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new file mode 100644
index 0000000000..2051f00f03
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/development/assets/www/lock/listings/ADA/adal02.lock b/development/assets/www/lock/listings/ADA/adal02.lock
new file mode 100644
index 0000000000..1153598087
--- /dev/null
+++ b/development/assets/www/lock/listings/ADA/adal02.lock
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new file mode 100644
index 0000000000..1153598087
--- /dev/null
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new file mode 100644
index 0000000000..1613543441
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new file mode 100644
index 0000000000..1613543441
--- /dev/null
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new file mode 100644
index 0000000000..1613543441
--- /dev/null
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new file mode 100644
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new file mode 100644
index 0000000000..1613543441
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new file mode 100644
index 0000000000..1613543441
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new file mode 100644
index 0000000000..1613543441
--- /dev/null
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new file mode 100644
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new file mode 100644
index 0000000000..1613543441
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new file mode 100644
index 0000000000..1153598087
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new file mode 100644
index 0000000000..1153598087
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new file mode 100644
index 0000000000..1613543441
--- /dev/null
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new file mode 100644
index 0000000000..1613543441
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new file mode 100644
index 0000000000..1613543441
--- /dev/null
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diff --git a/development/assets/www/lock/listings/pharmacokinetic/pkpl01.lock b/development/assets/www/lock/listings/pharmacokinetic/pkpl01.lock
new file mode 100644
index 0000000000..1153598087
--- /dev/null
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new file mode 100644
index 0000000000..1153598087
--- /dev/null
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new file mode 100644
index 0000000000..1153598087
--- /dev/null
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new file mode 100644
index 0000000000..1153598087
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new file mode 100644
index 0000000000..9c210c1352
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 0000000000..6e43478acc
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new file mode 100644
index 0000000000..6e43478acc
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new file mode 100644
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new file mode 100644
index 0000000000..9c210c1352
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new file mode 100644
index 0000000000..6e43478acc
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new file mode 100644
index 0000000000..9c210c1352
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 loaded via a namespace (and not attached):
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@@ -2015,7 +2015,7 @@
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  [16] htmlwidgets_1.6.4        fontawesome_0.5.2        testthat_3.2.1          
  [19] rootSolve_1.8.2.4        plotly_4.10.4            cachem_1.0.8            
- [22] mime_0.12                lifecycle_1.0.4          teal.widgets_0.4.2.9006 
+ [22] mime_0.12                lifecycle_1.0.4          teal.widgets_0.4.2.9008 
  [25] pkgconfig_2.0.3          webshot2_0.1.1           Matrix_1.6-5            
  [28] R6_2.5.1                 fastmap_1.1.1            rbibutils_2.2.16        
  [31] digest_0.6.34            Exact_3.2                colorspace_2.1-0        
diff --git a/development/graphs/other/bwg01_files/figure-html/teal-1.png b/development/graphs/other/bwg01_files/figure-html/teal-1.png
index ff49a4ea85..cffb451606 100644
Binary files a/development/graphs/other/bwg01_files/figure-html/teal-1.png and b/development/graphs/other/bwg01_files/figure-html/teal-1.png differ
diff --git a/development/graphs/other/cig01.html b/development/graphs/other/cig01.html
index dbfedab8b3..1ab8927030 100644
--- a/development/graphs/other/cig01.html
+++ b/development/graphs/other/cig01.html
@@ -1726,7 +1726,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 18:01:11 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:59:26 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1756,14 +1756,14 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      nestcolor_0.1.2.9009            
  [9] scda_0.1.6.9017                  dplyr_1.1.4                     
 [11] ggplot2_3.5.0                    tern_0.9.3.9024                 
 [13] rtables_0.6.6.9011               magrittr_2.0.3                  
-[15] formatters_0.5.5.9013           
+[15] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     
@@ -1782,7 +1782,7 @@
 [40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
 [46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         
-[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
+[49] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          
 [52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           
 [55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            
 [58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           
diff --git a/development/graphs/other/cig01_files/figure-html/teal-1.png b/development/graphs/other/cig01_files/figure-html/teal-1.png
index ea93f5fc76..e2acfa3472 100644
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diff --git a/development/graphs/other/ippg01.html b/development/graphs/other/ippg01.html
index 816a856671..cd21f617db 100644
--- a/development/graphs/other/ippg01.html
+++ b/development/graphs/other/ippg01.html
@@ -1647,7 +1647,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 18:00:32 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:58:48 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1677,14 +1677,14 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      nestcolor_0.1.2.9009            
  [9] ggplot2_3.5.0                    dplyr_1.1.4                     
 [11] scda_0.1.6.9017                  tern_0.9.3.9024                 
 [13] rtables_0.6.6.9011               magrittr_2.0.3                  
-[15] formatters_0.5.5.9013           
+[15] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     
@@ -1703,7 +1703,7 @@
 [40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            
 [43] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              
 [46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          
-[49] teal.widgets_0.4.2.9006  lattice_0.22-5           tibble_3.2.1            
+[49] teal.widgets_0.4.2.9008  lattice_0.22-5           tibble_3.2.1            
 [52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           
 [55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   
 [58] checkmate_2.3.1          shinyjs_2.1.0            generics_0.1.3          
diff --git a/development/graphs/other/ippg01_files/figure-html/teal-1.png b/development/graphs/other/ippg01_files/figure-html/teal-1.png
index 772b6f6a13..78953383c3 100644
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diff --git a/development/graphs/other/ltg01.html b/development/graphs/other/ltg01.html
index e286d14ae3..259194b02f 100644
--- a/development/graphs/other/ltg01.html
+++ b/development/graphs/other/ltg01.html
@@ -1697,7 +1697,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 18:00:55 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:59:11 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1728,12 +1728,12 @@
 
 other attached packages:
  [1] nestcolor_0.1.2.9009             dplyr_1.1.4                     
- [3] ggplot2_3.5.0                    teal.modules.clinical_0.9.0.9009
- [5] teal.transform_0.5.0.9001        teal_0.15.2.9003                
- [7] teal.slice_0.5.0.9004            teal.data_0.5.0.9002            
+ [3] ggplot2_3.5.0                    teal.modules.clinical_0.9.0.9010
+ [5] teal.transform_0.5.0.9002        teal_0.15.2.9004                
+ [7] teal.slice_0.5.0.9005            teal.data_0.5.0.9003            
  [9] teal.code_0.5.0.9003             shiny_1.8.0                     
 [11] tern_0.9.3.9024                  rtables_0.6.6.9011              
-[13] magrittr_2.0.3                   formatters_0.5.5.9013           
+[13] magrittr_2.0.3                   formatters_0.5.5.9015           
 [15] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
diff --git a/development/graphs/other/mng01.html b/development/graphs/other/mng01.html
index 50d8c07cd3..74cc394f6b 100644
--- a/development/graphs/other/mng01.html
+++ b/development/graphs/other/mng01.html
@@ -1694,7 +1694,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 18:01:31 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:59:46 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1724,13 +1724,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] forcats_1.0.0                    teal.modules.clinical_0.9.0.9009
- [3] teal.transform_0.5.0.9001        teal_0.15.2.9003                
- [5] teal.slice_0.5.0.9004            teal.data_0.5.0.9002            
+ [1] forcats_1.0.0                    teal.modules.clinical_0.9.0.9010
+ [3] teal.transform_0.5.0.9002        teal_0.15.2.9004                
+ [5] teal.slice_0.5.0.9005            teal.data_0.5.0.9003            
  [7] teal.code_0.5.0.9003             shiny_1.8.0                     
  [9] nestcolor_0.1.2.9009             scda_0.1.6.9017                 
 [11] tern_0.9.3.9024                  rtables_0.6.6.9011              
-[13] magrittr_2.0.3                   formatters_0.5.5.9013           
+[13] magrittr_2.0.3                   formatters_0.5.5.9015           
 [15] dplyr_1.1.4                     
 
 loaded via a namespace (and not attached):
@@ -1750,7 +1750,7 @@
 [40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            
 [43] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              
 [46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          
-[49] teal.widgets_0.4.2.9006  lattice_0.22-5           tibble_3.2.1            
+[49] teal.widgets_0.4.2.9008  lattice_0.22-5           tibble_3.2.1            
 [52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           
 [55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   
 [58] checkmate_2.3.1          shinyjs_2.1.0            generics_0.1.3          
diff --git a/development/graphs/other/mng01_files/figure-html/teal-1.png b/development/graphs/other/mng01_files/figure-html/teal-1.png
index 1f302b165e..b4cf92b19a 100644
Binary files a/development/graphs/other/mng01_files/figure-html/teal-1.png and b/development/graphs/other/mng01_files/figure-html/teal-1.png differ
diff --git a/development/graphs/pharmacokinetic/pkcg01.html b/development/graphs/pharmacokinetic/pkcg01.html
index e28bdc8c9b..c210d88a6b 100644
--- a/development/graphs/pharmacokinetic/pkcg01.html
+++ b/development/graphs/pharmacokinetic/pkcg01.html
@@ -1556,7 +1556,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:57:47 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:56:07 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1588,7 +1588,7 @@
 other attached packages:
 [1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          
 [4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   
-[7] magrittr_2.0.3        formatters_0.5.5.9013
+[7] magrittr_2.0.3        formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/graphs/pharmacokinetic/pkcg02.html b/development/graphs/pharmacokinetic/pkcg02.html
index 33bd18169a..b88879926b 100644
--- a/development/graphs/pharmacokinetic/pkcg02.html
+++ b/development/graphs/pharmacokinetic/pkcg02.html
@@ -1557,7 +1557,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:57:25 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:45 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1589,7 +1589,7 @@
 other attached packages:
 [1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          
 [4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   
-[7] magrittr_2.0.3        formatters_0.5.5.9013
+[7] magrittr_2.0.3        formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/graphs/pharmacokinetic/pkcg03.html b/development/graphs/pharmacokinetic/pkcg03.html
index 313fc25656..2eb99b8d5f 100644
--- a/development/graphs/pharmacokinetic/pkcg03.html
+++ b/development/graphs/pharmacokinetic/pkcg03.html
@@ -1573,7 +1573,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:57:39 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:58 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1605,7 +1605,7 @@
 other attached packages:
 [1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          
 [4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   
-[7] magrittr_2.0.3        formatters_0.5.5.9013
+[7] magrittr_2.0.3        formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/graphs/pharmacokinetic/pkpg01.html b/development/graphs/pharmacokinetic/pkpg01.html
index a220915e7d..87cc891240 100644
--- a/development/graphs/pharmacokinetic/pkpg01.html
+++ b/development/graphs/pharmacokinetic/pkpg01.html
@@ -1602,7 +1602,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:57:32 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:51 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1634,7 +1634,7 @@
 other attached packages:
 [1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          
 [4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   
-[7] magrittr_2.0.3        formatters_0.5.5.9013
+[7] magrittr_2.0.3        formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/graphs/pharmacokinetic/pkpg02.html b/development/graphs/pharmacokinetic/pkpg02.html
index 22d870698d..d2d195e013 100644
--- a/development/graphs/pharmacokinetic/pkpg02.html
+++ b/development/graphs/pharmacokinetic/pkpg02.html
@@ -1620,7 +1620,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:57:19 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:39 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1652,7 +1652,7 @@
 other attached packages:
 [1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          
 [4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   
-[7] magrittr_2.0.3        formatters_0.5.5.9013
+[7] magrittr_2.0.3        formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/graphs/pharmacokinetic/pkpg03.html b/development/graphs/pharmacokinetic/pkpg03.html
index 0482fb174a..cb1ff4102f 100644
--- a/development/graphs/pharmacokinetic/pkpg03.html
+++ b/development/graphs/pharmacokinetic/pkpg03.html
@@ -1515,7 +1515,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:57:06 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:26 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1547,7 +1547,7 @@
 other attached packages:
 [1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          
 [4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   
-[7] magrittr_2.0.3        formatters_0.5.5.9013
+[7] magrittr_2.0.3        formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/graphs/pharmacokinetic/pkpg04.html b/development/graphs/pharmacokinetic/pkpg04.html
index e83b408b53..6d34481620 100644
--- a/development/graphs/pharmacokinetic/pkpg04.html
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 </details><div class="cell-output cell-output-stdout">
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 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1549,7 +1549,7 @@
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 loaded via a namespace (and not attached):
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diff --git a/development/graphs/pharmacokinetic/pkpg04_files/figure-html/plot1-1.png b/development/graphs/pharmacokinetic/pkpg04_files/figure-html/plot1-1.png
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Binary files a/development/graphs/pharmacokinetic/pkpg04_files/figure-html/plot1-1.png and b/development/graphs/pharmacokinetic/pkpg04_files/figure-html/plot1-1.png differ
diff --git a/development/graphs/pharmacokinetic/pkpg06.html b/development/graphs/pharmacokinetic/pkpg06.html
index 662309f557..895418b372 100644
--- a/development/graphs/pharmacokinetic/pkpg06.html
+++ b/development/graphs/pharmacokinetic/pkpg06.html
@@ -1715,7 +1715,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:56:14 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1746,7 +1746,7 @@
 
 other attached packages:
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-[4] magrittr_2.0.3        formatters_0.5.5.9013 tidyr_1.3.1          
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 loaded via a namespace (and not attached):
diff --git a/development/listings/ADA/adal02.html b/development/listings/ADA/adal02.html
index 5ba3cca5ab..99e2b8a766 100644
--- a/development/listings/ADA/adal02.html
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@@ -1619,7 +1619,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:54:24 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1650,7 +1650,7 @@
 
 other attached packages:
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-[4] formatters_0.5.5.9013 dplyr_1.1.4          
+[4] formatters_0.5.5.9015 dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
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diff --git a/development/listings/ECG/egl01.html b/development/listings/ECG/egl01.html
index 0a133822bc..4d6f1df943 100644
--- a/development/listings/ECG/egl01.html
+++ b/development/listings/ECG/egl01.html
@@ -1576,7 +1576,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:54:21 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1606,7 +1606,7 @@
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 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
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 loaded via a namespace (and not attached):
diff --git a/development/listings/adverse-events/ael01.html b/development/listings/adverse-events/ael01.html
index c91493eb56..faf80178bf 100644
--- a/development/listings/adverse-events/ael01.html
+++ b/development/listings/adverse-events/ael01.html
@@ -1512,7 +1512,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:54:52 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1542,7 +1542,7 @@
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 other attached packages:
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 loaded via a namespace (and not attached):
diff --git a/development/listings/adverse-events/ael01_nollt.html b/development/listings/adverse-events/ael01_nollt.html
index d0b49d6e5f..2d8e370671 100644
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:54:41 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1541,7 +1541,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
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 loaded via a namespace (and not attached):
diff --git a/development/listings/adverse-events/ael02.html b/development/listings/adverse-events/ael02.html
index e7d22849e8..7427c6c488 100644
--- a/development/listings/adverse-events/ael02.html
+++ b/development/listings/adverse-events/ael02.html
@@ -1576,7 +1576,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:54:45 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1606,7 +1606,7 @@
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 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
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 loaded via a namespace (and not attached):
diff --git a/development/listings/adverse-events/ael02_ed.html b/development/listings/adverse-events/ael02_ed.html
index 6b3e843b87..7e7a81434f 100644
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@@ -1594,7 +1594,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:54:48 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1625,7 +1625,7 @@
 
 other attached packages:
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-[4] formatters_0.5.5.9013 scda_0.1.6.9017       dplyr_1.1.4          
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 loaded via a namespace (and not attached):
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diff --git a/development/listings/adverse-events/ael03.html b/development/listings/adverse-events/ael03.html
index cefed64c54..ec4ea2abe4 100644
--- a/development/listings/adverse-events/ael03.html
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@@ -1595,7 +1595,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:12 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:35 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1625,7 +1625,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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 loaded via a namespace (and not attached):
diff --git a/development/listings/adverse-events/ael04.html b/development/listings/adverse-events/ael04.html
index 1d044a4b11..76aa2e999c 100644
--- a/development/listings/adverse-events/ael04.html
+++ b/development/listings/adverse-events/ael04.html
@@ -1533,7 +1533,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:15 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:38 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1563,7 +1563,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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 loaded via a namespace (and not attached):
diff --git a/development/listings/concomitant-medications/cml01.html b/development/listings/concomitant-medications/cml01.html
index 77657825da..dfb2310d93 100644
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@@ -1544,7 +1544,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:46 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:11 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1574,7 +1574,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
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 loaded via a namespace (and not attached):
diff --git a/development/listings/concomitant-medications/cml02a_gl.html b/development/listings/concomitant-medications/cml02a_gl.html
index 512c2e5912..113849a50f 100644
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@@ -1518,7 +1518,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:50 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:14 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1548,7 +1548,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/concomitant-medications/cml02b_gl.html b/development/listings/concomitant-medications/cml02b_gl.html
index 11088a5228..3ceb5221eb 100644
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+++ b/development/listings/concomitant-medications/cml02b_gl.html
@@ -1521,7 +1521,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:53 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:17 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1551,7 +1551,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/development-safety-update-report/dsur4.html b/development/listings/development-safety-update-report/dsur4.html
index 8c3caba692..bad3f5d796 100644
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:57:00 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:21 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1553,7 +1553,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
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 loaded via a namespace (and not attached):
diff --git a/development/listings/disposition/dsl01.html b/development/listings/disposition/dsl01.html
index 71bb511e39..76512779c5 100644
--- a/development/listings/disposition/dsl01.html
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@@ -1532,7 +1532,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:04 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:28 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1562,7 +1562,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/disposition/dsl02.html b/development/listings/disposition/dsl02.html
index 5455d43855..6aadc4975d 100644
--- a/development/listings/disposition/dsl02.html
+++ b/development/listings/disposition/dsl02.html
@@ -1527,7 +1527,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:08 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:31 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1557,7 +1557,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/efficacy/oncl01.html b/development/listings/efficacy/oncl01.html
index f16748a30d..34064a270e 100644
--- a/development/listings/efficacy/oncl01.html
+++ b/development/listings/efficacy/oncl01.html
@@ -1587,7 +1587,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:42 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:07 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1617,7 +1617,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/exposure/exl01.html b/development/listings/exposure/exl01.html
index 6331ec9ef0..15311db3bf 100644
--- a/development/listings/exposure/exl01.html
+++ b/development/listings/exposure/exl01.html
@@ -1531,7 +1531,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:54:03 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1561,7 +1561,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/lab-results/lbl01.html b/development/listings/lab-results/lbl01.html
index f054e14cef..1c121991d1 100644
--- a/development/listings/lab-results/lbl01.html
+++ b/development/listings/lab-results/lbl01.html
@@ -1560,7 +1560,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:53 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:14 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1590,7 +1590,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/lab-results/lbl01_rls.html b/development/listings/lab-results/lbl01_rls.html
index fe2446fee1..b8709d90ec 100644
--- a/development/listings/lab-results/lbl01_rls.html
+++ b/development/listings/lab-results/lbl01_rls.html
@@ -1565,7 +1565,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:41 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:03 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1595,7 +1595,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/lab-results/lbl02a.html b/development/listings/lab-results/lbl02a.html
index c2f2aa4bc6..7f0a82d204 100644
--- a/development/listings/lab-results/lbl02a.html
+++ b/development/listings/lab-results/lbl02a.html
@@ -1560,7 +1560,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:45 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:06 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1590,7 +1590,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/lab-results/lbl02a_rls.html b/development/listings/lab-results/lbl02a_rls.html
index 63a5a4a9bf..f57fd4a839 100644
--- a/development/listings/lab-results/lbl02a_rls.html
+++ b/development/listings/lab-results/lbl02a_rls.html
@@ -1581,7 +1581,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:49 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:10 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1611,7 +1611,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/lab-results/lbl02b.html b/development/listings/lab-results/lbl02b.html
index 951cac6a04..38c2170c20 100644
--- a/development/listings/lab-results/lbl02b.html
+++ b/development/listings/lab-results/lbl02b.html
@@ -1561,7 +1561,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:57 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:55:17 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1591,7 +1591,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/medical-history/mhl01.html b/development/listings/medical-history/mhl01.html
index 6c079c3f51..b55ead9d13 100644
--- a/development/listings/medical-history/mhl01.html
+++ b/development/listings/medical-history/mhl01.html
@@ -1536,7 +1536,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:37 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:59 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1566,7 +1566,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/listings/pharmacokinetic/pkcl01.html b/development/listings/pharmacokinetic/pkcl01.html
index 83de38d06f..12d926b7b7 100644
--- a/development/listings/pharmacokinetic/pkcl01.html
+++ b/development/listings/pharmacokinetic/pkcl01.html
@@ -1534,7 +1534,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:31 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:56 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1565,7 +1565,7 @@
 
 other attached packages:
 [1] dplyr_1.1.4           scda_0.1.6.9017       rlistings_0.2.7.9010 
-[4] tibble_3.2.1          formatters_0.5.5.9013
+[4] tibble_3.2.1          formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      
diff --git a/development/listings/pharmacokinetic/pkcl02.html b/development/listings/pharmacokinetic/pkcl02.html
index 8a8c653dca..86ba2e1e2e 100644
--- a/development/listings/pharmacokinetic/pkcl02.html
+++ b/development/listings/pharmacokinetic/pkcl02.html
@@ -1568,7 +1568,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:27 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:53 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1599,7 +1599,7 @@
 
 other attached packages:
 [1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         
-[4] formatters_0.5.5.9013 dplyr_1.1.4          
+[4] formatters_0.5.5.9015 dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
  [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      
diff --git a/development/listings/pharmacokinetic/pkpl01.html b/development/listings/pharmacokinetic/pkpl01.html
index 476d1c6ea7..63d43c46c6 100644
--- a/development/listings/pharmacokinetic/pkpl01.html
+++ b/development/listings/pharmacokinetic/pkpl01.html
@@ -1536,7 +1536,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:24 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:49 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1567,7 +1567,7 @@
 
 other attached packages:
 [1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         
-[4] formatters_0.5.5.9013 dplyr_1.1.4          
+[4] formatters_0.5.5.9015 dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
  [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      
diff --git a/development/listings/pharmacokinetic/pkpl02.html b/development/listings/pharmacokinetic/pkpl02.html
index 04e47f3800..038046aa99 100644
--- a/development/listings/pharmacokinetic/pkpl02.html
+++ b/development/listings/pharmacokinetic/pkpl02.html
@@ -1537,7 +1537,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:20 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:46 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1568,7 +1568,7 @@
 
 other attached packages:
 [1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         
-[4] formatters_0.5.5.9013 dplyr_1.1.4          
+[4] formatters_0.5.5.9015 dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
  [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      
diff --git a/development/listings/pharmacokinetic/pkpl04.html b/development/listings/pharmacokinetic/pkpl04.html
index b3f865858c..f8d160c7fe 100644
--- a/development/listings/pharmacokinetic/pkpl04.html
+++ b/development/listings/pharmacokinetic/pkpl04.html
@@ -1555,7 +1555,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:35 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:00 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1586,7 +1586,7 @@
 
 other attached packages:
 [1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         
-[4] formatters_0.5.5.9013 dplyr_1.1.4          
+[4] formatters_0.5.5.9015 dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
  [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      
diff --git a/development/listings/vital-signs/vsl01.html b/development/listings/vital-signs/vsl01.html
index 411bb3232e..aef49b3bd8 100644
--- a/development/listings/vital-signs/vsl01.html
+++ b/development/listings/vital-signs/vsl01.html
@@ -1612,7 +1612,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:56:34 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:54:56 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1642,7 +1642,7 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013
+[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015
 [4] scda_0.1.6.9017       dplyr_1.1.4          
 
 loaded via a namespace (and not attached):
diff --git a/development/repro.html b/development/repro.html
index 28884df7bb..3d107812f7 100644
--- a/development/repro.html
+++ b/development/repro.html
@@ -184,7 +184,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 18:01:35 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:59:50 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
diff --git a/development/search.json b/development/search.json
index a795bbb688..13ed3bbc3f 100644
--- a/development/search.json
+++ b/development/search.json
@@ -72,7 +72,7 @@
     "href": "graphs/other/mng01.html",
     "title": "MNG01",
     "section": "",
-    "text": "Data Setup\nPlot of Mean  and CI\nPlot of Mean and CIs of Change  from Baseline (Changing the Input  Analysis Data Set and Analysis Variable)\nPlot of Mean (+/-SD)  (Changing the Statistics)\nPlot of Mean and CI  (Modify Alpha Level)\nPlot of Mean and CI (with Number  of Patients only in Table Section)\nPlot of Mean and CI  (with Table Section)\nPlot of Median and CI (Visits  Condensed in Table Section)\nPlot of Mean and  Upper Confidence Limit\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\nadsl_f &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\") %&gt;%\n  df_explicit_na()\n\nadlb_f &lt;- adlb %&gt;%\n  mutate(AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    ANL01FL == \"Y\",\n    ONTRTFL == \"Y\",\n    PARAMCD == \"ALT\",\n    AVISIT != \"SCREENING\"\n  ) %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\nadvs_f &lt;- advs %&gt;%\n  mutate(AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    ONTRTFL == \"Y\",\n    PARAMCD == \"DIABP\",\n    AVISIT != \"SCREENING\"\n  ) %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = advs_f,\n  alt_counts_df = adsl_f,\n  variables = control_lineplot_vars(y = \"CHG\"),\n  title = \"Plot of Mean and 95% Confidence Intervals of Change from Baseline by Visit\",\n  subtitle = \"Assessment:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = \"Plot of Mean (+/-SD)\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  control = control_analyze_vars(conf_level = 0.80),\n  title = \"Plot of Mean and 80% Confidence Limits by Visit\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  table = \"n\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  table = c(\"n\", \"mean\", \"mean_ci\"),\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\nThis option is not supported.\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  whiskers = \"mean_ci_upr\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  library(forcats)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    mutate(AVISIT = fct_reorder(AVISIT, AVISITN, min))\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_lineplot(\n      label = \"LINE PLOT\",\n      dataname = \"ADLB\",\n      strata = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      y = choices_selected(\n        variable_choices(ADLB, c(\"AVAL\", \"BASE\", \"CHG\", \"PCHG\")),\n        \"AVAL\"\n      ),\n      param = choices_selected(\n        value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        \"ALT\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 18:01:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] forcats_1.0.0                    teal.modules.clinical_0.9.0.9009\n [3] teal.transform_0.5.0.9001        teal_0.15.2.9003                \n [5] teal.slice_0.5.0.9004            teal.data_0.5.0.9002            \n [7] teal.code_0.5.0.9003             shiny_1.8.0                     \n [9] nestcolor_0.1.2.9009             scda_0.1.6.9017                 \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9013           \n[15] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[37] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[43] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              \n[46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          \n[49] teal.widgets_0.4.2.9006  lattice_0.22-5           tibble_3.2.1            \n[52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           \n[55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   \n[58] checkmate_2.3.1          shinyjs_2.1.0            generics_0.1.3          \n[61] chromote_0.2.0           ggplot2_3.5.0            munsell_0.5.0           \n[64] scales_1.3.0             xtable_1.8-4             glue_1.7.0              \n[67] emmeans_1.10.0           tools_4.3.3              webshot2_0.1.1          \n[70] webshot_0.5.5            mvtnorm_1.2-4            cowplot_1.1.3           \n[73] grid_4.3.3               tidyr_1.3.1              rbibutils_2.2.16        \n[76] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[79] textshaping_0.3.7        fansi_1.0.6              geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        farver_2.1.1            \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot of Mean  and CI\nPlot of Mean and CIs of Change  from Baseline (Changing the Input  Analysis Data Set and Analysis Variable)\nPlot of Mean (+/-SD)  (Changing the Statistics)\nPlot of Mean and CI  (Modify Alpha Level)\nPlot of Mean and CI (with Number  of Patients only in Table Section)\nPlot of Mean and CI  (with Table Section)\nPlot of Median and CI (Visits  Condensed in Table Section)\nPlot of Mean and  Upper Confidence Limit\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\nadsl_f &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\") %&gt;%\n  df_explicit_na()\n\nadlb_f &lt;- adlb %&gt;%\n  mutate(AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    ANL01FL == \"Y\",\n    ONTRTFL == \"Y\",\n    PARAMCD == \"ALT\",\n    AVISIT != \"SCREENING\"\n  ) %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\nadvs_f &lt;- advs %&gt;%\n  mutate(AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    ONTRTFL == \"Y\",\n    PARAMCD == \"DIABP\",\n    AVISIT != \"SCREENING\"\n  ) %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = advs_f,\n  alt_counts_df = adsl_f,\n  variables = control_lineplot_vars(y = \"CHG\"),\n  title = \"Plot of Mean and 95% Confidence Intervals of Change from Baseline by Visit\",\n  subtitle = \"Assessment:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = \"Plot of Mean (+/-SD)\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  control = control_analyze_vars(conf_level = 0.80),\n  title = \"Plot of Mean and 80% Confidence Limits by Visit\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  table = \"n\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  table = c(\"n\", \"mean\", \"mean_ci\"),\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\n\nThis option is not supported.\n\n\n\n\nCodeplot &lt;- g_lineplot(\n  df = adlb_f,\n  alt_counts_df = adsl_f,\n  whiskers = \"mean_ci_upr\",\n  subtitle = \"Laboratory Test:\"\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  library(forcats)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    mutate(AVISIT = fct_reorder(AVISIT, AVISITN, min))\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_lineplot(\n      label = \"LINE PLOT\",\n      dataname = \"ADLB\",\n      strata = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      y = choices_selected(\n        variable_choices(ADLB, c(\"AVAL\", \"BASE\", \"CHG\", \"PCHG\")),\n        \"AVAL\"\n      ),\n      param = choices_selected(\n        value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        \"ALT\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:59:46 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] forcats_1.0.0                    teal.modules.clinical_0.9.0.9010\n [3] teal.transform_0.5.0.9002        teal_0.15.2.9004                \n [5] teal.slice_0.5.0.9005            teal.data_0.5.0.9003            \n [7] teal.code_0.5.0.9003             shiny_1.8.0                     \n [9] nestcolor_0.1.2.9009             scda_0.1.6.9017                 \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9015           \n[15] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[37] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[43] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              \n[46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          \n[49] teal.widgets_0.4.2.9008  lattice_0.22-5           tibble_3.2.1            \n[52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           \n[55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   \n[58] checkmate_2.3.1          shinyjs_2.1.0            generics_0.1.3          \n[61] chromote_0.2.0           ggplot2_3.5.0            munsell_0.5.0           \n[64] scales_1.3.0             xtable_1.8-4             glue_1.7.0              \n[67] emmeans_1.10.0           tools_4.3.3              webshot2_0.1.1          \n[70] webshot_0.5.5            mvtnorm_1.2-4            cowplot_1.1.3           \n[73] grid_4.3.3               tidyr_1.3.1              rbibutils_2.2.16        \n[76] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[79] textshaping_0.3.7        fansi_1.0.6              geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        farver_2.1.1            \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Other",
@@ -84,7 +84,7 @@
     "href": "graphs/other/ltg01.html",
     "title": "LTG01",
     "section": "",
-    "text": "Lattice plots are natively handled by R, the examples below rely mostly on the package ggplot2.\n\n\nData Setup\nPlot of Liver Function Tests\nPlot of Liver Function Tests  Including Mean, Median, and 95% CIs\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(teal)\nlibrary(teal.modules.clinical)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(nestcolor)\n\n# Datasets\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:8)\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% adsl$USUBJID)\n\n# Pre-processing\nadlb$AVISIT_txt &lt;- adlb$AVISIT\nadlb$AVISIT &lt;- as.numeric(adlb$AVISIT)\nadlb$ARM_N &lt;- adlb$ARM\nlevels(adlb$ARM_N) &lt;- with(\n  data = adlb,\n  paste0(\n    levels(ARM_N), \" (N = \",\n    tapply(SUBJID, ARM_N, function(x) length(unique(x))), \")\"\n  )\n)\n\n# Plot utils\nnpch &lt;- 1:25\nnpatients &lt;- length(unique(adlb$SUBJID))\npch &lt;- c(\n  rep(npch, times = npatients %/% length(npch)),\n  npch[1:(npatients %% length(npch))]\n)\n\n\n\n\nBasic Plot\n\n\nCode# General mapping and \"lattice\" (\"facet\" in ggplot2 nomenclature).\ng1 &lt;- {\n  ggplot(\n    data = adlb,\n    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)\n  ) +\n    facet_grid(LBTESTCD ~ ARM, scales = \"free_y\") +\n    scale_shape_manual(values = pch)\n}\n\n# Add points and lines.\ng1 &lt;- g1 + geom_point()\ng1 &lt;- g1 + geom_line()\nplot &lt;- g1\nplot\n\n\n\n\n\n\n\nModifying Facets\nThe units describing rows of panes and the number of patients under each arm is specified by modifying facet_grid():\n\n\nCode# Include the units and the sample size N.\ng2 &lt;- g1 + facet_grid(\n  paste0(LBTESTCD, \"\\n(\", AVALU, \")\") ~ ARM_N,\n  scales = \"free_y\"\n)\n\nplot &lt;- g2\nplot\n\n\n\n\n\n\n\nModifying X-Axis\nThe graphic elements are modified through usual ggplot2 functions. For instance, the x-axis could be improved as follows:\n\n\nCodeg3 &lt;- g2 + theme(\n  axis.text.x = element_text(angle = 45, hjust = 1),\n  axis.title = element_blank()\n) + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)\n\nplot &lt;- g3\nplot\n\n\n\n\n\n\n\n\n\n\nThe functions stat_mean_ci and stat_median_ci from the tern package allow the addition of mean and/or median confidence intervals. The example below suggests a larger dataset, where the individual subject legend may not be relevant but the mean or the median are of special interest.\nPre-Processing\n\nCode# Datasets\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:40)\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% adsl$USUBJID)\n\n# Pre-processing\nadlb$AVISIT_txt &lt;- adlb$AVISIT\nadlb$AVISIT &lt;- as.numeric(adlb$AVISIT)\nadlb$ARM_N &lt;- adlb$ARM\nlevels(adlb$ARM_N) &lt;- with(\n  data = adlb,\n  paste0(\n    levels(ARM_N), \" (N = \",\n    tapply(SUBJID, ARM_N, function(x) length(unique(x))), \")\"\n  )\n)\n\n# Plot utils\nnpch &lt;- 1:25\nnpatients &lt;- length(unique(adlb$SUBJID))\npch &lt;- c(\n  rep(npch, times = npatients %/% length(npch)),\n  npch[1:(npatients %% length(npch))]\n)\n\n\nBasic Plot\n\nCode# General mapping and \"lattice\" (\"facet\" in ggplot2 nomenclature)\ng4 &lt;- {\n  ggplot(\n    data = adlb,\n    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)\n  ) +\n    facet_grid(LBTESTCD ~ ARM_N, scales = \"free_y\") +\n    scale_shape_manual(values = pch) +\n    scale_color_manual(values = rep(getOption(\"ggplot2.discrete.colour\"), 2))\n}\n\n# Add points and lines.\n# Note that with so many patients, legend might not be useful and transparency\n#   is advisable.\ng4 &lt;- g4 + geom_point(alpha = .3)\ng4 &lt;- g4 + geom_line(alpha = .3)\ng4 &lt;- g4 + guides(colour = \"none\", shape = \"none\")\ng4 &lt;- g4 + theme(\n  axis.text.x = element_text(angle = 45, hjust = 1),\n  axis.title  = element_blank()\n)\ng4 &lt;- g4 + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)\nplot &lt;- g4\nplot\n\n\n\n\n\n\n\nAdding Mean\n\n\nCode# Add the mean along with the 95% CI at every visit.\ng51 &lt;- g4 + stat_summary(\n  fun = mean, linewidth = 1, geom = \"line\",\n  aes(group = 1, linetype = \"Mean +/- 95% CI\")\n)\ng51 &lt;- g51 + stat_summary(\n  fun.data = tern::stat_mean_ci, geom = \"errorbar\",\n  aes(group = 1, linetype = \"Mean +/- 95% CI\")\n)\nplot &lt;- g51 + guides(linetype = guide_legend(title = NULL))\nplot\n\n\n\n\n\n\n\nAdding Median\n\n\nCode# Instead of a mean, the median could be more relevant.\ng52 &lt;- g51 + stat_summary(\n  fun = median, linewidth = 1, geom = \"line\",\n  aes(group = 1, linetype = \"Median +/- 95% CI\")\n)\ng52 &lt;- g52 + stat_summary(\n  fun.data = tern::stat_median_ci, geom = \"errorbar\",\n  aes(group = 1, linetype = \"Median +/- 95% CI\")\n)\nplot &lt;- g52 + guides(linetype = guide_legend(title = \"Aggregate\"))\nplot\n\n\n\n\n\n\n\nChanging Confidence Level\n\n\nCode# Change the confidence level of interval for the median.\n# Note: check `?stat_mean_ci()` and `?stat_median_ci()` for further fine tuning.\ng53 &lt;- g4 + stat_summary(\n  fun = median, linewidth = 1, geom = \"line\",\n  aes(group = 1, linetype = \"Median +/- 80% CI\")\n)\ng53 &lt;- g53 + stat_summary(\n  fun.data = function(x) tern::stat_median_ci(x, conf_level = 0.8),\n  geom = \"errorbar\", aes(group = 1, linetype = \"Median +/- 80% CI\")\n)\nplot &lt;- g53 + guides(linetype = guide_legend(title = NULL))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 18:00:55 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] nestcolor_0.1.2.9009             dplyr_1.1.4                     \n [3] ggplot2_3.5.0                    teal.modules.clinical_0.9.0.9009\n [5] teal.transform_0.5.0.9001        teal_0.15.2.9003                \n [7] teal.slice_0.5.0.9004            teal.data_0.5.0.9002            \n [9] teal.code_0.5.0.9003             shiny_1.8.0                     \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9013           \n[15] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] gtable_0.3.4           xfun_0.42              htmlwidgets_1.6.4     \n [4] lattice_0.22-5         vctrs_0.6.5            tools_4.3.3           \n [7] Rdpack_2.6             generics_0.1.3         sandwich_3.1-0        \n[10] tibble_3.2.1           fansi_1.0.6            pkgconfig_2.0.3       \n[13] Matrix_1.6-5           checkmate_2.3.1        lifecycle_1.0.4       \n[16] farver_2.1.1           compiler_4.3.3         brio_1.1.4            \n[19] munsell_0.5.0          codetools_0.2-19       httpuv_1.6.14         \n[22] htmltools_0.5.7        yaml_2.3.8             later_1.3.2           \n[25] pillar_1.9.0           tidyr_1.3.1            MASS_7.3-60.0.1       \n[28] ellipsis_0.3.2         tern.gee_0.1.3.9004    multcomp_1.4-25       \n[31] nlme_3.1-164           mime_0.12              tidyselect_1.2.0      \n[34] digest_0.6.34          mvtnorm_1.2-4          purrr_1.0.2           \n[37] labeling_0.4.3         geepack_1.3.10         splines_4.3.3         \n[40] fastmap_1.1.1          grid_4.3.3             colorspace_2.1-0      \n[43] cli_3.6.2              logger_0.3.0           survival_3.5-8        \n[46] utf8_1.2.4             TH.data_1.1-2          broom_1.0.5           \n[49] withr_3.0.0            scales_1.3.0           promises_1.2.1        \n[52] backports_1.4.1        estimability_1.5       rmarkdown_2.26        \n[55] emmeans_1.10.0         zoo_1.8-12             coda_0.19-4.1         \n[58] evaluate_0.23          knitr_1.45             rbibutils_2.2.16      \n[61] testthat_3.2.1         rlang_1.1.3            Rcpp_1.0.12           \n[64] xtable_1.8-4           glue_1.7.0             jsonlite_1.8.8        \n[67] teal.logger_0.1.3.9011 R6_2.5.1              \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Lattice plots are natively handled by R, the examples below rely mostly on the package ggplot2.\n\n\nData Setup\nPlot of Liver Function Tests\nPlot of Liver Function Tests  Including Mean, Median, and 95% CIs\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(teal)\nlibrary(teal.modules.clinical)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(nestcolor)\n\n# Datasets\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:8)\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% adsl$USUBJID)\n\n# Pre-processing\nadlb$AVISIT_txt &lt;- adlb$AVISIT\nadlb$AVISIT &lt;- as.numeric(adlb$AVISIT)\nadlb$ARM_N &lt;- adlb$ARM\nlevels(adlb$ARM_N) &lt;- with(\n  data = adlb,\n  paste0(\n    levels(ARM_N), \" (N = \",\n    tapply(SUBJID, ARM_N, function(x) length(unique(x))), \")\"\n  )\n)\n\n# Plot utils\nnpch &lt;- 1:25\nnpatients &lt;- length(unique(adlb$SUBJID))\npch &lt;- c(\n  rep(npch, times = npatients %/% length(npch)),\n  npch[1:(npatients %% length(npch))]\n)\n\n\n\n\nBasic Plot\n\n\nCode# General mapping and \"lattice\" (\"facet\" in ggplot2 nomenclature).\ng1 &lt;- {\n  ggplot(\n    data = adlb,\n    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)\n  ) +\n    facet_grid(LBTESTCD ~ ARM, scales = \"free_y\") +\n    scale_shape_manual(values = pch)\n}\n\n# Add points and lines.\ng1 &lt;- g1 + geom_point()\ng1 &lt;- g1 + geom_line()\nplot &lt;- g1\nplot\n\n\n\n\n\n\n\nModifying Facets\nThe units describing rows of panes and the number of patients under each arm is specified by modifying facet_grid():\n\n\nCode# Include the units and the sample size N.\ng2 &lt;- g1 + facet_grid(\n  paste0(LBTESTCD, \"\\n(\", AVALU, \")\") ~ ARM_N,\n  scales = \"free_y\"\n)\n\nplot &lt;- g2\nplot\n\n\n\n\n\n\n\nModifying X-Axis\nThe graphic elements are modified through usual ggplot2 functions. For instance, the x-axis could be improved as follows:\n\n\nCodeg3 &lt;- g2 + theme(\n  axis.text.x = element_text(angle = 45, hjust = 1),\n  axis.title = element_blank()\n) + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)\n\nplot &lt;- g3\nplot\n\n\n\n\n\n\n\n\n\n\nThe functions stat_mean_ci and stat_median_ci from the tern package allow the addition of mean and/or median confidence intervals. The example below suggests a larger dataset, where the individual subject legend may not be relevant but the mean or the median are of special interest.\nPre-Processing\n\nCode# Datasets\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:40)\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% adsl$USUBJID)\n\n# Pre-processing\nadlb$AVISIT_txt &lt;- adlb$AVISIT\nadlb$AVISIT &lt;- as.numeric(adlb$AVISIT)\nadlb$ARM_N &lt;- adlb$ARM\nlevels(adlb$ARM_N) &lt;- with(\n  data = adlb,\n  paste0(\n    levels(ARM_N), \" (N = \",\n    tapply(SUBJID, ARM_N, function(x) length(unique(x))), \")\"\n  )\n)\n\n# Plot utils\nnpch &lt;- 1:25\nnpatients &lt;- length(unique(adlb$SUBJID))\npch &lt;- c(\n  rep(npch, times = npatients %/% length(npch)),\n  npch[1:(npatients %% length(npch))]\n)\n\n\nBasic Plot\n\nCode# General mapping and \"lattice\" (\"facet\" in ggplot2 nomenclature)\ng4 &lt;- {\n  ggplot(\n    data = adlb,\n    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)\n  ) +\n    facet_grid(LBTESTCD ~ ARM_N, scales = \"free_y\") +\n    scale_shape_manual(values = pch) +\n    scale_color_manual(values = rep(getOption(\"ggplot2.discrete.colour\"), 2))\n}\n\n# Add points and lines.\n# Note that with so many patients, legend might not be useful and transparency\n#   is advisable.\ng4 &lt;- g4 + geom_point(alpha = .3)\ng4 &lt;- g4 + geom_line(alpha = .3)\ng4 &lt;- g4 + guides(colour = \"none\", shape = \"none\")\ng4 &lt;- g4 + theme(\n  axis.text.x = element_text(angle = 45, hjust = 1),\n  axis.title  = element_blank()\n)\ng4 &lt;- g4 + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)\nplot &lt;- g4\nplot\n\n\n\n\n\n\n\nAdding Mean\n\n\nCode# Add the mean along with the 95% CI at every visit.\ng51 &lt;- g4 + stat_summary(\n  fun = mean, linewidth = 1, geom = \"line\",\n  aes(group = 1, linetype = \"Mean +/- 95% CI\")\n)\ng51 &lt;- g51 + stat_summary(\n  fun.data = tern::stat_mean_ci, geom = \"errorbar\",\n  aes(group = 1, linetype = \"Mean +/- 95% CI\")\n)\nplot &lt;- g51 + guides(linetype = guide_legend(title = NULL))\nplot\n\n\n\n\n\n\n\nAdding Median\n\n\nCode# Instead of a mean, the median could be more relevant.\ng52 &lt;- g51 + stat_summary(\n  fun = median, linewidth = 1, geom = \"line\",\n  aes(group = 1, linetype = \"Median +/- 95% CI\")\n)\ng52 &lt;- g52 + stat_summary(\n  fun.data = tern::stat_median_ci, geom = \"errorbar\",\n  aes(group = 1, linetype = \"Median +/- 95% CI\")\n)\nplot &lt;- g52 + guides(linetype = guide_legend(title = \"Aggregate\"))\nplot\n\n\n\n\n\n\n\nChanging Confidence Level\n\n\nCode# Change the confidence level of interval for the median.\n# Note: check `?stat_mean_ci()` and `?stat_median_ci()` for further fine tuning.\ng53 &lt;- g4 + stat_summary(\n  fun = median, linewidth = 1, geom = \"line\",\n  aes(group = 1, linetype = \"Median +/- 80% CI\")\n)\ng53 &lt;- g53 + stat_summary(\n  fun.data = function(x) tern::stat_median_ci(x, conf_level = 0.8),\n  geom = \"errorbar\", aes(group = 1, linetype = \"Median +/- 80% CI\")\n)\nplot &lt;- g53 + guides(linetype = guide_legend(title = NULL))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:59:11 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] nestcolor_0.1.2.9009             dplyr_1.1.4                     \n [3] ggplot2_3.5.0                    teal.modules.clinical_0.9.0.9010\n [5] teal.transform_0.5.0.9002        teal_0.15.2.9004                \n [7] teal.slice_0.5.0.9005            teal.data_0.5.0.9003            \n [9] teal.code_0.5.0.9003             shiny_1.8.0                     \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9015           \n[15] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] gtable_0.3.4           xfun_0.42              htmlwidgets_1.6.4     \n [4] lattice_0.22-5         vctrs_0.6.5            tools_4.3.3           \n [7] Rdpack_2.6             generics_0.1.3         sandwich_3.1-0        \n[10] tibble_3.2.1           fansi_1.0.6            pkgconfig_2.0.3       \n[13] Matrix_1.6-5           checkmate_2.3.1        lifecycle_1.0.4       \n[16] farver_2.1.1           compiler_4.3.3         brio_1.1.4            \n[19] munsell_0.5.0          codetools_0.2-19       httpuv_1.6.14         \n[22] htmltools_0.5.7        yaml_2.3.8             later_1.3.2           \n[25] pillar_1.9.0           tidyr_1.3.1            MASS_7.3-60.0.1       \n[28] ellipsis_0.3.2         tern.gee_0.1.3.9004    multcomp_1.4-25       \n[31] nlme_3.1-164           mime_0.12              tidyselect_1.2.0      \n[34] digest_0.6.34          mvtnorm_1.2-4          purrr_1.0.2           \n[37] labeling_0.4.3         geepack_1.3.10         splines_4.3.3         \n[40] fastmap_1.1.1          grid_4.3.3             colorspace_2.1-0      \n[43] cli_3.6.2              logger_0.3.0           survival_3.5-8        \n[46] utf8_1.2.4             TH.data_1.1-2          broom_1.0.5           \n[49] withr_3.0.0            scales_1.3.0           promises_1.2.1        \n[52] backports_1.4.1        estimability_1.5       rmarkdown_2.26        \n[55] emmeans_1.10.0         zoo_1.8-12             coda_0.19-4.1         \n[58] evaluate_0.23          knitr_1.45             rbibutils_2.2.16      \n[61] testthat_3.2.1         rlang_1.1.3            Rcpp_1.0.12           \n[64] xtable_1.8-4           glue_1.7.0             jsonlite_1.8.8        \n[67] teal.logger_0.1.3.9011 R6_2.5.1              \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Other",
@@ -96,7 +96,7 @@
     "href": "graphs/other/ippg01.html",
     "title": "IPPG01",
     "section": "",
-    "text": "For illustration purposes, we will subset the adlb dataset for safety population in treatment arm A and a specific lab parameter (ALT).\n\n\nData Setup\nStandard Plot\nPlot with Patient Baselines as Reference\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\n# use small sample size\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:15)\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% adsl$USUBJID)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadlb &lt;- df_explicit_na(adlb)\n\nadlb_f &lt;- adlb %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    PARAMCD == \"ALT\",\n    AVISIT != \"SCREENING\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID))\n\n\n\n\nThe user can select different plotting_choices depending on their preference. To demonstrate, separate plots are produced with a maximum of 3 observations each.\n\n\nCodeplots &lt;- g_ipp(\n  df = adlb_f,\n  xvar = \"AVISIT\",\n  yvar = \"AVAL\",\n  xlab = \"Visit\",\n  ylab = \"SGOT/ALT (U/L)\",\n  id_var = \"Patient_ID\",\n  title = \"Individual Patient Plots\",\n  subtitle = \"Treatment Arm A\",\n  plotting_choices = \"split_by_max_obs\",\n  max_obs_per_plot = 3\n)\nplots\n\n[[1]]\n\n\n\n\n\n\n\n\n\n[[2]]\n\n\n\n\n\n\n\n\n\n\nHere, patients’ individual baseline values will be shown for reference. Note that users can provide their own custom theme to the function via the ggtheme argument.\n\n\nCodeplots &lt;- g_ipp(\n  df = adlb_f,\n  xvar = \"AVISIT\",\n  yvar = \"AVAL\",\n  xlab = \"Visit\",\n  ylab = \"SGOT/ALT (U/L)\",\n  id_var = \"Patient_ID\",\n  title = \"Individual Patient Plots\",\n  subtitle = \"Treatment Arm A\",\n  add_baseline_hline = TRUE,\n  yvar_baseline = \"BASE\",\n  ggtheme = theme_minimal(),\n  plotting_choices = \"split_by_max_obs\",\n  max_obs_per_plot = 3\n)\nplots\n\n[[1]]\n\n\n\n\n\n\n\n\n\n[[2]]\n\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(tern)\n  library(scda)\n  library(dplyr)\n\n  # use small sample size\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:15)\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% ADSL$USUBJID)\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADLB &lt;- df_explicit_na(ADLB) %&gt;%\n    filter(AVISIT != \"SCREENING\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_ipp(\n      label = \"Individual Patient Plot\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        value_choices(ADLB, c(\"ARMCD\")),\n        \"ARM A\"\n      ),\n      paramcd = choices_selected(\n        value_choices(ADLB, \"PARAMCD\"),\n        \"ALT\"\n      ),\n      aval_var = choices_selected(\n        variable_choices(ADLB, c(\"AVAL\")),\n        \"AVAL\"\n      ),\n      avalu_var = choices_selected(\n        variable_choices(ADLB, c(\"AVALU\")),\n        \"AVALU\",\n        fixed = TRUE\n      ),\n      id_var = choices_selected(\n        variable_choices(ADLB, c(\"USUBJID\")),\n        \"USUBJID\",\n        fixed = TRUE\n      ),\n      visit_var = choices_selected(\n        variable_choices(ADLB, c(\"AVISIT\")),\n        \"AVISIT\"\n      ),\n      baseline_var = choices_selected(\n        variable_choices(ADLB, c(\"BASE\")),\n        \"BASE\",\n        fixed = TRUE\n      ),\n      add_baseline_hline = FALSE,\n      separate_by_obs = FALSE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 18:00:32 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] ggplot2_3.5.0                    dplyr_1.1.4                     \n[11] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[13] rtables_0.6.6.9011               magrittr_2.0.3                  \n[15] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[37] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[43] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              \n[46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          \n[49] teal.widgets_0.4.2.9006  lattice_0.22-5           tibble_3.2.1            \n[52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           \n[55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   \n[58] checkmate_2.3.1          shinyjs_2.1.0            generics_0.1.3          \n[61] chromote_0.2.0           munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] tools_4.3.3              webshot2_0.1.1           webshot_0.5.5           \n[70] forcats_1.0.0            mvtnorm_1.2-4            grid_4.3.3              \n[73] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[76] nlme_3.1-164             cli_3.6.2                textshaping_0.3.7       \n[79] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[82] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[85] htmlwidgets_1.6.4        farver_2.1.1             memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[91] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "For illustration purposes, we will subset the adlb dataset for safety population in treatment arm A and a specific lab parameter (ALT).\n\n\nData Setup\nStandard Plot\nPlot with Patient Baselines as Reference\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\n# use small sample size\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:15)\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% adsl$USUBJID)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadlb &lt;- df_explicit_na(adlb)\n\nadlb_f &lt;- adlb %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    PARAMCD == \"ALT\",\n    AVISIT != \"SCREENING\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID))\n\n\n\n\nThe user can select different plotting_choices depending on their preference. To demonstrate, separate plots are produced with a maximum of 3 observations each.\n\n\nCodeplots &lt;- g_ipp(\n  df = adlb_f,\n  xvar = \"AVISIT\",\n  yvar = \"AVAL\",\n  xlab = \"Visit\",\n  ylab = \"SGOT/ALT (U/L)\",\n  id_var = \"Patient_ID\",\n  title = \"Individual Patient Plots\",\n  subtitle = \"Treatment Arm A\",\n  plotting_choices = \"split_by_max_obs\",\n  max_obs_per_plot = 3\n)\nplots\n\n[[1]]\n\n\n\n\n\n\n\n\n\n[[2]]\n\n\n\n\n\n\n\n\n\n\nHere, patients’ individual baseline values will be shown for reference. Note that users can provide their own custom theme to the function via the ggtheme argument.\n\n\nCodeplots &lt;- g_ipp(\n  df = adlb_f,\n  xvar = \"AVISIT\",\n  yvar = \"AVAL\",\n  xlab = \"Visit\",\n  ylab = \"SGOT/ALT (U/L)\",\n  id_var = \"Patient_ID\",\n  title = \"Individual Patient Plots\",\n  subtitle = \"Treatment Arm A\",\n  add_baseline_hline = TRUE,\n  yvar_baseline = \"BASE\",\n  ggtheme = theme_minimal(),\n  plotting_choices = \"split_by_max_obs\",\n  max_obs_per_plot = 3\n)\nplots\n\n[[1]]\n\n\n\n\n\n\n\n\n\n[[2]]\n\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(tern)\n  library(scda)\n  library(dplyr)\n\n  # use small sample size\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;% slice(1:15)\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;% filter(USUBJID %in% ADSL$USUBJID)\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADLB &lt;- df_explicit_na(ADLB) %&gt;%\n    filter(AVISIT != \"SCREENING\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_ipp(\n      label = \"Individual Patient Plot\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        value_choices(ADLB, c(\"ARMCD\")),\n        \"ARM A\"\n      ),\n      paramcd = choices_selected(\n        value_choices(ADLB, \"PARAMCD\"),\n        \"ALT\"\n      ),\n      aval_var = choices_selected(\n        variable_choices(ADLB, c(\"AVAL\")),\n        \"AVAL\"\n      ),\n      avalu_var = choices_selected(\n        variable_choices(ADLB, c(\"AVALU\")),\n        \"AVALU\",\n        fixed = TRUE\n      ),\n      id_var = choices_selected(\n        variable_choices(ADLB, c(\"USUBJID\")),\n        \"USUBJID\",\n        fixed = TRUE\n      ),\n      visit_var = choices_selected(\n        variable_choices(ADLB, c(\"AVISIT\")),\n        \"AVISIT\"\n      ),\n      baseline_var = choices_selected(\n        variable_choices(ADLB, c(\"BASE\")),\n        \"BASE\",\n        fixed = TRUE\n      ),\n      add_baseline_hline = FALSE,\n      separate_by_obs = FALSE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:58:48 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] ggplot2_3.5.0                    dplyr_1.1.4                     \n[11] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[13] rtables_0.6.6.9011               magrittr_2.0.3                  \n[15] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[37] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[43] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              \n[46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          \n[49] teal.widgets_0.4.2.9008  lattice_0.22-5           tibble_3.2.1            \n[52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           \n[55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   \n[58] checkmate_2.3.1          shinyjs_2.1.0            generics_0.1.3          \n[61] chromote_0.2.0           munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] tools_4.3.3              webshot2_0.1.1           webshot_0.5.5           \n[70] forcats_1.0.0            mvtnorm_1.2-4            grid_4.3.3              \n[73] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[76] nlme_3.1-164             cli_3.6.2                textshaping_0.3.7       \n[79] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[82] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[85] htmlwidgets_1.6.4        farver_2.1.1             memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[91] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Other",
@@ -108,7 +108,7 @@
     "href": "graphs/efficacy/mmrmg01.html",
     "title": "MMRMG01",
     "section": "",
-    "text": "Given an MMRM fitted with s_mmrm, g_mmrm_lsmeans displays for each visit the adjusted means within group and/or difference in adjusted means between groups. g_mmrm_diagnostic displays marginal residual plots for evaluating model fit.\n\n\nData Setup and  Model Fitting\nLeast Squares Means:  Estimates Within Groups\nLeast Squares Means:  Contrasts Between Groups\nModel Diagnostics: Marginal  Fitted Values vs. Residuals\nModel Diagnostics: Normality  of Marginal Residuals\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern.mmrm)\nlibrary(scda)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\nadqs_f &lt;- adqs %&gt;%\n  dplyr::filter(PARAMCD == \"FKSI-FWB\" & !AVISIT %in% c(\"BASELINE\")) %&gt;%\n  droplevels() %&gt;%\n  dplyr::mutate(ARM = factor(ARM, levels = c(\"B: Placebo\", \"A: Drug X\", \"C: Combination\"))) %&gt;%\n  dplyr::mutate(AVISITN = rank(AVISITN) %&gt;% as.factor() %&gt;% as.numeric() %&gt;% as.factor())\n\nmmrm_results &lt;- fit_mmrm(\n  vars = list(\n    response = \"AVAL\",\n    covariates = c(\"STRATA2\"),\n    id = \"USUBJID\",\n    arm = \"ARM\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f\n)\n\n\n\n\nConsidering the treatment variable in the model\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"estimates\",\n  xlab = \"Visit\"\n)\nplot\n\n\n\n\n\n\n\nConsidering the treatment variable in the model, with lines\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"estimates\",\n  xlab = \"Visit\",\n  show_lines = TRUE\n)\nplot\n\n\n\n\n\n\n\nConsidering the treatment variable in the model, with statistics table\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"estimates\",\n  xlab = \"Visit\",\n  table_stats = c(\"n\", \"estimate\", \"se\", \"ci\"),\n  table_font_size = 4,\n  table_rel_height = 0.6\n)\nplot\n\n\n\n\n\n\n\n\n\n\nUsers can choose to display both estimates and contrasts together by running g_mmrm_lsmeans(mmrm_results).\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"contrasts\",\n  titles = c(contrasts = \"Contrasts of FKSI-FWB means\"),\n  xlab = \"Visit\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_mmrm_diagnostic(mmrm_results)\nplot\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_mmrm_diagnostic(mmrm_results, type = \"q-q-residual\")\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\") %&gt;%\n    filter(ABLFL != \"Y\" & ABLFL2 != \"Y\") %&gt;%\n    filter(AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\")) %&gt;%\n    mutate(\n      AVISIT = as.factor(AVISIT),\n      AVISITN = rank(AVISITN) %&gt;%\n        as.factor() %&gt;%\n        as.numeric() %&gt;%\n        as.factor() # making consecutive numeric factor\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\narm_ref_comp &lt;- list(\n  ARMCD = list(\n    ref = \"ARM A\",\n    comp = c(\"ARM B\", \"ARM C\")\n  )\n)\n\n## Reusable Configuration For Modules\nADQS &lt;- data[[\"ADQS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_a_mmrm(\n      label = \"MMRM\",\n      dataname = \"ADQS\",\n      aval_var = choices_selected(c(\"AVAL\", \"CHG\"), \"AVAL\"),\n      id_var = choices_selected(c(\"USUBJID\", \"SUBJID\"), \"USUBJID\"),\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARMCD\"),\n      visit_var = choices_selected(c(\"AVISIT\", \"AVISITN\"), \"AVISIT\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"FKSI-FWB\"\n      ),\n      cov_var = choices_selected(c(\"BASE\", \"AGE\", \"SEX\", \"BASE:AVISIT\"), NULL),\n      conf_level = choices_selected(c(0.95, 0.9, 0.8), 0.95)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:59:52 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] scda_0.1.6.9017                  tern.mmrm_0.3.0.9005            \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9013           \n[15] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               mmrm_0.3.11              formatR_1.14            \n [4] tern.gee_0.1.3.9004      logger_0.3.0             testthat_3.2.1          \n [7] sandwich_3.1-0           rlang_1.1.3              multcomp_1.4-25         \n[10] compiler_4.3.3           callr_3.7.5              vctrs_0.6.5             \n[13] stringr_1.5.1            pkgconfig_2.0.3          fastmap_1.1.1           \n[16] backports_1.4.1          ellipsis_0.3.2           fontawesome_0.5.2       \n[19] labeling_0.4.3           utf8_1.2.4               promises_1.2.1          \n[22] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              parallel_4.3.3           R6_2.5.1                \n[34] bslib_0.6.1              stringi_1.8.3            parallelly_1.37.1       \n[37] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[40] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[43] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[46] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              \n[49] TMB_1.9.10               codetools_0.2-19         websocket_1.4.1         \n[52] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[55] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[58] evaluate_0.23            survival_3.5-8           shinycssloaders_1.0.0   \n[61] pillar_1.9.0             checkmate_2.3.1          shinyjs_2.1.0           \n[64] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[67] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[70] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[73] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[76] cowplot_1.1.3            grid_4.3.3               tidyr_1.3.1             \n[79] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[82] cli_3.6.2                fansi_1.0.6              geepack_1.3.10          \n[85] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[88] TH.data_1.1-2            htmlwidgets_1.6.4        farver_2.1.1            \n[91] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[94] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Given an MMRM fitted with s_mmrm, g_mmrm_lsmeans displays for each visit the adjusted means within group and/or difference in adjusted means between groups. g_mmrm_diagnostic displays marginal residual plots for evaluating model fit.\n\n\nData Setup and  Model Fitting\nLeast Squares Means:  Estimates Within Groups\nLeast Squares Means:  Contrasts Between Groups\nModel Diagnostics: Marginal  Fitted Values vs. Residuals\nModel Diagnostics: Normality  of Marginal Residuals\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern.mmrm)\nlibrary(scda)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\nadqs_f &lt;- adqs %&gt;%\n  dplyr::filter(PARAMCD == \"FKSI-FWB\" & !AVISIT %in% c(\"BASELINE\")) %&gt;%\n  droplevels() %&gt;%\n  dplyr::mutate(ARM = factor(ARM, levels = c(\"B: Placebo\", \"A: Drug X\", \"C: Combination\"))) %&gt;%\n  dplyr::mutate(AVISITN = rank(AVISITN) %&gt;% as.factor() %&gt;% as.numeric() %&gt;% as.factor())\n\nmmrm_results &lt;- fit_mmrm(\n  vars = list(\n    response = \"AVAL\",\n    covariates = c(\"STRATA2\"),\n    id = \"USUBJID\",\n    arm = \"ARM\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f\n)\n\n\n\n\nConsidering the treatment variable in the model\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"estimates\",\n  xlab = \"Visit\"\n)\nplot\n\n\n\n\n\n\n\nConsidering the treatment variable in the model, with lines\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"estimates\",\n  xlab = \"Visit\",\n  show_lines = TRUE\n)\nplot\n\n\n\n\n\n\n\nConsidering the treatment variable in the model, with statistics table\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"estimates\",\n  xlab = \"Visit\",\n  table_stats = c(\"n\", \"estimate\", \"se\", \"ci\"),\n  table_font_size = 4,\n  table_rel_height = 0.6\n)\nplot\n\n\n\n\n\n\n\n\n\n\nUsers can choose to display both estimates and contrasts together by running g_mmrm_lsmeans(mmrm_results).\n\n\nCodeplot &lt;- g_mmrm_lsmeans(\n  mmrm_results,\n  select = \"contrasts\",\n  titles = c(contrasts = \"Contrasts of FKSI-FWB means\"),\n  xlab = \"Visit\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_mmrm_diagnostic(mmrm_results)\nplot\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- g_mmrm_diagnostic(mmrm_results, type = \"q-q-residual\")\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\") %&gt;%\n    filter(ABLFL != \"Y\" & ABLFL2 != \"Y\") %&gt;%\n    filter(AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\")) %&gt;%\n    mutate(\n      AVISIT = as.factor(AVISIT),\n      AVISITN = rank(AVISITN) %&gt;%\n        as.factor() %&gt;%\n        as.numeric() %&gt;%\n        as.factor() # making consecutive numeric factor\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\narm_ref_comp &lt;- list(\n  ARMCD = list(\n    ref = \"ARM A\",\n    comp = c(\"ARM B\", \"ARM C\")\n  )\n)\n\n## Reusable Configuration For Modules\nADQS &lt;- data[[\"ADQS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_a_mmrm(\n      label = \"MMRM\",\n      dataname = \"ADQS\",\n      aval_var = choices_selected(c(\"AVAL\", \"CHG\"), \"AVAL\"),\n      id_var = choices_selected(c(\"USUBJID\", \"SUBJID\"), \"USUBJID\"),\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARMCD\"),\n      visit_var = choices_selected(c(\"AVISIT\", \"AVISITN\"), \"AVISIT\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"FKSI-FWB\"\n      ),\n      cov_var = choices_selected(c(\"BASE\", \"AGE\", \"SEX\", \"BASE:AVISIT\"), NULL),\n      conf_level = choices_selected(c(0.95, 0.9, 0.8), 0.95)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:58:10 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] scda_0.1.6.9017                  tern.mmrm_0.3.0.9005            \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9015           \n[15] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               mmrm_0.3.11              formatR_1.14            \n [4] tern.gee_0.1.3.9004      logger_0.3.0             testthat_3.2.1          \n [7] sandwich_3.1-0           rlang_1.1.3              multcomp_1.4-25         \n[10] compiler_4.3.3           callr_3.7.5              vctrs_0.6.5             \n[13] stringr_1.5.1            pkgconfig_2.0.3          fastmap_1.1.1           \n[16] backports_1.4.1          ellipsis_0.3.2           fontawesome_0.5.2       \n[19] labeling_0.4.3           utf8_1.2.4               promises_1.2.1          \n[22] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              parallel_4.3.3           R6_2.5.1                \n[34] bslib_0.6.1              stringi_1.8.3            parallelly_1.37.1       \n[37] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[40] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[43] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[46] splines_4.3.3            tidyselect_1.2.0         yaml_2.3.8              \n[49] TMB_1.9.10               codetools_0.2-19         websocket_1.4.1         \n[52] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[55] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[58] evaluate_0.23            survival_3.5-8           shinycssloaders_1.0.0   \n[61] pillar_1.9.0             checkmate_2.3.1          shinyjs_2.1.0           \n[64] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[67] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[70] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[73] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[76] cowplot_1.1.3            grid_4.3.3               tidyr_1.3.1             \n[79] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[82] cli_3.6.2                fansi_1.0.6              geepack_1.3.10          \n[85] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[88] TH.data_1.1-2            htmlwidgets_1.6.4        farver_2.1.1            \n[91] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[94] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Efficacy",
@@ -120,7 +120,7 @@
     "href": "graphs/efficacy/fstg02.html",
     "title": "FSTG02",
     "section": "",
-    "text": "Data Setup\nStandard Plot\nPlot Specifying Class Variables and  Options for the Treatment Variable\nPlot Selecting Columns and  Changing the Alpha Level\nPlot with Fixed  Symbol Size\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(forcats)\nlibrary(nestcolor)\nlibrary(scda)\n\npreprocess_adtte &lt;- function(adtte) {\n  # Save variable labels before data processing steps.\n  adtte_labels &lt;- var_labels(adtte)\n\n  adtte &lt;- adtte %&gt;%\n    df_explicit_na() %&gt;%\n    dplyr::filter(\n      PARAMCD == \"OS\",\n      ARM %in% c(\"B: Placebo\", \"A: Drug X\"),\n      SEX %in% c(\"M\", \"F\")\n    ) %&gt;%\n    dplyr::mutate(\n      # Reorder levels of ARM to display reference arm before treatment arm.\n      ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")),\n      SEX = droplevels(SEX),\n      is_event = CNSR == 0,\n      # Convert time to MONTH\n      AVAL = day2month(AVAL),\n      AVALU = \"Months\"\n    ) %&gt;%\n    var_relabel(\n      ARM = adtte_labels[\"ARM\"],\n      SEX = adtte_labels[\"SEX\"],\n      is_event = \"Event Flag\",\n      AVAL = adtte_labels[\"AVAL\"],\n      AVALU = adtte_labels[\"AVALU\"]\n    )\n\n  adtte\n}\n\nanl &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\") %&gt;%\n  preprocess_adtte()\n\n\n\n\n\n\nCodeanl1 &lt;- anl\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),\n  data = anl1\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"n\", \"median\", \"hr\", \"ci\"),\n    time_unit = anl1$AVALU[1]\n  )\nresult\n\n                                               B: Placebo               A: Drug X                                    \nBaseline Risk Factors           Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                      268     134         NA          134         NA              1.00       (0.69, 1.44)\nSex                                                                                                                  \n  F                               161     82          NA          79          NA              0.79       (0.49, 1.28)\n  M                               107     52          NA          55          9.6             1.39       (0.78, 2.47)\nCategorical Level Biomarker 2                                                                                        \n  LOW                             95      45          NA          50          9.3             1.14       (0.64, 2.02)\n  MEDIUM                          93      56          NA          37          NA              0.97       (0.52, 1.82)\n  HIGH                            80      33          NA          47          NA              0.97       (0.45, 2.12)\n\nCode# Add plot.\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl2 &lt;- anl %&gt;%\n  mutate(\n    # Recode levels of arm.\n    ARM = forcats::fct_recode(\n      ARM,\n      \"Placebo\" = \"B: Placebo\",\n      \"Drug X\" = \"A: Drug X\"\n    ),\n    # Reorder levels of `SEX`.\n    SEX = forcats::fct_relevel(SEX, \"M\", \"F\"),\n    # Reorder levels of `STRATA1`` by frequency.\n    STRATA1 = forcats::fct_infreq(STRATA1)\n  )\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA1\")),\n  data = anl2\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"n\", \"median\", \"hr\", \"ci\"),\n    time_unit = anl2$AVALU[1]\n  )\nresult\n\n                                           Placebo                 Drug X                                      \nBaseline Risk Factors     Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134         NA          134         NA              1.00       (0.69, 1.44)\nSex                                                                                                            \n  M                         107     52          NA          55          9.6             1.39       (0.78, 2.47)\n  F                         161     82          NA          79          NA              0.79       (0.49, 1.28)\nStratification Factor 1                                                                                        \n  C                         94      45          NA          49          NA              0.75       (0.41, 1.38)\n  B                         92      45          NA          47          NA              1.34       (0.71, 2.54)\n  A                         82      44          NA          38          NA              1.02       (0.53, 1.97)\n\nCodeplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl3 &lt;- anl\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),\n  control = control_coxph(conf_level = 0.9),\n  data = anl3\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"hr\", \"ci\")\n  )\nresult\n\n                                                                     \nBaseline Risk Factors           Total n   Hazard Ratio   90% Wald CI \n—————————————————————————————————————————————————————————————————————\nAll Patients                      268         1.00       (0.74, 1.36)\nSex                                                                  \n  F                               161         0.79       (0.53, 1.19)\n  M                               107         1.39       (0.86, 2.25)\nCategorical Level Biomarker 2                                        \n  LOW                             95          1.14       (0.71, 1.84)\n  MEDIUM                          93          0.97       (0.58, 1.64)\n  HIGH                            80          0.97       (0.51, 1.87)\n\nCode# Add plot.\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl4 &lt;- anl\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),\n  data = anl4\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"n\", \"median\", \"hr\", \"ci\"),\n    time_unit = anl4$AVALU[1]\n  )\nresult\n\n                                               B: Placebo               A: Drug X                                    \nBaseline Risk Factors           Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                      268     134         NA          134         NA              1.00       (0.69, 1.44)\nSex                                                                                                                  \n  F                               161     82          NA          79          NA              0.79       (0.49, 1.28)\n  M                               107     52          NA          55          9.6             1.39       (0.78, 2.47)\nCategorical Level Biomarker 2                                                                                        \n  LOW                             95      45          NA          50          9.3             1.14       (0.64, 2.02)\n  MEDIUM                          93      56          NA          37          NA              0.97       (0.52, 1.82)\n  HIGH                            80      33          NA          47          NA              0.97       (0.45, 2.12)\n\nCode# Add plot.\nplot &lt;- g_forest(\n  tbl = result,\n  col_symbol_size = NULL\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  library(forcats)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADSL &lt;- ADSL %&gt;%\n    filter(ARM %in% c(\"B: Placebo\", \"A: Drug X\")) %&gt;%\n    mutate(ARM = droplevels(fct_relevel(ARM, \"B: Placebo\"))) %&gt;%\n    mutate(ARMCD = droplevels(fct_relevel(ARMCD, \"ARM B\")))\n  ADSL$RACE &lt;- droplevels(ADSL$RACE)\n\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n  adtte_labels &lt;- col_labels(ADTTE)\n\n  ADTTE &lt;- ADTTE %&gt;%\n    filter(\n      PARAMCD == \"OS\",\n      ARM %in% c(\"B: Placebo\", \"A: Drug X\"),\n      SEX %in% c(\"M\", \"F\")\n    ) %&gt;%\n    mutate(\n      # Reorder levels of ARM to display reference arm before treatment arm.\n      ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),\n      SEX = droplevels(SEX),\n      is_event = CNSR == 0,\n      # Convert time to MONTH\n      AVAL = day2month(AVAL),\n      AVALU = \"Months\"\n    ) %&gt;%\n    col_relabel(\n      ARM = adtte_labels[\"ARM\"],\n      SEX = adtte_labels[\"SEX\"],\n      is_event = \"Event Flag\",\n      AVAL = adtte_labels[\"AVAL\"],\n      AVALU = adtte_labels[\"AVALU\"]\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_forest_tte(\n      label = \"Forest Survival\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      subgroup_var = choices_selected(names(ADSL), c(\"SEX\", \"BMRKR2\")),\n      paramcd = choices_selected(value_choices(ADTTE, \"PARAMCD\", \"PARAM\"), \"OS\"),\n      strata_var = choices_selected(c(\"STRATA1\", \"STRATA2\"), \"STRATA2\"),\n      plot_height = c(600, 200, 2000),\n      plot_width = c(1500, 200, 5000)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:58:55 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] nestcolor_0.1.2.9009             forcats_1.0.0                   \n[11] dplyr_1.1.4                      tern_0.9.3.9024                 \n[13] rtables_0.6.6.9011               magrittr_2.0.3                  \n[15] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[73] cowplot_1.1.3            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                textshaping_0.3.7        fansi_1.0.6             \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] farver_2.1.1             memoise_2.0.1            htmltools_0.5.7         \n[91] lifecycle_1.0.4          shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Plot\nPlot Specifying Class Variables and  Options for the Treatment Variable\nPlot Selecting Columns and  Changing the Alpha Level\nPlot with Fixed  Symbol Size\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(forcats)\nlibrary(nestcolor)\nlibrary(scda)\n\npreprocess_adtte &lt;- function(adtte) {\n  # Save variable labels before data processing steps.\n  adtte_labels &lt;- var_labels(adtte)\n\n  adtte &lt;- adtte %&gt;%\n    df_explicit_na() %&gt;%\n    dplyr::filter(\n      PARAMCD == \"OS\",\n      ARM %in% c(\"B: Placebo\", \"A: Drug X\"),\n      SEX %in% c(\"M\", \"F\")\n    ) %&gt;%\n    dplyr::mutate(\n      # Reorder levels of ARM to display reference arm before treatment arm.\n      ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")),\n      SEX = droplevels(SEX),\n      is_event = CNSR == 0,\n      # Convert time to MONTH\n      AVAL = day2month(AVAL),\n      AVALU = \"Months\"\n    ) %&gt;%\n    var_relabel(\n      ARM = adtte_labels[\"ARM\"],\n      SEX = adtte_labels[\"SEX\"],\n      is_event = \"Event Flag\",\n      AVAL = adtte_labels[\"AVAL\"],\n      AVALU = adtte_labels[\"AVALU\"]\n    )\n\n  adtte\n}\n\nanl &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\") %&gt;%\n  preprocess_adtte()\n\n\n\n\n\n\nCodeanl1 &lt;- anl\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),\n  data = anl1\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"n\", \"median\", \"hr\", \"ci\"),\n    time_unit = anl1$AVALU[1]\n  )\nresult\n\n                                               B: Placebo               A: Drug X                                    \nBaseline Risk Factors           Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                      268     134         NA          134         NA              1.00       (0.69, 1.44)\nSex                                                                                                                  \n  F                               161     82          NA          79          NA              0.79       (0.49, 1.28)\n  M                               107     52          NA          55          9.6             1.39       (0.78, 2.47)\nCategorical Level Biomarker 2                                                                                        \n  LOW                             95      45          NA          50          9.3             1.14       (0.64, 2.02)\n  MEDIUM                          93      56          NA          37          NA              0.97       (0.52, 1.82)\n  HIGH                            80      33          NA          47          NA              0.97       (0.45, 2.12)\n\nCode# Add plot.\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl2 &lt;- anl %&gt;%\n  mutate(\n    # Recode levels of arm.\n    ARM = forcats::fct_recode(\n      ARM,\n      \"Placebo\" = \"B: Placebo\",\n      \"Drug X\" = \"A: Drug X\"\n    ),\n    # Reorder levels of `SEX`.\n    SEX = forcats::fct_relevel(SEX, \"M\", \"F\"),\n    # Reorder levels of `STRATA1`` by frequency.\n    STRATA1 = forcats::fct_infreq(STRATA1)\n  )\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA1\")),\n  data = anl2\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"n\", \"median\", \"hr\", \"ci\"),\n    time_unit = anl2$AVALU[1]\n  )\nresult\n\n                                           Placebo                 Drug X                                      \nBaseline Risk Factors     Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134         NA          134         NA              1.00       (0.69, 1.44)\nSex                                                                                                            \n  M                         107     52          NA          55          9.6             1.39       (0.78, 2.47)\n  F                         161     82          NA          79          NA              0.79       (0.49, 1.28)\nStratification Factor 1                                                                                        \n  C                         94      45          NA          49          NA              0.75       (0.41, 1.38)\n  B                         92      45          NA          47          NA              1.34       (0.71, 2.54)\n  A                         82      44          NA          38          NA              1.02       (0.53, 1.97)\n\nCodeplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl3 &lt;- anl\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),\n  control = control_coxph(conf_level = 0.9),\n  data = anl3\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"hr\", \"ci\")\n  )\nresult\n\n                                                                     \nBaseline Risk Factors           Total n   Hazard Ratio   90% Wald CI \n—————————————————————————————————————————————————————————————————————\nAll Patients                      268         1.00       (0.74, 1.36)\nSex                                                                  \n  F                               161         0.79       (0.53, 1.19)\n  M                               107         1.39       (0.86, 2.25)\nCategorical Level Biomarker 2                                        \n  LOW                             95          1.14       (0.71, 1.84)\n  MEDIUM                          93          0.97       (0.58, 1.64)\n  HIGH                            80          0.97       (0.51, 1.87)\n\nCode# Add plot.\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl4 &lt;- anl\n\ndf &lt;- extract_survival_subgroups(\n  variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),\n  data = anl4\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_survival_subgroups(\n    df = df,\n    vars = c(\"n_tot\", \"n\", \"median\", \"hr\", \"ci\"),\n    time_unit = anl4$AVALU[1]\n  )\nresult\n\n                                               B: Placebo               A: Drug X                                    \nBaseline Risk Factors           Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                      268     134         NA          134         NA              1.00       (0.69, 1.44)\nSex                                                                                                                  \n  F                               161     82          NA          79          NA              0.79       (0.49, 1.28)\n  M                               107     52          NA          55          9.6             1.39       (0.78, 2.47)\nCategorical Level Biomarker 2                                                                                        \n  LOW                             95      45          NA          50          9.3             1.14       (0.64, 2.02)\n  MEDIUM                          93      56          NA          37          NA              0.97       (0.52, 1.82)\n  HIGH                            80      33          NA          47          NA              0.97       (0.45, 2.12)\n\nCode# Add plot.\nplot &lt;- g_forest(\n  tbl = result,\n  col_symbol_size = NULL\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  library(forcats)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADSL &lt;- ADSL %&gt;%\n    filter(ARM %in% c(\"B: Placebo\", \"A: Drug X\")) %&gt;%\n    mutate(ARM = droplevels(fct_relevel(ARM, \"B: Placebo\"))) %&gt;%\n    mutate(ARMCD = droplevels(fct_relevel(ARMCD, \"ARM B\")))\n  ADSL$RACE &lt;- droplevels(ADSL$RACE)\n\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n  adtte_labels &lt;- col_labels(ADTTE)\n\n  ADTTE &lt;- ADTTE %&gt;%\n    filter(\n      PARAMCD == \"OS\",\n      ARM %in% c(\"B: Placebo\", \"A: Drug X\"),\n      SEX %in% c(\"M\", \"F\")\n    ) %&gt;%\n    mutate(\n      # Reorder levels of ARM to display reference arm before treatment arm.\n      ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),\n      SEX = droplevels(SEX),\n      is_event = CNSR == 0,\n      # Convert time to MONTH\n      AVAL = day2month(AVAL),\n      AVALU = \"Months\"\n    ) %&gt;%\n    col_relabel(\n      ARM = adtte_labels[\"ARM\"],\n      SEX = adtte_labels[\"SEX\"],\n      is_event = \"Event Flag\",\n      AVAL = adtte_labels[\"AVAL\"],\n      AVALU = adtte_labels[\"AVALU\"]\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_forest_tte(\n      label = \"Forest Survival\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      subgroup_var = choices_selected(names(ADSL), c(\"SEX\", \"BMRKR2\")),\n      paramcd = choices_selected(value_choices(ADTTE, \"PARAMCD\", \"PARAM\"), \"OS\"),\n      strata_var = choices_selected(c(\"STRATA1\", \"STRATA2\"), \"STRATA2\"),\n      plot_height = c(600, 200, 2000),\n      plot_width = c(1500, 200, 5000)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:57:15 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] nestcolor_0.1.2.9009             forcats_1.0.0                   \n[11] dplyr_1.1.4                      tern_0.9.3.9024                 \n[13] rtables_0.6.6.9011               magrittr_2.0.3                  \n[15] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[73] cowplot_1.1.3            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                textshaping_0.3.7        fansi_1.0.6             \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] farver_2.1.1             memoise_2.0.1            htmltools_0.5.7         \n[91] lifecycle_1.0.4          shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Efficacy",
@@ -132,7 +132,7 @@
     "href": "graphs/efficacy/kmg01.html",
     "title": "KMG01",
     "section": "",
-    "text": "Data Setup\nStandard Plot\nPlot of Failures\nPlot Without  Comparative Statistics\nPlot Without  Censoring Marks\nPlot Modifying  Censoring Marks\nPlot Modifying Options for Statistics,  Tie Handling, Stratification, etc.\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(nestcolor)\n\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\nanl &lt;- adtte %&gt;%\n  dplyr::filter(PARAMCD == \"OS\") %&gt;%\n  dplyr::mutate(is_event = CNSR == 0)\nvariables &lt;- list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\")\n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.155] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  xlab = \"Time (Days)\",\n  yval = \"Failure\",\n  ylim = c(0, 1),\n  annot_surv_med = TRUE,\n  annot_coxph = TRUE,\n  font_size = 7.5,\n  position_coxph = c(0.3, 0),\n  position_surv_med = c(0.9, 0.3)\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.298] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_surv_med = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.415] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  censor_show = FALSE,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.546] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  pch = 1,\n  size = 2,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.689] \n\n\n\n\n\n\nCodevariables$strata &lt;- c(\"STRATA1\", \"STRATA2\")\nplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  control_surv = control_surv_timepoint(conf_level = 0.8),\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE,\n  control_coxph_pw = control_coxph(\n    pval_method = \"wald\",\n    ties = \"breslow\",\n    conf_level = 0.8\n  )\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.832] \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_km(\n      label = \"KM PLOT\",\n      plot_height = c(600, 100, 2000),\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"),\n        \"OS\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        \"SEX\"\n      ),\n      facet_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        NULL\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:58:12 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] dplyr_1.1.4                      scda_0.1.6.9017                 \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               gridExtra_2.3            formatR_1.14            \n [4] tern.gee_0.1.3.9004      logger_0.3.0             testthat_3.2.1          \n [7] sandwich_3.1-0           rlang_1.1.3              multcomp_1.4-25         \n[10] compiler_4.3.3           systemfonts_1.0.6        callr_3.7.5             \n[13] vctrs_0.6.5              pkgconfig_2.0.3          fastmap_1.1.1           \n[16] backports_1.4.1          ellipsis_0.3.2           fontawesome_0.5.2       \n[19] labeling_0.4.3           utf8_1.2.4               promises_1.2.1          \n[22] rmarkdown_2.26           ps_1.7.6                 ragg_1.2.7              \n[25] purrr_1.0.2              xfun_0.42                shinyvalidate_0.1.3     \n[28] cachem_1.0.8             teal.reporter_0.3.0.9005 jsonlite_1.8.8          \n[31] later_1.3.2              broom_1.0.5              R6_2.5.1                \n[34] bslib_0.6.1              brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[73] grid_4.3.3               tidyr_1.3.1              rbibutils_2.2.16        \n[76] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[79] textshaping_0.3.7        fansi_1.0.6              geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        farver_2.1.1            \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Plot\nPlot of Failures\nPlot Without  Comparative Statistics\nPlot Without  Censoring Marks\nPlot Modifying  Censoring Marks\nPlot Modifying Options for Statistics,  Tie Handling, Stratification, etc.\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(nestcolor)\n\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\nanl &lt;- adtte %&gt;%\n  dplyr::filter(PARAMCD == \"OS\") %&gt;%\n  dplyr::mutate(is_event = CNSR == 0)\nvariables &lt;- list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\")\n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.155] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  xlab = \"Time (Days)\",\n  yval = \"Failure\",\n  ylim = c(0, 1),\n  annot_surv_med = TRUE,\n  annot_coxph = TRUE,\n  font_size = 7.5,\n  position_coxph = c(0.3, 0),\n  position_surv_med = c(0.9, 0.3)\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.298] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_surv_med = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.415] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  censor_show = FALSE,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.546] \n\n\n\n\n\n\nCodeplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  pch = 1,\n  size = 2,\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.689] \n\n\n\n\n\n\nCodevariables$strata &lt;- c(\"STRATA1\", \"STRATA2\")\nplot &lt;- g_km(\n  df = anl,\n  variables = variables,\n  control_surv = control_surv_timepoint(conf_level = 0.8),\n  xlab = \"Time (Days)\",\n  ylim = c(0, 1),\n  annot_coxph = TRUE,\n  control_coxph_pw = control_coxph(\n    pval_method = \"wald\",\n    ties = \"breslow\",\n    conf_level = 0.8\n  )\n)\n\n\n\n\n\n\nCodeplot\n\ngTree[GRID.gTree.832] \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_km(\n      label = \"KM PLOT\",\n      plot_height = c(600, 100, 2000),\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"),\n        \"OS\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        \"SEX\"\n      ),\n      facet_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        NULL\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:56:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] dplyr_1.1.4                      scda_0.1.6.9017                 \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               gridExtra_2.3            formatR_1.14            \n [4] tern.gee_0.1.3.9004      logger_0.3.0             testthat_3.2.1          \n [7] sandwich_3.1-0           rlang_1.1.3              multcomp_1.4-25         \n[10] compiler_4.3.3           systemfonts_1.0.6        callr_3.7.5             \n[13] vctrs_0.6.5              pkgconfig_2.0.3          fastmap_1.1.1           \n[16] backports_1.4.1          ellipsis_0.3.2           fontawesome_0.5.2       \n[19] labeling_0.4.3           utf8_1.2.4               promises_1.2.1          \n[22] rmarkdown_2.26           ps_1.7.6                 ragg_1.2.7              \n[25] purrr_1.0.2              xfun_0.42                shinyvalidate_0.1.3     \n[28] cachem_1.0.8             teal.reporter_0.3.0.9005 jsonlite_1.8.8          \n[31] later_1.3.2              broom_1.0.5              R6_2.5.1                \n[34] bslib_0.6.1              brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[73] grid_4.3.3               tidyr_1.3.1              rbibutils_2.2.16        \n[76] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[79] textshaping_0.3.7        fansi_1.0.6              geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        farver_2.1.1            \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Efficacy",
@@ -144,7 +144,7 @@
     "href": "graphs/pharmacokinetic/pkcg01.html",
     "title": "PKCG01",
     "section": "",
-    "text": "Data Setup\nPlot in Linear Scale\nPlot in Log Scale\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID))\n\nuse_title &lt;- \"Plot of Plasma Drug X (ug/mL) Concentrations Over Time by\\nPatient: PK Evaluable Patients\"\nuse_subtitle &lt;- \"Analyte: Plasma Drug X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"AVAL\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  plotting_choices = \"separate_by_obs\"\n)\nplot &lt;- result[[1]] # only show the first subject\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(avalog = ifelse(AVAL != 0, AVAL, 0.001)) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID))\n\nuse_title2 &lt;- \"Plot of Plasma Drug X (ug/mL) Concentrations Over Time by\\nPatient: PK Evaluable Patients\"\nuse_subtitle2 &lt;- \"Analyte: Plasma Drug X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote2 &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"avalog\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title2,\n  subtitle = use_subtitle2,\n  caption = use_footnote2,\n  plotting_choices = \"separate_by_obs\"\n)\nplot &lt;- result[[1]] + ggplot2::scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10), labels = c(0.001, 0.01, 0.1, 1, 10))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:47 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    broom_1.0.5       xfun_0.42        \n[29] cli_3.6.2         withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        farver_2.1.1      codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot in Linear Scale\nPlot in Log Scale\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID))\n\nuse_title &lt;- \"Plot of Plasma Drug X (ug/mL) Concentrations Over Time by\\nPatient: PK Evaluable Patients\"\nuse_subtitle &lt;- \"Analyte: Plasma Drug X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"AVAL\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  plotting_choices = \"separate_by_obs\"\n)\nplot &lt;- result[[1]] # only show the first subject\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(avalog = ifelse(AVAL != 0, AVAL, 0.001)) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID))\n\nuse_title2 &lt;- \"Plot of Plasma Drug X (ug/mL) Concentrations Over Time by\\nPatient: PK Evaluable Patients\"\nuse_subtitle2 &lt;- \"Analyte: Plasma Drug X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote2 &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"avalog\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title2,\n  subtitle = use_subtitle2,\n  caption = use_footnote2,\n  plotting_choices = \"separate_by_obs\"\n)\nplot &lt;- result[[1]] + ggplot2::scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10), labels = c(0.001, 0.01, 0.1, 1, 10))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:56:07 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    broom_1.0.5       xfun_0.42        \n[29] cli_3.6.2         withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        farver_2.1.1      codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -156,7 +156,7 @@
     "href": "graphs/pharmacokinetic/pkpg01.html",
     "title": "PKPG01",
     "section": "",
-    "text": "Data Setup\nPlot with Two Cohorts\nPlot with Six Cohorts\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\nset.seed(123)\n\n# loading in the data\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n# filtering the rows for specific data entries\nadpp &lt;- adpp %&gt;%\n  filter(PARAMCD == \"RCPCINT\", AVISIT == \"CYCLE 1 DAY 1\", PPCAT == \"Plasma Drug X\")\n\n# adding or modifying specific columns\nadpp &lt;- adpp %&gt;%\n  mutate(ARM = as.factor(TRT01A)) %&gt;%\n  mutate(PARAM1 = \"Fe\") %&gt;% # re-format PK parameter name\n  mutate(Time = as.numeric(gsub(\"PT*|\\\\.|H$\", \"\", PPENINT))) %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\n# in cases where the cohorts are numeric it is possible to rename them\nlevels(adpp$ARM) &lt;- c(\n  \"A: Drug X\",\n  \"C: Combination\"\n)\n\n\n\n\n\n\nCodeuse_title &lt;- \"Plot of Mean (+/- SD) Cummulative Percentage (%) of Recovered Drug in Urine \\nby Analyte, Visit: PK Evaluable Patients\" \nuse_subtitle &lt;- \"Analyte: Plasma Drug X \\nVisit: CYCLE 1 DAY 1 \\nPK Parameter:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nseparation_between_barplots &lt;- 1.5\n\nresult &lt;- g_lineplot(\n  df = adpp,\n  variables = control_lineplot_vars(\n    x = \"Time\",\n    y = \"AVAL\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM1\",\n    y_unit = \"AVALU\"\n  ),\n  alt_counts_df = adpp,\n  y_lab = \"Cummulative Percentage\",\n  x_lab = \"Time (hours)\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest(),\n  position = ggplot2::position_dodge(width = 2)\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0)) +\n  scale_x_continuous(breaks = c(12, 24))\nplot\n\n\n\n\n\n\n\n\n\nIn this case we need to process the data further by artificially adding more random rows. Of course this step is not necessary in the case that data already has more cohorts.\n\n\nCodeadpp_hck &lt;- adpp %&gt;%\n  mutate(AVAL + 1 * rnorm(nrow(adpp), mean = 1, sd = 1)) %&gt;%\n  mutate(ARM = as.factor(sample(\n    c(\n      \"D: Drug D\",\n      \"E: Drug E\",\n      \"F: Drug F\",\n      \"G: Drug G\"\n    ),\n    nrow(adpp),\n    replace = TRUE,\n    prob = c(0.4, 0.3, 0.2, 0.1)\n  )))\n\nadpp &lt;- bind_rows(adpp, adpp_hck)\n\nuse_title &lt;- \"Plot of Mean (+/- SD) Cummulative Percentage (%) of Recovered Drug in Urine \\nby Analyte, Visit: PK Evaluable Patients\" \nuse_subtitle &lt;- \"Analyte: Plasma Drug X \\nVisit: CYCLE 1 DAY 1 \\nPK Parameter:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nseparation_between_barplots &lt;- 1.5\n\nresult &lt;- g_lineplot(\n  df = adpp,\n  variables = control_lineplot_vars(\n    x = \"Time\",\n    y = \"AVAL\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM1\",\n    y_unit = \"AVALU\"\n  ),\n  alt_counts_df = adpp,\n  y_lab = \"Cummulative Percentage\",\n  x_lab = \"Time (hours)\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest(),\n  position = ggplot2::position_dodge(width = 2)\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0)) +\n  scale_x_continuous(breaks = c(12, 24))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:32 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        farver_2.1.1      codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot with Two Cohorts\nPlot with Six Cohorts\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\nset.seed(123)\n\n# loading in the data\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n# filtering the rows for specific data entries\nadpp &lt;- adpp %&gt;%\n  filter(PARAMCD == \"RCPCINT\", AVISIT == \"CYCLE 1 DAY 1\", PPCAT == \"Plasma Drug X\")\n\n# adding or modifying specific columns\nadpp &lt;- adpp %&gt;%\n  mutate(ARM = as.factor(TRT01A)) %&gt;%\n  mutate(PARAM1 = \"Fe\") %&gt;% # re-format PK parameter name\n  mutate(Time = as.numeric(gsub(\"PT*|\\\\.|H$\", \"\", PPENINT))) %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\n# in cases where the cohorts are numeric it is possible to rename them\nlevels(adpp$ARM) &lt;- c(\n  \"A: Drug X\",\n  \"C: Combination\"\n)\n\n\n\n\n\n\nCodeuse_title &lt;- \"Plot of Mean (+/- SD) Cummulative Percentage (%) of Recovered Drug in Urine \\nby Analyte, Visit: PK Evaluable Patients\" \nuse_subtitle &lt;- \"Analyte: Plasma Drug X \\nVisit: CYCLE 1 DAY 1 \\nPK Parameter:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nseparation_between_barplots &lt;- 1.5\n\nresult &lt;- g_lineplot(\n  df = adpp,\n  variables = control_lineplot_vars(\n    x = \"Time\",\n    y = \"AVAL\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM1\",\n    y_unit = \"AVALU\"\n  ),\n  alt_counts_df = adpp,\n  y_lab = \"Cummulative Percentage\",\n  x_lab = \"Time (hours)\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest(),\n  position = ggplot2::position_dodge(width = 2)\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0)) +\n  scale_x_continuous(breaks = c(12, 24))\nplot\n\n\n\n\n\n\n\n\n\nIn this case we need to process the data further by artificially adding more random rows. Of course this step is not necessary in the case that data already has more cohorts.\n\n\nCodeadpp_hck &lt;- adpp %&gt;%\n  mutate(AVAL + 1 * rnorm(nrow(adpp), mean = 1, sd = 1)) %&gt;%\n  mutate(ARM = as.factor(sample(\n    c(\n      \"D: Drug D\",\n      \"E: Drug E\",\n      \"F: Drug F\",\n      \"G: Drug G\"\n    ),\n    nrow(adpp),\n    replace = TRUE,\n    prob = c(0.4, 0.3, 0.2, 0.1)\n  )))\n\nadpp &lt;- bind_rows(adpp, adpp_hck)\n\nuse_title &lt;- \"Plot of Mean (+/- SD) Cummulative Percentage (%) of Recovered Drug in Urine \\nby Analyte, Visit: PK Evaluable Patients\" \nuse_subtitle &lt;- \"Analyte: Plasma Drug X \\nVisit: CYCLE 1 DAY 1 \\nPK Parameter:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nseparation_between_barplots &lt;- 1.5\n\nresult &lt;- g_lineplot(\n  df = adpp,\n  variables = control_lineplot_vars(\n    x = \"Time\",\n    y = \"AVAL\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM1\",\n    y_unit = \"AVALU\"\n  ),\n  alt_counts_df = adpp,\n  y_lab = \"Cummulative Percentage\",\n  x_lab = \"Time (hours)\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest(),\n  position = ggplot2::position_dodge(width = 2)\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0)) +\n  scale_x_continuous(breaks = c(12, 24))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:51 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        farver_2.1.1      codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -168,7 +168,7 @@
     "href": "graphs/pharmacokinetic/pkpg02.html",
     "title": "PKPG02",
     "section": "",
-    "text": "Data Setup\nSummary of Pharmacokinetic  Parameters – Plasma\nSummary of Plasma Pharmacokinetic  Parameters with Median Points\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\n# need adex for dose info and adpp for AUC max info\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\nadpp_a &lt;- adpp %&gt;%\n  filter(\n    PPSPEC == \"Plasma\",\n    AVISITN == \"1\",\n    PARAMCD == \"AUCIFO\"\n  ) %&gt;%\n  mutate(AUCinf = AVAL)\n\nadex_a &lt;- adex %&gt;%\n  filter(\n    AVISITN == \"1\",\n    PARAMCD == \"DOSE\"\n  ) %&gt;%\n  mutate(Dose = AVAL) %&gt;%\n  select(USUBJID, Dose)\n\n# join the dose information to the adpp table\nadpp_adex &lt;- left_join(adpp_a, adex_a, by = \"USUBJID\") %&gt;%\n  group_by(`ARM`) %&gt;%\n  mutate(count = paste0(`ARM`, \" (\", n(), \")\"))\n\n# set x and y variable names\nx_var &lt;- \"Dose\"\ny_var &lt;- \"AUCinf\"\n\n\n\n\n\n\nCode# calculate Summary Statistics (mean and sd) for each group\nSummaryStat &lt;- adpp_adex %&gt;% \n  group_by(Dose = as.factor(Dose)) %&gt;%\n  summarise(AUCsd = sd(AUCinf), meanAUC = mean(AUCinf))\nSummaryStat$Dose &lt;- as.numeric(as.character(SummaryStat$Dose)) \n\n# generate linear model\nmod1 &lt;- lm(log(AUCinf) ~ log(Dose), adpp_adex)\n\n# obtain linear model coefficient values\ncf &lt;- round(coef(mod1), 2)\n\n# generate linear model equation\neq &lt;- paste0(\n  \"y = \", cf[1],\n  ifelse(sign(cf[2]) == 1, \" + \", \" - \"), abs(cf[2]), \" x , \",\n  \"R²\",\n  \" = \",\n  signif(summary(mod1)$adj.r.squared, 3)\n)\n\nplot &lt;- ggplot(adpp_adex, aes(x = .data[[x_var]], y = .data[[y_var]])) +\n  annotate(geom = \"text\", x = min(adpp_adex[[x_var]]), y = max(adpp_adex[[y_var]]), label = eq, hjust = 0.1) +\n  geom_point(size = 1, aes(color = factor(`count`))) +\n  scale_x_continuous(\n    name = \"Dose (mg/mL)\",\n    breaks = unique(adpp_adex$Dose)\n  ) +\n  scale_y_continuous(\n    name = paste(y_var, adpp_a$AVALU),\n    transform = \"log\",\n    breaks = exp(ceiling(seq(\n      from = min(log(adpp_adex$AUCinf)), to = max(log(adpp_adex$AUCinf)),\n      by = 1\n    ))),\n    labels = as.character(ceiling(seq(\n      from = min(log(adpp_adex$AUCinf)),\n      to = max(log(adpp_adex$AUCinf)), by = 1\n    )))\n  ) +\n  geom_smooth(method = \"lm\", formula = y ~ x, se = FALSE, color = \"black\", linewidth = 0.5) +\n  # Display error bars for each dosing group (this will only appear if the sd is less than the mean)\n  geom_errorbar(\n    data = SummaryStat,\n    aes(x = `Dose`, y = `meanAUC`, ymin = `meanAUC` - `AUCsd`, ymax = `meanAUC` + AUCsd),\n    width = .05,\n    position = position_dodge(.1)\n  ) +\n  geom_point(data = SummaryStat, aes(x = Dose, y = meanAUC, size = 1), shape = 2, show.legend = FALSE) +\n  ggtitle(paste(\n    \"Dose-Proportionality Plot of Serum\", as.character(unique(adex$TRT01P)),\n    y_var, \"in\", adpp_a$AVALU\n  ), subtitle = \"Summary of serum PK parameters by treatment\") +\n  labs(color = \"Treatment Arm\") +\n  theme_nest()\n\nplot\n\n\n\n\n\n\n\n\n\n\n\nCode# calculate median for each group if preferred\nSummaryStat &lt;- adpp_adex %&gt;% \n  group_by(Dose = as.factor(Dose)) %&gt;%\n  summarise(medAUC = median(AUCinf))\nSummaryStat$Dose &lt;- as.numeric(as.character(SummaryStat$Dose)) \n\nplot &lt;- ggplot(adpp_adex, aes(x = .data[[x_var]], y = .data[[y_var]])) +\n  annotate(geom = \"text\", x = min(adpp_adex[[x_var]]), y = max(adpp_adex[[y_var]]), label = eq, hjust = 0.1) +\n  geom_point(size = 1, aes(color = factor(`count`))) +\n  geom_smooth(method = \"lm\", formula = y ~ x, se = FALSE, color = \"black\", linewidth = 0.5) +\n  geom_point(data = SummaryStat, aes(x = Dose, y = medAUC, size = 1), shape = 2, show.legend = FALSE) +\n  ggtitle(\n    paste(\n      \"Dose-Proportionality Plot of Serum\",\n      as.character(unique(adex$TRT01P)),\n      y_var,\n      \"in\",\n      adpp_a$AVALU\n    ),\n    subtitle = \"Summary of serum PK parameters by treatment\"\n  ) +\n  labs(color = \"Treatment Arm\") +\n  scale_y_continuous(\n    name = paste(y_var, adpp_a$AVALU),\n    transform = \"log\",\n    breaks = exp(ceiling(seq(from = min(log(adpp_adex$AUCinf)), to = max(log(adpp_adex$AUCinf)), by = 1))),\n    labels = as.character(ceiling(seq(from = min(log(adpp_adex$AUCinf)), to = max(log(adpp_adex$AUCinf)), by = 1)))\n  ) +\n  scale_x_continuous(\n    name = \"Dose (mg/mL)\",\n    breaks = unique(adpp_adex$Dose)\n  ) +\n  theme_nest()\n\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:19 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          generics_0.1.3    knitr_1.45        rbibutils_2.2.16 \n[17] htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1   tibble_3.2.1     \n[21] munsell_0.5.0     pillar_1.9.0      rlang_1.1.3       utf8_1.2.4       \n[25] testthat_3.2.1    broom_1.0.5       xfun_0.42         cli_3.6.2        \n[29] mgcv_1.9-1        withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        nlme_3.1-164      lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        farver_2.1.1      codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nSummary of Pharmacokinetic  Parameters – Plasma\nSummary of Plasma Pharmacokinetic  Parameters with Median Points\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\n# need adex for dose info and adpp for AUC max info\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\nadpp_a &lt;- adpp %&gt;%\n  filter(\n    PPSPEC == \"Plasma\",\n    AVISITN == \"1\",\n    PARAMCD == \"AUCIFO\"\n  ) %&gt;%\n  mutate(AUCinf = AVAL)\n\nadex_a &lt;- adex %&gt;%\n  filter(\n    AVISITN == \"1\",\n    PARAMCD == \"DOSE\"\n  ) %&gt;%\n  mutate(Dose = AVAL) %&gt;%\n  select(USUBJID, Dose)\n\n# join the dose information to the adpp table\nadpp_adex &lt;- left_join(adpp_a, adex_a, by = \"USUBJID\") %&gt;%\n  group_by(`ARM`) %&gt;%\n  mutate(count = paste0(`ARM`, \" (\", n(), \")\"))\n\n# set x and y variable names\nx_var &lt;- \"Dose\"\ny_var &lt;- \"AUCinf\"\n\n\n\n\n\n\nCode# calculate Summary Statistics (mean and sd) for each group\nSummaryStat &lt;- adpp_adex %&gt;% \n  group_by(Dose = as.factor(Dose)) %&gt;%\n  summarise(AUCsd = sd(AUCinf), meanAUC = mean(AUCinf))\nSummaryStat$Dose &lt;- as.numeric(as.character(SummaryStat$Dose)) \n\n# generate linear model\nmod1 &lt;- lm(log(AUCinf) ~ log(Dose), adpp_adex)\n\n# obtain linear model coefficient values\ncf &lt;- round(coef(mod1), 2)\n\n# generate linear model equation\neq &lt;- paste0(\n  \"y = \", cf[1],\n  ifelse(sign(cf[2]) == 1, \" + \", \" - \"), abs(cf[2]), \" x , \",\n  \"R²\",\n  \" = \",\n  signif(summary(mod1)$adj.r.squared, 3)\n)\n\nplot &lt;- ggplot(adpp_adex, aes(x = .data[[x_var]], y = .data[[y_var]])) +\n  annotate(geom = \"text\", x = min(adpp_adex[[x_var]]), y = max(adpp_adex[[y_var]]), label = eq, hjust = 0.1) +\n  geom_point(size = 1, aes(color = factor(`count`))) +\n  scale_x_continuous(\n    name = \"Dose (mg/mL)\",\n    breaks = unique(adpp_adex$Dose)\n  ) +\n  scale_y_continuous(\n    name = paste(y_var, adpp_a$AVALU),\n    transform = \"log\",\n    breaks = exp(ceiling(seq(\n      from = min(log(adpp_adex$AUCinf)), to = max(log(adpp_adex$AUCinf)),\n      by = 1\n    ))),\n    labels = as.character(ceiling(seq(\n      from = min(log(adpp_adex$AUCinf)),\n      to = max(log(adpp_adex$AUCinf)), by = 1\n    )))\n  ) +\n  geom_smooth(method = \"lm\", formula = y ~ x, se = FALSE, color = \"black\", linewidth = 0.5) +\n  # Display error bars for each dosing group (this will only appear if the sd is less than the mean)\n  geom_errorbar(\n    data = SummaryStat,\n    aes(x = `Dose`, y = `meanAUC`, ymin = `meanAUC` - `AUCsd`, ymax = `meanAUC` + AUCsd),\n    width = .05,\n    position = position_dodge(.1)\n  ) +\n  geom_point(data = SummaryStat, aes(x = Dose, y = meanAUC, size = 1), shape = 2, show.legend = FALSE) +\n  ggtitle(paste(\n    \"Dose-Proportionality Plot of Serum\", as.character(unique(adex$TRT01P)),\n    y_var, \"in\", adpp_a$AVALU\n  ), subtitle = \"Summary of serum PK parameters by treatment\") +\n  labs(color = \"Treatment Arm\") +\n  theme_nest()\n\nplot\n\n\n\n\n\n\n\n\n\n\n\nCode# calculate median for each group if preferred\nSummaryStat &lt;- adpp_adex %&gt;% \n  group_by(Dose = as.factor(Dose)) %&gt;%\n  summarise(medAUC = median(AUCinf))\nSummaryStat$Dose &lt;- as.numeric(as.character(SummaryStat$Dose)) \n\nplot &lt;- ggplot(adpp_adex, aes(x = .data[[x_var]], y = .data[[y_var]])) +\n  annotate(geom = \"text\", x = min(adpp_adex[[x_var]]), y = max(adpp_adex[[y_var]]), label = eq, hjust = 0.1) +\n  geom_point(size = 1, aes(color = factor(`count`))) +\n  geom_smooth(method = \"lm\", formula = y ~ x, se = FALSE, color = \"black\", linewidth = 0.5) +\n  geom_point(data = SummaryStat, aes(x = Dose, y = medAUC, size = 1), shape = 2, show.legend = FALSE) +\n  ggtitle(\n    paste(\n      \"Dose-Proportionality Plot of Serum\",\n      as.character(unique(adex$TRT01P)),\n      y_var,\n      \"in\",\n      adpp_a$AVALU\n    ),\n    subtitle = \"Summary of serum PK parameters by treatment\"\n  ) +\n  labs(color = \"Treatment Arm\") +\n  scale_y_continuous(\n    name = paste(y_var, adpp_a$AVALU),\n    transform = \"log\",\n    breaks = exp(ceiling(seq(from = min(log(adpp_adex$AUCinf)), to = max(log(adpp_adex$AUCinf)), by = 1))),\n    labels = as.character(ceiling(seq(from = min(log(adpp_adex$AUCinf)), to = max(log(adpp_adex$AUCinf)), by = 1)))\n  ) +\n  scale_x_continuous(\n    name = \"Dose (mg/mL)\",\n    breaks = unique(adpp_adex$Dose)\n  ) +\n  theme_nest()\n\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:39 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          generics_0.1.3    knitr_1.45        rbibutils_2.2.16 \n[17] htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1   tibble_3.2.1     \n[21] munsell_0.5.0     pillar_1.9.0      rlang_1.1.3       utf8_1.2.4       \n[25] testthat_3.2.1    broom_1.0.5       xfun_0.42         cli_3.6.2        \n[29] mgcv_1.9-1        withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        nlme_3.1-164      lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        farver_2.1.1      codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -180,7 +180,7 @@
     "href": "graphs/pharmacokinetic/pkpg03.html",
     "title": "PKPG03",
     "section": "",
-    "text": "Data Setup\nPlot of CMAX by Visit\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n\n\n\n\n\nCode# filtered parameter\nadpp &lt;- adpp %&gt;% filter(PARAMCD == \"CMAX\", PPSPEC == \"Plasma\")\n\nuse_title &lt;- paste0(\"Boxplot of \", as.character(unique(adpp$PARAM)), \" by Visit\")\nuse_subtitle &lt;- paste0(\n  \"Analyte:\\nPK Parameter: \",\n  as.character(unique(adpp$PARAM)),\n  \" (\",\n  as.character(unique(adpp$AVALU)),\n  \")\"\n)\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nplot &lt;- ggplot(adpp, aes(x = AVISIT, y = AVAL, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(geom = \"point\", fun = \"mean\", col = \"black\", size = 5, shape = 8, position = position_dodge(0.75)) +\n  geom_text(\n    data = . %&gt;% dplyr::group_by(AVISIT, ACTARM) %&gt;% dplyr::filter(AVAL %in% boxplot.stats(AVAL)$out),\n    aes(x = AVISIT, y = AVAL, label = SUBJID, color = ACTARM),\n    size = 3, hjust = -0.2, position = position_dodge(0.75)\n  ) +\n  labs(\n    title = use_title,\n    subtitle = use_subtitle,\n    caption = use_footnote,\n    x = \"Visit\",\n    y = paste0(as.character(unique(adpp$PARAM)), \" (\", as.character(unique(adpp$AVALU)), \")\")\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:06 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    broom_1.0.5       xfun_0.42        \n[29] cli_3.6.2         withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        farver_2.1.1      codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot of CMAX by Visit\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n\n\n\n\n\nCode# filtered parameter\nadpp &lt;- adpp %&gt;% filter(PARAMCD == \"CMAX\", PPSPEC == \"Plasma\")\n\nuse_title &lt;- paste0(\"Boxplot of \", as.character(unique(adpp$PARAM)), \" by Visit\")\nuse_subtitle &lt;- paste0(\n  \"Analyte:\\nPK Parameter: \",\n  as.character(unique(adpp$PARAM)),\n  \" (\",\n  as.character(unique(adpp$AVALU)),\n  \")\"\n)\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nplot &lt;- ggplot(adpp, aes(x = AVISIT, y = AVAL, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(geom = \"point\", fun = \"mean\", col = \"black\", size = 5, shape = 8, position = position_dodge(0.75)) +\n  geom_text(\n    data = . %&gt;% dplyr::group_by(AVISIT, ACTARM) %&gt;% dplyr::filter(AVAL %in% boxplot.stats(AVAL)$out),\n    aes(x = AVISIT, y = AVAL, label = SUBJID, color = ACTARM),\n    size = 3, hjust = -0.2, position = position_dodge(0.75)\n  ) +\n  labs(\n    title = use_title,\n    subtitle = use_subtitle,\n    caption = use_footnote,\n    x = \"Visit\",\n    y = paste0(as.character(unique(adpp$PARAM)), \" (\", as.character(unique(adpp$AVALU)), \")\")\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:26 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    broom_1.0.5       xfun_0.42        \n[29] cli_3.6.2         withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        farver_2.1.1      codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -192,7 +192,7 @@
     "href": "listings/lab-results/lbl02b.html",
     "title": "LBL02B",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(\n    !sub(\"-\", \"\", ATOXGR) %in% c(\"\", \"0\", \"1\"),\n    LBTEST != \"\",\n    !is.na(ADY),\n    !ANRIND %in% c(\"\", \"NORMAL\")\n  ) %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ADTM = toupper(format(as.Date(ADTM), \"%d%b%Y\")),\n    AVAL = format(round(AVAL, 1), nsmall = 1),\n    LBNRNG = paste(format(round(ANRLO, 1), nsmall = 1), format(round(ANRHI, 1), nsmall = 1), sep = \" - \"),\n    ANRIND_GR = factor(\n      case_when(\n        ANRIND == \"LOW\" ~ paste0(\"L\", sub(\"-\", \"\", ATOXGR)),\n        ANRIND == \"HIGH\" ~ paste0(\"H\", ATOXGR)\n      )\n    )\n  ) %&gt;%\n  select(LBTEST, TRT01A, CPID, ADY, ADTM, AVAL, AVALU, LBNRNG, ANRIND_GR) %&gt;%\n  unique() %&gt;%\n  arrange(CPID, ADY) %&gt;%\n  group_by(LBTEST, CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  mutate(DLD = ifelse(is.na(DLD), 0, DLD))\n\nout &lt;- adlb_x %&gt;%\n  select(LBTEST, TRT01A, CPID, ADY, ADTM, DLD, AVAL, AVALU, LBNRNG, ANRIND_GR)\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    LBTEST = \"Lab Test\",\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    ADTM = \"Date\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\",\n    AVAL = \"Result\",\n    AVALU = \"Unit\",\n    LBNRNG = \"Lab Normal\\nRange\",\n    ANRIND_GR = \"NCI\\nCTCAE\\nGrade\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"LBTEST\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 2\",\n  main_footer = \"NCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 2\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                            Days Since                                  NCI \n                                                                       Study               Last Dose of                   Lab Normal   CTCAE\nTreatment                 Lab Test                 Center/Patient ID    Day      Date       Study Drug    Result   Unit     Range      Grade\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X   Alanine Aminotransferase Measurement     BRA-1/id-105       80     27MAY2020        0           4.3    U/L    7.0 - 55.0    L4  \n                                                                        398    10APR2021       318          3.7    U/L    7.0 - 55.0    L3  \n                                                      CAN-1/id-18       465    12APR2022        0           1.8    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-133       127    13DEC2020        0           5.6    U/L    7.0 - 55.0    L2  \n                                                                        197    21FEB2021        70          1.2    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-199       277    11OCT2020        0           5.3    U/L    7.0 - 55.0    L4  \n                                                                        672    10NOV2021       395          6.8    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-235       348    25JUN2020        0           3.0    U/L    7.0 - 55.0    L3  \n                                                     CHN-1/id-275       364    01JUN2021        0           5.1    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-277       483    12MAY2022        0           1.1    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-287       182    23MAY2020        0           5.9    U/L    7.0 - 55.0    L2  \n                                                                        191    01JUN2020        9           5.3    U/L    7.0 - 55.0    L3  \n                                                                        515    21APR2021       324          2.9    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-315       289    05JUN2020        0           4.6    U/L    7.0 - 55.0    L4  \n                                                                        599    11APR2021       310          5.2    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-316       971    14MAY2023        0           3.6    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-360       689    01APR2022        0           4.7    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-371       81     01MAY2021        0           5.9    U/L    7.0 - 55.0    L2  \n                                                      CHN-1/id-47       236    11SEP2020        0           4.4    U/L    7.0 - 55.0    L3  \n                                                      CHN-1/id-53       98     13FEB2020        0           6.4    U/L    7.0 - 55.0    L2  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:57 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(\n    !sub(\"-\", \"\", ATOXGR) %in% c(\"\", \"0\", \"1\"),\n    LBTEST != \"\",\n    !is.na(ADY),\n    !ANRIND %in% c(\"\", \"NORMAL\")\n  ) %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ADTM = toupper(format(as.Date(ADTM), \"%d%b%Y\")),\n    AVAL = format(round(AVAL, 1), nsmall = 1),\n    LBNRNG = paste(format(round(ANRLO, 1), nsmall = 1), format(round(ANRHI, 1), nsmall = 1), sep = \" - \"),\n    ANRIND_GR = factor(\n      case_when(\n        ANRIND == \"LOW\" ~ paste0(\"L\", sub(\"-\", \"\", ATOXGR)),\n        ANRIND == \"HIGH\" ~ paste0(\"H\", ATOXGR)\n      )\n    )\n  ) %&gt;%\n  select(LBTEST, TRT01A, CPID, ADY, ADTM, AVAL, AVALU, LBNRNG, ANRIND_GR) %&gt;%\n  unique() %&gt;%\n  arrange(CPID, ADY) %&gt;%\n  group_by(LBTEST, CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  mutate(DLD = ifelse(is.na(DLD), 0, DLD))\n\nout &lt;- adlb_x %&gt;%\n  select(LBTEST, TRT01A, CPID, ADY, ADTM, DLD, AVAL, AVALU, LBNRNG, ANRIND_GR)\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    LBTEST = \"Lab Test\",\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    ADTM = \"Date\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\",\n    AVAL = \"Result\",\n    AVALU = \"Unit\",\n    LBNRNG = \"Lab Normal\\nRange\",\n    ANRIND_GR = \"NCI\\nCTCAE\\nGrade\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"LBTEST\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 2\",\n  main_footer = \"NCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 2\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                            Days Since                                  NCI \n                                                                       Study               Last Dose of                   Lab Normal   CTCAE\nTreatment                 Lab Test                 Center/Patient ID    Day      Date       Study Drug    Result   Unit     Range      Grade\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X   Alanine Aminotransferase Measurement     BRA-1/id-105       80     27MAY2020        0           4.3    U/L    7.0 - 55.0    L4  \n                                                                        398    10APR2021       318          3.7    U/L    7.0 - 55.0    L3  \n                                                      CAN-1/id-18       465    12APR2022        0           1.8    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-133       127    13DEC2020        0           5.6    U/L    7.0 - 55.0    L2  \n                                                                        197    21FEB2021        70          1.2    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-199       277    11OCT2020        0           5.3    U/L    7.0 - 55.0    L4  \n                                                                        672    10NOV2021       395          6.8    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-235       348    25JUN2020        0           3.0    U/L    7.0 - 55.0    L3  \n                                                     CHN-1/id-275       364    01JUN2021        0           5.1    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-277       483    12MAY2022        0           1.1    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-287       182    23MAY2020        0           5.9    U/L    7.0 - 55.0    L2  \n                                                                        191    01JUN2020        9           5.3    U/L    7.0 - 55.0    L3  \n                                                                        515    21APR2021       324          2.9    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-315       289    05JUN2020        0           4.6    U/L    7.0 - 55.0    L4  \n                                                                        599    11APR2021       310          5.2    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-316       971    14MAY2023        0           3.6    U/L    7.0 - 55.0    L4  \n                                                     CHN-1/id-360       689    01APR2022        0           4.7    U/L    7.0 - 55.0    L2  \n                                                     CHN-1/id-371       81     01MAY2021        0           5.9    U/L    7.0 - 55.0    L2  \n                                                      CHN-1/id-47       236    11SEP2020        0           4.4    U/L    7.0 - 55.0    L3  \n                                                      CHN-1/id-53       98     13FEB2020        0           6.4    U/L    7.0 - 55.0    L2  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:17 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Lab Results",
@@ -204,7 +204,7 @@
     "href": "listings/lab-results/lbl02a_rls.html",
     "title": "LBL02A_RLS",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(\n    LBTEST != \"\"\n  ) %&gt;%\n  mutate(\n    LBTEST_U = paste0(LBTEST, \" (\", AVALU, \")\"),\n    CPID = paste(SITEID, SUBJID, sep = \"/\")\n  ) %&gt;%\n  mutate(CRC = paste(\"40%\", \"40%\", sep = \" / \"))\n\nstd_rng &lt;- adlb_x %&gt;%\n  group_by(LBTEST_U) %&gt;%\n  summarise(\n    STD_RNG_LO = stats::quantile(AVAL, probs = c(0.1), na.rm = TRUE),\n    STD_RNG_HI = stats::quantile(AVAL, probs = c(0.9), na.rm = TRUE)\n  ) %&gt;%\n  ungroup()\n\nadlb_x &lt;- adlb_x %&gt;%\n  left_join(std_rng, by = \"LBTEST_U\") %&gt;%\n  mutate(\n    AVAL = format(round(AVAL, 1), nsmall = 1),\n    PCHG = format(round(PCHG, 1), nsmall = 1),\n    LBNRNG = paste(ANRLO, ANRHI, sep = \" - \"),\n    STD_RNG_LO = format(round(STD_RNG_LO, 1), nsmall = 1),\n    STD_RNG_HI = format(round(STD_RNG_HI, 1), nsmall = 1)\n  ) %&gt;%\n  mutate(\n    STD_RNG = paste(STD_RNG_LO, STD_RNG_HI, sep = \" - \"),\n    ANRIND = factor(case_when(\n      ANRIND == \"LOW\" & AVAL &gt; STD_RNG_LO ~ \"L\",\n      ANRIND == \"HIGH\" & AVAL &lt; STD_RNG_HI ~ \"H\",\n      ANRIND == \"LOW\" & AVAL &lt;= STD_RNG_LO ~ \"LL\",\n      ANRIND == \"HIGH\" & AVAL &gt;= STD_RNG_HI ~ \"HH\",\n      TRUE ~ \"\"\n    ))\n  ) %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, AVAL, PCHG, STD_RNG, LBNRNG, CRC, ANRIND) %&gt;%\n  unique() %&gt;%\n  arrange(CPID, ADY) %&gt;%\n  group_by(LBTEST_U, CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  mutate(DLD = ifelse(is.na(DLD), 0, DLD))\n\nout &lt;- adlb_x %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, DLD, AVAL, PCHG, STD_RNG, LBNRNG, CRC, ANRIND)\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    LBTEST_U = \"Lab Test (Unit)\",\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\",\n    AVAL = \"Result\",\n    PCHG = \"% Change\\nfrom\\nBaseline\",\n    STD_RNG = \"Standard\\nReference\\nRange\",\n    LBNRNG = \"Marked\\nReference\\nRange\",\n    CRC = \"Clinically\\nRelevant\\nChange\\nDec./Inc.\",\n    ANRIND = \"Abnormality\\nFlag\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"LBTEST_U\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization\",\n  main_footer = \"Standard reference range, marked reference range and clinically relevant change from baseline are from the Roche Safety Lab Standardization guideline.  Abnormalities are flagged as high (H) or low (L) if outside the standard reference range; high high (HH) or low low (LL) if outside the marked reference range with a clinically relevant change from baseline.\" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                                                                  Clinically              \n                                                                                      Days Since             % Change     Standard     Marked      Relevant               \n                                                                             Study   Last Dose of              from      Reference    Reference     Change     Abnormality\nTreatment                Lab Test (Unit)                 Center/Patient ID    Day     Study Drug    Result   Baseline      Range        Range     Dec./Inc.       Flag    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X   Alanine Aminotransferase Measurement (U/L)     BRA-1/id-105       80          0           4.3           NA   5.5 - 31.1    7 - 55     40% / 40%        LL     \n                                                                              149         69         24.7          0.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              314        165         24.9          0.7   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              398         84          3.7        -85.2   5.5 - 31.1    7 - 55     40% / 40%        LL     \n                                                                              470         72         18.5        -24.9   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              528         58          7.7        -68.7   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              535         7          23.5         -4.8   5.5 - 31.1    7 - 55     40% / 40%               \n                                                           BRA-1/id-134       225         0           7.4           NA   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              227         2          16.4          0.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              255         28         11.2        -32.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              269         14         15.9         -3.2   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              357         88         24.4         48.3   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              436         79         16.0         -2.4   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              440         4          17.2          4.8   5.5 - 31.1    7 - 55     40% / 40%               \n                                                            BRA-1/id-42       176         0          16.6           NA   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              477        301         16.5          0.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              534         57         18.7         13.4   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              550         16         11.4        -30.6   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              569         19         21.7         31.3   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              608         39         24.6         49.2   5.5 - 31.1    7 - 55     40% / 40%               \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nStandard reference range, marked reference range and clinically relevant change from baseline are from the Roche Safety Lab Standardization guideline.  Abnormalities are flagged as high (H) or low (L) if outside the standard reference range; high high (HH) or low low (LL) if outside the marked reference range with a clinically relevant change from baseline.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:49 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(\n    LBTEST != \"\"\n  ) %&gt;%\n  mutate(\n    LBTEST_U = paste0(LBTEST, \" (\", AVALU, \")\"),\n    CPID = paste(SITEID, SUBJID, sep = \"/\")\n  ) %&gt;%\n  mutate(CRC = paste(\"40%\", \"40%\", sep = \" / \"))\n\nstd_rng &lt;- adlb_x %&gt;%\n  group_by(LBTEST_U) %&gt;%\n  summarise(\n    STD_RNG_LO = stats::quantile(AVAL, probs = c(0.1), na.rm = TRUE),\n    STD_RNG_HI = stats::quantile(AVAL, probs = c(0.9), na.rm = TRUE)\n  ) %&gt;%\n  ungroup()\n\nadlb_x &lt;- adlb_x %&gt;%\n  left_join(std_rng, by = \"LBTEST_U\") %&gt;%\n  mutate(\n    AVAL = format(round(AVAL, 1), nsmall = 1),\n    PCHG = format(round(PCHG, 1), nsmall = 1),\n    LBNRNG = paste(ANRLO, ANRHI, sep = \" - \"),\n    STD_RNG_LO = format(round(STD_RNG_LO, 1), nsmall = 1),\n    STD_RNG_HI = format(round(STD_RNG_HI, 1), nsmall = 1)\n  ) %&gt;%\n  mutate(\n    STD_RNG = paste(STD_RNG_LO, STD_RNG_HI, sep = \" - \"),\n    ANRIND = factor(case_when(\n      ANRIND == \"LOW\" & AVAL &gt; STD_RNG_LO ~ \"L\",\n      ANRIND == \"HIGH\" & AVAL &lt; STD_RNG_HI ~ \"H\",\n      ANRIND == \"LOW\" & AVAL &lt;= STD_RNG_LO ~ \"LL\",\n      ANRIND == \"HIGH\" & AVAL &gt;= STD_RNG_HI ~ \"HH\",\n      TRUE ~ \"\"\n    ))\n  ) %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, AVAL, PCHG, STD_RNG, LBNRNG, CRC, ANRIND) %&gt;%\n  unique() %&gt;%\n  arrange(CPID, ADY) %&gt;%\n  group_by(LBTEST_U, CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  mutate(DLD = ifelse(is.na(DLD), 0, DLD))\n\nout &lt;- adlb_x %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, DLD, AVAL, PCHG, STD_RNG, LBNRNG, CRC, ANRIND)\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    LBTEST_U = \"Lab Test (Unit)\",\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\",\n    AVAL = \"Result\",\n    PCHG = \"% Change\\nfrom\\nBaseline\",\n    STD_RNG = \"Standard\\nReference\\nRange\",\n    LBNRNG = \"Marked\\nReference\\nRange\",\n    CRC = \"Clinically\\nRelevant\\nChange\\nDec./Inc.\",\n    ANRIND = \"Abnormality\\nFlag\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"LBTEST_U\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization\",\n  main_footer = \"Standard reference range, marked reference range and clinically relevant change from baseline are from the Roche Safety Lab Standardization guideline.  Abnormalities are flagged as high (H) or low (L) if outside the standard reference range; high high (HH) or low low (LL) if outside the marked reference range with a clinically relevant change from baseline.\" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                                                                  Clinically              \n                                                                                      Days Since             % Change     Standard     Marked      Relevant               \n                                                                             Study   Last Dose of              from      Reference    Reference     Change     Abnormality\nTreatment                Lab Test (Unit)                 Center/Patient ID    Day     Study Drug    Result   Baseline      Range        Range     Dec./Inc.       Flag    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X   Alanine Aminotransferase Measurement (U/L)     BRA-1/id-105       80          0           4.3           NA   5.5 - 31.1    7 - 55     40% / 40%        LL     \n                                                                              149         69         24.7          0.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              314        165         24.9          0.7   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              398         84          3.7        -85.2   5.5 - 31.1    7 - 55     40% / 40%        LL     \n                                                                              470         72         18.5        -24.9   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              528         58          7.7        -68.7   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              535         7          23.5         -4.8   5.5 - 31.1    7 - 55     40% / 40%               \n                                                           BRA-1/id-134       225         0           7.4           NA   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              227         2          16.4          0.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              255         28         11.2        -32.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              269         14         15.9         -3.2   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              357         88         24.4         48.3   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              436         79         16.0         -2.4   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              440         4          17.2          4.8   5.5 - 31.1    7 - 55     40% / 40%               \n                                                            BRA-1/id-42       176         0          16.6           NA   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              477        301         16.5          0.0   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              534         57         18.7         13.4   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              550         16         11.4        -30.6   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              569         19         21.7         31.3   5.5 - 31.1    7 - 55     40% / 40%               \n                                                                              608         39         24.6         49.2   5.5 - 31.1    7 - 55     40% / 40%               \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nStandard reference range, marked reference range and clinically relevant change from baseline are from the Roche Safety Lab Standardization guideline.  Abnormalities are flagged as high (H) or low (L) if outside the standard reference range; high high (HH) or low low (LL) if outside the marked reference range with a clinically relevant change from baseline.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:10 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Lab Results",
@@ -216,7 +216,7 @@
     "href": "listings/lab-results/lbl01_rls.html",
     "title": "LBL01_RLS",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n  filter(!is.na(AVAL))\n\nadlb_x &lt;- adlb %&gt;%\n  mutate(ANRIND = factor(\n    case_when(\n      ANRIND == \"LOW\" & AVAL &gt; stats::quantile(adlb$AVAL, probs = c(0.1)) ~ \"L\",\n      ANRIND == \"HIGH\" & AVAL &lt; stats::quantile(adlb$AVAL, probs = c(0.9)) ~ \"H\",\n      ANRIND == \"LOW\" & AVAL &lt;= stats::quantile(adlb$AVAL, probs = c(0.1)) ~ \"LL\",\n      ANRIND == \"HIGH\" & AVAL &gt;= stats::quantile(adlb$AVAL, probs = c(0.9)) ~ \"HH\",\n      TRUE ~ as.character(ANRIND)\n    ),\n    levels = c(\"\", \"H\", \"HH\", \"L\", \"LL\", \"NORMAL\")\n  ))\n\nadlb_x &lt;- adlb_x %&gt;%\n  filter(!is.na(LBSEQ) & !is.na(ADY) & ANRIND != \"\") %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    PARAM_U = paste0(PARAMCD, \"\\n(\", AVALU, \")\"),\n    AVALC = as.character(format(round(AVAL, 2), nsmall = 2))\n  ) %&gt;%\n  mutate(AVAL_GR = ifelse(\n    !ANRIND %in% c(\"H\", \"HH\", \"L\", \"LL\"),\n    AVALC,\n    paste(AVALC, ANRIND, sep = \" / \")\n  )) %&gt;%\n  select(CPID, TRT01A, ADY, LBSEQ, PARAM_U, AVAL_GR) %&gt;%\n  unique()\n\nout &lt;- adlb_x %&gt;%\n  arrange(CPID, ADY, PARAM_U, LBSEQ) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, CPID, ADY),\n    names_from = PARAM_U,\n    values_from = AVAL_GR\n  ) %&gt;%\n  group_by(CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  select(CPID, TRT01A, ADY, DLD, unique(adlb_x$PARAM_U))\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Test Results Using Roche Safety Lab Standardization\",\n  main_footer = \"Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range; high high (HH) or low low (LL) if outside the Roche marked reference range with a clinically relevant change from baseline. \" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Test Results Using Roche Safety Lab Standardization\n\n————————————————————————————————————————————————————————————————————————————————————————\n                                         Days Since                                     \n                                Study   Last Dose of      ALT         CRP         IGA   \nTreatment   Center/Patient ID    Day     Study Drug      (U/L)      (mg/L)       (g/L)  \n————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105       80          NA         4.30 / L      9.09        2.84  \n                                 149         69          24.70        9.15        2.93  \n                                 314        165          24.87     10.89 / H      2.66  \n                                 398         84         3.67 / L      8.14        2.86  \n                                 470         72          18.55     10.59 / H      2.88  \n                                 528         58           7.73     10.09 / H      2.93  \n                                 535         7           23.51        9.20        2.88  \n              BRA-1/id-134       225         NA           7.37     10.46 / H      2.86  \n                                 227         2           16.42      7.43 / L      2.76  \n                                 255         28          11.16        9.41        2.75  \n                                 269         14          15.89     10.02 / H      2.84  \n                                 357         88          24.35        8.01        2.86  \n                                 436         79          16.03        8.51        2.98  \n                                 440         4           17.21      7.63 / L      2.78  \n               BRA-1/id-42       176         NA          16.56        8.88        2.75  \n                                 477        301          16.49        8.11        2.95  \n                                 534         57          18.71        8.78        2.95  \n                                 550         16          11.44        9.53        2.99  \n                                 569         19          21.66     10.32 / H    3.00 / H\n                                 608         39          24.61      7.95 / L      2.78  \n————————————————————————————————————————————————————————————————————————————————————————\n\nAbnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range; high high (HH) or low low (LL) if outside the Roche marked reference range with a clinically relevant change from baseline. \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:41 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n  filter(!is.na(AVAL))\n\nadlb_x &lt;- adlb %&gt;%\n  mutate(ANRIND = factor(\n    case_when(\n      ANRIND == \"LOW\" & AVAL &gt; stats::quantile(adlb$AVAL, probs = c(0.1)) ~ \"L\",\n      ANRIND == \"HIGH\" & AVAL &lt; stats::quantile(adlb$AVAL, probs = c(0.9)) ~ \"H\",\n      ANRIND == \"LOW\" & AVAL &lt;= stats::quantile(adlb$AVAL, probs = c(0.1)) ~ \"LL\",\n      ANRIND == \"HIGH\" & AVAL &gt;= stats::quantile(adlb$AVAL, probs = c(0.9)) ~ \"HH\",\n      TRUE ~ as.character(ANRIND)\n    ),\n    levels = c(\"\", \"H\", \"HH\", \"L\", \"LL\", \"NORMAL\")\n  ))\n\nadlb_x &lt;- adlb_x %&gt;%\n  filter(!is.na(LBSEQ) & !is.na(ADY) & ANRIND != \"\") %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    PARAM_U = paste0(PARAMCD, \"\\n(\", AVALU, \")\"),\n    AVALC = as.character(format(round(AVAL, 2), nsmall = 2))\n  ) %&gt;%\n  mutate(AVAL_GR = ifelse(\n    !ANRIND %in% c(\"H\", \"HH\", \"L\", \"LL\"),\n    AVALC,\n    paste(AVALC, ANRIND, sep = \" / \")\n  )) %&gt;%\n  select(CPID, TRT01A, ADY, LBSEQ, PARAM_U, AVAL_GR) %&gt;%\n  unique()\n\nout &lt;- adlb_x %&gt;%\n  arrange(CPID, ADY, PARAM_U, LBSEQ) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, CPID, ADY),\n    names_from = PARAM_U,\n    values_from = AVAL_GR\n  ) %&gt;%\n  group_by(CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  select(CPID, TRT01A, ADY, DLD, unique(adlb_x$PARAM_U))\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Test Results Using Roche Safety Lab Standardization\",\n  main_footer = \"Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range; high high (HH) or low low (LL) if outside the Roche marked reference range with a clinically relevant change from baseline. \" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Test Results Using Roche Safety Lab Standardization\n\n————————————————————————————————————————————————————————————————————————————————————————\n                                         Days Since                                     \n                                Study   Last Dose of      ALT         CRP         IGA   \nTreatment   Center/Patient ID    Day     Study Drug      (U/L)      (mg/L)       (g/L)  \n————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105       80          NA         4.30 / L      9.09        2.84  \n                                 149         69          24.70        9.15        2.93  \n                                 314        165          24.87     10.89 / H      2.66  \n                                 398         84         3.67 / L      8.14        2.86  \n                                 470         72          18.55     10.59 / H      2.88  \n                                 528         58           7.73     10.09 / H      2.93  \n                                 535         7           23.51        9.20        2.88  \n              BRA-1/id-134       225         NA           7.37     10.46 / H      2.86  \n                                 227         2           16.42      7.43 / L      2.76  \n                                 255         28          11.16        9.41        2.75  \n                                 269         14          15.89     10.02 / H      2.84  \n                                 357         88          24.35        8.01        2.86  \n                                 436         79          16.03        8.51        2.98  \n                                 440         4           17.21      7.63 / L      2.78  \n               BRA-1/id-42       176         NA          16.56        8.88        2.75  \n                                 477        301          16.49        8.11        2.95  \n                                 534         57          18.71        8.78        2.95  \n                                 550         16          11.44        9.53        2.99  \n                                 569         19          21.66     10.32 / H    3.00 / H\n                                 608         39          24.61      7.95 / L      2.78  \n————————————————————————————————————————————————————————————————————————————————————————\n\nAbnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range; high high (HH) or low low (LL) if outside the Roche marked reference range with a clinically relevant change from baseline. \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:03 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Lab Results",
@@ -228,7 +228,7 @@
     "href": "listings/vital-signs/vsl01.html",
     "title": "VSL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\nget_param_unit_range &lt;- function(dataset) {\n  u_rng &lt;- lapply(unique(dataset$PARAMCD), function(p) {\n    dat &lt;- dataset %&gt;% filter(PARAMCD == p)\n    list(\n      unit = unique(dat$AVALU),\n      range = paste0(unique(dat$ANRLO), \"-\", unique(dat$ANRHI))\n    )\n  })\n  names(u_rng) &lt;- unique(dataset$PARAMCD)\n  u_rng\n}\n\nvs_u_rng &lt;- get_param_unit_range(advs)\n\nadvs_sub &lt;- advs %&gt;%\n  filter(!is.na(AVAL) & SAFFL == \"Y\" & ONTRTFL == \"Y\" & !is.na(VSSEQ)) %&gt;%\n  mutate(\n    CRTNPT = paste(SITEID, sub(\"^.*-([[:alnum:]]+)$\", \"\\\\1\", SUBJID), sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    AVAL = format(round(AVAL, 2), nsmall = 2),\n    AVAL_ANRIND = ifelse(ANRIND %in% c(\"NORMAL\", \"\"), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = \"/\")),\n    CHG = format(round(CHG, 2), nsmall = 2)\n  )\n\nanl_vs &lt;- advs_sub %&gt;%\n  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),\n    names_from = PARAMCD,\n    values_from = c(AVAL_ANRIND, CHG)\n  )\n\nout &lt;- anl_vs %&gt;%\n  select(\n    CRTNPT, AGSXRC, TRT01A, AVISIT, ADY, AVAL_ANRIND_WEIGHT, CHG_WEIGHT, AVAL_ANRIND_TEMP, CHG_TEMP, AVAL_ANRIND_DIABP,\n    CHG_DIABP, AVAL_ANRIND_SYSBP, CHG_SYSBP, AVAL_ANRIND_PULSE, CHG_PULSE, AVAL_ANRIND_RESP, CHG_RESP\n  ) %&gt;%\n  var_relabel(\n    CRTNPT = \"Center/Subject ID\",\n    AGSXRC = \"Age/Sex/Race\",\n    TRT01A = \"Treatment\",\n    AVISIT = \"Visit\",\n    ADY = \"Study\\nDay\",\n    AVAL_ANRIND_WEIGHT = paste0(\n      \"Weight\\nResult\\n(\",\n      vs_u_rng$WEIGHT$unit,\n      \");\\nRange:(\",\n      vs_u_rng$WEIGHT$range,\n      \")\"\n    ),\n    CHG_WEIGHT = \"Weight\\nChange from BL\",\n    AVAL_ANRIND_TEMP = paste0(\n      \"Temperature Result\\n(\",\n      vs_u_rng$TEMP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$TEMP$range,\n      \")\"\n    ),\n    CHG_TEMP = \"Temperature Rate\\nChange from BL\",\n    AVAL_ANRIND_DIABP = paste0(\n      \"Diastolic Blood\\nPressure,\\nSitting\\nResult\\n(\",\n      vs_u_rng$DIABP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$DIABP$range,\n      \")\"\n    ),\n    CHG_DIABP = \"Diastolic Blood\\nPressure,\\nSitting\\nChange from BL\",\n    AVAL_ANRIND_SYSBP = paste0(\n      \"Systolic Blood\\nPressure,\\nSitting\\n(\",\n      vs_u_rng$SYSBP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$SYSBP$range,\n      \")\"\n    ),\n    CHG_SYSBP = \"Systolic Blood\\nPressure,\\nSitting\\nChange from BL\",\n    AVAL_ANRIND_PULSE = paste0(\n      \"Pulse Rate Result\\n(\",\n      vs_u_rng$PULSE$unit,\n      \");\\nRange:(\",\n      vs_u_rng$PULSE$range,\n      \")\"\n    ),\n    CHG_PULSE = \"Pulse Rate\\nChange from BL\",\n    AVAL_ANRIND_RESP = paste0(\n      \"Respiratory Rate\\nResult\\n(\",\n      vs_u_rng$RESP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$RESP$range,\n      \")\"\n    ),\n    CHG_RESP = \"Respiratory Rate\\nChange from BL\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CRTNPT\", \"AGSXRC\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Vital Signs: Safety-Evaluable Patients\",\n  main_footer = \"Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Vital Signs: Safety-Evaluable Patients\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                                                                                   Diastolic Blood                                                                                                                                \n                                                                                                                                                                      Pressure,                        Systolic Blood                                                                                             \n                                                                                             Weight                                                                    Sitting       Diastolic Blood      Pressure,      Systolic Blood                                        Respiratory Rate                   \n                                                                                             Result                        Temperature Result                          Result           Pressure,          Sitting         Pressure,      Pulse Rate Result                         Result                        \n                                                                                 Study       (Kg);            Weight              (C);          Temperature Rate        (Pa);            Sitting            (Pa);           Sitting         (beats/min);        Pulse Rate      (breaths/min);    Respiratory Rate\nTreatment   Center/Subject ID            Age/Sex/Race                Visit        Day    Range:(40-100)   Change from BL   Range:(36.1-37.2)     Change from BL    Range:(80-120)    Change from BL    Range:(120-180)   Change from BL    Range:(60-100)     Change from BL    Range:(12-20)      Change from BL \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X       BRA-1/105       38/M/BLACK OR AFRICAN AMERICAN   WEEK 1 DAY 8     189         90.96            24.62              36.79               -1.88            101.10             -12.65          181.05/H             3.70             84.91              -2.05             18.34               1.13     \n                                                                 WEEK 2 DAY 15    323         41.40           -24.93              36.52               -2.14            111.02              -2.72           132.47            -44.88             71.54             -15.42             9.81/L             -7.40     \n                                                                 WEEK 3 DAY 22    492         60.55            -5.78             35.03/L              -3.64             88.35             -25.39          112.67/L           -64.68            33.75/L            -53.22            21.23/H              4.02     \n                                                                 WEEK 4 DAY 29    550         69.69             3.36             35.71/L              -2.95            100.74             -13.00          194.88/H            17.53             97.51              10.54             12.50              -4.71     \n                                                                 WEEK 5 DAY 36    628         81.64            15.30             36.01/L              -2.65           123.17/H              9.43           140.33            -37.02             62.50             -24.47            10.91/L             -6.30     \n                BRA-1/134                 47/M/WHITE             WEEK 1 DAY 8     203         42.95           -12.51              36.39               -1.38            112.71              26.38           148.69            -52.75             69.12               3.72             15.31              -2.84     \n                                                                 WEEK 2 DAY 15    326        20.36/L          -35.09             35.75/L              -2.03             98.42              12.09          188.08/H           -13.36             68.79               3.40             17.60              -0.55     \n                                                                 WEEK 3 DAY 22    363         46.33            -9.12             35.43/L              -2.34             97.88              11.55           160.05            -41.39             90.21              24.81             18.15               0.00     \n                                                                 WEEK 4 DAY 29    367         43.83           -11.62             36.01/L              -1.76             98.97              12.65           132.19            -69.25             88.39              23.00             19.62               1.48     \n                                                                 WEEK 5 DAY 36    417        31.75/L          -23.71              36.35               -1.42           124.26/H             37.94           134.30            -67.14             88.64              23.25            20.08/H              1.94     \n                BRA-1/42        36/M/BLACK OR AFRICAN AMERICAN   WEEK 1 DAY 8     265       115.27/H            8.42             37.35/H              -1.67            107.32               5.90           173.89             27.40             66.14              26.13             9.23/L              7.13     \n                                                                 WEEK 2 DAY 15    313         45.84           -61.01             36.01/L              -3.01            110.97               9.55           144.75             -1.74           109.71/H             69.70             7.18/L              5.08     \n                                                                 WEEK 3 DAY 22    325         85.70           -21.14             37.71/H              -1.31           124.13/H             22.71           89.08/L           -57.41             90.29              50.28            25.16/H             23.05     \n                                                                 WEEK 4 DAY 29    535         44.64           -62.21              36.44               -2.58            79.51/L            -21.91           140.78             -5.71             82.44              42.43             14.06              11.95     \n                                                                 WEEK 5 DAY 36    729         85.21           -21.64             35.78/L              -3.23            105.26               3.84           126.60            -19.89             88.71              48.70            25.98/H             23.87     \n                BRA-1/93                  34/F/ASIAN             WEEK 1 DAY 8     402         47.51           -35.34             35.50/L              -0.83             94.27              17.98           98.82/L           -61.71             72.44              -7.85             14.97               0.22     \n                                                                 WEEK 2 DAY 15    460         67.64           -15.21             37.96/H               1.62            109.98              33.69          104.21/L           -56.32           104.70/H             24.41             14.90               0.16     \n                                                                 WEEK 3 DAY 22    464         53.06           -29.78              36.33               -0.01             90.17              13.87           139.66            -20.87           111.13/H             30.85            22.86/H              8.12     \n                                                                 WEEK 4 DAY 29    496         61.68           -21.17             33.52/L              -2.82            112.76              36.47           128.82            -31.70             72.89              -7.39            20.77/H              6.03     \n                                                                 WEEK 5 DAY 36    538         53.98           -28.86             35.71/L              -0.63             80.20               3.91           137.16            -23.37             96.63              16.35             18.10               3.36     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nBaseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:34 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\nget_param_unit_range &lt;- function(dataset) {\n  u_rng &lt;- lapply(unique(dataset$PARAMCD), function(p) {\n    dat &lt;- dataset %&gt;% filter(PARAMCD == p)\n    list(\n      unit = unique(dat$AVALU),\n      range = paste0(unique(dat$ANRLO), \"-\", unique(dat$ANRHI))\n    )\n  })\n  names(u_rng) &lt;- unique(dataset$PARAMCD)\n  u_rng\n}\n\nvs_u_rng &lt;- get_param_unit_range(advs)\n\nadvs_sub &lt;- advs %&gt;%\n  filter(!is.na(AVAL) & SAFFL == \"Y\" & ONTRTFL == \"Y\" & !is.na(VSSEQ)) %&gt;%\n  mutate(\n    CRTNPT = paste(SITEID, sub(\"^.*-([[:alnum:]]+)$\", \"\\\\1\", SUBJID), sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    AVAL = format(round(AVAL, 2), nsmall = 2),\n    AVAL_ANRIND = ifelse(ANRIND %in% c(\"NORMAL\", \"\"), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = \"/\")),\n    CHG = format(round(CHG, 2), nsmall = 2)\n  )\n\nanl_vs &lt;- advs_sub %&gt;%\n  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),\n    names_from = PARAMCD,\n    values_from = c(AVAL_ANRIND, CHG)\n  )\n\nout &lt;- anl_vs %&gt;%\n  select(\n    CRTNPT, AGSXRC, TRT01A, AVISIT, ADY, AVAL_ANRIND_WEIGHT, CHG_WEIGHT, AVAL_ANRIND_TEMP, CHG_TEMP, AVAL_ANRIND_DIABP,\n    CHG_DIABP, AVAL_ANRIND_SYSBP, CHG_SYSBP, AVAL_ANRIND_PULSE, CHG_PULSE, AVAL_ANRIND_RESP, CHG_RESP\n  ) %&gt;%\n  var_relabel(\n    CRTNPT = \"Center/Subject ID\",\n    AGSXRC = \"Age/Sex/Race\",\n    TRT01A = \"Treatment\",\n    AVISIT = \"Visit\",\n    ADY = \"Study\\nDay\",\n    AVAL_ANRIND_WEIGHT = paste0(\n      \"Weight\\nResult\\n(\",\n      vs_u_rng$WEIGHT$unit,\n      \");\\nRange:(\",\n      vs_u_rng$WEIGHT$range,\n      \")\"\n    ),\n    CHG_WEIGHT = \"Weight\\nChange from BL\",\n    AVAL_ANRIND_TEMP = paste0(\n      \"Temperature Result\\n(\",\n      vs_u_rng$TEMP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$TEMP$range,\n      \")\"\n    ),\n    CHG_TEMP = \"Temperature Rate\\nChange from BL\",\n    AVAL_ANRIND_DIABP = paste0(\n      \"Diastolic Blood\\nPressure,\\nSitting\\nResult\\n(\",\n      vs_u_rng$DIABP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$DIABP$range,\n      \")\"\n    ),\n    CHG_DIABP = \"Diastolic Blood\\nPressure,\\nSitting\\nChange from BL\",\n    AVAL_ANRIND_SYSBP = paste0(\n      \"Systolic Blood\\nPressure,\\nSitting\\n(\",\n      vs_u_rng$SYSBP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$SYSBP$range,\n      \")\"\n    ),\n    CHG_SYSBP = \"Systolic Blood\\nPressure,\\nSitting\\nChange from BL\",\n    AVAL_ANRIND_PULSE = paste0(\n      \"Pulse Rate Result\\n(\",\n      vs_u_rng$PULSE$unit,\n      \");\\nRange:(\",\n      vs_u_rng$PULSE$range,\n      \")\"\n    ),\n    CHG_PULSE = \"Pulse Rate\\nChange from BL\",\n    AVAL_ANRIND_RESP = paste0(\n      \"Respiratory Rate\\nResult\\n(\",\n      vs_u_rng$RESP$unit,\n      \");\\nRange:(\",\n      vs_u_rng$RESP$range,\n      \")\"\n    ),\n    CHG_RESP = \"Respiratory Rate\\nChange from BL\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CRTNPT\", \"AGSXRC\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Vital Signs: Safety-Evaluable Patients\",\n  main_footer = \"Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Vital Signs: Safety-Evaluable Patients\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                                                                                   Diastolic Blood                                                                                                                                \n                                                                                                                                                                      Pressure,                        Systolic Blood                                                                                             \n                                                                                             Weight                                                                    Sitting       Diastolic Blood      Pressure,      Systolic Blood                                        Respiratory Rate                   \n                                                                                             Result                        Temperature Result                          Result           Pressure,          Sitting         Pressure,      Pulse Rate Result                         Result                        \n                                                                                 Study       (Kg);            Weight              (C);          Temperature Rate        (Pa);            Sitting            (Pa);           Sitting         (beats/min);        Pulse Rate      (breaths/min);    Respiratory Rate\nTreatment   Center/Subject ID            Age/Sex/Race                Visit        Day    Range:(40-100)   Change from BL   Range:(36.1-37.2)     Change from BL    Range:(80-120)    Change from BL    Range:(120-180)   Change from BL    Range:(60-100)     Change from BL    Range:(12-20)      Change from BL \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X       BRA-1/105       38/M/BLACK OR AFRICAN AMERICAN   WEEK 1 DAY 8     189         90.96            24.62              36.79               -1.88            101.10             -12.65          181.05/H             3.70             84.91              -2.05             18.34               1.13     \n                                                                 WEEK 2 DAY 15    323         41.40           -24.93              36.52               -2.14            111.02              -2.72           132.47            -44.88             71.54             -15.42             9.81/L             -7.40     \n                                                                 WEEK 3 DAY 22    492         60.55            -5.78             35.03/L              -3.64             88.35             -25.39          112.67/L           -64.68            33.75/L            -53.22            21.23/H              4.02     \n                                                                 WEEK 4 DAY 29    550         69.69             3.36             35.71/L              -2.95            100.74             -13.00          194.88/H            17.53             97.51              10.54             12.50              -4.71     \n                                                                 WEEK 5 DAY 36    628         81.64            15.30             36.01/L              -2.65           123.17/H              9.43           140.33            -37.02             62.50             -24.47            10.91/L             -6.30     \n                BRA-1/134                 47/M/WHITE             WEEK 1 DAY 8     203         42.95           -12.51              36.39               -1.38            112.71              26.38           148.69            -52.75             69.12               3.72             15.31              -2.84     \n                                                                 WEEK 2 DAY 15    326        20.36/L          -35.09             35.75/L              -2.03             98.42              12.09          188.08/H           -13.36             68.79               3.40             17.60              -0.55     \n                                                                 WEEK 3 DAY 22    363         46.33            -9.12             35.43/L              -2.34             97.88              11.55           160.05            -41.39             90.21              24.81             18.15               0.00     \n                                                                 WEEK 4 DAY 29    367         43.83           -11.62             36.01/L              -1.76             98.97              12.65           132.19            -69.25             88.39              23.00             19.62               1.48     \n                                                                 WEEK 5 DAY 36    417        31.75/L          -23.71              36.35               -1.42           124.26/H             37.94           134.30            -67.14             88.64              23.25            20.08/H              1.94     \n                BRA-1/42        36/M/BLACK OR AFRICAN AMERICAN   WEEK 1 DAY 8     265       115.27/H            8.42             37.35/H              -1.67            107.32               5.90           173.89             27.40             66.14              26.13             9.23/L              7.13     \n                                                                 WEEK 2 DAY 15    313         45.84           -61.01             36.01/L              -3.01            110.97               9.55           144.75             -1.74           109.71/H             69.70             7.18/L              5.08     \n                                                                 WEEK 3 DAY 22    325         85.70           -21.14             37.71/H              -1.31           124.13/H             22.71           89.08/L           -57.41             90.29              50.28            25.16/H             23.05     \n                                                                 WEEK 4 DAY 29    535         44.64           -62.21              36.44               -2.58            79.51/L            -21.91           140.78             -5.71             82.44              42.43             14.06              11.95     \n                                                                 WEEK 5 DAY 36    729         85.21           -21.64             35.78/L              -3.23            105.26               3.84           126.60            -19.89             88.71              48.70            25.98/H             23.87     \n                BRA-1/93                  34/F/ASIAN             WEEK 1 DAY 8     402         47.51           -35.34             35.50/L              -0.83             94.27              17.98           98.82/L           -61.71             72.44              -7.85             14.97               0.22     \n                                                                 WEEK 2 DAY 15    460         67.64           -15.21             37.96/H               1.62            109.98              33.69          104.21/L           -56.32           104.70/H             24.41             14.90               0.16     \n                                                                 WEEK 3 DAY 22    464         53.06           -29.78              36.33               -0.01             90.17              13.87           139.66            -20.87           111.13/H             30.85            22.86/H              8.12     \n                                                                 WEEK 4 DAY 29    496         61.68           -21.17             33.52/L              -2.82            112.76              36.47           128.82            -31.70             72.89              -7.39            20.77/H              6.03     \n                                                                 WEEK 5 DAY 36    538         53.98           -28.86             35.71/L              -0.63             80.20               3.91           137.16            -23.37             96.63              16.35             18.10               3.36     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nBaseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:56 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Vital Signs",
@@ -240,7 +240,7 @@
     "href": "listings/adverse-events/ael02_ed.html",
     "title": "AEL02_ED",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\nlibrary(lubridate)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadae_anl &lt;- adae %&gt;%\n  filter(SAFFL == \"Y\") %&gt;%\n  mutate(\n    DHM = as.period(LDOSEDTM %--% ASTDTM),\n    TMOM = minute(DHM) + ifelse(second(DHM) &gt;= 30, 1, 0),\n    TMOH = hour(DHM) + ifelse(TMOM == 60, 1, 0),\n    TMOD = day(DHM) + ifelse(TMOH == 24, 1, 0)\n  ) %&gt;%\n  mutate(\n    TMOM = TMOM %% 60,\n    TMOH = TMOH %% 24\n  )\n\nout &lt;- adae_anl %&gt;%\n  mutate(\n    ADURN = AENDY - ASTDY + 1,\n    cent_subj = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    AESER_F = if_else(AESER == \"Y\", \"Yes\", \"No\", \"\"),\n    AEREL_F = if_else(AEREL == \"Y\", \"Yes\", \"No\", \"\"),\n    AECONTRT_F = if_else(AECONTRT == \"Y\", \"Yes\", \"No\", \"\"),\n    AEOUT_F = case_when(\n      AEOUT == \"FATAL\" ~ 1,\n      AEOUT == \"NOT RECOVERED/NOT RESOLVED\" ~ 2,\n      AEOUT == \"RECOVERED/RESOLVED\" ~ 3,\n      AEOUT == \"RECOVERED/RESOLVED WITH SEQUELAE\" ~ 4,\n      AEOUT == \"RECOVERING/RESOLVING\" ~ 5,\n      AEOUT == \"UNKNOWN\" ~ 6\n    ),\n    AEACN_F = case_when(\n      AEACN == \"DOSE INCREASED\" ~ 1,\n      AEACN == \"DOSE NOT CHANGED\" ~ 2,\n      AEACN == \"DOSE REDUCED\" | AEACN == \"DOSE RATE REDUCED\" ~ 3,\n      AEACN == \"DRUG INTERRUPTED\" ~ 4,\n      AEACN == \"DRUG WITHDRAWN\" ~ 5,\n      AEACN == \"NOT APPLICABLE\" | AEACN == \"NOT EVALUABLE\" ~ 6,\n      AEACN == \"UNKNOWN\" ~ 7\n    )\n  ) %&gt;%\n  select(\n    cent_subj, ASR, TRT01A, AEDECOD, ASTDY, TMOD, TMOH, TMOM,\n    ADURN, AESER_F, AESEV, AEREL_F, AEOUT_F, AECONTRT_F, AEACN_F\n  )\n\nout &lt;- out %&gt;% var_relabel(\n  cent_subj  = \"Center/Patient ID\",\n  ASR        = \"Age/Sex/Race\",\n  TRT01A     = \"Treatment\",\n  AEDECOD    = \"Adverse\\nEvent MedDRA\\nPreferred Term\",\n  ASTDY      = \"Study\\nDay of\\nOnset\",\n  TMOD       = \"Time from\\nLast Dose\\nto Onset\\ndays\",\n  TMOH       = \"Time from\\nLast Dose\\nto Onset\\nhrs\",\n  TMOM       = \"Time from\\nLast Dose\\nto Onset\\nmins\",\n  ADURN      = \"AE\\nDuration\\nin Days\",\n  AESER_F    = \"Serious\",\n  AESEV      = \"Most\\nExtreme\\nIntensity\",\n  AEREL_F    = \"Caused by\\nStudy\\nDrug\",\n  AEOUT_F    = \"Outcome\\n(1)\",\n  AECONTRT_F = \"Treatment\\nfor AE\",\n  AEACN_F    = \"Action\\nTaken\\n(2)\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"cent_subj\", \"ASR\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Adverse Events (for early development studies)\",\n  main_footer = \"\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Adverse Events (for early development studies)\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                           Time from   Time from   Time from                                                                            \n                                                                    Adverse       Study    Last Dose   Last Dose   Last Dose      AE                  Most      Caused by                         Action\n                                                                  Event MedDRA    Day of   to Onset    to Onset    to Onset    Duration              Extreme      Study     Outcome   Treatment   Taken \nTreatment   Center/Patient ID            Age/Sex/Race            Preferred Term   Onset      days         hrs        mins      in Days    Serious   Intensity     Drug        (1)      for AE      (2)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             dcd B.2.1.2.1     162         7          19           4         173        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.1.1.4.2     196         1          20          48         166        No      MODERATE       No          5         No         2   \n                                                                 dcd A.1.1.1.2     321        28          12          54         149        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd A.1.1.1.2     393        12          19          17          43        Yes     MODERATE       No          5         No         5   \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   dcd C.2.1.2.1      39        30           9           4         506        No      MODERATE       Yes         2         No         6   \n                                                                 dcd D.1.1.1.1      64        26          17          10         630        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd C.2.1.2.1      95         4          12          58         306        No      MODERATE       Yes         5         No         2   \n                                                                 dcd A.1.1.1.2     160        24           5          57         259        Yes     MODERATE       No          2         Yes        2   \n                                                                 dcd B.2.2.3.1     292         9          23           4          50        Yes       MILD         No          3         No         2   \n                                                                 dcd A.1.1.1.2     459         9           5           4         195        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd B.1.1.1.1     470         6           5           0         131        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd A.1.1.1.2     475        20          16          16         113        Yes     MODERATE       No          4         Yes        2   \n                                                                 dcd C.1.1.1.3     496         5           5          28         162        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd B.1.1.1.1     641         8          17           4          15        Yes      SEVERE        Yes         1         Yes        6   \n               BRA-1/id-93                34/F/ASIAN             dcd D.1.1.4.2      44        18          15          39          47        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.2.1.5.3      79         2           3          58         300        No        MILD         Yes         5         No         3   \n                                                                 dcd A.1.1.1.1     136        22           1          19          78        No        MILD         No          3         Yes        2   \n                                                                 dcd D.2.1.5.3     260        12          12          26         305        No        MILD         Yes         5         No         2   \n                                                                 dcd C.1.1.1.3     281         4          14          52         106        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd D.1.1.1.1     316         6          12          43         250        Yes      SEVERE        Yes         1         No         6   \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:26 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] lubridate_1.9.3       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9013 scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  timechange_0.3.0  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   digest_0.6.34     R6_2.5.1          tidyselect_1.2.0 \n[29] utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1  \n[33] withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\nlibrary(lubridate)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadae_anl &lt;- adae %&gt;%\n  filter(SAFFL == \"Y\") %&gt;%\n  mutate(\n    DHM = as.period(LDOSEDTM %--% ASTDTM),\n    TMOM = minute(DHM) + ifelse(second(DHM) &gt;= 30, 1, 0),\n    TMOH = hour(DHM) + ifelse(TMOM == 60, 1, 0),\n    TMOD = day(DHM) + ifelse(TMOH == 24, 1, 0)\n  ) %&gt;%\n  mutate(\n    TMOM = TMOM %% 60,\n    TMOH = TMOH %% 24\n  )\n\nout &lt;- adae_anl %&gt;%\n  mutate(\n    ADURN = AENDY - ASTDY + 1,\n    cent_subj = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    AESER_F = if_else(AESER == \"Y\", \"Yes\", \"No\", \"\"),\n    AEREL_F = if_else(AEREL == \"Y\", \"Yes\", \"No\", \"\"),\n    AECONTRT_F = if_else(AECONTRT == \"Y\", \"Yes\", \"No\", \"\"),\n    AEOUT_F = case_when(\n      AEOUT == \"FATAL\" ~ 1,\n      AEOUT == \"NOT RECOVERED/NOT RESOLVED\" ~ 2,\n      AEOUT == \"RECOVERED/RESOLVED\" ~ 3,\n      AEOUT == \"RECOVERED/RESOLVED WITH SEQUELAE\" ~ 4,\n      AEOUT == \"RECOVERING/RESOLVING\" ~ 5,\n      AEOUT == \"UNKNOWN\" ~ 6\n    ),\n    AEACN_F = case_when(\n      AEACN == \"DOSE INCREASED\" ~ 1,\n      AEACN == \"DOSE NOT CHANGED\" ~ 2,\n      AEACN == \"DOSE REDUCED\" | AEACN == \"DOSE RATE REDUCED\" ~ 3,\n      AEACN == \"DRUG INTERRUPTED\" ~ 4,\n      AEACN == \"DRUG WITHDRAWN\" ~ 5,\n      AEACN == \"NOT APPLICABLE\" | AEACN == \"NOT EVALUABLE\" ~ 6,\n      AEACN == \"UNKNOWN\" ~ 7\n    )\n  ) %&gt;%\n  select(\n    cent_subj, ASR, TRT01A, AEDECOD, ASTDY, TMOD, TMOH, TMOM,\n    ADURN, AESER_F, AESEV, AEREL_F, AEOUT_F, AECONTRT_F, AEACN_F\n  )\n\nout &lt;- out %&gt;% var_relabel(\n  cent_subj  = \"Center/Patient ID\",\n  ASR        = \"Age/Sex/Race\",\n  TRT01A     = \"Treatment\",\n  AEDECOD    = \"Adverse\\nEvent MedDRA\\nPreferred Term\",\n  ASTDY      = \"Study\\nDay of\\nOnset\",\n  TMOD       = \"Time from\\nLast Dose\\nto Onset\\ndays\",\n  TMOH       = \"Time from\\nLast Dose\\nto Onset\\nhrs\",\n  TMOM       = \"Time from\\nLast Dose\\nto Onset\\nmins\",\n  ADURN      = \"AE\\nDuration\\nin Days\",\n  AESER_F    = \"Serious\",\n  AESEV      = \"Most\\nExtreme\\nIntensity\",\n  AEREL_F    = \"Caused by\\nStudy\\nDrug\",\n  AEOUT_F    = \"Outcome\\n(1)\",\n  AECONTRT_F = \"Treatment\\nfor AE\",\n  AEACN_F    = \"Action\\nTaken\\n(2)\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"cent_subj\", \"ASR\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Adverse Events (for early development studies)\",\n  main_footer = \"\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Adverse Events (for early development studies)\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                           Time from   Time from   Time from                                                                            \n                                                                    Adverse       Study    Last Dose   Last Dose   Last Dose      AE                  Most      Caused by                         Action\n                                                                  Event MedDRA    Day of   to Onset    to Onset    to Onset    Duration              Extreme      Study     Outcome   Treatment   Taken \nTreatment   Center/Patient ID            Age/Sex/Race            Preferred Term   Onset      days         hrs        mins      in Days    Serious   Intensity     Drug        (1)      for AE      (2)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             dcd B.2.1.2.1     162         7          19           4         173        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.1.1.4.2     196         1          20          48         166        No      MODERATE       No          5         No         2   \n                                                                 dcd A.1.1.1.2     321        28          12          54         149        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd A.1.1.1.2     393        12          19          17          43        Yes     MODERATE       No          5         No         5   \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   dcd C.2.1.2.1      39        30           9           4         506        No      MODERATE       Yes         2         No         6   \n                                                                 dcd D.1.1.1.1      64        26          17          10         630        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd C.2.1.2.1      95         4          12          58         306        No      MODERATE       Yes         5         No         2   \n                                                                 dcd A.1.1.1.2     160        24           5          57         259        Yes     MODERATE       No          2         Yes        2   \n                                                                 dcd B.2.2.3.1     292         9          23           4          50        Yes       MILD         No          3         No         2   \n                                                                 dcd A.1.1.1.2     459         9           5           4         195        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd B.1.1.1.1     470         6           5           0         131        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd A.1.1.1.2     475        20          16          16         113        Yes     MODERATE       No          4         Yes        2   \n                                                                 dcd C.1.1.1.3     496         5           5          28         162        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd B.1.1.1.1     641         8          17           4          15        Yes      SEVERE        Yes         1         Yes        6   \n               BRA-1/id-93                34/F/ASIAN             dcd D.1.1.4.2      44        18          15          39          47        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.2.1.5.3      79         2           3          58         300        No        MILD         Yes         5         No         3   \n                                                                 dcd A.1.1.1.1     136        22           1          19          78        No        MILD         No          3         Yes        2   \n                                                                 dcd D.2.1.5.3     260        12          12          26         305        No        MILD         Yes         5         No         2   \n                                                                 dcd C.1.1.1.3     281         4          14          52         106        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd D.1.1.1.1     316         6          12          43         250        Yes      SEVERE        Yes         1         No         6   \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:48 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] lubridate_1.9.3       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9015 scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  timechange_0.3.0  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   digest_0.6.34     R6_2.5.1          tidyselect_1.2.0 \n[29] utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1  \n[33] withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Adverse Events",
@@ -252,7 +252,7 @@
     "href": "listings/adverse-events/ael01_nollt.html",
     "title": "AEL01_NOLLT",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  select(AESOC, AEDECOD, AETERM) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  AESOC = \"MedDRA System Organ Class\",\n  AEDECOD = \"MedDRA Preferred Term\",\n  AETERM = \"Investigator-Specified\\nAdverse Event Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"AESOC\", \"AEDECOD\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Preferred Terms and Investigator-Specified Adverse Event Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Preferred Terms and Investigator-Specified Adverse Event Terms\n\n——————————————————————————————————————————————————————————————————————————\n                                                    Investigator-Specified\nMedDRA System Organ Class   MedDRA Preferred Term     Adverse Event Term  \n——————————————————————————————————————————————————————————————————————————\n          cl A                  dcd A.1.1.1.1           trm A.1.1.1.1     \n                                dcd A.1.1.1.2           trm A.1.1.1.2     \n          cl B                  dcd B.1.1.1.1           trm B.1.1.1.1     \n                                dcd B.2.1.2.1           trm B.2.1.2.1     \n                                dcd B.2.2.3.1           trm B.2.2.3.1     \n          cl C                  dcd C.1.1.1.3           trm C.1.1.1.3     \n                                dcd C.2.1.2.1           trm C.2.1.2.1     \n          cl D                  dcd D.1.1.1.1           trm D.1.1.1.1     \n                                dcd D.1.1.4.2           trm D.1.1.4.2     \n                                dcd D.2.1.5.3           trm D.2.1.5.3     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:19 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  select(AESOC, AEDECOD, AETERM) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  AESOC = \"MedDRA System Organ Class\",\n  AEDECOD = \"MedDRA Preferred Term\",\n  AETERM = \"Investigator-Specified\\nAdverse Event Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"AESOC\", \"AEDECOD\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Preferred Terms and Investigator-Specified Adverse Event Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Preferred Terms and Investigator-Specified Adverse Event Terms\n\n——————————————————————————————————————————————————————————————————————————\n                                                    Investigator-Specified\nMedDRA System Organ Class   MedDRA Preferred Term     Adverse Event Term  \n——————————————————————————————————————————————————————————————————————————\n          cl A                  dcd A.1.1.1.1           trm A.1.1.1.1     \n                                dcd A.1.1.1.2           trm A.1.1.1.2     \n          cl B                  dcd B.1.1.1.1           trm B.1.1.1.1     \n                                dcd B.2.1.2.1           trm B.2.1.2.1     \n                                dcd B.2.2.3.1           trm B.2.2.3.1     \n          cl C                  dcd C.1.1.1.3           trm C.1.1.1.3     \n                                dcd C.2.1.2.1           trm C.2.1.2.1     \n          cl D                  dcd D.1.1.1.1           trm D.1.1.1.1     \n                                dcd D.1.1.4.2           trm D.1.1.4.2     \n                                dcd D.2.1.5.3           trm D.2.1.5.3     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:41 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Adverse Events",
@@ -264,7 +264,7 @@
     "href": "listings/adverse-events/ael03.html",
     "title": "AEL03",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  filter(AESER == \"Y\") %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    Date_First = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    Duration = AENDY - ASTDY + 1,\n    Related = ifelse(AEREL == \"Y\", \"Yes\", ifelse(AEREL == \"N\", \"No\", \"\")),\n    Outcome = case_when(\n      AEOUT == \"FATAL\" ~ 1,\n      AEOUT == \"NOT RECOVERED/NOT RESOLVED\" ~ 2,\n      AEOUT == \"RECOVERED/RESOLVED\" ~ 3,\n      AEOUT == \"RECOVERED/RESOLVED WITH SEQUELAE\" ~ 4,\n      AEOUT == \"RECOVERING/RESOLVING\" ~ 5,\n      AEOUT == \"UNKNOWN\" ~ 6\n    ),\n    Treated = ifelse(AECONTRT == \"Y\", \"Yes\", ifelse(AECONTRT == \"N\", \"No\", \"\")),\n    Action = case_when(\n      AEACN == \"DOSE INCREASED\" ~ 1,\n      AEACN == \"DOSE NOT CHANGED\" ~ 2,\n      AEACN == \"DOSE REDUCED\" | AEACN == \"DOSE RATE REDUCED\" ~ 3,\n      AEACN == \"DRUG INTERRUPTED\" ~ 4,\n      AEACN == \"DRUG WITHDRAWN\" ~ 5,\n      AEACN == \"NOT APPLICABLE\" | AEACN == \"NOT EVALUABLE\" ~ 6,\n      AEACN == \"UNKNOWN\" ~ 7\n    ),\n    SERREAS = case_when(\n      AESDTH == \"Y\" ~ \"1\",\n      AESLIFE == \"Y\" ~ \"2\",\n      AESHOSP == \"Y\" ~ \"3\",\n      AESDISAB == \"Y\" ~ \"4\",\n      AESCONG == \"Y\" ~ \"5\",\n      AESMIE == \"Y\" ~ \"6\",\n      TRUE ~ \" \"\n    )\n  ) %&gt;%\n  select(CPID, ASR, TRT01A, AEDECOD, Date_First, ASTDY, Duration, AESEV, Related, Outcome, Treated, Action, SERREAS)\n\nvar_labels(out) &lt;- c(\n  CPID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  AEDECOD = \"Adverse\\nEvent MedDRA\\nPreferred Term\",\n  Date_First = \"Date of\\nFirst Study\\nDrug\\nAdministration\",\n  ASTDY = \"Study\\nDay of\\nOnset\",\n  Duration = \"AE\\nDuration\\nin Days\",\n  AESEV = \"Most\\nExtreme\\nIntensity\",\n  Related = \"Caused by\\nStudy\\nDrug\",\n  Outcome = \"Outcome\\n(1)\",\n  Treated = \"Treatment\\nfor AE\",\n  Action = \"Action\\nTaken\\n(2)\",\n  SERREAS = \"Reason\\nClassified\\nas Serious\\n(3)\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\", \"ASR\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Serious Adverse Events\",\n  main_footer = \"\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n(3) Reason classified as serious: 1 = resulted in death; 2 = life threatening;\n    3 = required prolonged in patient hospitalization; 4 = disabling;\n    5 = a congenital anomaly/birth defect in offspring of study subject;\n    6 = does not meet any of the above serious criteria, but may jeopardize the subject,\n    and may require medical or surgical intervention to prevent one of the outcomes listed above.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Serious Adverse Events\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                     Date of                                                                                    Reason  \n                                                                    Adverse        First Study     Study       AE        Most      Caused by                         Action   Classified\n                                                                  Event MedDRA         Drug        Day of   Duration    Extreme      Study     Outcome   Treatment   Taken    as Serious\nTreatment   Center/Patient ID            Age/Sex/Race            Preferred Term   Administration   Onset    in Days    Intensity     Drug        (1)      for AE      (2)        (3)    \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             dcd A.1.1.1.2      04NOV2020       321       149      MODERATE       No          5         Yes        2          4     \n                                                                 dcd A.1.1.1.2      04NOV2020       393        43      MODERATE       No          5         No         5          5     \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   dcd D.1.1.1.1      01JAN2020        64       630       SEVERE        Yes         1         No         6          1     \n                                                                 dcd A.1.1.1.2      01JAN2020       160       259      MODERATE       No          2         Yes        2          4     \n                                                                 dcd B.2.2.3.1      01JAN2020       292        50        MILD         No          3         No         2          4     \n                                                                 dcd A.1.1.1.2      01JAN2020       459       195      MODERATE       No          5         Yes        2          5     \n                                                                 dcd B.1.1.1.1      01JAN2020       470       131       SEVERE        Yes         1         No         6          1     \n                                                                 dcd A.1.1.1.2      01JAN2020       475       113      MODERATE       No          4         Yes        2          4     \n                                                                 dcd B.1.1.1.1      01JAN2020       641        15       SEVERE        Yes         1         Yes        6          1     \n               BRA-1/id-93                34/F/ASIAN             dcd D.1.1.1.1      20JUN2020       316       250       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.1.1.1.1      20JUN2020       523        24       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.2.2.3.1      20JUN2020       596        10        MILD         No          3         Yes        2          4     \n              BRA-11/id-345               37/F/WHITE             dcd B.1.1.1.1      18AUG2020       1009       35       SEVERE        Yes         1         No         6          1     \n              BRA-11/id-397               38/M/ASIAN             dcd D.1.1.1.1      27JUN2020       516       441       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.1.1.1.1      27JUN2020       1011       30       SEVERE        Yes         1         Yes        6          1     \n              BRA-11/id-50      26/M/BLACK OR AFRICAN AMERICAN   dcd A.1.1.1.2      28APR2019       1007       68      MODERATE       No          3         Yes        2          5     \n              BRA-13/id-177               24/M/WHITE             dcd B.1.1.1.1      30AUG2020       249       470       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.1.1.1.1      30AUG2020       1012       33       SEVERE        Yes         1         No         6          1     \n              BRA-15/id-36                38/F/ASIAN             dcd B.1.1.1.1      08JAN2020       372       154       SEVERE        Yes         1         Yes        6          1     \n                                                                 dcd A.1.1.1.2      08JAN2020       573       134      MODERATE       No          3         Yes        2          3     \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n(3) Reason classified as serious: 1 = resulted in death; 2 = life threatening;\n    3 = required prolonged in patient hospitalization; 4 = disabling;\n    5 = a congenital anomaly/birth defect in offspring of study subject;\n    6 = does not meet any of the above serious criteria, but may jeopardize the subject,\n    and may require medical or surgical intervention to prevent one of the outcomes listed above.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:12 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  filter(AESER == \"Y\") %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    Date_First = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    Duration = AENDY - ASTDY + 1,\n    Related = ifelse(AEREL == \"Y\", \"Yes\", ifelse(AEREL == \"N\", \"No\", \"\")),\n    Outcome = case_when(\n      AEOUT == \"FATAL\" ~ 1,\n      AEOUT == \"NOT RECOVERED/NOT RESOLVED\" ~ 2,\n      AEOUT == \"RECOVERED/RESOLVED\" ~ 3,\n      AEOUT == \"RECOVERED/RESOLVED WITH SEQUELAE\" ~ 4,\n      AEOUT == \"RECOVERING/RESOLVING\" ~ 5,\n      AEOUT == \"UNKNOWN\" ~ 6\n    ),\n    Treated = ifelse(AECONTRT == \"Y\", \"Yes\", ifelse(AECONTRT == \"N\", \"No\", \"\")),\n    Action = case_when(\n      AEACN == \"DOSE INCREASED\" ~ 1,\n      AEACN == \"DOSE NOT CHANGED\" ~ 2,\n      AEACN == \"DOSE REDUCED\" | AEACN == \"DOSE RATE REDUCED\" ~ 3,\n      AEACN == \"DRUG INTERRUPTED\" ~ 4,\n      AEACN == \"DRUG WITHDRAWN\" ~ 5,\n      AEACN == \"NOT APPLICABLE\" | AEACN == \"NOT EVALUABLE\" ~ 6,\n      AEACN == \"UNKNOWN\" ~ 7\n    ),\n    SERREAS = case_when(\n      AESDTH == \"Y\" ~ \"1\",\n      AESLIFE == \"Y\" ~ \"2\",\n      AESHOSP == \"Y\" ~ \"3\",\n      AESDISAB == \"Y\" ~ \"4\",\n      AESCONG == \"Y\" ~ \"5\",\n      AESMIE == \"Y\" ~ \"6\",\n      TRUE ~ \" \"\n    )\n  ) %&gt;%\n  select(CPID, ASR, TRT01A, AEDECOD, Date_First, ASTDY, Duration, AESEV, Related, Outcome, Treated, Action, SERREAS)\n\nvar_labels(out) &lt;- c(\n  CPID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  AEDECOD = \"Adverse\\nEvent MedDRA\\nPreferred Term\",\n  Date_First = \"Date of\\nFirst Study\\nDrug\\nAdministration\",\n  ASTDY = \"Study\\nDay of\\nOnset\",\n  Duration = \"AE\\nDuration\\nin Days\",\n  AESEV = \"Most\\nExtreme\\nIntensity\",\n  Related = \"Caused by\\nStudy\\nDrug\",\n  Outcome = \"Outcome\\n(1)\",\n  Treated = \"Treatment\\nfor AE\",\n  Action = \"Action\\nTaken\\n(2)\",\n  SERREAS = \"Reason\\nClassified\\nas Serious\\n(3)\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\", \"ASR\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Serious Adverse Events\",\n  main_footer = \"\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n(3) Reason classified as serious: 1 = resulted in death; 2 = life threatening;\n    3 = required prolonged in patient hospitalization; 4 = disabling;\n    5 = a congenital anomaly/birth defect in offspring of study subject;\n    6 = does not meet any of the above serious criteria, but may jeopardize the subject,\n    and may require medical or surgical intervention to prevent one of the outcomes listed above.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Serious Adverse Events\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                     Date of                                                                                    Reason  \n                                                                    Adverse        First Study     Study       AE        Most      Caused by                         Action   Classified\n                                                                  Event MedDRA         Drug        Day of   Duration    Extreme      Study     Outcome   Treatment   Taken    as Serious\nTreatment   Center/Patient ID            Age/Sex/Race            Preferred Term   Administration   Onset    in Days    Intensity     Drug        (1)      for AE      (2)        (3)    \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             dcd A.1.1.1.2      04NOV2020       321       149      MODERATE       No          5         Yes        2          4     \n                                                                 dcd A.1.1.1.2      04NOV2020       393        43      MODERATE       No          5         No         5          5     \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   dcd D.1.1.1.1      01JAN2020        64       630       SEVERE        Yes         1         No         6          1     \n                                                                 dcd A.1.1.1.2      01JAN2020       160       259      MODERATE       No          2         Yes        2          4     \n                                                                 dcd B.2.2.3.1      01JAN2020       292        50        MILD         No          3         No         2          4     \n                                                                 dcd A.1.1.1.2      01JAN2020       459       195      MODERATE       No          5         Yes        2          5     \n                                                                 dcd B.1.1.1.1      01JAN2020       470       131       SEVERE        Yes         1         No         6          1     \n                                                                 dcd A.1.1.1.2      01JAN2020       475       113      MODERATE       No          4         Yes        2          4     \n                                                                 dcd B.1.1.1.1      01JAN2020       641        15       SEVERE        Yes         1         Yes        6          1     \n               BRA-1/id-93                34/F/ASIAN             dcd D.1.1.1.1      20JUN2020       316       250       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.1.1.1.1      20JUN2020       523        24       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.2.2.3.1      20JUN2020       596        10        MILD         No          3         Yes        2          4     \n              BRA-11/id-345               37/F/WHITE             dcd B.1.1.1.1      18AUG2020       1009       35       SEVERE        Yes         1         No         6          1     \n              BRA-11/id-397               38/M/ASIAN             dcd D.1.1.1.1      27JUN2020       516       441       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.1.1.1.1      27JUN2020       1011       30       SEVERE        Yes         1         Yes        6          1     \n              BRA-11/id-50      26/M/BLACK OR AFRICAN AMERICAN   dcd A.1.1.1.2      28APR2019       1007       68      MODERATE       No          3         Yes        2          5     \n              BRA-13/id-177               24/M/WHITE             dcd B.1.1.1.1      30AUG2020       249       470       SEVERE        Yes         1         No         6          1     \n                                                                 dcd B.1.1.1.1      30AUG2020       1012       33       SEVERE        Yes         1         No         6          1     \n              BRA-15/id-36                38/F/ASIAN             dcd B.1.1.1.1      08JAN2020       372       154       SEVERE        Yes         1         Yes        6          1     \n                                                                 dcd A.1.1.1.2      08JAN2020       573       134      MODERATE       No          3         Yes        2          3     \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n(3) Reason classified as serious: 1 = resulted in death; 2 = life threatening;\n    3 = required prolonged in patient hospitalization; 4 = disabling;\n    5 = a congenital anomaly/birth defect in offspring of study subject;\n    6 = does not meet any of the above serious criteria, but may jeopardize the subject,\n    and may require medical or surgical intervention to prevent one of the outcomes listed above.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Adverse Events",
@@ -276,7 +276,7 @@
     "href": "listings/disposition/dsl01.html",
     "title": "DSL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(AEWITHFL == \"Y\") %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    SSADM = toupper(format(as.Date(TRTSDTM), format = \"%d%b%Y\")),\n    STDWD = as.numeric(ceiling(difftime(TRTEDTM, TRTSDTM, units = \"days\"))),\n    DISCONT = ifelse(!is.na(DCSREAS) & toupper(EOSSTT) == \"DISCONTINUED\", \"Yes\", \"No\")\n  ) %&gt;%\n  select(ID, ASR, TRT01A, SSADM, STDWD, DISCONT)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  SSADM = \"Date of First\\nStudy Drug\\nAdministration\",\n  STDWD = \"Study Day\\nof Withdrawal\",\n  DISCONT = \"Discontinued\\nEarly from Study?\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"TRT01A\",\n  disp_cols = names(out),\n  main_title = \"Listing of Patients with Study Drug Withdrawn Due to Adverse Events\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patients with Study Drug Withdrawn Due to Adverse Events\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                      Date of First                                     \n                                                                                        Study Drug       Study Day       Discontinued   \n  Treatment      Center/Patient ID                    Age/Sex/Race                    Administration   of Withdrawal   Early from Study?\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n  A: Drug X        RUS-18/id-359             47/F/BLACK OR AFRICAN AMERICAN             08JUN2019          1096               No        \n                    BRA-1/id-42              36/M/BLACK OR AFRICAN AMERICAN             01JAN2020           NA                No        \n                   CHN-11/id-256                       23/M/ASIAN                       17MAR2020          1096               No        \n                    CHN-1/id-26                        29/M/WHITE                       16JUL2020          1096               No        \n                    CHN-1/id-62                        36/F/WHITE                       22NOV2020           455               Yes       \n  B: Placebo        CHN-7/id-28          40/M/AMERICAN INDIAN OR ALASKA NATIVE          11MAR2019          1096               No        \n                   CHN-13/id-102                       37/M/ASIAN                       23MAR2019          1096               No        \n                   CHN-3/id-333              30/F/BLACK OR AFRICAN AMERICAN             13JAN2020           NA                No        \n                   USA-12/id-226                       30/M/WHITE                       29JAN2020           NA                No        \n                   CHN-9/id-147                        26/F/ASIAN                       06FEB2020           NA                No        \n                   USA-11/id-157                       50/M/WHITE                       14JUN2020          1096               No        \n                    CHN-9/id-11      28/F/NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER     27JAN2021           388               Yes       \nC: Combination     CHN-15/id-262             35/M/BLACK OR AFRICAN AMERICAN             26FEB2019          1096               No        \n                   NGA-11/id-173             24/F/BLACK OR AFRICAN AMERICAN             10MAR2019          1096               No        \n                   CHN-11/id-263                       34/F/ASIAN                       03APR2019          1096               No        \n                   PAK-2/id-191          38/F/AMERICAN INDIAN OR ALASKA NATIVE          07APR2019          1096               No        \n                   USA-11/id-136                       38/F/ASIAN                       02OCT2019           865               Yes       \n                   CHN-17/id-31                        35/F/ASIAN                       17NOV2019           NA                No        \n                   BRA-11/id-237                       64/F/ASIAN                       10MAR2020          1096               No        \n                   USA-11/id-100                       40/F/ASIAN                       10MAR2020           705               Yes       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:04 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(AEWITHFL == \"Y\") %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    SSADM = toupper(format(as.Date(TRTSDTM), format = \"%d%b%Y\")),\n    STDWD = as.numeric(ceiling(difftime(TRTEDTM, TRTSDTM, units = \"days\"))),\n    DISCONT = ifelse(!is.na(DCSREAS) & toupper(EOSSTT) == \"DISCONTINUED\", \"Yes\", \"No\")\n  ) %&gt;%\n  select(ID, ASR, TRT01A, SSADM, STDWD, DISCONT)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  SSADM = \"Date of First\\nStudy Drug\\nAdministration\",\n  STDWD = \"Study Day\\nof Withdrawal\",\n  DISCONT = \"Discontinued\\nEarly from Study?\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"TRT01A\",\n  disp_cols = names(out),\n  main_title = \"Listing of Patients with Study Drug Withdrawn Due to Adverse Events\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patients with Study Drug Withdrawn Due to Adverse Events\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                      Date of First                                     \n                                                                                        Study Drug       Study Day       Discontinued   \n  Treatment      Center/Patient ID                    Age/Sex/Race                    Administration   of Withdrawal   Early from Study?\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n  A: Drug X        RUS-18/id-359             47/F/BLACK OR AFRICAN AMERICAN             08JUN2019          1096               No        \n                    BRA-1/id-42              36/M/BLACK OR AFRICAN AMERICAN             01JAN2020           NA                No        \n                   CHN-11/id-256                       23/M/ASIAN                       17MAR2020          1096               No        \n                    CHN-1/id-26                        29/M/WHITE                       16JUL2020          1096               No        \n                    CHN-1/id-62                        36/F/WHITE                       22NOV2020           455               Yes       \n  B: Placebo        CHN-7/id-28          40/M/AMERICAN INDIAN OR ALASKA NATIVE          11MAR2019          1096               No        \n                   CHN-13/id-102                       37/M/ASIAN                       23MAR2019          1096               No        \n                   CHN-3/id-333              30/F/BLACK OR AFRICAN AMERICAN             13JAN2020           NA                No        \n                   USA-12/id-226                       30/M/WHITE                       29JAN2020           NA                No        \n                   CHN-9/id-147                        26/F/ASIAN                       06FEB2020           NA                No        \n                   USA-11/id-157                       50/M/WHITE                       14JUN2020          1096               No        \n                    CHN-9/id-11      28/F/NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER     27JAN2021           388               Yes       \nC: Combination     CHN-15/id-262             35/M/BLACK OR AFRICAN AMERICAN             26FEB2019          1096               No        \n                   NGA-11/id-173             24/F/BLACK OR AFRICAN AMERICAN             10MAR2019          1096               No        \n                   CHN-11/id-263                       34/F/ASIAN                       03APR2019          1096               No        \n                   PAK-2/id-191          38/F/AMERICAN INDIAN OR ALASKA NATIVE          07APR2019          1096               No        \n                   USA-11/id-136                       38/F/ASIAN                       02OCT2019           865               Yes       \n                   CHN-17/id-31                        35/F/ASIAN                       17NOV2019           NA                No        \n                   BRA-11/id-237                       64/F/ASIAN                       10MAR2020          1096               No        \n                   USA-11/id-100                       40/F/ASIAN                       10MAR2020           705               Yes       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:28 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Disposition",
@@ -288,7 +288,7 @@
     "href": "listings/ECG/egl01.html",
     "title": "EGL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\nget_param_unit_range &lt;- function(dataset) {\n  u_rng &lt;- lapply(unique(dataset$PARAMCD), function(p) {\n    dat &lt;- dataset %&gt;% filter(PARAMCD == p)\n    list(\n      unit = unique(dat$AVALU),\n      range = paste0(unique(dat$ANRLO), \"-\", unique(dat$ANRHI))\n    )\n  })\n  names(u_rng) &lt;- unique(dataset$PARAMCD)\n  u_rng\n}\n\neg_u_rng &lt;- get_param_unit_range(adeg)\n\nadeg_sub &lt;- adeg %&gt;%\n  filter(!is.na(AVAL) & SAFFL == \"Y\" & ANL01FL == \"Y\" & !is.na(EGSEQ) & PARAMCD != \"ECGINTP\") %&gt;%\n  mutate(\n    CRTNPT = paste(SITEID, sub(\"^.*-([[:alnum:]]+)$\", \"\\\\1\", SUBJID), sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    AVAL = format(round(AVAL, 2), nsmall = 2),\n    AVAL_ANRIND = ifelse(ANRIND %in% c(\"NORMAL\", \"\"), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = \"/\")),\n    CHG = format(round(CHG, 2), nsmall = 2)\n  )\n\nanl_eg &lt;- adeg_sub %&gt;%\n  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),\n    names_from = PARAMCD,\n    values_from = c(AVAL_ANRIND, CHG)\n  )\n\nout &lt;- anl_eg %&gt;%\n  select(\n    CRTNPT,\n    AGSXRC,\n    TRT01A,\n    AVISIT,\n    ADY,\n    AVAL_ANRIND_HR,\n    CHG_HR,\n    AVAL_ANRIND_QT,\n    CHG_QT,\n    AVAL_ANRIND_RR,\n    CHG_RR\n  ) %&gt;%\n  var_relabel(\n    CRTNPT = \"Center/Subject ID\",\n    AGSXRC = \"Age/Sex/Race\",\n    TRT01A = \"Treatment\",\n    AVISIT = \"Visit\",\n    ADY = \"Study\\nDay\",\n    AVAL_ANRIND_HR = paste0(\"Heart Rate Result\\n(\", eg_u_rng$HR$unit, \");\\nRange:(\", eg_u_rng$HR$range, \")\"),\n    CHG_HR = \"Heart Rate\\nChange from BL\",\n    AVAL_ANRIND_QT = paste0(\"QT Duration Result\\n(\", eg_u_rng$QT$unit, \");\\nRange:(\", eg_u_rng$QT$range, \")\"),\n    CHG_QT = \"QT Duration\\nChange from BL\",\n    AVAL_ANRIND_RR = paste0(\"RR Duration Result\\n(\", eg_u_rng$RR$unit, \");\\nRange:(\", eg_u_rng$RR$range, \")\"),\n    CHG_RR = \"RR Duration\\nChange from BL\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CRTNPT\", \"AGSXRC\", \"AVISIT\", \"ADY\"),\n  disp_cols = names(out),\n  main_title = \"Listing of ECG Data: Safety-Evaluable Patients\",\n  main_footer = \"Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of ECG Data: Safety-Evaluable Patients\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                         Heart Rate Result                    QT Duration Result                    RR Duration Result                 \n                                                                                 Study     (beats/min);        Heart Rate          (msec);          QT Duration          (msec);          RR Duration  \nTreatment   Center/Subject ID            Age/Sex/Race                Visit        Day     Range:(40-100)     Change from BL    Range:(200-500)     Change from BL    Range:(600-1500)    Change from BL\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X       BRA-1/105       38/M/BLACK OR AFRICAN AMERICAN     BASELINE       149           66.94                 NA            441.78                  NA            896.59                  NA   \n                                                                 WEEK 1 DAY 8     314           63.18              -3.76            365.68              -76.10           1376.26              479.67   \n                                                                 WEEK 2 DAY 15    398           57.57              -9.37            355.48              -86.29            799.57              -97.02   \n                                                                 WEEK 3 DAY 22    470           73.36               6.42            352.45              -89.33           1015.00              118.41   \n                                                                 WEEK 4 DAY 29    528           53.72             -13.22            388.42              -53.36           1189.29              292.70   \n                                                                 WEEK 5 DAY 36    535           58.25              -8.69            291.77             -150.01            936.09               39.50   \n                BRA-1/134                 47/M/WHITE               BASELINE       227           46.99                 NA            385.29                  NA            835.17                  NA   \n                                                                 WEEK 1 DAY 8     255           48.38               1.39            453.74               68.46            901.86               66.68   \n                                                                 WEEK 2 DAY 15    269          35.40/L            -11.59            413.54               28.26            892.01               56.84   \n                                                                 WEEK 3 DAY 22    357           60.50              13.51            394.23                8.95           490.18/L            -344.99   \n                                                                 WEEK 4 DAY 29    436           82.28              35.29            398.29               13.00            757.96              -77.21   \n                                                                 WEEK 5 DAY 36    440          38.89/L             -8.10            339.38              -45.91            880.26               45.08   \n                BRA-1/42        36/M/BLACK OR AFRICAN AMERICAN     BASELINE       477           56.19                 NA           190.94/L                 NA           1276.65                  NA   \n                                                                 WEEK 1 DAY 8     534           85.24              29.05            483.71              292.77            735.11             -541.54   \n                                                                 WEEK 2 DAY 15    550           55.53              -0.66            386.60              195.66           1092.71             -183.94   \n                                                                 WEEK 3 DAY 22    569           65.37               9.18            311.08              120.14           1227.15              -49.50   \n                                                                 WEEK 4 DAY 29    608           65.13               8.93            329.78              138.83           250.18/L           -1026.47   \n                                                                 WEEK 5 DAY 36    675           49.03              -7.16            470.67              279.73           1321.21               44.56   \n                BRA-1/93                  34/F/ASIAN               BASELINE       73            72.12                 NA            306.64                  NA           1324.80                  NA   \n                                                                 WEEK 1 DAY 8     207           66.88              -5.24           190.50/L            -116.14           1495.37              170.58   \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nBaseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:57 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\nget_param_unit_range &lt;- function(dataset) {\n  u_rng &lt;- lapply(unique(dataset$PARAMCD), function(p) {\n    dat &lt;- dataset %&gt;% filter(PARAMCD == p)\n    list(\n      unit = unique(dat$AVALU),\n      range = paste0(unique(dat$ANRLO), \"-\", unique(dat$ANRHI))\n    )\n  })\n  names(u_rng) &lt;- unique(dataset$PARAMCD)\n  u_rng\n}\n\neg_u_rng &lt;- get_param_unit_range(adeg)\n\nadeg_sub &lt;- adeg %&gt;%\n  filter(!is.na(AVAL) & SAFFL == \"Y\" & ANL01FL == \"Y\" & !is.na(EGSEQ) & PARAMCD != \"ECGINTP\") %&gt;%\n  mutate(\n    CRTNPT = paste(SITEID, sub(\"^.*-([[:alnum:]]+)$\", \"\\\\1\", SUBJID), sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    AVAL = format(round(AVAL, 2), nsmall = 2),\n    AVAL_ANRIND = ifelse(ANRIND %in% c(\"NORMAL\", \"\"), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = \"/\")),\n    CHG = format(round(CHG, 2), nsmall = 2)\n  )\n\nanl_eg &lt;- adeg_sub %&gt;%\n  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),\n    names_from = PARAMCD,\n    values_from = c(AVAL_ANRIND, CHG)\n  )\n\nout &lt;- anl_eg %&gt;%\n  select(\n    CRTNPT,\n    AGSXRC,\n    TRT01A,\n    AVISIT,\n    ADY,\n    AVAL_ANRIND_HR,\n    CHG_HR,\n    AVAL_ANRIND_QT,\n    CHG_QT,\n    AVAL_ANRIND_RR,\n    CHG_RR\n  ) %&gt;%\n  var_relabel(\n    CRTNPT = \"Center/Subject ID\",\n    AGSXRC = \"Age/Sex/Race\",\n    TRT01A = \"Treatment\",\n    AVISIT = \"Visit\",\n    ADY = \"Study\\nDay\",\n    AVAL_ANRIND_HR = paste0(\"Heart Rate Result\\n(\", eg_u_rng$HR$unit, \");\\nRange:(\", eg_u_rng$HR$range, \")\"),\n    CHG_HR = \"Heart Rate\\nChange from BL\",\n    AVAL_ANRIND_QT = paste0(\"QT Duration Result\\n(\", eg_u_rng$QT$unit, \");\\nRange:(\", eg_u_rng$QT$range, \")\"),\n    CHG_QT = \"QT Duration\\nChange from BL\",\n    AVAL_ANRIND_RR = paste0(\"RR Duration Result\\n(\", eg_u_rng$RR$unit, \");\\nRange:(\", eg_u_rng$RR$range, \")\"),\n    CHG_RR = \"RR Duration\\nChange from BL\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CRTNPT\", \"AGSXRC\", \"AVISIT\", \"ADY\"),\n  disp_cols = names(out),\n  main_title = \"Listing of ECG Data: Safety-Evaluable Patients\",\n  main_footer = \"Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\" \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of ECG Data: Safety-Evaluable Patients\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                         Heart Rate Result                    QT Duration Result                    RR Duration Result                 \n                                                                                 Study     (beats/min);        Heart Rate          (msec);          QT Duration          (msec);          RR Duration  \nTreatment   Center/Subject ID            Age/Sex/Race                Visit        Day     Range:(40-100)     Change from BL    Range:(200-500)     Change from BL    Range:(600-1500)    Change from BL\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X       BRA-1/105       38/M/BLACK OR AFRICAN AMERICAN     BASELINE       149           66.94                 NA            441.78                  NA            896.59                  NA   \n                                                                 WEEK 1 DAY 8     314           63.18              -3.76            365.68              -76.10           1376.26              479.67   \n                                                                 WEEK 2 DAY 15    398           57.57              -9.37            355.48              -86.29            799.57              -97.02   \n                                                                 WEEK 3 DAY 22    470           73.36               6.42            352.45              -89.33           1015.00              118.41   \n                                                                 WEEK 4 DAY 29    528           53.72             -13.22            388.42              -53.36           1189.29              292.70   \n                                                                 WEEK 5 DAY 36    535           58.25              -8.69            291.77             -150.01            936.09               39.50   \n                BRA-1/134                 47/M/WHITE               BASELINE       227           46.99                 NA            385.29                  NA            835.17                  NA   \n                                                                 WEEK 1 DAY 8     255           48.38               1.39            453.74               68.46            901.86               66.68   \n                                                                 WEEK 2 DAY 15    269          35.40/L            -11.59            413.54               28.26            892.01               56.84   \n                                                                 WEEK 3 DAY 22    357           60.50              13.51            394.23                8.95           490.18/L            -344.99   \n                                                                 WEEK 4 DAY 29    436           82.28              35.29            398.29               13.00            757.96              -77.21   \n                                                                 WEEK 5 DAY 36    440          38.89/L             -8.10            339.38              -45.91            880.26               45.08   \n                BRA-1/42        36/M/BLACK OR AFRICAN AMERICAN     BASELINE       477           56.19                 NA           190.94/L                 NA           1276.65                  NA   \n                                                                 WEEK 1 DAY 8     534           85.24              29.05            483.71              292.77            735.11             -541.54   \n                                                                 WEEK 2 DAY 15    550           55.53              -0.66            386.60              195.66           1092.71             -183.94   \n                                                                 WEEK 3 DAY 22    569           65.37               9.18            311.08              120.14           1227.15              -49.50   \n                                                                 WEEK 4 DAY 29    608           65.13               8.93            329.78              138.83           250.18/L           -1026.47   \n                                                                 WEEK 5 DAY 36    675           49.03              -7.16            470.67              279.73           1321.21               44.56   \n                BRA-1/93                  34/F/ASIAN               BASELINE       73            72.12                 NA            306.64                  NA           1324.80                  NA   \n                                                                 WEEK 1 DAY 8     207           66.88              -5.24           190.50/L            -116.14           1495.37              170.58   \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nBaseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if outside the Roche standard reference range.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:21 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "ECG",
@@ -300,7 +300,7 @@
     "href": "listings/concomitant-medications/cml02a_gl.html",
     "title": "CML02A_GL",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nout &lt;- adcm %&gt;%\n  select(ATC2, CMDECOD, CMTRT) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  ATC2 = \"ATC Class Level 2\",\n  CMDECOD = \"WHODrug Preferred Name\",\n  CMTRT = \"Investigator-Specified\\nTreatment Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ATC2\", \"CMDECOD\", \"CMTRT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms\n\n———————————————————————————————————————————————————————————————————\n                                             Investigator-Specified\nATC Class Level 2   WHODrug Preferred Name       Treatment Term    \n———————————————————————————————————————————————————————————————————\n   ATCCLAS2 A           medname A_1/3                A_1/3         \n                        medname A_2/3                A_2/3         \n                        medname A_3/3                A_3/3         \n  ATCCLAS2 A p2         medname A_3/3                A_3/3         \n   ATCCLAS2 B           medname B_1/4                B_1/4         \n                        medname B_2/4                B_2/4         \n                        medname B_3/4                B_3/4         \n                        medname B_4/4                B_4/4         \n  ATCCLAS2 B p2         medname B_1/4                B_1/4         \n                        medname B_2/4                B_2/4         \n  ATCCLAS2 B p3         medname B_1/4                B_1/4         \n                        medname B_2/4                B_2/4         \n   ATCCLAS2 C           medname C_1/2                C_1/2         \n                        medname C_2/2                C_2/2         \n  ATCCLAS2 C p2         medname C_1/2                C_1/2         \n                        medname C_2/2                C_2/2         \n  ATCCLAS2 C p3         medname C_2/2                C_2/2         \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:50 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nout &lt;- adcm %&gt;%\n  select(ATC2, CMDECOD, CMTRT) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  ATC2 = \"ATC Class Level 2\",\n  CMDECOD = \"WHODrug Preferred Name\",\n  CMTRT = \"Investigator-Specified\\nTreatment Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ATC2\", \"CMDECOD\", \"CMTRT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms\n\n———————————————————————————————————————————————————————————————————\n                                             Investigator-Specified\nATC Class Level 2   WHODrug Preferred Name       Treatment Term    \n———————————————————————————————————————————————————————————————————\n   ATCCLAS2 A           medname A_1/3                A_1/3         \n                        medname A_2/3                A_2/3         \n                        medname A_3/3                A_3/3         \n  ATCCLAS2 A p2         medname A_3/3                A_3/3         \n   ATCCLAS2 B           medname B_1/4                B_1/4         \n                        medname B_2/4                B_2/4         \n                        medname B_3/4                B_3/4         \n                        medname B_4/4                B_4/4         \n  ATCCLAS2 B p2         medname B_1/4                B_1/4         \n                        medname B_2/4                B_2/4         \n  ATCCLAS2 B p3         medname B_1/4                B_1/4         \n                        medname B_2/4                B_2/4         \n   ATCCLAS2 C           medname C_1/2                C_1/2         \n                        medname C_2/2                C_2/2         \n  ATCCLAS2 C p2         medname C_1/2                C_1/2         \n                        medname C_2/2                C_2/2         \n  ATCCLAS2 C p3         medname C_2/2                C_2/2         \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:14 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Concomitant Medications",
@@ -312,7 +312,7 @@
     "href": "listings/efficacy/oncl01.html",
     "title": "ONCL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\nout_ex &lt;- adex %&gt;%\n  filter(PARAMCD == \"TNDOSE\") %&gt;%\n  mutate(ID = paste(SITEID, SUBJID, sep = \"/\")) %&gt;%\n  select(ID, AVAL)\n\nout_rs &lt;- adrs %&gt;%\n  filter(PARAM %in% c(\"Investigator End Of Induction Response\", \"Best Confirmed Overall Response by Investigator\")) %&gt;%\n  mutate(ID = paste(SITEID, SUBJID, sep = \"/\")) %&gt;%\n  select(ID, AVALC, PARAM) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = ID,\n    names_from = PARAM,\n    values_from = AVALC\n  ) %&gt;%\n  right_join(out_ex, ., by = \"ID\", multiple = \"all\")\n\nadtte_flt &lt;- adtte %&gt;%\n  filter(PARAMCD %in% c(\"OS\", \"PFS\", \"CRSD\")) %&gt;%\n  mutate(\n    PARAM = paste(PARAM, paste0(\"(\", AVALU, \")\"), sep = \" \"), ID = paste(SITEID, SUBJID, sep = \"/\"),\n    trigeventpfs = ifelse(CNSR == 0, EVNTDESC, NA)\n  )\n\n# Select triggering event variable\nout_trg &lt;- adtte_flt %&gt;%\n  select(ID, trigeventpfs)\n\nout_tte &lt;- adtte_flt %&gt;%\n  select(ID, TRT01A, PARAM, AVAL, trigeventpfs) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(ID, TRT01A),\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate_at(\n    c(\"Overall Survival (DAYS)\", \"Progression Free Survival (DAYS)\", \"Duration of Confirmed Response (DAYS)\"),\n    function(x) format(round(x, 1), nsmall = 1)\n  ) %&gt;%\n  select(\n    ID,\n    TRT01A,\n    `Overall Survival (DAYS)`,\n    `Progression Free Survival (DAYS)`,\n    `Duration of Confirmed Response (DAYS)`\n  ) %&gt;%\n  right_join(out_trg, ., by = \"ID\", multiple = \"all\")\n\nout &lt;- out_tte %&gt;%\n  right_join(out_rs, ., by = \"ID\", multiple = \"all\") %&gt;%\n  select(\n    \"ID\", \"TRT01A\", \"AVAL\", \"Best Confirmed Overall Response by Investigator\",\n    \"Investigator End Of Induction Response\", \"Overall Survival (DAYS)\", \"Progression Free Survival (DAYS)\",\n    \"trigeventpfs\", \"Duration of Confirmed Response (DAYS)\"\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    ID = \"Center/Patient ID\",\n    TRT01A = \"Treatment\",\n    AVAL = \"Number of Doses\\nReceived\",\n    `Best Confirmed Overall Response by Investigator` = \"Best Confirmed\\nOverall Response\\nby Investigator\",\n    `Investigator End Of Induction Response` = \"Investigator\\nEnd Of Induction\\nResponse\",\n    trigeventpfs = \"Triggering Event\\n for PFS\",\n    `Overall Survival (DAYS)` = \"Overall Survival\\n(days)\",\n    `Progression Free Survival (DAYS)` = \"PFS\\n(days)\",\n    `Duration of Confirmed Response (DAYS)` = \"Duration of\\nConfirmed Response\\n(days)\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Individual Efficacy Data\",\n  main_footer = \"PFS = Progression Free Survival\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Individual Efficacy Data\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                   Best Confirmed      Investigator                                                          Duration of    \n                                Number of Doses   Overall Response   End Of Induction   Overall Survival    PFS      Triggering Event     Confirmed Response\nTreatment   Center/Patient ID      Received       by Investigator        Response            (days)        (days)         for PFS               (days)      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105             7                 CR                 CR               402.6         235.7    Disease Progression         119.6       \n                                       7                 CR                 CR               402.6         235.7            NA                  119.6       \n                                       7                 CR                 CR               402.6         235.7            NA                  119.6       \n              BRA-1/id-134             7                 CR                 CR                90.1          90.1           Death                 90.1       \n                                       7                 CR                 CR                90.1          90.1           Death                 90.1       \n                                       7                 CR                 CR                90.1          90.1           Death                 90.1       \n               BRA-1/id-42             7                 PR                 PR               407.0         204.6    Disease Progression         193.9       \n                                       7                 PR                 PR               407.0         204.6            NA                  193.9       \n                                       7                 PR                 PR               407.0         204.6            NA                  193.9       \n               BRA-1/id-93             7                 CR                 PD               316.0         255.7            NA                  140.3       \n                                       7                 CR                 PD               316.0         255.7            NA                  140.3       \n                                       7                 CR                 PD               316.0         255.7            NA                  140.3       \n              BRA-11/id-217            7                 CR                 CR               293.0         293.0    Disease Progression         153.2       \n                                       7                 CR                 CR               293.0         293.0           Death                153.2       \n                                       7                 CR                 CR               293.0         293.0           Death                153.2       \n              BRA-11/id-345            7                 CR                 CR               305.5         272.6            NA                  150.6       \n                                       7                 CR                 CR               305.5         272.6            NA                  150.6       \n                                       7                 CR                 CR               305.5         272.6            NA                  150.6       \n              BRA-11/id-397            7                 PR                 PR               138.7         138.7           Death                138.7       \n                                       7                 PR                 PR               138.7         138.7           Death                138.7       \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nPFS = Progression Free Survival\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:42 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4\n[25] testthat_3.2.1    tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\nout_ex &lt;- adex %&gt;%\n  filter(PARAMCD == \"TNDOSE\") %&gt;%\n  mutate(ID = paste(SITEID, SUBJID, sep = \"/\")) %&gt;%\n  select(ID, AVAL)\n\nout_rs &lt;- adrs %&gt;%\n  filter(PARAM %in% c(\"Investigator End Of Induction Response\", \"Best Confirmed Overall Response by Investigator\")) %&gt;%\n  mutate(ID = paste(SITEID, SUBJID, sep = \"/\")) %&gt;%\n  select(ID, AVALC, PARAM) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = ID,\n    names_from = PARAM,\n    values_from = AVALC\n  ) %&gt;%\n  right_join(out_ex, ., by = \"ID\", multiple = \"all\")\n\nadtte_flt &lt;- adtte %&gt;%\n  filter(PARAMCD %in% c(\"OS\", \"PFS\", \"CRSD\")) %&gt;%\n  mutate(\n    PARAM = paste(PARAM, paste0(\"(\", AVALU, \")\"), sep = \" \"), ID = paste(SITEID, SUBJID, sep = \"/\"),\n    trigeventpfs = ifelse(CNSR == 0, EVNTDESC, NA)\n  )\n\n# Select triggering event variable\nout_trg &lt;- adtte_flt %&gt;%\n  select(ID, trigeventpfs)\n\nout_tte &lt;- adtte_flt %&gt;%\n  select(ID, TRT01A, PARAM, AVAL, trigeventpfs) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(ID, TRT01A),\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate_at(\n    c(\"Overall Survival (DAYS)\", \"Progression Free Survival (DAYS)\", \"Duration of Confirmed Response (DAYS)\"),\n    function(x) format(round(x, 1), nsmall = 1)\n  ) %&gt;%\n  select(\n    ID,\n    TRT01A,\n    `Overall Survival (DAYS)`,\n    `Progression Free Survival (DAYS)`,\n    `Duration of Confirmed Response (DAYS)`\n  ) %&gt;%\n  right_join(out_trg, ., by = \"ID\", multiple = \"all\")\n\nout &lt;- out_tte %&gt;%\n  right_join(out_rs, ., by = \"ID\", multiple = \"all\") %&gt;%\n  select(\n    \"ID\", \"TRT01A\", \"AVAL\", \"Best Confirmed Overall Response by Investigator\",\n    \"Investigator End Of Induction Response\", \"Overall Survival (DAYS)\", \"Progression Free Survival (DAYS)\",\n    \"trigeventpfs\", \"Duration of Confirmed Response (DAYS)\"\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    ID = \"Center/Patient ID\",\n    TRT01A = \"Treatment\",\n    AVAL = \"Number of Doses\\nReceived\",\n    `Best Confirmed Overall Response by Investigator` = \"Best Confirmed\\nOverall Response\\nby Investigator\",\n    `Investigator End Of Induction Response` = \"Investigator\\nEnd Of Induction\\nResponse\",\n    trigeventpfs = \"Triggering Event\\n for PFS\",\n    `Overall Survival (DAYS)` = \"Overall Survival\\n(days)\",\n    `Progression Free Survival (DAYS)` = \"PFS\\n(days)\",\n    `Duration of Confirmed Response (DAYS)` = \"Duration of\\nConfirmed Response\\n(days)\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Individual Efficacy Data\",\n  main_footer = \"PFS = Progression Free Survival\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Individual Efficacy Data\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                   Best Confirmed      Investigator                                                          Duration of    \n                                Number of Doses   Overall Response   End Of Induction   Overall Survival    PFS      Triggering Event     Confirmed Response\nTreatment   Center/Patient ID      Received       by Investigator        Response            (days)        (days)         for PFS               (days)      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105             7                 CR                 CR               402.6         235.7    Disease Progression         119.6       \n                                       7                 CR                 CR               402.6         235.7            NA                  119.6       \n                                       7                 CR                 CR               402.6         235.7            NA                  119.6       \n              BRA-1/id-134             7                 CR                 CR                90.1          90.1           Death                 90.1       \n                                       7                 CR                 CR                90.1          90.1           Death                 90.1       \n                                       7                 CR                 CR                90.1          90.1           Death                 90.1       \n               BRA-1/id-42             7                 PR                 PR               407.0         204.6    Disease Progression         193.9       \n                                       7                 PR                 PR               407.0         204.6            NA                  193.9       \n                                       7                 PR                 PR               407.0         204.6            NA                  193.9       \n               BRA-1/id-93             7                 CR                 PD               316.0         255.7            NA                  140.3       \n                                       7                 CR                 PD               316.0         255.7            NA                  140.3       \n                                       7                 CR                 PD               316.0         255.7            NA                  140.3       \n              BRA-11/id-217            7                 CR                 CR               293.0         293.0    Disease Progression         153.2       \n                                       7                 CR                 CR               293.0         293.0           Death                153.2       \n                                       7                 CR                 CR               293.0         293.0           Death                153.2       \n              BRA-11/id-345            7                 CR                 CR               305.5         272.6            NA                  150.6       \n                                       7                 CR                 CR               305.5         272.6            NA                  150.6       \n                                       7                 CR                 CR               305.5         272.6            NA                  150.6       \n              BRA-11/id-397            7                 PR                 PR               138.7         138.7           Death                138.7       \n                                       7                 PR                 PR               138.7         138.7           Death                138.7       \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nPFS = Progression Free Survival\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:07 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4\n[25] testthat_3.2.1    tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Efficacy",
@@ -324,7 +324,7 @@
     "href": "listings/pharmacokinetic/pkpl04.html",
     "title": "PKPL04",
     "section": "",
-    "text": "Data Setup\nStandard Listing - CYCLE 1 DAY 1\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\nvisit &lt;- \"CYCLE 1 DAY 1\"\ndrug_a &lt;- \"Plasma Drug X\" # numerator drug\ndrug_b &lt;- \"Plasma Drug Y\" # denominator drug\n\nadpp_x &lt;- adpp %&gt;%\n  filter(\n    AVISIT == visit,\n    PPCAT %in% c(drug_a, drug_b),\n    PARAMCD %in% c(\"CMAX\", \"AUCIFO\")\n  ) %&gt;%\n  mutate(\n    PARAM_U = paste0(PARAM, \" (\", AVALU, \")\")\n  )\n\nadpp_ratio &lt;- adpp_x %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(USUBJID, PARAM_U),\n    names_from = PPCAT,\n    values_from = AVAL\n  )\n\nadpp_ratio[, paste0(drug_a, \"/\", drug_b)] &lt;- as.numeric(unlist(adpp_ratio[, drug_a] / adpp_ratio[, drug_b]))\n\nout &lt;- adpp_ratio %&gt;%\n  tidyr::pivot_wider(\n    id_cols = USUBJID,\n    names_from = PARAM_U,\n    names_glue = \"{PARAM_U}\\n{.value}\",\n    values_from = c(all_of(drug_a), all_of(drug_b), paste0(drug_a, \"/\", drug_b))\n  ) %&gt;%\n  select(names(.)[c(1:2, 4, 6, 3, 5, 7)])\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(USUBJID = \"Subject ID\")\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"USUBJID\",\n  disp_cols = names(out),\n  main_title = paste0(\n    \"Listing of Individual \", drug_a, \" \", paste(unique(adpp_x$PARAM), collapse = \" and \"), \" Ratios following \",\n    unique(adpp_x$REGIMEN), \" \", paste(drug_a, drug_b, sep = \" or \"), \", PK Population\\nProtocol: xxnnnnn\",\n    \"\\nVisit: \", unique(adpp_x$AVISIT)\n  ),\n  subtitles = paste0(\"\\nAnalytes: \", paste(drug_a, drug_b, sep = \" and \"))\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Individual Plasma Drug X AUC Infinity Obs and Max Conc Ratios following BID Plasma Drug X or Plasma Drug Y, PK Population\nProtocol: xxnnnnn\nVisit: CYCLE 1 DAY 1\n\nAnalytes: Plasma Drug X and Plasma Drug Y\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                        AUC Infinity Obs (day*ug/mL)   AUC Infinity Obs (day*ug/mL)   AUC Infinity Obs (day*ug/mL)   Max Conc (ug/mL)   Max Conc (ug/mL)        Max Conc (ug/mL)      \n     Subject ID                Plasma Drug X                  Plasma Drug Y           Plasma Drug X/Plasma Drug Y     Plasma Drug X      Plasma Drug Y     Plasma Drug X/Plasma Drug Y\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAB12345-BRA-1-id-105          246.349321001289                      NA                             NA                31.2264481880236          NA                      NA             \nAB12345-BRA-1-id-134          248.519038520728                      NA                             NA                21.7795763901966          NA                      NA             \nAB12345-BRA-1-id-141          204.038850147211               191.797779791421               1.06382279486812         29.5409649407056   38.8911230490524        0.759581174949522     \nAB12345-BRA-1-id-265          196.360864588266               218.652833415662              0.898048570973609         35.6328283094637   34.2215467219396        1.04123956170044      \n AB12345-BRA-1-id-42          210.528307366678                      NA                             NA                26.7845825535397          NA                      NA             \n AB12345-BRA-1-id-93          160.406798051843                      NA                             NA                17.5296885232566          NA                      NA             \nAB12345-BRA-11-id-171         217.543269983548               180.798322952882               1.20323721166508         30.3782220474834   31.615072576154         0.960877820992148     \nAB12345-BRA-11-id-217         272.094354596647                      NA                             NA                34.2812274648873          NA                      NA             \nAB12345-BRA-11-id-237         148.094218281784               193.694022424355              0.764578154907287         32.9103242471385   38.7385450224575        0.849549827647366     \nAB12345-BRA-11-id-321         206.178847103577               216.464946980475               0.95248145244585         33.0064256549217   28.4783979285037         1.1589986816599      \nAB12345-BRA-11-id-345         228.51025073098                       NA                             NA                39.7523164840323          NA                      NA             \nAB12345-BRA-11-id-397         201.536920713046                      NA                             NA                36.5841424116449          NA                      NA             \nAB12345-BRA-11-id-50          189.012389008811                      NA                             NA                21.1977400008473          NA                      NA             \n AB12345-BRA-11-id-9          235.477941027941               207.851453783084               1.13291457308587         27.1559637630099   28.2923295654501        0.959834844995305     \nAB12345-BRA-13-id-177         185.956676853099                      NA                             NA                31.0405525087113          NA                      NA             \nAB12345-BRA-14-id-120         209.878908685802               301.584778421544              0.695920098435608         31.8076450080362   22.9229189133965        1.38759139393227      \nAB12345-BRA-14-id-23          270.799396057463                      NA                             NA                29.2804222629517          NA                      NA             \nAB12345-BRA-15-id-36          183.877364155027                      NA                             NA                37.9405096212457          NA                      NA             \nAB12345-BRA-2-id-296          216.092875093089                      NA                             NA                27.3509307000219          NA                      NA             \nAB12345-BRA-4-id-368          203.23438144437                149.30280248041                1.36122281744199         38.3952821076341   30.8121148640987        1.24610992387189      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9013 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing - CYCLE 1 DAY 1\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\nvisit &lt;- \"CYCLE 1 DAY 1\"\ndrug_a &lt;- \"Plasma Drug X\" # numerator drug\ndrug_b &lt;- \"Plasma Drug Y\" # denominator drug\n\nadpp_x &lt;- adpp %&gt;%\n  filter(\n    AVISIT == visit,\n    PPCAT %in% c(drug_a, drug_b),\n    PARAMCD %in% c(\"CMAX\", \"AUCIFO\")\n  ) %&gt;%\n  mutate(\n    PARAM_U = paste0(PARAM, \" (\", AVALU, \")\")\n  )\n\nadpp_ratio &lt;- adpp_x %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(USUBJID, PARAM_U),\n    names_from = PPCAT,\n    values_from = AVAL\n  )\n\nadpp_ratio[, paste0(drug_a, \"/\", drug_b)] &lt;- as.numeric(unlist(adpp_ratio[, drug_a] / adpp_ratio[, drug_b]))\n\nout &lt;- adpp_ratio %&gt;%\n  tidyr::pivot_wider(\n    id_cols = USUBJID,\n    names_from = PARAM_U,\n    names_glue = \"{PARAM_U}\\n{.value}\",\n    values_from = c(all_of(drug_a), all_of(drug_b), paste0(drug_a, \"/\", drug_b))\n  ) %&gt;%\n  select(names(.)[c(1:2, 4, 6, 3, 5, 7)])\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(USUBJID = \"Subject ID\")\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"USUBJID\",\n  disp_cols = names(out),\n  main_title = paste0(\n    \"Listing of Individual \", drug_a, \" \", paste(unique(adpp_x$PARAM), collapse = \" and \"), \" Ratios following \",\n    unique(adpp_x$REGIMEN), \" \", paste(drug_a, drug_b, sep = \" or \"), \", PK Population\\nProtocol: xxnnnnn\",\n    \"\\nVisit: \", unique(adpp_x$AVISIT)\n  ),\n  subtitles = paste0(\"\\nAnalytes: \", paste(drug_a, drug_b, sep = \" and \"))\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Individual Plasma Drug X AUC Infinity Obs and Max Conc Ratios following BID Plasma Drug X or Plasma Drug Y, PK Population\nProtocol: xxnnnnn\nVisit: CYCLE 1 DAY 1\n\nAnalytes: Plasma Drug X and Plasma Drug Y\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                        AUC Infinity Obs (day*ug/mL)   AUC Infinity Obs (day*ug/mL)   AUC Infinity Obs (day*ug/mL)   Max Conc (ug/mL)   Max Conc (ug/mL)        Max Conc (ug/mL)      \n     Subject ID                Plasma Drug X                  Plasma Drug Y           Plasma Drug X/Plasma Drug Y     Plasma Drug X      Plasma Drug Y     Plasma Drug X/Plasma Drug Y\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAB12345-BRA-1-id-105          246.349321001289                      NA                             NA                31.2264481880236          NA                      NA             \nAB12345-BRA-1-id-134          248.519038520728                      NA                             NA                21.7795763901966          NA                      NA             \nAB12345-BRA-1-id-141          204.038850147211               191.797779791421               1.06382279486812         29.5409649407056   38.8911230490524        0.759581174949522     \nAB12345-BRA-1-id-265          196.360864588266               218.652833415662              0.898048570973609         35.6328283094637   34.2215467219396        1.04123956170044      \n AB12345-BRA-1-id-42          210.528307366678                      NA                             NA                26.7845825535397          NA                      NA             \n AB12345-BRA-1-id-93          160.406798051843                      NA                             NA                17.5296885232566          NA                      NA             \nAB12345-BRA-11-id-171         217.543269983548               180.798322952882               1.20323721166508         30.3782220474834   31.615072576154         0.960877820992148     \nAB12345-BRA-11-id-217         272.094354596647                      NA                             NA                34.2812274648873          NA                      NA             \nAB12345-BRA-11-id-237         148.094218281784               193.694022424355              0.764578154907287         32.9103242471385   38.7385450224575        0.849549827647366     \nAB12345-BRA-11-id-321         206.178847103577               216.464946980475               0.95248145244585         33.0064256549217   28.4783979285037         1.1589986816599      \nAB12345-BRA-11-id-345         228.51025073098                       NA                             NA                39.7523164840323          NA                      NA             \nAB12345-BRA-11-id-397         201.536920713046                      NA                             NA                36.5841424116449          NA                      NA             \nAB12345-BRA-11-id-50          189.012389008811                      NA                             NA                21.1977400008473          NA                      NA             \n AB12345-BRA-11-id-9          235.477941027941               207.851453783084               1.13291457308587         27.1559637630099   28.2923295654501        0.959834844995305     \nAB12345-BRA-13-id-177         185.956676853099                      NA                             NA                31.0405525087113          NA                      NA             \nAB12345-BRA-14-id-120         209.878908685802               301.584778421544              0.695920098435608         31.8076450080362   22.9229189133965        1.38759139393227      \nAB12345-BRA-14-id-23          270.799396057463                      NA                             NA                29.2804222629517          NA                      NA             \nAB12345-BRA-15-id-36          183.877364155027                      NA                             NA                37.9405096212457          NA                      NA             \nAB12345-BRA-2-id-296          216.092875093089                      NA                             NA                27.3509307000219          NA                      NA             \nAB12345-BRA-4-id-368          203.23438144437                149.30280248041                1.36122281744199         38.3952821076341   30.8121148640987        1.24610992387189      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:00 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9015 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Pharmacokinetic",
@@ -336,7 +336,7 @@
     "href": "listings/pharmacokinetic/pkcl02.html",
     "title": "PKCL02",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\ndrug_a &lt;- \"Drug X\"\nspec &lt;- \"URINE\"\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\nadpc_x &lt;- adpc %&gt;%\n  mutate(REGIMEN = ifelse(\"REGIMEN\" %in% names(adpc), REGIMEN, \"BID\")) %&gt;%\n  filter(\n    grepl(drug_a, PARAM),\n    ASMED == spec\n  )\n\nout &lt;- adpc_x %&gt;%\n  tidyr::pivot_longer(\n    cols = c(AVAL, PCVOL),\n    names_to = \"URCD\",\n    values_to = \"VALUE\"\n  ) %&gt;%\n  mutate(\n    URCD = case_when(\n      URCD == \"AVAL\" ~ \"UR_Conc\",\n      URCD == \"PCVOL\" ~ \"Vurine\",\n      TRUE ~ URCD\n    ),\n    UNIT = case_when(\n      URCD == \"UR_Conc\" ~ as.character(AVALU),\n      URCD == \"Vurine\" ~ as.character(PCVOLU),\n      TRUE ~ \"NA\"\n    )\n  ) %&gt;%\n  mutate(\n    PARAM_INT = paste0(\n      URCD, \" (\", UNIT, \") -\\nUrine Collection\\nInterval\",\n      ifelse(PCTPT == \"Predose\", \"\", \" (hours)\"), \":\\n\",\n      gsub(\"[PTH]\", \"\", PCTPT)\n    )\n  ) %&gt;%\n  select(TRT01A, USUBJID, VISIT, PARAM_INT, VALUE) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, USUBJID, VISIT),\n    names_from = PARAM_INT,\n    values_from = VALUE\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(\n  TRT01A = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  VISIT = \"Visit\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"USUBJID\", \"VISIT\"),\n  disp_cols = names(out),\n  main_title = paste0(\n    \"Listing of \", drug_a, \" Urine Concentration and Volumes following \", unique(adpc_x$REGIMEN)[1],\n    \" of \", drug_a, \", PK Population\\nProtocol: xxnnnnn\"\n  ),\n  subtitles = paste(\"Analyte:\", drug_a)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Drug X Urine Concentration and Volumes following BID of Drug X, PK Population\nProtocol: xxnnnnn\nAnalyte: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                  UR_Conc (ug/mL) -    Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -  \n                                                  Urine Collection    Urine Collection   Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection \n                                                      Interval:          Interval:       Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):\nTreatment Group        Subject ID         Visit        redose              redose              0 - 4               0 - 4               4 - 8               4 - 8              8 - 12              8 - 12              0 - 24              0 - 24      \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105    Day 1           0                713.94              5.461               96.92               0.562              235.38               0.049              384.98                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                735.22      \n                  AB12345-BRA-1-id-134    Day 1           0               1068.83              4.064               145.1               0.178              352.39               0.006              576.35                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                1080.14     \n                   AB12345-BRA-1-id-42    Day 1           0                817.09              4.277              110.93               0.305              269.39               0.018               440.6                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                737.25      \n                   AB12345-BRA-1-id-93    Day 1           0                864.61              4.324              117.38               0.278              285.06               0.015              466.23                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                757.23      \n                  AB12345-BRA-11-id-217   Day 1           0                724.93              6.225               98.41               0.672              239.01               0.059              390.91                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                1185.46     \n                  AB12345-BRA-11-id-345   Day 1           0                840.09              6.511              114.05               0.588              276.98               0.04               453.01                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                883.89      \n                  AB12345-BRA-11-id-397   Day 1           0                974.41              5.478              132.28               0.354              321.26               0.017              525.44                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                918.67      \n                  AB12345-BRA-11-id-50    Day 1           0                922.97              4.122               125.3               0.228               304.3               0.01                497.7                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                808.08      \n                  AB12345-BRA-13-id-177   Day 1           0                694.09              4.267               94.23               0.421              228.84               0.038              374.28                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                908.45      \n                  AB12345-BRA-14-id-23    Day 1           0                722.84              4.254               98.13               0.386              238.32               0.032              389.78                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                 862.1      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:27 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9013 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\ndrug_a &lt;- \"Drug X\"\nspec &lt;- \"URINE\"\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\nadpc_x &lt;- adpc %&gt;%\n  mutate(REGIMEN = ifelse(\"REGIMEN\" %in% names(adpc), REGIMEN, \"BID\")) %&gt;%\n  filter(\n    grepl(drug_a, PARAM),\n    ASMED == spec\n  )\n\nout &lt;- adpc_x %&gt;%\n  tidyr::pivot_longer(\n    cols = c(AVAL, PCVOL),\n    names_to = \"URCD\",\n    values_to = \"VALUE\"\n  ) %&gt;%\n  mutate(\n    URCD = case_when(\n      URCD == \"AVAL\" ~ \"UR_Conc\",\n      URCD == \"PCVOL\" ~ \"Vurine\",\n      TRUE ~ URCD\n    ),\n    UNIT = case_when(\n      URCD == \"UR_Conc\" ~ as.character(AVALU),\n      URCD == \"Vurine\" ~ as.character(PCVOLU),\n      TRUE ~ \"NA\"\n    )\n  ) %&gt;%\n  mutate(\n    PARAM_INT = paste0(\n      URCD, \" (\", UNIT, \") -\\nUrine Collection\\nInterval\",\n      ifelse(PCTPT == \"Predose\", \"\", \" (hours)\"), \":\\n\",\n      gsub(\"[PTH]\", \"\", PCTPT)\n    )\n  ) %&gt;%\n  select(TRT01A, USUBJID, VISIT, PARAM_INT, VALUE) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, USUBJID, VISIT),\n    names_from = PARAM_INT,\n    values_from = VALUE\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(\n  TRT01A = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  VISIT = \"Visit\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"USUBJID\", \"VISIT\"),\n  disp_cols = names(out),\n  main_title = paste0(\n    \"Listing of \", drug_a, \" Urine Concentration and Volumes following \", unique(adpc_x$REGIMEN)[1],\n    \" of \", drug_a, \", PK Population\\nProtocol: xxnnnnn\"\n  ),\n  subtitles = paste(\"Analyte:\", drug_a)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Drug X Urine Concentration and Volumes following BID of Drug X, PK Population\nProtocol: xxnnnnn\nAnalyte: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                  UR_Conc (ug/mL) -    Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -     UR_Conc (ug/mL) -     Vurine (mL) -  \n                                                  Urine Collection    Urine Collection   Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection    Urine Collection \n                                                      Interval:          Interval:       Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):   Interval (hours):\nTreatment Group        Subject ID         Visit        redose              redose              0 - 4               0 - 4               4 - 8               4 - 8              8 - 12              8 - 12              0 - 24              0 - 24      \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105    Day 1           0                713.94              5.461               96.92               0.562              235.38               0.049              384.98                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                735.22      \n                  AB12345-BRA-1-id-134    Day 1           0               1068.83              4.064               145.1               0.178              352.39               0.006              576.35                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                1080.14     \n                   AB12345-BRA-1-id-42    Day 1           0                817.09              4.277              110.93               0.305              269.39               0.018               440.6                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                737.25      \n                   AB12345-BRA-1-id-93    Day 1           0                864.61              4.324              117.38               0.278              285.06               0.015              466.23                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                757.23      \n                  AB12345-BRA-11-id-217   Day 1           0                724.93              6.225               98.41               0.672              239.01               0.059              390.91                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                1185.46     \n                  AB12345-BRA-11-id-345   Day 1           0                840.09              6.511              114.05               0.588              276.98               0.04               453.01                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                883.89      \n                  AB12345-BRA-11-id-397   Day 1           0                974.41              5.478              132.28               0.354              321.26               0.017              525.44                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                918.67      \n                  AB12345-BRA-11-id-50    Day 1           0                922.97              4.122               125.3               0.228               304.3               0.01                497.7                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                808.08      \n                  AB12345-BRA-13-id-177   Day 1           0                694.09              4.267               94.23               0.421              228.84               0.038              374.28                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                908.45      \n                  AB12345-BRA-14-id-23    Day 1           0                722.84              4.254               98.13               0.386              238.32               0.032              389.78                NA                  NA        \n                                          Day 2          NA                  NA                 NA                  NA                  NA                  NA                  NA                  NA                   0                 862.1      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9015 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Pharmacokinetic",
@@ -348,7 +348,7 @@
     "href": "listings/pharmacokinetic/pkpl02.html",
     "title": "PKPL02",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\ndrug_a &lt;- \"Plasma Drug X\"\nspec &lt;- \"Urine\"\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp_x &lt;- adpp %&gt;% filter(\n  PPCAT == drug_a,\n  PPSPEC == spec\n)\n\nout &lt;- adpp_x %&gt;%\n  mutate(PARAM = paste0(PARAM, \" (\", AVALU, \")\")) %&gt;%\n  select(TRT01A, USUBJID, AVISIT, PARAM, AVAL) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, USUBJID, AVISIT),\n    names_from = PARAM,\n    values_from = AVAL\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(\n  TRT01A = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  AVISIT = \"Visit\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"USUBJID\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = paste(\"Listing of\", drug_a, spec, \"PK Parameters, PK Population\\nProtocol: xxnnnnn\"),\n  subtitles = paste(\"Analyte:\", drug_a)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Plasma Drug X Urine PK Parameters, PK Population\nProtocol: xxnnnnn\nAnalyte: Plasma Drug X\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group        Subject ID             Visit        Renal CL (L/hr)     Renal CL Norm by Dose (L/hr/mg)   Amt Rec from T1 to T2 (mg)   Pct Rec from T1 to T2 (%)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105      SCREENING     0.0538423152160391         0.00441769893771616              2.15210785813109            17.8220657702531     \n                                          CYCLE 1 DAY 1   0.0480828455497337         0.00418360874479016              1.77968465488986            13.4821873677008     \n                                          CYCLE 1 DAY 2   0.0524191320174391         0.00574562025709617              1.7751362634173             11.7748899262287     \n                  AB12345-BRA-1-id-134      SCREENING     0.054828756388858          0.00489378524965625              1.69069405572207            14.6782255303037     \n                                          CYCLE 1 DAY 1   0.0463628883891636         0.00281388401660215              1.03364773182262            15.2049132671477     \n                                          CYCLE 1 DAY 2   0.0542853013687975         0.00480239019141706              2.07978470495693            16.6499332185177     \n                   AB12345-BRA-1-id-42      SCREENING     0.0479776607513692         0.00620670551445586              1.49259200502984            12.4632371401908     \n                                          CYCLE 1 DAY 1   0.060136833920038          0.00423749524975822               2.002399829348             16.1228873656652     \n                                          CYCLE 1 DAY 2   0.0222050225259149         0.0041513339034891               1.65173021070619             10.129985981426     \n                   AB12345-BRA-1-id-93      SCREENING     0.0429283028076499         0.00450841008626937             0.882348047230192            19.8088728992851     \n                                          CYCLE 1 DAY 1   0.0343747382858835         0.00495342578722049              1.73500915591676            14.8733500411057     \n                                          CYCLE 1 DAY 2   0.0474831008867497         0.00415970555107753              1.30298887256052            14.9616973312841     \n                  AB12345-BRA-11-id-217     SCREENING     0.0524432563610182         0.00439177770746837              1.16344390767412             16.584779611686     \n                                          CYCLE 1 DAY 1   0.0411628011374518         0.00567801310981526              1.11763922430636            19.3000982041101     \n                                          CYCLE 1 DAY 2   0.0672581292323284         0.00400060865194939              1.45655867480937            10.1596117673893     \n                  AB12345-BRA-11-id-345     SCREENING     0.044070189259969          0.00561835429596993              1.43739076299198            15.4875095868721     \n                                          CYCLE 1 DAY 1   0.0362275034574886         0.0038765297501434                0.96604915921              17.1567306808389     \n                                          CYCLE 1 DAY 2   0.0416331541840018         0.00462187415243145              1.27070193721417            19.2547757988484     \n                  AB12345-BRA-11-id-397     SCREENING     0.0379747899730378         0.0047876948377162               1.67729762147252            11.6015615292251     \n                                          CYCLE 1 DAY 1   0.0430467407701829         0.00463308440642305              1.1695965192594             12.5511219923965     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:20 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9013 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\ndrug_a &lt;- \"Plasma Drug X\"\nspec &lt;- \"Urine\"\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp_x &lt;- adpp %&gt;% filter(\n  PPCAT == drug_a,\n  PPSPEC == spec\n)\n\nout &lt;- adpp_x %&gt;%\n  mutate(PARAM = paste0(PARAM, \" (\", AVALU, \")\")) %&gt;%\n  select(TRT01A, USUBJID, AVISIT, PARAM, AVAL) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, USUBJID, AVISIT),\n    names_from = PARAM,\n    values_from = AVAL\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(\n  TRT01A = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  AVISIT = \"Visit\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"USUBJID\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = paste(\"Listing of\", drug_a, spec, \"PK Parameters, PK Population\\nProtocol: xxnnnnn\"),\n  subtitles = paste(\"Analyte:\", drug_a)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Plasma Drug X Urine PK Parameters, PK Population\nProtocol: xxnnnnn\nAnalyte: Plasma Drug X\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group        Subject ID             Visit        Renal CL (L/hr)     Renal CL Norm by Dose (L/hr/mg)   Amt Rec from T1 to T2 (mg)   Pct Rec from T1 to T2 (%)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105      SCREENING     0.0538423152160391         0.00441769893771616              2.15210785813109            17.8220657702531     \n                                          CYCLE 1 DAY 1   0.0480828455497337         0.00418360874479016              1.77968465488986            13.4821873677008     \n                                          CYCLE 1 DAY 2   0.0524191320174391         0.00574562025709617              1.7751362634173             11.7748899262287     \n                  AB12345-BRA-1-id-134      SCREENING     0.054828756388858          0.00489378524965625              1.69069405572207            14.6782255303037     \n                                          CYCLE 1 DAY 1   0.0463628883891636         0.00281388401660215              1.03364773182262            15.2049132671477     \n                                          CYCLE 1 DAY 2   0.0542853013687975         0.00480239019141706              2.07978470495693            16.6499332185177     \n                   AB12345-BRA-1-id-42      SCREENING     0.0479776607513692         0.00620670551445586              1.49259200502984            12.4632371401908     \n                                          CYCLE 1 DAY 1   0.060136833920038          0.00423749524975822               2.002399829348             16.1228873656652     \n                                          CYCLE 1 DAY 2   0.0222050225259149         0.0041513339034891               1.65173021070619             10.129985981426     \n                   AB12345-BRA-1-id-93      SCREENING     0.0429283028076499         0.00450841008626937             0.882348047230192            19.8088728992851     \n                                          CYCLE 1 DAY 1   0.0343747382858835         0.00495342578722049              1.73500915591676            14.8733500411057     \n                                          CYCLE 1 DAY 2   0.0474831008867497         0.00415970555107753              1.30298887256052            14.9616973312841     \n                  AB12345-BRA-11-id-217     SCREENING     0.0524432563610182         0.00439177770746837              1.16344390767412             16.584779611686     \n                                          CYCLE 1 DAY 1   0.0411628011374518         0.00567801310981526              1.11763922430636            19.3000982041101     \n                                          CYCLE 1 DAY 2   0.0672581292323284         0.00400060865194939              1.45655867480937            10.1596117673893     \n                  AB12345-BRA-11-id-345     SCREENING     0.044070189259969          0.00561835429596993              1.43739076299198            15.4875095868721     \n                                          CYCLE 1 DAY 1   0.0362275034574886         0.0038765297501434                0.96604915921              17.1567306808389     \n                                          CYCLE 1 DAY 2   0.0416331541840018         0.00462187415243145              1.27070193721417            19.2547757988484     \n                  AB12345-BRA-11-id-397     SCREENING     0.0379747899730378         0.0047876948377162               1.67729762147252            11.6015615292251     \n                                          CYCLE 1 DAY 1   0.0430467407701829         0.00463308440642305              1.1695965192594             12.5511219923965     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:46 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9015 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Pharmacokinetic",
@@ -360,7 +360,7 @@
     "href": "tables/risk-management-plan/rmpt06.html",
     "title": "RMPT06",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable with Differences\nTable Modifying Alpha Level and  Type of Confidence Interval\nTable by SMQ\nteal App\nReproducibility\n\n\n\nThe adae and adsl datasets need to undergo data processing before table production. Some new variables are added to these datasets to create the desired tables.\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(\n  adae,\n  omit_columns = c(\"SMQ01NAM\", \"SMQ01SC\", \"SMQ02NAM\", \"SMQ02SC\", \"CQ01NAM\", \"STUDYID\", \"USUBJID\")\n)\n\ndf_max &lt;- aggregate(as.numeric(AETOXGR) ~ USUBJID, data = adae, FUN = max, drop = FALSE)\ncolnames(df_max) &lt;- c(\"USUBJID\", \"WTOXGR\")\n\nadae &lt;- adae %&gt;%\n  left_join(df_max, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    WTOXGR = factor(WTOXGR, levels = c(\"1\", \"2\", \"3\", \"4\", \"5\")),\n    fl_ser = AESER == \"Y\"\n  ) %&gt;%\n  mutate(\n    WTOXGR = forcats::fct_recode(\n      WTOXGR,\n      \"Grade 1\" = \"1\",\n      \"Grade 2\" = \"2\",\n      \"Grade 3\" = \"3\",\n      \"Grade 4\" = \"4\",\n      \"Grade 5\" = \"5\"\n    ),\n    AEOUT = forcats::fct_recode(\n      AEOUT,\n      \"Fatal outcome\" = \"FATAL\",\n      \"Unresolved\" = \"NOT RECOVERED/NOT RESOLVED\",\n      \"Recovered/Resolved\" = \"RECOVERED/RESOLVED\",\n      \"Resolved with sequelae\" = \"RECOVERED/RESOLVED WITH SEQUELAE\",\n      \"Recovering/Resolving\" = \"RECOVERING/RESOLVING\",\n      \"Unknown outcome\" = \"UNKNOWN\"\n    )\n  ) %&gt;%\n  var_relabel(\n    WTOXGR = \"Worst overall grade\",\n    fl_ser = \"Number of patients with at least one serious AE\"\n  )\n\n# Add AE flag to adsl.\nadsl1 &lt;- adsl %&gt;%\n  mutate(AEFL = ifelse(USUBJID %in% adae$USUBJID, TRUE, FALSE)) %&gt;%\n  var_relabel(AEFL = \"At least one AE\")\n\n\n\n\nPlease note that by default the percentage analysis uses Wald’s confidence interval with continuity correction and the default confidence interval for percentage is 95%. To use a different method for confidence interval calculation, the method name needs to be provided to method argument.\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"95% CI for % of patients with at least one AE (Clopper-Pearson)\"\n    ),\n    table_names = \"est_prop\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\"\n  ) %&gt;%\n  count_occurrences(\n    \"AEOUT\",\n    denom = \"n\",\n    var_labels = \"Number of patients with at least one AE by outcome\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                                   A: Drug X      B: Placebo    C: Combination\n                                                                    (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  \n95% CI for % of patients with at least one AE (Clopper-Pearson)   (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) \nTotal number of AEs                                                   609            622             703      \nNumber of patients with at least one AE by worst grade                                                        \n  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE                   104 (85.2%)    101 (82.1%)      99 (82.5%)  \nNumber of patients with at least one AE by outcome                                                            \n  Fatal outcome                                                    76 (62.3%)     70 (56.9%)      75 (62.5%)  \n  Unresolved                                                       66 (54.1%)     64 (52.0%)      70 (58.3%)  \n  Recovered/Resolved                                               73 (59.8%)     83 (67.5%)      88 (73.3%)  \n  Resolved with sequelae                                           44 (36.1%)     40 (32.5%)      43 (35.8%)  \n  Recovering/Resolving                                             81 (66.4%)     84 (68.3%)      88 (73.3%)  \n  Unknown outcome                                                  38 (31.1%)     49 (39.8%)      44 (36.7%)  \n\n\n\n\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"95% CI for % of patients with at least one AE (Clopper-Pearson)\"\n    ),\n    table_names = \"est_prop\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\",\n    denom = \"N_col\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                                   A: Drug X      B: Placebo    C: Combination\n                                                                    (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  \n95% CI for % of patients with at least one AE (Clopper-Pearson)   (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) \nTotal number of AEs                                                   609            622             703      \nNumber of patients with at least one AE by worst grade                                                        \n  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE                   104 (77.6%)    101 (75.4%)      99 (75.0%)  \n\n\n\n\nPlease note that by default the percentage difference analysis also uses Wald’s confidence interval with continuity correction and the default confidence interval is 95%.\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"95% CI for % of patients with at least one AE\"\n    ),\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"AEFL\",\n    .labels = c(\n      diff = \"Difference in % of patients with at least one AE\",\n      diff_ci = \"95% CI of difference (Wald, with correction)\"\n    ),\n    table_names = \"est_prop_diff\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\",\n    denom = \"N_col\"\n  ) %&gt;%\n  count_occurrences(\n    \"AEOUT\",\n    denom = \"n\",\n    var_labels = \"Number of patients with at least one AE by outcome\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                          A: Drug X      B: Placebo    C: Combination\n                                                           (N=134)        (N=134)         (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event       122 (91.0%)    123 (91.8%)     120 (90.9%)  \n95% CI for % of patients with at least one AE            (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) \nDifference in % of patients with at least one AE                            0.7             -0.1     \n  95% CI of difference (Wald, with correction)                          (-6.7, 8.2)     (-7.8, 7.5)  \nTotal number of AEs                                          609            622             703      \nNumber of patients with at least one AE by worst grade                                               \n  Grade 1                                                  7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                  6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                 18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                 15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                 76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE          104 (77.6%)    101 (75.4%)      99 (75.0%)  \nNumber of patients with at least one AE by outcome                                                   \n  Fatal outcome                                           76 (62.3%)     70 (56.9%)      75 (62.5%)  \n  Unresolved                                              66 (54.1%)     64 (52.0%)      70 (58.3%)  \n  Recovered/Resolved                                      73 (59.8%)     83 (67.5%)      88 (73.3%)  \n  Resolved with sequelae                                  44 (36.1%)     40 (32.5%)      43 (35.8%)  \n  Recovering/Resolving                                    81 (66.4%)     84 (68.3%)      88 (73.3%)  \n  Unknown outcome                                         38 (31.1%)     49 (39.8%)      44 (36.7%)  \n\n\n\n\nThe confidence level can be changed through the conf_level argument.\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    conf_level = 0.90,\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"90% CI for % of patients with at least one AE (Clopper-Pearson)\"\n    ),\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"AEFL\",\n    conf_level = 0.90,\n    method = \"newcombe\",\n    .labels = c(\n      diff = \"Difference in % of patients with at least one AE\",\n      diff_ci = \"90% CI of difference (Newcombe)\"\n    ),\n    table_names = \"est_prop_diff\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\",\n    denom = \"N_col\"\n  ) %&gt;%\n  count_occurrences(\n    \"AEOUT\",\n    denom = \"n\",\n    var_labels = \"Number of patients with at least one AE by outcome\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                                   A: Drug X      B: Placebo    C: Combination\n                                                                    (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  \n90% CI for % of patients with at least one AE (Clopper-Pearson)   (85.9, 94.8)   (86.8, 95.3)    (85.7, 94.7) \nDifference in % of patients with at least one AE                                     0.7             -0.1     \n  90% CI of difference (Newcombe)                                                (-5.1, 6.6)     (-6.1, 5.8)  \nTotal number of AEs                                                   609            622             703      \nNumber of patients with at least one AE by worst grade                                                        \n  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE                   104 (77.6%)    101 (75.4%)      99 (75.0%)  \nNumber of patients with at least one AE by outcome                                                            \n  Fatal outcome                                                    76 (62.3%)     70 (56.9%)      75 (62.5%)  \n  Unresolved                                                       66 (54.1%)     64 (52.0%)      70 (58.3%)  \n  Recovered/Resolved                                               73 (59.8%)     83 (67.5%)      88 (73.3%)  \n  Resolved with sequelae                                           44 (36.1%)     40 (32.5%)      43 (35.8%)  \n  Recovering/Resolving                                             81 (66.4%)     84 (68.3%)      88 (73.3%)  \n  Unknown outcome                                                  38 (31.1%)     49 (39.8%)      44 (36.7%)  \n\n\n\n\n\nCode# In progress\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:00 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       dplyr_1.1.4           tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable with Differences\nTable Modifying Alpha Level and  Type of Confidence Interval\nTable by SMQ\nteal App\nReproducibility\n\n\n\nThe adae and adsl datasets need to undergo data processing before table production. Some new variables are added to these datasets to create the desired tables.\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(\n  adae,\n  omit_columns = c(\"SMQ01NAM\", \"SMQ01SC\", \"SMQ02NAM\", \"SMQ02SC\", \"CQ01NAM\", \"STUDYID\", \"USUBJID\")\n)\n\ndf_max &lt;- aggregate(as.numeric(AETOXGR) ~ USUBJID, data = adae, FUN = max, drop = FALSE)\ncolnames(df_max) &lt;- c(\"USUBJID\", \"WTOXGR\")\n\nadae &lt;- adae %&gt;%\n  left_join(df_max, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    WTOXGR = factor(WTOXGR, levels = c(\"1\", \"2\", \"3\", \"4\", \"5\")),\n    fl_ser = AESER == \"Y\"\n  ) %&gt;%\n  mutate(\n    WTOXGR = forcats::fct_recode(\n      WTOXGR,\n      \"Grade 1\" = \"1\",\n      \"Grade 2\" = \"2\",\n      \"Grade 3\" = \"3\",\n      \"Grade 4\" = \"4\",\n      \"Grade 5\" = \"5\"\n    ),\n    AEOUT = forcats::fct_recode(\n      AEOUT,\n      \"Fatal outcome\" = \"FATAL\",\n      \"Unresolved\" = \"NOT RECOVERED/NOT RESOLVED\",\n      \"Recovered/Resolved\" = \"RECOVERED/RESOLVED\",\n      \"Resolved with sequelae\" = \"RECOVERED/RESOLVED WITH SEQUELAE\",\n      \"Recovering/Resolving\" = \"RECOVERING/RESOLVING\",\n      \"Unknown outcome\" = \"UNKNOWN\"\n    )\n  ) %&gt;%\n  var_relabel(\n    WTOXGR = \"Worst overall grade\",\n    fl_ser = \"Number of patients with at least one serious AE\"\n  )\n\n# Add AE flag to adsl.\nadsl1 &lt;- adsl %&gt;%\n  mutate(AEFL = ifelse(USUBJID %in% adae$USUBJID, TRUE, FALSE)) %&gt;%\n  var_relabel(AEFL = \"At least one AE\")\n\n\n\n\nPlease note that by default the percentage analysis uses Wald’s confidence interval with continuity correction and the default confidence interval for percentage is 95%. To use a different method for confidence interval calculation, the method name needs to be provided to method argument.\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"95% CI for % of patients with at least one AE (Clopper-Pearson)\"\n    ),\n    table_names = \"est_prop\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\"\n  ) %&gt;%\n  count_occurrences(\n    \"AEOUT\",\n    denom = \"n\",\n    var_labels = \"Number of patients with at least one AE by outcome\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                                   A: Drug X      B: Placebo    C: Combination\n                                                                    (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  \n95% CI for % of patients with at least one AE (Clopper-Pearson)   (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) \nTotal number of AEs                                                   609            622             703      \nNumber of patients with at least one AE by worst grade                                                        \n  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE                   104 (85.2%)    101 (82.1%)      99 (82.5%)  \nNumber of patients with at least one AE by outcome                                                            \n  Fatal outcome                                                    76 (62.3%)     70 (56.9%)      75 (62.5%)  \n  Unresolved                                                       66 (54.1%)     64 (52.0%)      70 (58.3%)  \n  Recovered/Resolved                                               73 (59.8%)     83 (67.5%)      88 (73.3%)  \n  Resolved with sequelae                                           44 (36.1%)     40 (32.5%)      43 (35.8%)  \n  Recovering/Resolving                                             81 (66.4%)     84 (68.3%)      88 (73.3%)  \n  Unknown outcome                                                  38 (31.1%)     49 (39.8%)      44 (36.7%)  \n\n\n\n\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"95% CI for % of patients with at least one AE (Clopper-Pearson)\"\n    ),\n    table_names = \"est_prop\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\",\n    denom = \"N_col\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                                   A: Drug X      B: Placebo    C: Combination\n                                                                    (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  \n95% CI for % of patients with at least one AE (Clopper-Pearson)   (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) \nTotal number of AEs                                                   609            622             703      \nNumber of patients with at least one AE by worst grade                                                        \n  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE                   104 (77.6%)    101 (75.4%)      99 (75.0%)  \n\n\n\n\nPlease note that by default the percentage difference analysis also uses Wald’s confidence interval with continuity correction and the default confidence interval is 95%.\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"95% CI for % of patients with at least one AE\"\n    ),\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"AEFL\",\n    .labels = c(\n      diff = \"Difference in % of patients with at least one AE\",\n      diff_ci = \"95% CI of difference (Wald, with correction)\"\n    ),\n    table_names = \"est_prop_diff\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\",\n    denom = \"N_col\"\n  ) %&gt;%\n  count_occurrences(\n    \"AEOUT\",\n    denom = \"n\",\n    var_labels = \"Number of patients with at least one AE by outcome\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                          A: Drug X      B: Placebo    C: Combination\n                                                           (N=134)        (N=134)         (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event       122 (91.0%)    123 (91.8%)     120 (90.9%)  \n95% CI for % of patients with at least one AE            (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) \nDifference in % of patients with at least one AE                            0.7             -0.1     \n  95% CI of difference (Wald, with correction)                          (-6.7, 8.2)     (-7.8, 7.5)  \nTotal number of AEs                                          609            622             703      \nNumber of patients with at least one AE by worst grade                                               \n  Grade 1                                                  7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                  6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                 18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                 15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                 76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE          104 (77.6%)    101 (75.4%)      99 (75.0%)  \nNumber of patients with at least one AE by outcome                                                   \n  Fatal outcome                                           76 (62.3%)     70 (56.9%)      75 (62.5%)  \n  Unresolved                                              66 (54.1%)     64 (52.0%)      70 (58.3%)  \n  Recovered/Resolved                                      73 (59.8%)     83 (67.5%)      88 (73.3%)  \n  Resolved with sequelae                                  44 (36.1%)     40 (32.5%)      43 (35.8%)  \n  Recovering/Resolving                                    81 (66.4%)     84 (68.3%)      88 (73.3%)  \n  Unknown outcome                                         38 (31.1%)     49 (39.8%)      44 (36.7%)  \n\n\n\n\nThe confidence level can be changed through the conf_level argument.\n\nCodelyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"AEFL\",\n    conf_level = 0.90,\n    method = \"clopper-pearson\",\n    .labels = c(\n      n_prop = \"Number of patients with at least one adverse event\",\n      prop_ci = \"90% CI for % of patients with at least one AE (Clopper-Pearson)\"\n    ),\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"AEFL\",\n    conf_level = 0.90,\n    method = \"newcombe\",\n    .labels = c(\n      diff = \"Difference in % of patients with at least one AE\",\n      diff_ci = \"90% CI of difference (Newcombe)\"\n    ),\n    table_names = \"est_prop_diff\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences(\n    \"WTOXGR\",\n    var_labels = \"Number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"fl_ser\",\n    denom = \"N_col\"\n  ) %&gt;%\n  count_occurrences(\n    \"AEOUT\",\n    denom = \"n\",\n    var_labels = \"Number of patients with at least one AE by outcome\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adsl,\n  result_adae[seq_len(nrow(result_adae)), ]\n)\n\nresult\n\n                                                                   A: Drug X      B: Placebo    C: Combination\n                                                                    (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  \n90% CI for % of patients with at least one AE (Clopper-Pearson)   (85.9, 94.8)   (86.8, 95.3)    (85.7, 94.7) \nDifference in % of patients with at least one AE                                     0.7             -0.1     \n  90% CI of difference (Newcombe)                                                (-5.1, 6.6)     (-6.1, 5.8)  \nTotal number of AEs                                                   609            622             703      \nNumber of patients with at least one AE by worst grade                                                        \n  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   \n  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   \n  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  \n  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  \n  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  \nNumber of patients with at least one serious AE                   104 (77.6%)    101 (75.4%)      99 (75.0%)  \nNumber of patients with at least one AE by outcome                                                            \n  Fatal outcome                                                    76 (62.3%)     70 (56.9%)      75 (62.5%)  \n  Unresolved                                                       66 (54.1%)     64 (52.0%)      70 (58.3%)  \n  Recovered/Resolved                                               73 (59.8%)     83 (67.5%)      88 (73.3%)  \n  Resolved with sequelae                                           44 (36.1%)     40 (32.5%)      43 (35.8%)  \n  Recovering/Resolving                                             81 (66.4%)     84 (68.3%)      88 (73.3%)  \n  Unknown outcome                                                  38 (31.1%)     49 (39.8%)      44 (36.7%)  \n\n\n\n\n\nCode# In progress\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:26 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       dplyr_1.1.4           tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Risk Management Plan",
@@ -372,7 +372,7 @@
     "href": "tables/risk-management-plan/rmpt01.html",
     "title": "RMPT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in sample scda dataset.\nset.seed(1, kind = \"Mersenne-Twister\")\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  ) %&gt;%\n  mutate(\n    aval_months = day2month(AVAL),\n    aval_months_cat = factor(case_when(\n      aval_months &lt; 1 ~ \"&lt; 1 month\",\n      aval_months &gt;= 1 & aval_months &lt; 3 ~ \"1 to &lt;3 months\",\n      aval_months &gt;= 3 & aval_months &lt; 6 ~ \"3 to &lt;6 months\",\n      TRUE ~ \"&gt;=6 months\"\n    ), levels = c(\"&lt; 1 month\", \"1 to &lt;3 months\", \"3 to &lt;6 months\", \"&gt;=6 months\"))\n  )\n\nadsl_f &lt;- adsl %&gt;% filter(adsl$SAFFL == \"Y\")\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Duration of Exposure: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\",\n  show_colcounts = TRUE\n) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Number of Patients\", sum_exposure = \"Patient Time*\"),\n    custom_label = \"Total Number of Patients and Patient Time\"\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"aval_months_cat\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", \"Duration of exposure\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nDuration of Exposure: Safety-Evaluable Patients\n\n——————————————————————————————————————————————————————————————————————————————\n                                            Number of Patients   Patient Time*\nDuration of exposure                             (N=400)            (N=400)   \n——————————————————————————————————————————————————————————————————————————————\nTotal Number of Patients and Patient Time      217 (54.2%)           20641    \n  &lt; 1 month                                     28 (7.0%)             504     \n  1 to &lt;3 months                                79 (19.8%)           4727     \n  3 to &lt;6 months                               101 (25.2%)           13690    \n  &gt;=6 months                                     9 (2.2%)            1720     \n——————————————————————————————————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\"\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  ADEX &lt;- ADEX %&gt;%\n    mutate(\n      aval_months = day2month(AVAL),\n      aval_months_cat = factor(case_when(\n        aval_months &lt; 1 ~ \"&lt; 1 month\",\n        aval_months &gt;= 1 & aval_months &lt; 3 ~ \"1 to &lt;3 months\",\n        aval_months &gt;= 3 & aval_months &lt; 6 ~ \"3 to &lt;6 months\",\n        TRUE ~ \"&gt;=6 months\"\n      ), levels = c(\"&lt; 1 month\", \"1 to &lt;3 months\", \"3 to &lt;6 months\", \"&gt;=6 months\")) %&gt;% with_label(\"Overall duration\")\n    ) %&gt;%\n    select(-aval_months)\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"aval_months_cat\", \"RACE\", \"STRATA1\", \"SEX\")),\n        selected = \"aval_months_cat\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:54:36 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in sample scda dataset.\nset.seed(1, kind = \"Mersenne-Twister\")\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  ) %&gt;%\n  mutate(\n    aval_months = day2month(AVAL),\n    aval_months_cat = factor(case_when(\n      aval_months &lt; 1 ~ \"&lt; 1 month\",\n      aval_months &gt;= 1 & aval_months &lt; 3 ~ \"1 to &lt;3 months\",\n      aval_months &gt;= 3 & aval_months &lt; 6 ~ \"3 to &lt;6 months\",\n      TRUE ~ \"&gt;=6 months\"\n    ), levels = c(\"&lt; 1 month\", \"1 to &lt;3 months\", \"3 to &lt;6 months\", \"&gt;=6 months\"))\n  )\n\nadsl_f &lt;- adsl %&gt;% filter(adsl$SAFFL == \"Y\")\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Duration of Exposure: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\",\n  show_colcounts = TRUE\n) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Number of Patients\", sum_exposure = \"Patient Time*\"),\n    custom_label = \"Total Number of Patients and Patient Time\"\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"aval_months_cat\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", \"Duration of exposure\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nDuration of Exposure: Safety-Evaluable Patients\n\n——————————————————————————————————————————————————————————————————————————————\n                                            Number of Patients   Patient Time*\nDuration of exposure                             (N=400)            (N=400)   \n——————————————————————————————————————————————————————————————————————————————\nTotal Number of Patients and Patient Time      217 (54.2%)           20641    \n  &lt; 1 month                                     28 (7.0%)             504     \n  1 to &lt;3 months                                79 (19.8%)           4727     \n  3 to &lt;6 months                               101 (25.2%)           13690    \n  &gt;=6 months                                     9 (2.2%)            1720     \n——————————————————————————————————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\"\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  ADEX &lt;- ADEX %&gt;%\n    mutate(\n      aval_months = day2month(AVAL),\n      aval_months_cat = factor(case_when(\n        aval_months &lt; 1 ~ \"&lt; 1 month\",\n        aval_months &gt;= 1 & aval_months &lt; 3 ~ \"1 to &lt;3 months\",\n        aval_months &gt;= 3 & aval_months &lt; 6 ~ \"3 to &lt;6 months\",\n        TRUE ~ \"&gt;=6 months\"\n      ), levels = c(\"&lt; 1 month\", \"1 to &lt;3 months\", \"3 to &lt;6 months\", \"&gt;=6 months\")) %&gt;% with_label(\"Overall duration\")\n    ) %&gt;%\n    select(-aval_months)\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"aval_months_cat\", \"RACE\", \"STRATA1\", \"SEX\")),\n        selected = \"aval_months_cat\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:05 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Risk Management Plan",
@@ -384,7 +384,7 @@
     "href": "tables/lab-results/lbt10.html",
     "title": "LBT10",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting Sections\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nadhy_liver &lt;- adhy %&gt;%\n  filter(PARAMCD %in% c(\n    \"BL2AL2CU\", \"BL2AS2CU\", \"BG2AL2CU\", \"BG2AS2CU\", \"B2A2L2CU\", \"B2A2S2CU\", \"B2A5L2CU\", \"B2A5S2CU\"\n  )) %&gt;%\n  mutate(PARAMCAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CU\", \"BL2AS2CU\") ~ \"Total Bilirubin &lt;= 2xULN\",\n    PARAMCD %in% c(\"BG2AL2CU\", \"BG2AS2CU\") ~ \"Total Bilirubin &gt; 2xULN\",\n    PARAMCD %in% c(\"B2A2L2CU\", \"B2A2S2CU\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n    PARAMCD %in% c(\"B2A5L2CU\", \"B2A5S2CU\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n  )) %&gt;%\n  mutate(AVALC_FORMAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CU\", \"BG2AL2CU\", \"B2A2L2CU\", \"B2A5L2CU\") & AVALC == \"Y\" ~ \"ALT &gt;3xULN at 2 Visits\",\n    PARAMCD %in% c(\"BL2AS2CU\", \"BG2AS2CU\", \"B2A2S2CU\", \"B2A5S2CU\") & AVALC == \"Y\" ~ \"AST &gt;3xULN at 2 Visits\",\n    TRUE ~ \"Criteria not met\"\n  ))\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nanl &lt;- df_explicit_na(adhy_liver)\n\n\n\n\n\nCodetbl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   20/134 (14.9%)   21/134 (15.7%)   20/132 (15.2%)\n  AST &gt;3xULN at 2 Visits   30/134 (22.4%)   25/134 (18.7%)   27/132 (20.5%)\nTotal Bilirubin &gt; 2xULN                                                    \n  ALT &gt;3xULN at 2 Visits   24/134 (17.9%)   32/134 (23.9%)   24/132 (18.2%)\n  AST &gt;3xULN at 2 Visits   33/134 (24.6%)   29/134 (21.6%)   25/132 (18.9%)\n\n\n\n\n\nCodetbl2 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\", split_fun = remove_split_levels(\"Total Bilirubin &gt; 2xULN\")) %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl2 %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   20/134 (14.9%)   21/134 (15.7%)   20/132 (15.2%)\n  AST &gt;3xULN at 2 Visits   30/134 (22.4%)   25/134 (18.7%)   27/132 (20.5%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:54:06 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting Sections\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nadhy_liver &lt;- adhy %&gt;%\n  filter(PARAMCD %in% c(\n    \"BL2AL2CU\", \"BL2AS2CU\", \"BG2AL2CU\", \"BG2AS2CU\", \"B2A2L2CU\", \"B2A2S2CU\", \"B2A5L2CU\", \"B2A5S2CU\"\n  )) %&gt;%\n  mutate(PARAMCAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CU\", \"BL2AS2CU\") ~ \"Total Bilirubin &lt;= 2xULN\",\n    PARAMCD %in% c(\"BG2AL2CU\", \"BG2AS2CU\") ~ \"Total Bilirubin &gt; 2xULN\",\n    PARAMCD %in% c(\"B2A2L2CU\", \"B2A2S2CU\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n    PARAMCD %in% c(\"B2A5L2CU\", \"B2A5S2CU\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n  )) %&gt;%\n  mutate(AVALC_FORMAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CU\", \"BG2AL2CU\", \"B2A2L2CU\", \"B2A5L2CU\") & AVALC == \"Y\" ~ \"ALT &gt;3xULN at 2 Visits\",\n    PARAMCD %in% c(\"BL2AS2CU\", \"BG2AS2CU\", \"B2A2S2CU\", \"B2A5S2CU\") & AVALC == \"Y\" ~ \"AST &gt;3xULN at 2 Visits\",\n    TRUE ~ \"Criteria not met\"\n  ))\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nanl &lt;- df_explicit_na(adhy_liver)\n\n\n\n\n\nCodetbl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   20/134 (14.9%)   21/134 (15.7%)   20/132 (15.2%)\n  AST &gt;3xULN at 2 Visits   30/134 (22.4%)   25/134 (18.7%)   27/132 (20.5%)\nTotal Bilirubin &gt; 2xULN                                                    \n  ALT &gt;3xULN at 2 Visits   24/134 (17.9%)   32/134 (23.9%)   24/132 (18.2%)\n  AST &gt;3xULN at 2 Visits   33/134 (24.6%)   29/134 (21.6%)   25/132 (18.9%)\n\n\n\n\n\nCodetbl2 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\", split_fun = remove_split_levels(\"Total Bilirubin &gt; 2xULN\")) %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl2 %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   20/134 (14.9%)   21/134 (15.7%)   20/132 (15.2%)\n  AST &gt;3xULN at 2 Visits   30/134 (22.4%)   25/134 (18.7%)   27/132 (20.5%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:52:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -396,7 +396,7 @@
     "href": "tables/lab-results/lbt15.html",
     "title": "LBT15",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Modifying Cut Point\nteal App\nReproducibility\n\n\n\nBecause the scda dataset adlb doesn’t have the PARCAT1 variable, this variable is created from LBCAT in pre-processing.\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nlibrary(forcats)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb &lt;- adlb %&gt;%\n  mutate(\n    PARAM = with_label(.data$PARAM, \"Laboratory Test\"),\n    ANRIND = with_label(.data$ANRIND, \"Direction of Abnormality\")\n  ) %&gt;%\n  mutate(\n    PARCAT1 = LBCAT\n  ) %&gt;%\n  mutate(\n    ANRIND = fct_collapse(.data$ATOXGR,\n      \"LOW\" = c(\"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n      \"HIGH\" = c(\"3\", \"4\")\n    ),\n    BNRIND = fct_collapse(.data$BTOXGR,\n      \"LOW\" = c(\"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n      \"HIGH\" = c(\"3\", \"4\")\n    ),\n    PARCAT1 = LBCAT\n  ) %&gt;%\n  filter(ONTRTFL == \"Y\")\n\n\nadlb_alt_cut &lt;- adlb %&gt;%\n  mutate(\n    ANRIND = fct_collapse(.data$ATOXGR,\n      \"LOW\" = c(\"-2\", \"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-1\", \"0\", \"1\"),\n      \"HIGH\" = c(\"2\", \"3\", \"4\")\n    ),\n    BNRIND = fct_collapse(.data$BTOXGR,\n      \"LOW\" = c(\"-2\", \"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-1\", \"0\", \"1\"),\n      \"HIGH\" = c(\"2\", \"3\", \"4\")\n    )\n  ) %&gt;%\n  filter(ONTRTFL == \"Y\")\n\nadlb &lt;- adlb %&gt;% var_relabel(\n  PARCAT1 = \"Category for Lab Test\",\n  PARAM = \"Parameter\"\n)\n\n\n\n\n\nCode# Argument exclude_base_abn must be TRUE to include patients with normal or abnormal in the opposite\n# direction in the denom.\n# So for \"High\" direction, subjects with baseline grade 3-4 are not in the denom.\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\n    \"PARCAT1\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb$PARCAT1)\n  ) %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb$PARAM)\n  ) %&gt;%\n  count_abnormal(\n    var = \"ANRIND\",\n    abnormal = list(Low = \"LOW\", High = \"HIGH\"),\n    variables = list(id = \"USUBJID\", baseline = \"BNRIND\"),\n    exclude_base_abn = TRUE,\n    .indent_mods = 4L\n  ) %&gt;%\n  append_topleft(\"            Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, adlb, alt_counts_df = adsl)\nresult\n\nCategory for Lab Test                                                                                      \n  Parameter                                A: Drug X        B: Placebo     C: Combination    All Patients  \n            Direction of Abnormality        (N=134)          (N=134)          (N=132)           (N=400)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————\nCHEMISTRY                                                                                                  \n  Alanine Aminotransferase Measurement                                                                     \n            Low                          32/127 (25.2%)    32/128 (25%)     32/128 (25%)    96/383 (25.1%) \n            High                             0/134            0/134            0/132             0/400     \n  C-Reactive Protein Measurement                                                                           \n            Low                          36/127 (28.3%)   36/127 (28.3%)   42/123 (34.1%)   114/377 (30.2%)\n            High                         34/124 (27.4%)   33/128 (25.8%)    31/129 (24%)    98/381 (25.7%) \nIMMUNOLOGY                                                                                                 \n  Immunoglobulin A Measurement                                                                             \n            Low                              0/134            0/134            0/132             0/400     \n            High                         33/128 (25.8%)   32/124 (25.8%)   28/120 (23.3%)    93/372 (25%)  \n\n\n\n\n\nCoderesult &lt;- build_table(lyt, adlb_alt_cut, alt_counts_df = adsl)\nresult\n\nCategory for Lab Test                                                                                      \n  Parameter                                A: Drug X        B: Placebo     C: Combination    All Patients  \n            Direction of Abnormality        (N=134)          (N=134)          (N=132)           (N=400)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————\nCHEMISTRY                                                                                                  \n  Alanine Aminotransferase Measurement                                                                     \n            Low                          46/119 (38.7%)   54/123 (43.9%)    47/127 (37%)    147/369 (39.8%)\n            High                             0/134            0/134            0/132             0/400     \n  C-Reactive Protein Measurement                                                                           \n            Low                          59/122 (48.4%)   56/118 (47.5%)   58/121 (47.9%)   173/361 (47.9%)\n            High                         54/122 (44.3%)   46/122 (37.7%)   44/122 (36.1%)   144/366 (39.3%)\nIMMUNOLOGY                                                                                                 \n  Immunoglobulin A Measurement                                                                             \n            Low                              0/134            0/134            0/132             0/400     \n            High                         47/124 (37.9%)    54/120 (45%)    47/115 (40.9%)   148/359 (41.2%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nThe current module tm_t_abnormality is only displaying rows that are not all-zero, so the result could be a little different from static output where all-zero rows can be shown.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  library(forcats)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    mutate(\n      ANRIND = fct_collapse(ATOXGR,\n        \"LOW\" = c(\"-3\", \"-4\"),\n        \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n        \"HIGH\" = c(\"3\", \"4\")\n      ),\n      BNRIND = fct_collapse(.data$BTOXGR,\n        \"LOW\" = c(\"-3\", \"-4\"),\n        \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n        \"HIGH\" = c(\"3\", \"4\")\n      )\n    ) %&gt;%\n    filter(ONTRTFL == \"Y\") %&gt;%\n    col_relabel(ANRIND = \"Direction of Abnormality\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"PARAM\")),\n        selected = c(\"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADLB, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      baseline_var = choices_selected(\n        choices = variable_choices(ADLB, subset = \"BNRIND\"),\n        selected = \"BNRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(Low = \"LOW\", High = \"HIGH\"),\n      exclude_base_abn = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:53:54 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      forcats_1.0.0                   \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013            scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Modifying Cut Point\nteal App\nReproducibility\n\n\n\nBecause the scda dataset adlb doesn’t have the PARCAT1 variable, this variable is created from LBCAT in pre-processing.\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nlibrary(forcats)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb &lt;- adlb %&gt;%\n  mutate(\n    PARAM = with_label(.data$PARAM, \"Laboratory Test\"),\n    ANRIND = with_label(.data$ANRIND, \"Direction of Abnormality\")\n  ) %&gt;%\n  mutate(\n    PARCAT1 = LBCAT\n  ) %&gt;%\n  mutate(\n    ANRIND = fct_collapse(.data$ATOXGR,\n      \"LOW\" = c(\"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n      \"HIGH\" = c(\"3\", \"4\")\n    ),\n    BNRIND = fct_collapse(.data$BTOXGR,\n      \"LOW\" = c(\"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n      \"HIGH\" = c(\"3\", \"4\")\n    ),\n    PARCAT1 = LBCAT\n  ) %&gt;%\n  filter(ONTRTFL == \"Y\")\n\n\nadlb_alt_cut &lt;- adlb %&gt;%\n  mutate(\n    ANRIND = fct_collapse(.data$ATOXGR,\n      \"LOW\" = c(\"-2\", \"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-1\", \"0\", \"1\"),\n      \"HIGH\" = c(\"2\", \"3\", \"4\")\n    ),\n    BNRIND = fct_collapse(.data$BTOXGR,\n      \"LOW\" = c(\"-2\", \"-3\", \"-4\"),\n      \"MODERATE/NORMAL\" = c(\"-1\", \"0\", \"1\"),\n      \"HIGH\" = c(\"2\", \"3\", \"4\")\n    )\n  ) %&gt;%\n  filter(ONTRTFL == \"Y\")\n\nadlb &lt;- adlb %&gt;% var_relabel(\n  PARCAT1 = \"Category for Lab Test\",\n  PARAM = \"Parameter\"\n)\n\n\n\n\n\nCode# Argument exclude_base_abn must be TRUE to include patients with normal or abnormal in the opposite\n# direction in the denom.\n# So for \"High\" direction, subjects with baseline grade 3-4 are not in the denom.\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\n    \"PARCAT1\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb$PARCAT1)\n  ) %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb$PARAM)\n  ) %&gt;%\n  count_abnormal(\n    var = \"ANRIND\",\n    abnormal = list(Low = \"LOW\", High = \"HIGH\"),\n    variables = list(id = \"USUBJID\", baseline = \"BNRIND\"),\n    exclude_base_abn = TRUE,\n    .indent_mods = 4L\n  ) %&gt;%\n  append_topleft(\"            Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, adlb, alt_counts_df = adsl)\nresult\n\nCategory for Lab Test                                                                                      \n  Parameter                                A: Drug X        B: Placebo     C: Combination    All Patients  \n            Direction of Abnormality        (N=134)          (N=134)          (N=132)           (N=400)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————\nCHEMISTRY                                                                                                  \n  Alanine Aminotransferase Measurement                                                                     \n            Low                          32/127 (25.2%)    32/128 (25%)     32/128 (25%)    96/383 (25.1%) \n            High                             0/134            0/134            0/132             0/400     \n  C-Reactive Protein Measurement                                                                           \n            Low                          36/127 (28.3%)   36/127 (28.3%)   42/123 (34.1%)   114/377 (30.2%)\n            High                         34/124 (27.4%)   33/128 (25.8%)    31/129 (24%)    98/381 (25.7%) \nIMMUNOLOGY                                                                                                 \n  Immunoglobulin A Measurement                                                                             \n            Low                              0/134            0/134            0/132             0/400     \n            High                         33/128 (25.8%)   32/124 (25.8%)   28/120 (23.3%)    93/372 (25%)  \n\n\n\n\n\nCoderesult &lt;- build_table(lyt, adlb_alt_cut, alt_counts_df = adsl)\nresult\n\nCategory for Lab Test                                                                                      \n  Parameter                                A: Drug X        B: Placebo     C: Combination    All Patients  \n            Direction of Abnormality        (N=134)          (N=134)          (N=132)           (N=400)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————\nCHEMISTRY                                                                                                  \n  Alanine Aminotransferase Measurement                                                                     \n            Low                          46/119 (38.7%)   54/123 (43.9%)    47/127 (37%)    147/369 (39.8%)\n            High                             0/134            0/134            0/132             0/400     \n  C-Reactive Protein Measurement                                                                           \n            Low                          59/122 (48.4%)   56/118 (47.5%)   58/121 (47.9%)   173/361 (47.9%)\n            High                         54/122 (44.3%)   46/122 (37.7%)   44/122 (36.1%)   144/366 (39.3%)\nIMMUNOLOGY                                                                                                 \n  Immunoglobulin A Measurement                                                                             \n            Low                              0/134            0/134            0/132             0/400     \n            High                         47/124 (37.9%)    54/120 (45%)    47/115 (40.9%)   148/359 (41.2%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nThe current module tm_t_abnormality is only displaying rows that are not all-zero, so the result could be a little different from static output where all-zero rows can be shown.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  library(forcats)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    mutate(\n      ANRIND = fct_collapse(ATOXGR,\n        \"LOW\" = c(\"-3\", \"-4\"),\n        \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n        \"HIGH\" = c(\"3\", \"4\")\n      ),\n      BNRIND = fct_collapse(.data$BTOXGR,\n        \"LOW\" = c(\"-3\", \"-4\"),\n        \"MODERATE/NORMAL\" = c(\"-2\", \"-1\", \"0\", \"1\", \"2\"),\n        \"HIGH\" = c(\"3\", \"4\")\n      )\n    ) %&gt;%\n    filter(ONTRTFL == \"Y\") %&gt;%\n    col_relabel(ANRIND = \"Direction of Abnormality\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"PARAM\")),\n        selected = c(\"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADLB, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      baseline_var = choices_selected(\n        choices = variable_choices(ADLB, subset = \"BNRIND\"),\n        selected = \"BNRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(Low = \"LOW\", High = \"HIGH\"),\n      exclude_base_abn = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:52:23 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      forcats_1.0.0                   \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015            scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -408,7 +408,7 @@
     "href": "tables/lab-results/lbt09.html",
     "title": "LBT09",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting Sections\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadhy_liver &lt;- df_explicit_na(adhy)\n\n# Define values of interest in PARAMCD variable.\nparamcd_tbili_alt &lt;- c(\"BLAL\", \"BGAL\", \"BA2AL\", \"BA5AL\")\nparamcd_tbili_ast &lt;- c(\"BLAS\", \"BGAS\", \"BA2AS\", \"BA5AS\")\n\n# Select LBT09 parameters.\nadhy_liver &lt;- adhy_liver %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    AVISIT %in% c(\"BASELINE\", \"POST-BASELINE\"),\n    PARAMCD %in% c(paramcd_tbili_alt, paramcd_tbili_ast)\n  )\n\n# Let's be explicit about factor levels for AVISIT and PARAMCD.\nadhy_liver &lt;- adhy_liver %&gt;%\n  mutate(\n    AVISIT = factor(AVISIT, levels = c(\"BASELINE\", \"POST-BASELINE\")),\n    PARAMCD = factor(PARAMCD, levels = c(paramcd_tbili_alt, paramcd_tbili_ast))\n  )\n\n# Create indicator and category variables.\nadhy_liver &lt;- adhy_liver %&gt;%\n  mutate(\n    # Create TBILI_CAT categories variable - this is needed so we get the right nesting in the table.\n    TBILI_CAT = factor(\n      case_when(\n        PARAMCD %in% c(paramcd_tbili_alt[1], paramcd_tbili_ast[1]) ~ \"Total Bilirubin &lt;= 2xULN\",\n        PARAMCD %in% c(paramcd_tbili_alt[2], paramcd_tbili_ast[2]) ~ \"Total Bilirubin &gt; 2xULN\",\n        PARAMCD %in% c(paramcd_tbili_alt[3], paramcd_tbili_ast[3]) ~\n          \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n        PARAMCD %in% c(paramcd_tbili_alt[4], paramcd_tbili_ast[4]) ~\n          \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n      ),\n      levels = c(\n        \"Total Bilirubin &lt;= 2xULN\",\n        \"Total Bilirubin &gt; 2xULN\",\n        \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n        \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n      )\n    ),\n    # Create ALTAST_CAT categories variable\n    # this will be the labels for different ALT/AST categories displayed in the table.\n    ALTAST_CAT = factor(\n      case_when(\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;3-5ULN\" ~ \"ALT &gt;3 - &lt;= 5xULN\",\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;5-10ULN\" ~ \"ALT &gt;5 - &lt;= 10xULN\",\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;10-20ULN\" ~ \"ALT &gt;10 - &lt;= 20xULN\",\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;20ULN\" ~ \"ALT &gt; 20xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;3-5ULN\" ~ \"AST &gt;3 - &lt;= 5xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;5-10ULN\" ~ \"AST &gt;5 - &lt;= 10xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;10-20ULN\" ~ \"AST &gt;10 - &lt;= 20xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;20ULN\" ~ \"AST &gt; 20xULN\",\n        TRUE ~ \"Criteria not met\"\n      ),\n      levels = c(\n        \"ALT &gt;3 - &lt;= 5xULN\", \"ALT &gt;5 - &lt;= 10xULN\", \"ALT &gt;10 - &lt;= 20xULN\",\n        \"20\" = \"ALT &gt; 20xULN\",\n        \"AST &gt;3 - &lt;= 5xULN\", \"AST &gt;5 - &lt;= 10xULN\", \"AST &gt;10 - &lt;= 20xULN\", \"AST &gt; 20xULN\",\n        \"Criteria not met\"\n      )\n    ),\n    ALTAST_ind = factor(\n      case_when(\n        PARAMCD %in% paramcd_tbili_alt ~ \"ALT\",\n        PARAMCD %in% paramcd_tbili_ast ~ \"AST\"\n      ),\n      levels = c(\"ALT\", \"AST\")\n    )\n  )\n\nmap &lt;- data.frame(\n  ALTAST_ind = c(rep(\"ALT\", 5), rep(\"AST\", 5)),\n  ALTAST_CAT = c(\n    \"ALT &gt;3 - &lt;= 5xULN\", \"ALT &gt;5 - &lt;= 10xULN\", \"ALT &gt;10 - &lt;= 20xULN\",\n    \"20\" = \"ALT &gt; 20xULN\", \"Criteria not met\",\n    \"AST &gt;3 - &lt;= 5xULN\", \"AST &gt;5 - &lt;= 10xULN\", \"AST &gt;10 - &lt;= 20xULN\", \"AST &gt; 20xULN\", \"Criteria not met\"\n  ),\n  stringsAsFactors = FALSE\n)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"AVISIT\") %&gt;%\n  split_rows_by(\"TBILI_CAT\") %&gt;%\n  # below split helps us get the right denominator between ALT/AST but it can be hidden\n  split_rows_by(\"ALTAST_ind\", split_fun = trim_levels_to_map(map), child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"ALTAST_CAT\",\n    .stats = \"fraction\",\n    denom = \"n\",\n    drop = FALSE\n  ) %&gt;%\n  append_topleft(\"Liver Laboratory Test Criterion\") %&gt;%\n  build_table(df = adhy_liver)\n\n# trim away rows with criteria not met\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                                                                       A: Drug X                        B: Placebo                      C: Combination         \nLiver Laboratory Test Criterion                                BASELINE      POST-BASELINE       BASELINE      POST-BASELINE       BASELINE      POST-BASELINE \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                                                                                                       \n  ALT &gt;3 - &lt;= 5xULN                                         23/134 (17.2%)   15/134 (11.2%)   25/134 (18.7%)   27/134 (20.1%)   27/132 (20.5%)   30/132 (22.7%)\n  ALT &gt;5 - &lt;= 10xULN                                        28/134 (20.9%)   29/134 (21.6%)   29/134 (21.6%)   19/134 (14.2%)   36/132 (27.3%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   30/134 (22.4%)   32/134 (23.9%)   33/134 (24.6%)   28/132 (21.2%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              32/134 (23.9%)   35/134 (26.1%)   24/134 (17.9%)   23/134 (17.2%)   20/132 (15.2%)   26/132 (19.7%)\n  AST &gt;3 - &lt;= 5xULN                                         31/134 (23.1%)   34/134 (25.4%)   30/134 (22.4%)   26/134 (19.4%)   34/132 (25.8%)   24/132 (18.2%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   25/134 (18.7%)   26/134 (19.4%)   26/134 (19.4%)   22/132 (16.7%)   27/132 (20.5%)\n  AST &gt;10 - &lt;= 20xULN                                       22/134 (16.4%)   29/134 (21.6%)   16/134 (11.9%)   22/134 (16.4%)   34/132 (25.8%)   19/132 (14.4%)\n  AST &gt; 20xULN                                              20/134 (14.9%)   16/134 (11.9%)   28/134 (20.9%)   31/134 (23.1%)   21/132 (15.9%)   39/132 (29.5%)\nTotal Bilirubin &gt; 2xULN                                                                                                                                        \n  ALT &gt;3 - &lt;= 5xULN                                         40/134 (29.9%)   28/134 (20.9%)   31/134 (23.1%)   28/134 (20.9%)   18/132 (13.6%)   32/132 (24.2%)\n  ALT &gt;5 - &lt;= 10xULN                                        24/134 (17.9%)   29/134 (21.6%)   30/134 (22.4%)   37/134 (27.6%)   33/132 (25.0%)   24/132 (18.2%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   22/134 (16.4%)   26/134 (19.4%)   18/134 (13.4%)   21/132 (15.9%)   30/132 (22.7%)\n  ALT &gt; 20xULN                                              19/134 (14.2%)   33/134 (24.6%)   23/134 (17.2%)   19/134 (14.2%)   30/132 (22.7%)   22/132 (16.7%)\n  AST &gt;3 - &lt;= 5xULN                                         17/134 (12.7%)   27/134 (20.1%)   36/134 (26.9%)   25/134 (18.7%)   26/132 (19.7%)   34/132 (25.8%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   28/134 (20.9%)   24/134 (17.9%)   28/134 (20.9%)   20/132 (15.2%)   23/132 (17.4%)\n  AST &gt;10 - &lt;= 20xULN                                       36/134 (26.9%)   28/134 (20.9%)   16/134 (11.9%)   26/134 (19.4%)   33/132 (25.0%)   26/132 (19.7%)\n  AST &gt; 20xULN                                              30/134 (22.4%)   21/134 (15.7%)   30/134 (22.4%)   24/134 (17.9%)   24/132 (18.2%)   25/132 (18.9%)\nTotal Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN                                                                                                      \n  ALT &gt;3 - &lt;= 5xULN                                         24/134 (17.9%)   22/134 (16.4%)   28/134 (20.9%)   23/134 (17.2%)   38/132 (28.8%)   23/132 (17.4%)\n  ALT &gt;5 - &lt;= 10xULN                                        26/134 (19.4%)   25/134 (18.7%)   19/134 (14.2%)   22/134 (16.4%)   32/132 (24.2%)   32/132 (24.2%)\n  ALT &gt;10 - &lt;= 20xULN                                       26/134 (19.4%)   30/134 (22.4%)   31/134 (23.1%)   30/134 (22.4%)   21/132 (15.9%)   27/132 (20.5%)\n  ALT &gt; 20xULN                                              30/134 (22.4%)   28/134 (20.9%)   24/134 (17.9%)   36/134 (26.9%)   20/132 (15.2%)   23/132 (17.4%)\n  AST &gt;3 - &lt;= 5xULN                                         37/134 (27.6%)   25/134 (18.7%)   31/134 (23.1%)   26/134 (19.4%)   24/132 (18.2%)   36/132 (27.3%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   32/134 (23.9%)   29/134 (21.6%)   30/134 (22.4%)   26/132 (19.7%)   24/132 (18.2%)\n  AST &gt;10 - &lt;= 20xULN                                       25/134 (18.7%)   31/134 (23.1%)   28/134 (20.9%)   22/134 (16.4%)   29/132 (22.0%)   23/132 (17.4%)\n  AST &gt; 20xULN                                              26/134 (19.4%)   25/134 (18.7%)   23/134 (17.2%)   30/134 (22.4%)   23/132 (17.4%)   24/132 (18.2%)\nTotal Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN                                                                                                      \n  ALT &gt;3 - &lt;= 5xULN                                         24/134 (17.9%)   21/134 (15.7%)   30/134 (22.4%)   17/134 (12.7%)   28/132 (21.2%)   31/132 (23.5%)\n  ALT &gt;5 - &lt;= 10xULN                                        36/134 (26.9%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   26/132 (19.7%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       30/134 (22.4%)   21/134 (15.7%)   28/134 (20.9%)   36/134 (26.9%)   25/132 (18.9%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              14/134 (10.4%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   31/132 (23.5%)   23/132 (17.4%)\n  AST &gt;3 - &lt;= 5xULN                                         21/134 (15.7%)   26/134 (19.4%)   29/134 (21.6%)   31/134 (23.1%)   33/132 (25.0%)   19/132 (14.4%)\n  AST &gt;5 - &lt;= 10xULN                                        29/134 (21.6%)   26/134 (19.4%)   24/134 (17.9%)   26/134 (19.4%)   23/132 (17.4%)   32/132 (24.2%)\n  AST &gt;10 - &lt;= 20xULN                                       29/134 (21.6%)   31/134 (23.1%)   28/134 (20.9%)   22/134 (16.4%)   22/132 (16.7%)   21/132 (15.9%)\n  AST &gt; 20xULN                                              34/134 (25.4%)   21/134 (15.7%)   19/134 (14.2%)   28/134 (20.9%)   21/132 (15.9%)   33/132 (25.0%)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"AVISIT\") %&gt;%\n  split_rows_by(\n    \"TBILI_CAT\",\n    split_fun = remove_split_levels(\"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\")\n  ) %&gt;%\n  # below split helps us get the right denominator between ALT/AST but it can be hidden\n  split_rows_by(\n    \"ALTAST_ind\",\n    split_fun = trim_levels_to_map(map), child_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"ALTAST_CAT\",\n    .stats = \"fraction\",\n    denom = \"n\",\n    drop = FALSE\n  ) %&gt;%\n  append_topleft(\"Liver Laboratory Test Criterion\") %&gt;%\n  build_table(df = adhy_liver)\n\n# trim away rows with criteria not met\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                                                                       A: Drug X                        B: Placebo                      C: Combination         \nLiver Laboratory Test Criterion                                BASELINE      POST-BASELINE       BASELINE      POST-BASELINE       BASELINE      POST-BASELINE \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                                                                                                       \n  ALT &gt;3 - &lt;= 5xULN                                         23/134 (17.2%)   15/134 (11.2%)   25/134 (18.7%)   27/134 (20.1%)   27/132 (20.5%)   30/132 (22.7%)\n  ALT &gt;5 - &lt;= 10xULN                                        28/134 (20.9%)   29/134 (21.6%)   29/134 (21.6%)   19/134 (14.2%)   36/132 (27.3%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   30/134 (22.4%)   32/134 (23.9%)   33/134 (24.6%)   28/132 (21.2%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              32/134 (23.9%)   35/134 (26.1%)   24/134 (17.9%)   23/134 (17.2%)   20/132 (15.2%)   26/132 (19.7%)\n  AST &gt;3 - &lt;= 5xULN                                         31/134 (23.1%)   34/134 (25.4%)   30/134 (22.4%)   26/134 (19.4%)   34/132 (25.8%)   24/132 (18.2%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   25/134 (18.7%)   26/134 (19.4%)   26/134 (19.4%)   22/132 (16.7%)   27/132 (20.5%)\n  AST &gt;10 - &lt;= 20xULN                                       22/134 (16.4%)   29/134 (21.6%)   16/134 (11.9%)   22/134 (16.4%)   34/132 (25.8%)   19/132 (14.4%)\n  AST &gt; 20xULN                                              20/134 (14.9%)   16/134 (11.9%)   28/134 (20.9%)   31/134 (23.1%)   21/132 (15.9%)   39/132 (29.5%)\nTotal Bilirubin &gt; 2xULN                                                                                                                                        \n  ALT &gt;3 - &lt;= 5xULN                                         40/134 (29.9%)   28/134 (20.9%)   31/134 (23.1%)   28/134 (20.9%)   18/132 (13.6%)   32/132 (24.2%)\n  ALT &gt;5 - &lt;= 10xULN                                        24/134 (17.9%)   29/134 (21.6%)   30/134 (22.4%)   37/134 (27.6%)   33/132 (25.0%)   24/132 (18.2%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   22/134 (16.4%)   26/134 (19.4%)   18/134 (13.4%)   21/132 (15.9%)   30/132 (22.7%)\n  ALT &gt; 20xULN                                              19/134 (14.2%)   33/134 (24.6%)   23/134 (17.2%)   19/134 (14.2%)   30/132 (22.7%)   22/132 (16.7%)\n  AST &gt;3 - &lt;= 5xULN                                         17/134 (12.7%)   27/134 (20.1%)   36/134 (26.9%)   25/134 (18.7%)   26/132 (19.7%)   34/132 (25.8%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   28/134 (20.9%)   24/134 (17.9%)   28/134 (20.9%)   20/132 (15.2%)   23/132 (17.4%)\n  AST &gt;10 - &lt;= 20xULN                                       36/134 (26.9%)   28/134 (20.9%)   16/134 (11.9%)   26/134 (19.4%)   33/132 (25.0%)   26/132 (19.7%)\n  AST &gt; 20xULN                                              30/134 (22.4%)   21/134 (15.7%)   30/134 (22.4%)   24/134 (17.9%)   24/132 (18.2%)   25/132 (18.9%)\nTotal Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN                                                                                                      \n  ALT &gt;3 - &lt;= 5xULN                                         24/134 (17.9%)   21/134 (15.7%)   30/134 (22.4%)   17/134 (12.7%)   28/132 (21.2%)   31/132 (23.5%)\n  ALT &gt;5 - &lt;= 10xULN                                        36/134 (26.9%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   26/132 (19.7%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       30/134 (22.4%)   21/134 (15.7%)   28/134 (20.9%)   36/134 (26.9%)   25/132 (18.9%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              14/134 (10.4%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   31/132 (23.5%)   23/132 (17.4%)\n  AST &gt;3 - &lt;= 5xULN                                         21/134 (15.7%)   26/134 (19.4%)   29/134 (21.6%)   31/134 (23.1%)   33/132 (25.0%)   19/132 (14.4%)\n  AST &gt;5 - &lt;= 10xULN                                        29/134 (21.6%)   26/134 (19.4%)   24/134 (17.9%)   26/134 (19.4%)   23/132 (17.4%)   32/132 (24.2%)\n  AST &gt;10 - &lt;= 20xULN                                       29/134 (21.6%)   31/134 (23.1%)   28/134 (20.9%)   22/134 (16.4%)   22/132 (16.7%)   21/132 (15.9%)\n  AST &gt; 20xULN                                              34/134 (25.4%)   21/134 (15.7%)   19/134 (14.2%)   28/134 (20.9%)   21/132 (15.9%)   33/132 (25.0%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:53:32 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting Sections\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadhy_liver &lt;- df_explicit_na(adhy)\n\n# Define values of interest in PARAMCD variable.\nparamcd_tbili_alt &lt;- c(\"BLAL\", \"BGAL\", \"BA2AL\", \"BA5AL\")\nparamcd_tbili_ast &lt;- c(\"BLAS\", \"BGAS\", \"BA2AS\", \"BA5AS\")\n\n# Select LBT09 parameters.\nadhy_liver &lt;- adhy_liver %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    AVISIT %in% c(\"BASELINE\", \"POST-BASELINE\"),\n    PARAMCD %in% c(paramcd_tbili_alt, paramcd_tbili_ast)\n  )\n\n# Let's be explicit about factor levels for AVISIT and PARAMCD.\nadhy_liver &lt;- adhy_liver %&gt;%\n  mutate(\n    AVISIT = factor(AVISIT, levels = c(\"BASELINE\", \"POST-BASELINE\")),\n    PARAMCD = factor(PARAMCD, levels = c(paramcd_tbili_alt, paramcd_tbili_ast))\n  )\n\n# Create indicator and category variables.\nadhy_liver &lt;- adhy_liver %&gt;%\n  mutate(\n    # Create TBILI_CAT categories variable - this is needed so we get the right nesting in the table.\n    TBILI_CAT = factor(\n      case_when(\n        PARAMCD %in% c(paramcd_tbili_alt[1], paramcd_tbili_ast[1]) ~ \"Total Bilirubin &lt;= 2xULN\",\n        PARAMCD %in% c(paramcd_tbili_alt[2], paramcd_tbili_ast[2]) ~ \"Total Bilirubin &gt; 2xULN\",\n        PARAMCD %in% c(paramcd_tbili_alt[3], paramcd_tbili_ast[3]) ~\n          \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n        PARAMCD %in% c(paramcd_tbili_alt[4], paramcd_tbili_ast[4]) ~\n          \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n      ),\n      levels = c(\n        \"Total Bilirubin &lt;= 2xULN\",\n        \"Total Bilirubin &gt; 2xULN\",\n        \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n        \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n      )\n    ),\n    # Create ALTAST_CAT categories variable\n    # this will be the labels for different ALT/AST categories displayed in the table.\n    ALTAST_CAT = factor(\n      case_when(\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;3-5ULN\" ~ \"ALT &gt;3 - &lt;= 5xULN\",\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;5-10ULN\" ~ \"ALT &gt;5 - &lt;= 10xULN\",\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;10-20ULN\" ~ \"ALT &gt;10 - &lt;= 20xULN\",\n        PARAMCD %in% paramcd_tbili_alt & AVALC == \"&gt;20ULN\" ~ \"ALT &gt; 20xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;3-5ULN\" ~ \"AST &gt;3 - &lt;= 5xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;5-10ULN\" ~ \"AST &gt;5 - &lt;= 10xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;10-20ULN\" ~ \"AST &gt;10 - &lt;= 20xULN\",\n        PARAMCD %in% paramcd_tbili_ast & AVALC == \"&gt;20ULN\" ~ \"AST &gt; 20xULN\",\n        TRUE ~ \"Criteria not met\"\n      ),\n      levels = c(\n        \"ALT &gt;3 - &lt;= 5xULN\", \"ALT &gt;5 - &lt;= 10xULN\", \"ALT &gt;10 - &lt;= 20xULN\",\n        \"20\" = \"ALT &gt; 20xULN\",\n        \"AST &gt;3 - &lt;= 5xULN\", \"AST &gt;5 - &lt;= 10xULN\", \"AST &gt;10 - &lt;= 20xULN\", \"AST &gt; 20xULN\",\n        \"Criteria not met\"\n      )\n    ),\n    ALTAST_ind = factor(\n      case_when(\n        PARAMCD %in% paramcd_tbili_alt ~ \"ALT\",\n        PARAMCD %in% paramcd_tbili_ast ~ \"AST\"\n      ),\n      levels = c(\"ALT\", \"AST\")\n    )\n  )\n\nmap &lt;- data.frame(\n  ALTAST_ind = c(rep(\"ALT\", 5), rep(\"AST\", 5)),\n  ALTAST_CAT = c(\n    \"ALT &gt;3 - &lt;= 5xULN\", \"ALT &gt;5 - &lt;= 10xULN\", \"ALT &gt;10 - &lt;= 20xULN\",\n    \"20\" = \"ALT &gt; 20xULN\", \"Criteria not met\",\n    \"AST &gt;3 - &lt;= 5xULN\", \"AST &gt;5 - &lt;= 10xULN\", \"AST &gt;10 - &lt;= 20xULN\", \"AST &gt; 20xULN\", \"Criteria not met\"\n  ),\n  stringsAsFactors = FALSE\n)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"AVISIT\") %&gt;%\n  split_rows_by(\"TBILI_CAT\") %&gt;%\n  # below split helps us get the right denominator between ALT/AST but it can be hidden\n  split_rows_by(\"ALTAST_ind\", split_fun = trim_levels_to_map(map), child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"ALTAST_CAT\",\n    .stats = \"fraction\",\n    denom = \"n\",\n    drop = FALSE\n  ) %&gt;%\n  append_topleft(\"Liver Laboratory Test Criterion\") %&gt;%\n  build_table(df = adhy_liver)\n\n# trim away rows with criteria not met\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                                                                       A: Drug X                        B: Placebo                      C: Combination         \nLiver Laboratory Test Criterion                                BASELINE      POST-BASELINE       BASELINE      POST-BASELINE       BASELINE      POST-BASELINE \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                                                                                                       \n  ALT &gt;3 - &lt;= 5xULN                                         23/134 (17.2%)   15/134 (11.2%)   25/134 (18.7%)   27/134 (20.1%)   27/132 (20.5%)   30/132 (22.7%)\n  ALT &gt;5 - &lt;= 10xULN                                        28/134 (20.9%)   29/134 (21.6%)   29/134 (21.6%)   19/134 (14.2%)   36/132 (27.3%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   30/134 (22.4%)   32/134 (23.9%)   33/134 (24.6%)   28/132 (21.2%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              32/134 (23.9%)   35/134 (26.1%)   24/134 (17.9%)   23/134 (17.2%)   20/132 (15.2%)   26/132 (19.7%)\n  AST &gt;3 - &lt;= 5xULN                                         31/134 (23.1%)   34/134 (25.4%)   30/134 (22.4%)   26/134 (19.4%)   34/132 (25.8%)   24/132 (18.2%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   25/134 (18.7%)   26/134 (19.4%)   26/134 (19.4%)   22/132 (16.7%)   27/132 (20.5%)\n  AST &gt;10 - &lt;= 20xULN                                       22/134 (16.4%)   29/134 (21.6%)   16/134 (11.9%)   22/134 (16.4%)   34/132 (25.8%)   19/132 (14.4%)\n  AST &gt; 20xULN                                              20/134 (14.9%)   16/134 (11.9%)   28/134 (20.9%)   31/134 (23.1%)   21/132 (15.9%)   39/132 (29.5%)\nTotal Bilirubin &gt; 2xULN                                                                                                                                        \n  ALT &gt;3 - &lt;= 5xULN                                         40/134 (29.9%)   28/134 (20.9%)   31/134 (23.1%)   28/134 (20.9%)   18/132 (13.6%)   32/132 (24.2%)\n  ALT &gt;5 - &lt;= 10xULN                                        24/134 (17.9%)   29/134 (21.6%)   30/134 (22.4%)   37/134 (27.6%)   33/132 (25.0%)   24/132 (18.2%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   22/134 (16.4%)   26/134 (19.4%)   18/134 (13.4%)   21/132 (15.9%)   30/132 (22.7%)\n  ALT &gt; 20xULN                                              19/134 (14.2%)   33/134 (24.6%)   23/134 (17.2%)   19/134 (14.2%)   30/132 (22.7%)   22/132 (16.7%)\n  AST &gt;3 - &lt;= 5xULN                                         17/134 (12.7%)   27/134 (20.1%)   36/134 (26.9%)   25/134 (18.7%)   26/132 (19.7%)   34/132 (25.8%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   28/134 (20.9%)   24/134 (17.9%)   28/134 (20.9%)   20/132 (15.2%)   23/132 (17.4%)\n  AST &gt;10 - &lt;= 20xULN                                       36/134 (26.9%)   28/134 (20.9%)   16/134 (11.9%)   26/134 (19.4%)   33/132 (25.0%)   26/132 (19.7%)\n  AST &gt; 20xULN                                              30/134 (22.4%)   21/134 (15.7%)   30/134 (22.4%)   24/134 (17.9%)   24/132 (18.2%)   25/132 (18.9%)\nTotal Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN                                                                                                      \n  ALT &gt;3 - &lt;= 5xULN                                         24/134 (17.9%)   22/134 (16.4%)   28/134 (20.9%)   23/134 (17.2%)   38/132 (28.8%)   23/132 (17.4%)\n  ALT &gt;5 - &lt;= 10xULN                                        26/134 (19.4%)   25/134 (18.7%)   19/134 (14.2%)   22/134 (16.4%)   32/132 (24.2%)   32/132 (24.2%)\n  ALT &gt;10 - &lt;= 20xULN                                       26/134 (19.4%)   30/134 (22.4%)   31/134 (23.1%)   30/134 (22.4%)   21/132 (15.9%)   27/132 (20.5%)\n  ALT &gt; 20xULN                                              30/134 (22.4%)   28/134 (20.9%)   24/134 (17.9%)   36/134 (26.9%)   20/132 (15.2%)   23/132 (17.4%)\n  AST &gt;3 - &lt;= 5xULN                                         37/134 (27.6%)   25/134 (18.7%)   31/134 (23.1%)   26/134 (19.4%)   24/132 (18.2%)   36/132 (27.3%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   32/134 (23.9%)   29/134 (21.6%)   30/134 (22.4%)   26/132 (19.7%)   24/132 (18.2%)\n  AST &gt;10 - &lt;= 20xULN                                       25/134 (18.7%)   31/134 (23.1%)   28/134 (20.9%)   22/134 (16.4%)   29/132 (22.0%)   23/132 (17.4%)\n  AST &gt; 20xULN                                              26/134 (19.4%)   25/134 (18.7%)   23/134 (17.2%)   30/134 (22.4%)   23/132 (17.4%)   24/132 (18.2%)\nTotal Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN                                                                                                      \n  ALT &gt;3 - &lt;= 5xULN                                         24/134 (17.9%)   21/134 (15.7%)   30/134 (22.4%)   17/134 (12.7%)   28/132 (21.2%)   31/132 (23.5%)\n  ALT &gt;5 - &lt;= 10xULN                                        36/134 (26.9%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   26/132 (19.7%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       30/134 (22.4%)   21/134 (15.7%)   28/134 (20.9%)   36/134 (26.9%)   25/132 (18.9%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              14/134 (10.4%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   31/132 (23.5%)   23/132 (17.4%)\n  AST &gt;3 - &lt;= 5xULN                                         21/134 (15.7%)   26/134 (19.4%)   29/134 (21.6%)   31/134 (23.1%)   33/132 (25.0%)   19/132 (14.4%)\n  AST &gt;5 - &lt;= 10xULN                                        29/134 (21.6%)   26/134 (19.4%)   24/134 (17.9%)   26/134 (19.4%)   23/132 (17.4%)   32/132 (24.2%)\n  AST &gt;10 - &lt;= 20xULN                                       29/134 (21.6%)   31/134 (23.1%)   28/134 (20.9%)   22/134 (16.4%)   22/132 (16.7%)   21/132 (15.9%)\n  AST &gt; 20xULN                                              34/134 (25.4%)   21/134 (15.7%)   19/134 (14.2%)   28/134 (20.9%)   21/132 (15.9%)   33/132 (25.0%)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"AVISIT\") %&gt;%\n  split_rows_by(\n    \"TBILI_CAT\",\n    split_fun = remove_split_levels(\"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\")\n  ) %&gt;%\n  # below split helps us get the right denominator between ALT/AST but it can be hidden\n  split_rows_by(\n    \"ALTAST_ind\",\n    split_fun = trim_levels_to_map(map), child_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"ALTAST_CAT\",\n    .stats = \"fraction\",\n    denom = \"n\",\n    drop = FALSE\n  ) %&gt;%\n  append_topleft(\"Liver Laboratory Test Criterion\") %&gt;%\n  build_table(df = adhy_liver)\n\n# trim away rows with criteria not met\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                                                                       A: Drug X                        B: Placebo                      C: Combination         \nLiver Laboratory Test Criterion                                BASELINE      POST-BASELINE       BASELINE      POST-BASELINE       BASELINE      POST-BASELINE \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                                                                                                       \n  ALT &gt;3 - &lt;= 5xULN                                         23/134 (17.2%)   15/134 (11.2%)   25/134 (18.7%)   27/134 (20.1%)   27/132 (20.5%)   30/132 (22.7%)\n  ALT &gt;5 - &lt;= 10xULN                                        28/134 (20.9%)   29/134 (21.6%)   29/134 (21.6%)   19/134 (14.2%)   36/132 (27.3%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   30/134 (22.4%)   32/134 (23.9%)   33/134 (24.6%)   28/132 (21.2%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              32/134 (23.9%)   35/134 (26.1%)   24/134 (17.9%)   23/134 (17.2%)   20/132 (15.2%)   26/132 (19.7%)\n  AST &gt;3 - &lt;= 5xULN                                         31/134 (23.1%)   34/134 (25.4%)   30/134 (22.4%)   26/134 (19.4%)   34/132 (25.8%)   24/132 (18.2%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   25/134 (18.7%)   26/134 (19.4%)   26/134 (19.4%)   22/132 (16.7%)   27/132 (20.5%)\n  AST &gt;10 - &lt;= 20xULN                                       22/134 (16.4%)   29/134 (21.6%)   16/134 (11.9%)   22/134 (16.4%)   34/132 (25.8%)   19/132 (14.4%)\n  AST &gt; 20xULN                                              20/134 (14.9%)   16/134 (11.9%)   28/134 (20.9%)   31/134 (23.1%)   21/132 (15.9%)   39/132 (29.5%)\nTotal Bilirubin &gt; 2xULN                                                                                                                                        \n  ALT &gt;3 - &lt;= 5xULN                                         40/134 (29.9%)   28/134 (20.9%)   31/134 (23.1%)   28/134 (20.9%)   18/132 (13.6%)   32/132 (24.2%)\n  ALT &gt;5 - &lt;= 10xULN                                        24/134 (17.9%)   29/134 (21.6%)   30/134 (22.4%)   37/134 (27.6%)   33/132 (25.0%)   24/132 (18.2%)\n  ALT &gt;10 - &lt;= 20xULN                                       24/134 (17.9%)   22/134 (16.4%)   26/134 (19.4%)   18/134 (13.4%)   21/132 (15.9%)   30/132 (22.7%)\n  ALT &gt; 20xULN                                              19/134 (14.2%)   33/134 (24.6%)   23/134 (17.2%)   19/134 (14.2%)   30/132 (22.7%)   22/132 (16.7%)\n  AST &gt;3 - &lt;= 5xULN                                         17/134 (12.7%)   27/134 (20.1%)   36/134 (26.9%)   25/134 (18.7%)   26/132 (19.7%)   34/132 (25.8%)\n  AST &gt;5 - &lt;= 10xULN                                        25/134 (18.7%)   28/134 (20.9%)   24/134 (17.9%)   28/134 (20.9%)   20/132 (15.2%)   23/132 (17.4%)\n  AST &gt;10 - &lt;= 20xULN                                       36/134 (26.9%)   28/134 (20.9%)   16/134 (11.9%)   26/134 (19.4%)   33/132 (25.0%)   26/132 (19.7%)\n  AST &gt; 20xULN                                              30/134 (22.4%)   21/134 (15.7%)   30/134 (22.4%)   24/134 (17.9%)   24/132 (18.2%)   25/132 (18.9%)\nTotal Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN                                                                                                      \n  ALT &gt;3 - &lt;= 5xULN                                         24/134 (17.9%)   21/134 (15.7%)   30/134 (22.4%)   17/134 (12.7%)   28/132 (21.2%)   31/132 (23.5%)\n  ALT &gt;5 - &lt;= 10xULN                                        36/134 (26.9%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   26/132 (19.7%)   29/132 (22.0%)\n  ALT &gt;10 - &lt;= 20xULN                                       30/134 (22.4%)   21/134 (15.7%)   28/134 (20.9%)   36/134 (26.9%)   25/132 (18.9%)   24/132 (18.2%)\n  ALT &gt; 20xULN                                              14/134 (10.4%)   28/134 (20.9%)   25/134 (18.7%)   27/134 (20.1%)   31/132 (23.5%)   23/132 (17.4%)\n  AST &gt;3 - &lt;= 5xULN                                         21/134 (15.7%)   26/134 (19.4%)   29/134 (21.6%)   31/134 (23.1%)   33/132 (25.0%)   19/132 (14.4%)\n  AST &gt;5 - &lt;= 10xULN                                        29/134 (21.6%)   26/134 (19.4%)   24/134 (17.9%)   26/134 (19.4%)   23/132 (17.4%)   32/132 (24.2%)\n  AST &gt;10 - &lt;= 20xULN                                       29/134 (21.6%)   31/134 (23.1%)   28/134 (20.9%)   22/134 (16.4%)   22/132 (16.7%)   21/132 (15.9%)\n  AST &gt; 20xULN                                              34/134 (25.4%)   21/134 (15.7%)   19/134 (14.2%)   28/134 (20.9%)   21/132 (15.9%)   33/132 (25.0%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:52:02 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -420,7 +420,7 @@
     "href": "tables/lab-results/lbt01.html",
     "title": "LBT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nIn order to generate the LBT01 standard tabulation, the adlb dataset may be pre-processed so as to discriminate baseline from follow-up visits.\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n# For illustration purpose, the example focuses on \"Alanine Aminotransferase\n# Measurement\" starting from baseline, while excluding visit at week 1 for\n# subjects who were prescribed a placebo.\nadlb_f &lt;- adlb %&gt;%\n  dplyr::filter(\n    PARAM == \"Alanine Aminotransferase Measurement\" &\n      !(ACTARM == \"B: Placebo\" & AVISIT == \"WEEK 1 DAY 8\") &\n      AVISIT != \"SCREENING\"\n  )\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$PARAM)) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$AVISIT)) %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt, adlb_f)\nresult\n\n                                                  A: Drug X                        B: Placebo                      C: Combination         \n                                                         Change from                       Change from                       Change from  \nParameter                              Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   \n  Analysis Visit                          (N=804)          (N=804)          (N=670)          (N=670)          (N=792)          (N=792)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                                                                      \n  BASELINE                                                                                                                                \n    n                                       134                               134                               132                       \n    Mean (SD)                           17.74 (9.93)                      18.71 (9.83)                      19.46 (9.08)                  \n    Median                                 17.46                             18.19                             18.97                      \n    Min - Max                           0.00 - 44.06                      1.48 - 54.40                      0.57 - 39.81                  \n  WEEK 1 DAY 8                                                                                                                            \n    n                                       134              134               0                0               132              132      \n    Mean (SD)                           16.75 (9.08)    -0.99 (13.49)       NE (NE)          NE (NE)        19.61 (9.27)     0.14 (12.85) \n    Median                                 16.02            -1.28              NE               NE             19.00             0.06     \n    Min - Max                           0.05 - 36.30    -31.31 - 27.89      NE - NE          NE - NE        0.91 - 44.75    -32.45 - 38.85\n  WEEK 2 DAY 15                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                           17.82 (9.60)     0.08 (14.15)     18.82 (9.73)     0.11 (14.45)     16.55 (8.15)    -2.92 (12.64) \n    Median                                 15.92             0.28            17.96            -0.93            17.02            -1.11     \n    Min - Max                           0.40 - 44.33    -32.89 - 40.55    0.18 - 44.34    -45.93 - 29.85    0.35 - 34.69    -28.36 - 23.98\n  WEEK 3 DAY 22                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                           18.37 (9.30)     0.63 (13.85)     17.65 (9.58)    -1.06 (13.58)     16.75 (9.54)    -2.71 (13.22) \n    Median                                 18.11             1.13            17.68            -0.49            15.10            -2.66     \n    Min - Max                           0.59 - 41.73    -40.09 - 31.24    0.02 - 38.61    -46.30 - 31.38    0.48 - 39.23    -30.63 - 26.51\n  WEEK 4 DAY 29                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                          19.17 (10.95)     1.44 (15.39)    17.22 (10.64)    -1.48 (15.20)     17.92 (9.32)    -1.54 (12.63) \n    Median                                 17.41             0.88            15.88            -2.96            17.71            -1.53     \n    Min - Max                           0.93 - 54.24    -32.93 - 46.98    0.39 - 47.96    -41.45 - 43.08    0.25 - 41.27    -30.33 - 27.99\n  WEEK 5 DAY 36                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                           19.22 (9.47)     1.48 (14.49)     18.01 (9.92)    -0.69 (14.65)     18.51 (9.43)    -0.95 (12.92) \n    Median                                 19.80             2.31            18.28             1.82            19.26            -2.48     \n    Min - Max                           0.01 - 43.42    -40.08 - 30.07    0.11 - 40.64    -47.60 - 26.04    0.02 - 37.46    -29.78 - 25.00\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADLB &lt;- df_explicit_na(ADLB)\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Laboratory Test Results and Change from Baseline by Visit\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\", \"CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      ),\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADLB\", \"AVAL\", selected = NULL))\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:53:01 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nIn order to generate the LBT01 standard tabulation, the adlb dataset may be pre-processed so as to discriminate baseline from follow-up visits.\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n# For illustration purpose, the example focuses on \"Alanine Aminotransferase\n# Measurement\" starting from baseline, while excluding visit at week 1 for\n# subjects who were prescribed a placebo.\nadlb_f &lt;- adlb %&gt;%\n  dplyr::filter(\n    PARAM == \"Alanine Aminotransferase Measurement\" &\n      !(ACTARM == \"B: Placebo\" & AVISIT == \"WEEK 1 DAY 8\") &\n      AVISIT != \"SCREENING\"\n  )\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$PARAM)) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$AVISIT)) %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt, adlb_f)\nresult\n\n                                                  A: Drug X                        B: Placebo                      C: Combination         \n                                                         Change from                       Change from                       Change from  \nParameter                              Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   \n  Analysis Visit                          (N=804)          (N=804)          (N=670)          (N=670)          (N=792)          (N=792)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                                                                      \n  BASELINE                                                                                                                                \n    n                                       134                               134                               132                       \n    Mean (SD)                           17.74 (9.93)                      18.71 (9.83)                      19.46 (9.08)                  \n    Median                                 17.46                             18.19                             18.97                      \n    Min - Max                           0.00 - 44.06                      1.48 - 54.40                      0.57 - 39.81                  \n  WEEK 1 DAY 8                                                                                                                            \n    n                                       134              134               0                0               132              132      \n    Mean (SD)                           16.75 (9.08)    -0.99 (13.49)       NE (NE)          NE (NE)        19.61 (9.27)     0.14 (12.85) \n    Median                                 16.02            -1.28              NE               NE             19.00             0.06     \n    Min - Max                           0.05 - 36.30    -31.31 - 27.89      NE - NE          NE - NE        0.91 - 44.75    -32.45 - 38.85\n  WEEK 2 DAY 15                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                           17.82 (9.60)     0.08 (14.15)     18.82 (9.73)     0.11 (14.45)     16.55 (8.15)    -2.92 (12.64) \n    Median                                 15.92             0.28            17.96            -0.93            17.02            -1.11     \n    Min - Max                           0.40 - 44.33    -32.89 - 40.55    0.18 - 44.34    -45.93 - 29.85    0.35 - 34.69    -28.36 - 23.98\n  WEEK 3 DAY 22                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                           18.37 (9.30)     0.63 (13.85)     17.65 (9.58)    -1.06 (13.58)     16.75 (9.54)    -2.71 (13.22) \n    Median                                 18.11             1.13            17.68            -0.49            15.10            -2.66     \n    Min - Max                           0.59 - 41.73    -40.09 - 31.24    0.02 - 38.61    -46.30 - 31.38    0.48 - 39.23    -30.63 - 26.51\n  WEEK 4 DAY 29                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                          19.17 (10.95)     1.44 (15.39)    17.22 (10.64)    -1.48 (15.20)     17.92 (9.32)    -1.54 (12.63) \n    Median                                 17.41             0.88            15.88            -2.96            17.71            -1.53     \n    Min - Max                           0.93 - 54.24    -32.93 - 46.98    0.39 - 47.96    -41.45 - 43.08    0.25 - 41.27    -30.33 - 27.99\n  WEEK 5 DAY 36                                                                                                                           \n    n                                       134              134              134              134              132              132      \n    Mean (SD)                           19.22 (9.47)     1.48 (14.49)     18.01 (9.92)    -0.69 (14.65)     18.51 (9.43)    -0.95 (12.92) \n    Median                                 19.80             2.31            18.28             1.82            19.26            -2.48     \n    Min - Max                           0.01 - 43.42    -40.08 - 30.07    0.11 - 40.64    -47.60 - 26.04    0.02 - 37.46    -29.78 - 25.00\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADLB &lt;- df_explicit_na(ADLB)\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Laboratory Test Results and Change from Baseline by Visit\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\", \"CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      ),\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADLB\", \"AVAL\", selected = NULL))\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:51:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -432,7 +432,7 @@
     "href": "tables/lab-results/lbt03.html",
     "title": "LBT03",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nFor illustration purposes, this example focuses on “C-Reactive Protein Measurement” starting from baseline, while excluding visit at week 1 for subjects who were randomized to the placebo group.\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\nsaved_labels &lt;- var_labels(adlb)\n\nadlb_f &lt;- adlb %&gt;%\n  filter(\n    PARAM == \"C-Reactive Protein Measurement\",\n    !(ARM == \"B: Placebo\" & AVISIT == \"WEEK 1 DAY 8\"),\n    AVISIT != \"SCREENING\"\n  ) %&gt;%\n  dplyr::mutate(\n    AVISIT = droplevels(AVISIT),\n    ABLFLL = ABLFL == \"Y\"\n  )\n\nvar_labels(adlb_f) &lt;- c(saved_labels, \"\")\n\n\n\n\nThe LBT03 template is the result of a junction between the analysis of AVAL at baseline and CHG at visit time. AVAL is summarized for baseline visits and and CHG is summarized for post-baseline visits.\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$AVISIT)) %&gt;%\n  summarize_change(\n    \"CHG\",\n    variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\"),\n    na.rm = TRUE\n  )\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f,\n  alt_counts_df = adsl\n)\n\nresult\n\n                  A: Drug X      B: Placebo    C: Combination\nAnalysis Visit     (N=134)        (N=134)         (N=132)    \n—————————————————————————————————————————————————————————————\nBASELINE                                                     \n  n                  134            134             132      \n  Mean (SD)      9.06 (0.93)    8.99 (0.98)     8.98 (0.89)  \n  Median             9.07           8.92            8.96     \n  Min - Max      6.21 - 11.87   6.23 - 11.63    6.24 - 11.18 \nWEEK 1 DAY 8                                                 \n  n                  134             0              132      \n  Mean (SD)      -0.05 (1.38)        NA         -0.02 (1.30) \n  Median            -0.17            NA             0.02     \n  Min - Max      -3.56 - 3.48        NA         -3.28 - 3.33 \nWEEK 2 DAY 15                                                \n  n                  134            134             132      \n  Mean (SD)      -0.19 (1.47)   0.01 (1.45)     0.15 (1.25)  \n  Median            -0.27          -0.00            0.15     \n  Min - Max      -4.53 - 4.45   -3.79 - 3.43    -2.92 - 3.28 \nWEEK 3 DAY 22                                                \n  n                  134            134             132      \n  Mean (SD)      0.03 (1.38)    -0.02 (1.49)    0.02 (1.34)  \n  Median             0.15          -0.04            0.20     \n  Min - Max      -3.95 - 2.99   -4.28 - 4.24    -2.76 - 3.26 \nWEEK 4 DAY 29                                                \n  n                  134            134             132      \n  Mean (SD)      -0.26 (1.45)   0.05 (1.24)     -0.01 (1.17) \n  Median            -0.37           0.10           -0.06     \n  Min - Max      -3.74 - 4.15   -3.34 - 3.71    -3.06 - 3.22 \nWEEK 5 DAY 36                                                \n  n                  134            134             132      \n  Mean (SD)      -0.02 (1.50)   0.07 (1.34)     0.03 (1.27)  \n  Median             0.01           0.15            0.05     \n  Min - Max      -4.15 - 3.96   -3.50 - 3.53    -3.63 - 4.78 \n\n\nIn the final step, a new variable is derived from AVISIT that can specify the method of estimation of the evaluated change.\n\nCodeadlb_f &lt;- adlb_f %&gt;% mutate(AVISIT_header = recode(AVISIT,\n  \"BASELINE\" = \"BASELINE\",\n  \"WEEK 1 DAY 8\" = \"WEEK 1 DAY 8 value minus baseline\",\n  \"WEEK 2 DAY 15\" = \"WEEK 2 DAY 15 value minus baseline\",\n  \"WEEK 3 DAY 22\" = \"WEEK 3 DAY 22 value minus baseline\",\n  \"WEEK 4 DAY 29\" = \"WEEK 4 DAY 29 value minus baseline\",\n  \"WEEK 5 DAY 36\" = \"WEEK 5 DAY 36 value minus baseline\"\n))\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"AVISIT_header\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$AVISIT)\n  ) %&gt;%\n  summarize_change(\n    \"CHG\",\n    variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\"),\n    na.rm = TRUE\n  )\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f,\n  alt_counts_df = adsl\n)\n\nresult\n\n                                      A: Drug X      B: Placebo    C: Combination\nAnalysis Visit                         (N=134)        (N=134)         (N=132)    \n—————————————————————————————————————————————————————————————————————————————————\nBASELINE                                                                         \n  n                                      134            134             132      \n  Mean (SD)                          9.06 (0.93)    8.99 (0.98)     8.98 (0.89)  \n  Median                                 9.07           8.92            8.96     \n  Min - Max                          6.21 - 11.87   6.23 - 11.63    6.24 - 11.18 \nWEEK 1 DAY 8 value minus baseline                                                \n  n                                      134             0              132      \n  Mean (SD)                          -0.05 (1.38)        NA         -0.02 (1.30) \n  Median                                -0.17            NA             0.02     \n  Min - Max                          -3.56 - 3.48        NA         -3.28 - 3.33 \nWEEK 2 DAY 15 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          -0.19 (1.47)   0.01 (1.45)     0.15 (1.25)  \n  Median                                -0.27          -0.00            0.15     \n  Min - Max                          -4.53 - 4.45   -3.79 - 3.43    -2.92 - 3.28 \nWEEK 3 DAY 22 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          0.03 (1.38)    -0.02 (1.49)    0.02 (1.34)  \n  Median                                 0.15          -0.04            0.20     \n  Min - Max                          -3.95 - 2.99   -4.28 - 4.24    -2.76 - 3.26 \nWEEK 4 DAY 29 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          -0.26 (1.45)   0.05 (1.24)     -0.01 (1.17) \n  Median                                -0.37           0.10           -0.06     \n  Min - Max                          -3.74 - 4.15   -3.34 - 3.71    -3.06 - 3.22 \nWEEK 5 DAY 36 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          -0.02 (1.50)   0.07 (1.34)     0.03 (1.27)  \n  Median                                 0.01           0.15            0.05     \n  Min - Max                          -4.15 - 3.96   -3.50 - 3.53    -3.63 - 4.78 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nHere, we pre-process and manually define the variable “Baseline or Absolute Change from Baseline”.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- df_explicit_na(synthetic_cdisc_dataset(\"latest\", \"adsl\"))\n  ADLB &lt;- df_explicit_na(synthetic_cdisc_dataset(\"latest\", \"adlb\")) %&gt;%\n    filter(\n      !(ARM == \"B: Placebo\" & AVISIT == \"WEEK 1 DAY 8\"),\n      AVISIT != \"SCREENING\"\n    ) %&gt;%\n    mutate(\n      AVISIT = droplevels(AVISIT),\n      ABLFLL = ABLFL == \"Y\",\n      AVISIT_header = recode(AVISIT,\n        \"BASELINE\" = \"BASELINE\",\n        \"WEEK 1 DAY 8\" = \"WEEK 1 DAY 8 value minus baseline\",\n        \"WEEK 2 DAY 15\" = \"WEEK 2 DAY 15 value minus baseline\",\n        \"WEEK 3 DAY 22\" = \"WEEK 3 DAY 22 value minus baseline\",\n        \"WEEK 4 DAY 29\" = \"WEEK 4 DAY 29 value minus baseline\",\n        \"WEEK 5 DAY 36\" = \"WEEK 5 DAY 36 value minus baseline\"\n      )\n    ) %&gt;%\n    group_by(USUBJID, PARAM) %&gt;%\n    mutate(\n      AVAL_CHG = AVAL - (!ABLFLL) * sum(AVAL * ABLFLL)\n    ) %&gt;%\n    ungroup() %&gt;%\n    col_relabel(\n      AVAL_CHG = \"Baseline or Absolute Change from Baseline\",\n      ABLFLL = \"Baseline Flag (TRUE/FALSE)\",\n      AVISIT_header = \"Analysis Visit\"\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Laboratory Test Results Change from Baseline by Visit\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        # note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`\n        choices = variable_choices(ADLB, c(\"PARAM\", \"AVISIT_header\")),\n        selected = c(\"PARAM\", \"AVISIT_header\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"AVAL\", \"CHG\", \"AVAL_CHG\")),\n        selected = c(\"AVAL_CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"CRP\"\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADLB\", \"AVAL\", selected = NULL))\n)\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:52:39 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nFor illustration purposes, this example focuses on “C-Reactive Protein Measurement” starting from baseline, while excluding visit at week 1 for subjects who were randomized to the placebo group.\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\nsaved_labels &lt;- var_labels(adlb)\n\nadlb_f &lt;- adlb %&gt;%\n  filter(\n    PARAM == \"C-Reactive Protein Measurement\",\n    !(ARM == \"B: Placebo\" & AVISIT == \"WEEK 1 DAY 8\"),\n    AVISIT != \"SCREENING\"\n  ) %&gt;%\n  dplyr::mutate(\n    AVISIT = droplevels(AVISIT),\n    ABLFLL = ABLFL == \"Y\"\n  )\n\nvar_labels(adlb_f) &lt;- c(saved_labels, \"\")\n\n\n\n\nThe LBT03 template is the result of a junction between the analysis of AVAL at baseline and CHG at visit time. AVAL is summarized for baseline visits and and CHG is summarized for post-baseline visits.\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$AVISIT)) %&gt;%\n  summarize_change(\n    \"CHG\",\n    variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\"),\n    na.rm = TRUE\n  )\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f,\n  alt_counts_df = adsl\n)\n\nresult\n\n                  A: Drug X      B: Placebo    C: Combination\nAnalysis Visit     (N=134)        (N=134)         (N=132)    \n—————————————————————————————————————————————————————————————\nBASELINE                                                     \n  n                  134            134             132      \n  Mean (SD)      9.06 (0.93)    8.99 (0.98)     8.98 (0.89)  \n  Median             9.07           8.92            8.96     \n  Min - Max      6.21 - 11.87   6.23 - 11.63    6.24 - 11.18 \nWEEK 1 DAY 8                                                 \n  n                  134             0              132      \n  Mean (SD)      -0.05 (1.38)        NA         -0.02 (1.30) \n  Median            -0.17            NA             0.02     \n  Min - Max      -3.56 - 3.48        NA         -3.28 - 3.33 \nWEEK 2 DAY 15                                                \n  n                  134            134             132      \n  Mean (SD)      -0.19 (1.47)   0.01 (1.45)     0.15 (1.25)  \n  Median            -0.27          -0.00            0.15     \n  Min - Max      -4.53 - 4.45   -3.79 - 3.43    -2.92 - 3.28 \nWEEK 3 DAY 22                                                \n  n                  134            134             132      \n  Mean (SD)      0.03 (1.38)    -0.02 (1.49)    0.02 (1.34)  \n  Median             0.15          -0.04            0.20     \n  Min - Max      -3.95 - 2.99   -4.28 - 4.24    -2.76 - 3.26 \nWEEK 4 DAY 29                                                \n  n                  134            134             132      \n  Mean (SD)      -0.26 (1.45)   0.05 (1.24)     -0.01 (1.17) \n  Median            -0.37           0.10           -0.06     \n  Min - Max      -3.74 - 4.15   -3.34 - 3.71    -3.06 - 3.22 \nWEEK 5 DAY 36                                                \n  n                  134            134             132      \n  Mean (SD)      -0.02 (1.50)   0.07 (1.34)     0.03 (1.27)  \n  Median             0.01           0.15            0.05     \n  Min - Max      -4.15 - 3.96   -3.50 - 3.53    -3.63 - 4.78 \n\n\nIn the final step, a new variable is derived from AVISIT that can specify the method of estimation of the evaluated change.\n\nCodeadlb_f &lt;- adlb_f %&gt;% mutate(AVISIT_header = recode(AVISIT,\n  \"BASELINE\" = \"BASELINE\",\n  \"WEEK 1 DAY 8\" = \"WEEK 1 DAY 8 value minus baseline\",\n  \"WEEK 2 DAY 15\" = \"WEEK 2 DAY 15 value minus baseline\",\n  \"WEEK 3 DAY 22\" = \"WEEK 3 DAY 22 value minus baseline\",\n  \"WEEK 4 DAY 29\" = \"WEEK 4 DAY 29 value minus baseline\",\n  \"WEEK 5 DAY 36\" = \"WEEK 5 DAY 36 value minus baseline\"\n))\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"AVISIT_header\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$AVISIT)\n  ) %&gt;%\n  summarize_change(\n    \"CHG\",\n    variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\"),\n    na.rm = TRUE\n  )\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f,\n  alt_counts_df = adsl\n)\n\nresult\n\n                                      A: Drug X      B: Placebo    C: Combination\nAnalysis Visit                         (N=134)        (N=134)         (N=132)    \n—————————————————————————————————————————————————————————————————————————————————\nBASELINE                                                                         \n  n                                      134            134             132      \n  Mean (SD)                          9.06 (0.93)    8.99 (0.98)     8.98 (0.89)  \n  Median                                 9.07           8.92            8.96     \n  Min - Max                          6.21 - 11.87   6.23 - 11.63    6.24 - 11.18 \nWEEK 1 DAY 8 value minus baseline                                                \n  n                                      134             0              132      \n  Mean (SD)                          -0.05 (1.38)        NA         -0.02 (1.30) \n  Median                                -0.17            NA             0.02     \n  Min - Max                          -3.56 - 3.48        NA         -3.28 - 3.33 \nWEEK 2 DAY 15 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          -0.19 (1.47)   0.01 (1.45)     0.15 (1.25)  \n  Median                                -0.27          -0.00            0.15     \n  Min - Max                          -4.53 - 4.45   -3.79 - 3.43    -2.92 - 3.28 \nWEEK 3 DAY 22 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          0.03 (1.38)    -0.02 (1.49)    0.02 (1.34)  \n  Median                                 0.15          -0.04            0.20     \n  Min - Max                          -3.95 - 2.99   -4.28 - 4.24    -2.76 - 3.26 \nWEEK 4 DAY 29 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          -0.26 (1.45)   0.05 (1.24)     -0.01 (1.17) \n  Median                                -0.37           0.10           -0.06     \n  Min - Max                          -3.74 - 4.15   -3.34 - 3.71    -3.06 - 3.22 \nWEEK 5 DAY 36 value minus baseline                                               \n  n                                      134            134             132      \n  Mean (SD)                          -0.02 (1.50)   0.07 (1.34)     0.03 (1.27)  \n  Median                                 0.01           0.15            0.05     \n  Min - Max                          -4.15 - 3.96   -3.50 - 3.53    -3.63 - 4.78 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nHere, we pre-process and manually define the variable “Baseline or Absolute Change from Baseline”.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- df_explicit_na(synthetic_cdisc_dataset(\"latest\", \"adsl\"))\n  ADLB &lt;- df_explicit_na(synthetic_cdisc_dataset(\"latest\", \"adlb\")) %&gt;%\n    filter(\n      !(ARM == \"B: Placebo\" & AVISIT == \"WEEK 1 DAY 8\"),\n      AVISIT != \"SCREENING\"\n    ) %&gt;%\n    mutate(\n      AVISIT = droplevels(AVISIT),\n      ABLFLL = ABLFL == \"Y\",\n      AVISIT_header = recode(AVISIT,\n        \"BASELINE\" = \"BASELINE\",\n        \"WEEK 1 DAY 8\" = \"WEEK 1 DAY 8 value minus baseline\",\n        \"WEEK 2 DAY 15\" = \"WEEK 2 DAY 15 value minus baseline\",\n        \"WEEK 3 DAY 22\" = \"WEEK 3 DAY 22 value minus baseline\",\n        \"WEEK 4 DAY 29\" = \"WEEK 4 DAY 29 value minus baseline\",\n        \"WEEK 5 DAY 36\" = \"WEEK 5 DAY 36 value minus baseline\"\n      )\n    ) %&gt;%\n    group_by(USUBJID, PARAM) %&gt;%\n    mutate(\n      AVAL_CHG = AVAL - (!ABLFLL) * sum(AVAL * ABLFLL)\n    ) %&gt;%\n    ungroup() %&gt;%\n    col_relabel(\n      AVAL_CHG = \"Baseline or Absolute Change from Baseline\",\n      ABLFLL = \"Baseline Flag (TRUE/FALSE)\",\n      AVISIT_header = \"Analysis Visit\"\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Laboratory Test Results Change from Baseline by Visit\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        # note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`\n        choices = variable_choices(ADLB, c(\"PARAM\", \"AVISIT_header\")),\n        selected = c(\"PARAM\", \"AVISIT_header\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"AVAL\", \"CHG\", \"AVAL_CHG\")),\n        selected = c(\"AVAL_CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"CRP\"\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADLB\", \"AVAL\", selected = NULL))\n)\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:51:10 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -444,7 +444,7 @@
     "href": "tables/lab-results/lbt10_bl.html",
     "title": "LBT10_BL",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting Sections\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nadhy_liver &lt;- adhy %&gt;%\n  filter(PARAMCD %in% c(\n    \"BL2AL2CB\", \"BL2AS2CB\", \"BG2AL2CB\", \"BG2AS2CB\", \"B2A2L2CB\", \"B2A2S2CB\", \"B2A5L2CB\", \"B2A5S2CB\"\n  )) %&gt;%\n  mutate(PARAMCAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CB\", \"BL2AS2CB\") ~ \"Total Bilirubin &lt;= 2xULN\",\n    PARAMCD %in% c(\"BG2AL2CB\", \"BG2AS2CB\") ~ \"Total Bilirubin &gt; 2xULN\",\n    PARAMCD %in% c(\"B2A2L2CB\", \"B2A2S2CB\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n    PARAMCD %in% c(\"B2A5L2CB\", \"B2A5S2CB\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n  )) %&gt;%\n  mutate(AVALC_FORMAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CB\", \"BG2AL2CB\", \"B2A2L2CB\", \"B2A5L2CB\") & AVALC == \"Y\" ~ \"ALT &gt;3xULN at 2 Visits\",\n    PARAMCD %in% c(\"BL2AS2CB\", \"BG2AS2CB\", \"B2A2S2CB\", \"B2A5S2CB\") & AVALC == \"Y\" ~ \"AST &gt;3xULN at 2 Visits\",\n    TRUE ~ \"Criteria not met\"\n  ))\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nanl &lt;- df_explicit_na(adhy_liver)\n\n\n\n\n\nCodetbl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   25/134 (18.7%)   20/134 (14.9%)   27/132 (20.5%)\n  AST &gt;3xULN at 2 Visits   22/134 (16.4%)   14/134 (10.4%)   37/132 (28.0%)\nTotal Bilirubin &gt; 2xULN                                                    \n  ALT &gt;3xULN at 2 Visits   26/134 (19.4%)   31/134 (23.1%)   23/132 (17.4%)\n  AST &gt;3xULN at 2 Visits   30/134 (22.4%)   27/134 (20.1%)   26/132 (19.7%)\n\n\n\n\n\nCodetbl2 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\", split_fun = remove_split_levels(\"Total Bilirubin &gt; 2xULN\")) %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl2 %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   25/134 (18.7%)   20/134 (14.9%)   27/132 (20.5%)\n  AST &gt;3xULN at 2 Visits   22/134 (16.4%)   14/134 (10.4%)   37/132 (28.0%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:52:16 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting Sections\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nadhy_liver &lt;- adhy %&gt;%\n  filter(PARAMCD %in% c(\n    \"BL2AL2CB\", \"BL2AS2CB\", \"BG2AL2CB\", \"BG2AS2CB\", \"B2A2L2CB\", \"B2A2S2CB\", \"B2A5L2CB\", \"B2A5S2CB\"\n  )) %&gt;%\n  mutate(PARAMCAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CB\", \"BL2AS2CB\") ~ \"Total Bilirubin &lt;= 2xULN\",\n    PARAMCD %in% c(\"BG2AL2CB\", \"BG2AS2CB\") ~ \"Total Bilirubin &gt; 2xULN\",\n    PARAMCD %in% c(\"B2A2L2CB\", \"B2A2S2CB\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 2xULN\",\n    PARAMCD %in% c(\"B2A5L2CB\", \"B2A5S2CB\") ~ \"Total Bilirubin &gt; 2xULN and Alkaline Phosphatase &lt;= 5xULN\"\n  )) %&gt;%\n  mutate(AVALC_FORMAT = case_when(\n    PARAMCD %in% c(\"BL2AL2CB\", \"BG2AL2CB\", \"B2A2L2CB\", \"B2A5L2CB\") & AVALC == \"Y\" ~ \"ALT &gt;3xULN at 2 Visits\",\n    PARAMCD %in% c(\"BL2AS2CB\", \"BG2AS2CB\", \"B2A2S2CB\", \"B2A5S2CB\") & AVALC == \"Y\" ~ \"AST &gt;3xULN at 2 Visits\",\n    TRUE ~ \"Criteria not met\"\n  ))\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nanl &lt;- df_explicit_na(adhy_liver)\n\n\n\n\n\nCodetbl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   25/134 (18.7%)   20/134 (14.9%)   27/132 (20.5%)\n  AST &gt;3xULN at 2 Visits   22/134 (16.4%)   14/134 (10.4%)   37/132 (28.0%)\nTotal Bilirubin &gt; 2xULN                                                    \n  ALT &gt;3xULN at 2 Visits   26/134 (19.4%)   31/134 (23.1%)   23/132 (17.4%)\n  AST &gt;3xULN at 2 Visits   30/134 (22.4%)   27/134 (20.1%)   26/132 (19.7%)\n\n\n\n\n\nCodetbl2 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_rows_by(\"PARAMCAT\", split_fun = remove_split_levels(\"Total Bilirubin &gt; 2xULN\")) %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\n    vars = \"AVALC_FORMAT\",\n    .stats = c(\"fraction\"),\n    denom = \"n\",\n    drop = TRUE\n  ) %&gt;%\n  build_table(anl, alt_counts_df = adsl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  ifelse(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- tbl2 %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                               ARM A            ARM B            ARM C     \n                              (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————\nTotal Bilirubin &lt;= 2xULN                                                   \n  ALT &gt;3xULN at 2 Visits   25/134 (18.7%)   20/134 (14.9%)   27/132 (20.5%)\n  AST &gt;3xULN at 2 Visits   22/134 (16.4%)   14/134 (10.4%)   37/132 (28.0%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:50:47 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -456,7 +456,7 @@
     "href": "tables/lab-results/lbt02.html",
     "title": "LBT02",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCode# Preparation of an illustrative dataset\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_labels &lt;- var_labels(adlb)\n\n# For illustration purposes, the example focuses on ALT\n# Measurements starting from baseline and excluding all screening visits\nadlb &lt;- subset(adlb, AVISIT != \"SCREENING\" & PARAMCD == \"ALT\")\nadlb$AVISIT &lt;- droplevels(adlb$AVISIT)\n\nvar_labels(adlb) &lt;- adlb_labels\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\n    var = \"AVISIT\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb$AVISIT)\n  ) %&gt;%\n  analyze_vars(vars = \"AVAL\")\n\nresult &lt;- build_table(l,\n  df = adlb,\n  alt_counts_df = adsl\n)\nresult\n\n                  A: Drug X    B: Placebo    C: Combination   All Patients\nAnalysis Visit     (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————\nBASELINE                                                                  \n  n                  134           134            132             400     \n  Mean (SD)      17.7 (9.9)    18.7 (9.8)      19.5 (9.1)      18.6 (9.6) \n  Median            17.5          18.2            19.0            18.0    \n  Min - Max      0.0 - 44.1    1.5 - 54.4      0.6 - 39.8      0.0 - 54.4 \nWEEK 1 DAY 8                                                              \n  n                  134           134            132             400     \n  Mean (SD)      16.8 (9.1)    18.9 (9.2)      19.6 (9.3)      18.4 (9.2) \n  Median            16.0          18.2            19.0            18.0    \n  Min - Max      0.1 - 36.3    0.7 - 39.9      0.9 - 44.7      0.1 - 44.7 \nWEEK 2 DAY 15                                                             \n  n                  134           134            132             400     \n  Mean (SD)      17.8 (9.6)    18.8 (9.7)      16.5 (8.2)      17.7 (9.2) \n  Median            15.9          18.0            17.0            17.0    \n  Min - Max      0.4 - 44.3    0.2 - 44.3      0.3 - 34.7      0.2 - 44.3 \nWEEK 3 DAY 22                                                             \n  n                  134           134            132             400     \n  Mean (SD)      18.4 (9.3)    17.6 (9.6)      16.8 (9.5)      17.6 (9.5) \n  Median            18.1          17.7            15.1            17.2    \n  Min - Max      0.6 - 41.7    0.0 - 38.6      0.5 - 39.2      0.0 - 41.7 \nWEEK 4 DAY 29                                                             \n  n                  134           134            132             400     \n  Mean (SD)      19.2 (11.0)   17.2 (10.6)     17.9 (9.3)     18.1 (10.3) \n  Median            17.4          15.9            17.7            17.3    \n  Min - Max      0.9 - 54.2    0.4 - 48.0      0.2 - 41.3      0.2 - 54.2 \nWEEK 5 DAY 36                                                             \n  n                  134           134            132             400     \n  Mean (SD)      19.2 (9.5)    18.0 (9.9)      18.5 (9.4)      18.6 (9.6) \n  Median            19.8          18.3            19.3            19.0    \n  Min - Max      0.0 - 43.4    0.1 - 40.6      0.0 - 37.5      0.0 - 43.4 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADLB &lt;- df_explicit_na(ADLB)\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Laboratory Test Results by Visit\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        # note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`\n        choices = variable_choices(ADLB, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"PARAM\", \"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"AVAL\")),\n        selected = c(\"AVAL\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADLB\", \"AVAL\", selected = NULL))\n)\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:51:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] dplyr_1.1.4                      teal.modules.clinical_0.9.0.9009\n [3] teal.transform_0.5.0.9001        teal_0.15.2.9003                \n [5] teal.slice_0.5.0.9004            teal.data_0.5.0.9002            \n [7] teal.code_0.5.0.9003             shiny_1.8.0                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCode# Preparation of an illustrative dataset\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_labels &lt;- var_labels(adlb)\n\n# For illustration purposes, the example focuses on ALT\n# Measurements starting from baseline and excluding all screening visits\nadlb &lt;- subset(adlb, AVISIT != \"SCREENING\" & PARAMCD == \"ALT\")\nadlb$AVISIT &lt;- droplevels(adlb$AVISIT)\n\nvar_labels(adlb) &lt;- adlb_labels\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\n    var = \"AVISIT\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb$AVISIT)\n  ) %&gt;%\n  analyze_vars(vars = \"AVAL\")\n\nresult &lt;- build_table(l,\n  df = adlb,\n  alt_counts_df = adsl\n)\nresult\n\n                  A: Drug X    B: Placebo    C: Combination   All Patients\nAnalysis Visit     (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————\nBASELINE                                                                  \n  n                  134           134            132             400     \n  Mean (SD)      17.7 (9.9)    18.7 (9.8)      19.5 (9.1)      18.6 (9.6) \n  Median            17.5          18.2            19.0            18.0    \n  Min - Max      0.0 - 44.1    1.5 - 54.4      0.6 - 39.8      0.0 - 54.4 \nWEEK 1 DAY 8                                                              \n  n                  134           134            132             400     \n  Mean (SD)      16.8 (9.1)    18.9 (9.2)      19.6 (9.3)      18.4 (9.2) \n  Median            16.0          18.2            19.0            18.0    \n  Min - Max      0.1 - 36.3    0.7 - 39.9      0.9 - 44.7      0.1 - 44.7 \nWEEK 2 DAY 15                                                             \n  n                  134           134            132             400     \n  Mean (SD)      17.8 (9.6)    18.8 (9.7)      16.5 (8.2)      17.7 (9.2) \n  Median            15.9          18.0            17.0            17.0    \n  Min - Max      0.4 - 44.3    0.2 - 44.3      0.3 - 34.7      0.2 - 44.3 \nWEEK 3 DAY 22                                                             \n  n                  134           134            132             400     \n  Mean (SD)      18.4 (9.3)    17.6 (9.6)      16.8 (9.5)      17.6 (9.5) \n  Median            18.1          17.7            15.1            17.2    \n  Min - Max      0.6 - 41.7    0.0 - 38.6      0.5 - 39.2      0.0 - 41.7 \nWEEK 4 DAY 29                                                             \n  n                  134           134            132             400     \n  Mean (SD)      19.2 (11.0)   17.2 (10.6)     17.9 (9.3)     18.1 (10.3) \n  Median            17.4          15.9            17.7            17.3    \n  Min - Max      0.9 - 54.2    0.4 - 48.0      0.2 - 41.3      0.2 - 54.2 \nWEEK 5 DAY 36                                                             \n  n                  134           134            132             400     \n  Mean (SD)      19.2 (9.5)    18.0 (9.9)      18.5 (9.4)      18.6 (9.6) \n  Median            19.8          18.3            19.3            19.0    \n  Min - Max      0.0 - 43.4    0.1 - 40.6      0.0 - 37.5      0.0 - 43.4 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADLB &lt;- df_explicit_na(ADLB)\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Laboratory Test Results by Visit\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        # note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`\n        choices = variable_choices(ADLB, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"PARAM\", \"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADLB, c(\"AVAL\")),\n        selected = c(\"AVAL\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADLB\", \"AVAL\", selected = NULL))\n)\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:50:24 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] dplyr_1.1.4                      teal.modules.clinical_0.9.0.9010\n [3] teal.transform_0.5.0.9002        teal_0.15.2.9004                \n [5] teal.slice_0.5.0.9005            teal.data_0.5.0.9003            \n [7] teal.code_0.5.0.9003             shiny_1.8.0                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -468,7 +468,7 @@
     "href": "tables/lab-results/lbt04.html",
     "title": "LBT04",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\nadlb_f &lt;- adlb %&gt;%\n  filter(ONTRTFL == \"Y\", ANRIND != \"&lt;Missing&gt;\") %&gt;%\n  var_relabel(\n    PARAM = \"Laboratory Test\",\n    ANRIND = \"Direction of Abnormality\"\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$PARAM)\n  ) %&gt;%\n  count_abnormal(\n    var = \"ANRIND\",\n    abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n    exclude_base_abn = TRUE\n  ) %&gt;%\n  append_varlabels(adlb_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adlb_f, alt_counts_df = adsl)\n\nresult\n\nLaboratory Test                          A: Drug X        B: Placebo     C: Combination\n  Direction of Abnormality                (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                   \n  Low                                  57/113 (50.4%)    62/117 (53%)    61/123 (49.6%)\n  High                                     0/134            0/134            0/132     \nC-Reactive Protein Measurement                                                         \n  Low                                  78/119 (65.5%)   71/113 (62.8%)   62/112 (55.4%)\n  High                                 69/114 (60.5%)   57/112 (50.9%)   58/115 (50.4%)\nImmunoglobulin A Measurement                                                           \n  Low                                      0/134            0/134            0/132     \n  High                                 64/119 (53.8%)   62/116 (53.4%)   62/113 (54.9%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    col_relabel(PARAM = \"Laboratory Test\", ANRIND = \"Direction of Abnormality\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"LBCAT\", \"PARAM\", \"AVISIT\")),\n        selected = c(\"LBCAT\", \"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADLB, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(low = \"LOW\", high = \"HIGH\"),\n      exclude_base_abn = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:51:21 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\nadlb_f &lt;- adlb %&gt;%\n  filter(ONTRTFL == \"Y\", ANRIND != \"&lt;Missing&gt;\") %&gt;%\n  var_relabel(\n    PARAM = \"Laboratory Test\",\n    ANRIND = \"Direction of Abnormality\"\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$PARAM)\n  ) %&gt;%\n  count_abnormal(\n    var = \"ANRIND\",\n    abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n    exclude_base_abn = TRUE\n  ) %&gt;%\n  append_varlabels(adlb_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adlb_f, alt_counts_df = adsl)\n\nresult\n\nLaboratory Test                          A: Drug X        B: Placebo     C: Combination\n  Direction of Abnormality                (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                   \n  Low                                  57/113 (50.4%)    62/117 (53%)    61/123 (49.6%)\n  High                                     0/134            0/134            0/132     \nC-Reactive Protein Measurement                                                         \n  Low                                  78/119 (65.5%)   71/113 (62.8%)   62/112 (55.4%)\n  High                                 69/114 (60.5%)   57/112 (50.9%)   58/115 (50.4%)\nImmunoglobulin A Measurement                                                           \n  Low                                      0/134            0/134            0/132     \n  High                                 64/119 (53.8%)   62/116 (53.4%)   62/113 (54.9%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    col_relabel(PARAM = \"Laboratory Test\", ANRIND = \"Direction of Abnormality\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"LBCAT\", \"PARAM\", \"AVISIT\")),\n        selected = c(\"LBCAT\", \"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADLB, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(low = \"LOW\", high = \"HIGH\"),\n      exclude_base_abn = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:49:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -480,7 +480,7 @@
     "href": "tables/lab-results/lbt07.html",
     "title": "LBT07",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nlibrary(forcats)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_labels &lt;- var_labels(adlb)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n# Select worst post-baseline records.\nadlb_f &lt;- adlb %&gt;%\n  filter(ATOXGR != \"&lt;Missing&gt;\") %&gt;%\n  filter(ONTRTFL == \"Y\") %&gt;%\n  filter(WGRLOFL == \"Y\" | WGRHIFL == \"Y\")\n\nvar_labels(adlb_f) &lt;- adlb_labels\n\n# Derive GRADE_DIR and GRADE_ANL to use in layout from ATOXGR\nadlb_f &lt;- adlb_f %&gt;%\n  mutate(\n    GRADE_DIR = factor(\n      case_when(\n        ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\") & .data$WGRLOFL == \"Y\" ~ \"LOW\",\n        ATOXGR == \"0\" ~ \"ZERO\",\n        ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\") & .data$WGRHIFL == \"Y\" ~ \"HIGH\",\n        TRUE ~ \"NONE\"\n      ),\n      levels = c(\"LOW\", \"ZERO\", \"HIGH\", \"NONE\")\n    ),\n    GRADE_ANL = forcats::fct_relevel(\n      forcats::fct_recode(ATOXGR,\n        `1` = \"-1\", `2` = \"-2\", `3` = \"-3\", `4` = \"-4\"\n      ),\n      c(\"0\", \"1\", \"2\", \"3\", \"4\")\n    )\n  ) %&gt;%\n  var_relabel(\n    GRADE_DIR = \"Direction of Abnormality\",\n    GRADE_ANL = \"Analysis Grade\"\n  )\n\n# Construct analysis map\nmap &lt;- expand.grid(\n  PARAM = levels(adlb$PARAM),\n  GRADE_DIR = c(\"LOW\", \"HIGH\"),\n  GRADE_ANL = as.character(1:4),\n  stringsAsFactors = FALSE\n) %&gt;%\n  arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$PARAM)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    required = \"ATOXGR\",\n    .stats = \"unique_count\"\n  ) %&gt;%\n  split_rows_by(\n    \"GRADE_DIR\",\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$GRADE_DIR),\n    split_fun = trim_levels_to_map(map)\n  ) %&gt;%\n  count_abnormal_by_worst_grade(\n    var = \"GRADE_ANL\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\"),\n    .indent_mods = 4L\n  ) %&gt;%\n  append_topleft(\"            Highest NCI CTCAE Grade\")\n\nresult &lt;- build_table(lyt = lyt, df = adlb_f, alt_counts_df = adsl)\nresult &lt;- result %&gt;% prune_table()\n\nresult\n\nParameter                                                                          \n  Direction of Abnormality                 A: Drug X    B: Placebo   C: Combination\n            Highest NCI CTCAE Grade         (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      134          134            132      \n  LOW                                                                              \n            1                              15 (11.2%)   11 (8.2%)      15 (11.4%)  \n            2                              18 (13.4%)   24 (17.9%)     17 (12.9%)  \n            3                              16 (11.9%)   24 (17.9%)     16 (12.1%)  \n            4                              17 (12.7%)   10 (7.5%)      18 (13.6%)  \n            Any                            66 (49.3%)   69 (51.5%)      66 (50%)   \nC-Reactive Protein Measurement (n)            134          134            132      \n  LOW                                                                              \n            1                              21 (15.7%)   22 (16.4%)     12 (9.1%)   \n            2                              24 (17.9%)   22 (16.4%)     18 (13.6%)  \n            3                              29 (21.6%)   21 (15.7%)     25 (18.9%)  \n            4                              10 (7.5%)    16 (11.9%)     22 (16.7%)  \n            Any                            84 (62.7%)   81 (60.4%)     77 (58.3%)  \n  HIGH                                                                             \n            1                              20 (14.9%)   22 (16.4%)     22 (16.7%)  \n            2                              21 (15.7%)   15 (11.2%)     14 (10.6%)  \n            3                              25 (18.7%)   16 (11.9%)     20 (15.2%)  \n            4                              13 (9.7%)    20 (14.9%)     11 (8.3%)   \n            Any                             79 (59%)    73 (54.5%)     67 (50.8%)  \nImmunoglobulin A Measurement (n)              134          134            132      \n  HIGH                                                                             \n            1                              19 (14.2%)    12 (9%)       18 (13.6%)  \n            2                              17 (12.7%)   24 (17.9%)     19 (14.4%)  \n            3                              21 (15.7%)   23 (17.2%)     23 (17.4%)  \n            4                              13 (9.7%)    13 (9.7%)       9 (6.8%)   \n            Any                            70 (52.2%)   72 (53.7%)     69 (52.3%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    filter(!AVISIT %in% c(\"SCREENING\", \"BASELINE\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality_by_worst_grade(\n      label = \"Laboratory Test Results with Highest Grade Post-Baseline\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = c(\"ALT\", \"CRP\", \"IGA\")\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = (\n    teal_slices(\n      teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n      teal_slice(\"ADLB\", \"ONTRTFL\", selected = \"Y\")\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:51:01 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      forcats_1.0.0                   \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013            scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nlibrary(forcats)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_labels &lt;- var_labels(adlb)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n# Select worst post-baseline records.\nadlb_f &lt;- adlb %&gt;%\n  filter(ATOXGR != \"&lt;Missing&gt;\") %&gt;%\n  filter(ONTRTFL == \"Y\") %&gt;%\n  filter(WGRLOFL == \"Y\" | WGRHIFL == \"Y\")\n\nvar_labels(adlb_f) &lt;- adlb_labels\n\n# Derive GRADE_DIR and GRADE_ANL to use in layout from ATOXGR\nadlb_f &lt;- adlb_f %&gt;%\n  mutate(\n    GRADE_DIR = factor(\n      case_when(\n        ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\") & .data$WGRLOFL == \"Y\" ~ \"LOW\",\n        ATOXGR == \"0\" ~ \"ZERO\",\n        ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\") & .data$WGRHIFL == \"Y\" ~ \"HIGH\",\n        TRUE ~ \"NONE\"\n      ),\n      levels = c(\"LOW\", \"ZERO\", \"HIGH\", \"NONE\")\n    ),\n    GRADE_ANL = forcats::fct_relevel(\n      forcats::fct_recode(ATOXGR,\n        `1` = \"-1\", `2` = \"-2\", `3` = \"-3\", `4` = \"-4\"\n      ),\n      c(\"0\", \"1\", \"2\", \"3\", \"4\")\n    )\n  ) %&gt;%\n  var_relabel(\n    GRADE_DIR = \"Direction of Abnormality\",\n    GRADE_ANL = \"Analysis Grade\"\n  )\n\n# Construct analysis map\nmap &lt;- expand.grid(\n  PARAM = levels(adlb$PARAM),\n  GRADE_DIR = c(\"LOW\", \"HIGH\"),\n  GRADE_ANL = as.character(1:4),\n  stringsAsFactors = FALSE\n) %&gt;%\n  arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$PARAM)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    required = \"ATOXGR\",\n    .stats = \"unique_count\"\n  ) %&gt;%\n  split_rows_by(\n    \"GRADE_DIR\",\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_f$GRADE_DIR),\n    split_fun = trim_levels_to_map(map)\n  ) %&gt;%\n  count_abnormal_by_worst_grade(\n    var = \"GRADE_ANL\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\"),\n    .indent_mods = 4L\n  ) %&gt;%\n  append_topleft(\"            Highest NCI CTCAE Grade\")\n\nresult &lt;- build_table(lyt = lyt, df = adlb_f, alt_counts_df = adsl)\nresult &lt;- result %&gt;% prune_table()\n\nresult\n\nParameter                                                                          \n  Direction of Abnormality                 A: Drug X    B: Placebo   C: Combination\n            Highest NCI CTCAE Grade         (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      134          134            132      \n  LOW                                                                              \n            1                              15 (11.2%)   11 (8.2%)      15 (11.4%)  \n            2                              18 (13.4%)   24 (17.9%)     17 (12.9%)  \n            3                              16 (11.9%)   24 (17.9%)     16 (12.1%)  \n            4                              17 (12.7%)   10 (7.5%)      18 (13.6%)  \n            Any                            66 (49.3%)   69 (51.5%)      66 (50%)   \nC-Reactive Protein Measurement (n)            134          134            132      \n  LOW                                                                              \n            1                              21 (15.7%)   22 (16.4%)     12 (9.1%)   \n            2                              24 (17.9%)   22 (16.4%)     18 (13.6%)  \n            3                              29 (21.6%)   21 (15.7%)     25 (18.9%)  \n            4                              10 (7.5%)    16 (11.9%)     22 (16.7%)  \n            Any                            84 (62.7%)   81 (60.4%)     77 (58.3%)  \n  HIGH                                                                             \n            1                              20 (14.9%)   22 (16.4%)     22 (16.7%)  \n            2                              21 (15.7%)   15 (11.2%)     14 (10.6%)  \n            3                              25 (18.7%)   16 (11.9%)     20 (15.2%)  \n            4                              13 (9.7%)    20 (14.9%)     11 (8.3%)   \n            Any                             79 (59%)    73 (54.5%)     67 (50.8%)  \nImmunoglobulin A Measurement (n)              134          134            132      \n  HIGH                                                                             \n            1                              19 (14.2%)    12 (9%)       18 (13.6%)  \n            2                              17 (12.7%)   24 (17.9%)     19 (14.4%)  \n            3                              21 (15.7%)   23 (17.2%)     23 (17.4%)  \n            4                              13 (9.7%)    13 (9.7%)       9 (6.8%)   \n            Any                            70 (52.2%)   72 (53.7%)     69 (52.3%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n    filter(!AVISIT %in% c(\"SCREENING\", \"BASELINE\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality_by_worst_grade(\n      label = \"Laboratory Test Results with Highest Grade Post-Baseline\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = c(\"ALT\", \"CRP\", \"IGA\")\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = (\n    teal_slices(\n      teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n      teal_slice(\"ADLB\", \"ONTRTFL\", selected = \"Y\")\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:49:33 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      forcats_1.0.0                   \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015            scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -492,7 +492,7 @@
     "href": "tables/medical-history/mht01.html",
     "title": "MHT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable of History  Prior to Study\nTable with Total Number  of Conditions Suppressed\nTable with Total Number of Conditions Per Body  System After The Summary of Patients\nTable Showing Additional  “All Patients” Column\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadmh &lt;- synthetic_cdisc_dataset(\"latest\", \"admh\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadmh &lt;- df_explicit_na(admh)\n\nadsl_f &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\") %&gt;%\n  select(USUBJID, ACTARM)\n\nadmh_f &lt;- admh %&gt;%\n  filter(SAFFL == \"Y\" & MHBODSYS != \"\" & MHDECOD != \"\") %&gt;%\n  var_relabel(\n    MHBODSYS = \"MedDRA System Organ Class\",\n    MHDECOD = \"MedDRA Preferred Term\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\"Total number of patients with at least one condition\", \"Total number of conditions\")\n  ) %&gt;%\n  split_rows_by(\n    var = \"MHBODSYS\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(admh_f$MHBODSYS)\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\"Total number of patients with at least one condition\", \"Total number of conditions\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"MHDECOD\") %&gt;%\n  append_varlabels(admh_f, \"MHDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, admh_f, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nMedDRA System Organ Class                                 A: Drug X    B: Placebo    C: Combination\n  MedDRA Preferred Term                                    (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one condition     122 (91.0%)   123 (91.8%)    120 (90.9%)  \nTotal number of conditions                                   609           622            703      \ncl A                                                                                               \n  Total number of patients with at least one condition   78 (58.2%)    75 (56.0%)      89 (67.4%)  \n  Total number of conditions                                 132           130            160      \n  trm A_1/2                                              50 (37.3%)    45 (33.6%)      63 (47.7%)  \n  trm A_2/2                                              48 (35.8%)    48 (35.8%)      50 (37.9%)  \ncl B                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    89 (66.4%)      97 (73.5%)  \n  Total number of conditions                                 185           198            205      \n  trm B_1/3                                              47 (35.1%)    49 (36.6%)      43 (32.6%)  \n  trm B_2/3                                              49 (36.6%)    44 (32.8%)      52 (39.4%)  \n  trm B_3/3                                              48 (35.8%)    54 (40.3%)      51 (38.6%)  \ncl C                                                                                               \n  Total number of patients with at least one condition   67 (50.0%)    75 (56.0%)      79 (59.8%)  \n  Total number of conditions                                 103           116            129      \n  trm C_1/2                                              43 (32.1%)    46 (34.3%)      43 (32.6%)  \n  trm C_2/2                                              35 (26.1%)    48 (35.8%)      55 (41.7%)  \ncl D                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    90 (67.2%)      98 (74.2%)  \n  Total number of conditions                                 189           178            209      \n  trm D_1/3                                              50 (37.3%)    42 (31.3%)      51 (38.6%)  \n  trm D_2/3                                              48 (35.8%)    42 (31.3%)      50 (37.9%)  \n  trm D_3/3                                              47 (35.1%)    58 (43.3%)      57 (43.2%)  \n\n\n\n\n\nCodeadmh_f_prior &lt;- admh_f %&gt;%\n  filter(ASTDY &lt;= 0)\n\nresult &lt;- build_table(lyt, admh_f_prior, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nMedDRA System Organ Class                                A: Drug X   B: Placebo   C: Combination\n  MedDRA Preferred Term                                   (N=134)     (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one condition         0           0           1 (0.8%)   \nTotal number of conditions                                   0           0              1       \ncl D                                                                                            \n  Total number of patients with at least one condition       0           0           1 (0.8%)   \n  Total number of conditions                                 0           0              1       \n  trm D_2/3                                                  0           0           1 (0.8%)   \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\"Total number of patients with at least one condition\")\n  ) %&gt;%\n  split_rows_by(\n    var = \"MHBODSYS\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(admh_f$MHBODSYS)\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\"Total number of patients with at least one condition\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"MHDECOD\") %&gt;%\n  append_varlabels(admh_f, \"MHDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, admh_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\nMedDRA System Organ Class                                 A: Drug X    B: Placebo    C: Combination\n  MedDRA Preferred Term                                    (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one condition     122 (91.0%)   123 (91.8%)    120 (90.9%)  \ncl A                                                                                               \n  Total number of patients with at least one condition   78 (58.2%)    75 (56.0%)      89 (67.4%)  \n  trm A_1/2                                              50 (37.3%)    45 (33.6%)      63 (47.7%)  \n  trm A_2/2                                              48 (35.8%)    48 (35.8%)      50 (37.9%)  \ncl B                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    89 (66.4%)      97 (73.5%)  \n  trm B_1/3                                              47 (35.1%)    49 (36.6%)      43 (32.6%)  \n  trm B_2/3                                              49 (36.6%)    44 (32.8%)      52 (39.4%)  \n  trm B_3/3                                              48 (35.8%)    54 (40.3%)      51 (38.6%)  \ncl C                                                                                               \n  Total number of patients with at least one condition   67 (50.0%)    75 (56.0%)      79 (59.8%)  \n  trm C_1/2                                              43 (32.1%)    46 (34.3%)      43 (32.6%)  \n  trm C_2/2                                              35 (26.1%)    48 (35.8%)      55 (41.7%)  \ncl D                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    90 (67.2%)      98 (74.2%)  \n  trm D_1/3                                              50 (37.3%)    42 (31.3%)      51 (38.6%)  \n  trm D_2/3                                              48 (35.8%)    42 (31.3%)      50 (37.9%)  \n  trm D_3/3                                              47 (35.1%)    58 (43.3%)      57 (43.2%)  \n\n\n\n\nNot currently supported by rtables. Users, please notify us if this variation is important to you.\n\n\nConditions are further sorted by decreasing high level terms and low level terms.\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_num_patients(\n    \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(unique = \"Total number of patients with at least one event\", nonunique = \"Total number of conditions\")\n  ) %&gt;%\n  split_rows_by(\n    var = \"MHBODSYS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    label_pos = \"topleft\",\n    split_label = obj_label(admh_f$MHBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(unique = \"Total number of patients with at least one event\", nonunique = \"Total number of conditions\")\n  ) %&gt;%\n  count_occurrences(vars = \"MHDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(admh_f, \"MHDECOD\", indent = 1L)\n\nscorefun_hlt &lt;- cont_n_allcols\nscorefun_llt &lt;- score_occurrences_cols(col_indices = nlevels(adsl_f$ACTARM) + 1)\n\nresult &lt;- build_table(lyt, admh_f, alt_counts_df = adsl_f) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(path = c(\"MHBODSYS\"), scorefun = scorefun_hlt) %&gt;%\n  sort_at_path(path = c(\"MHBODSYS\", \"*\", \"MHDECOD\"), scorefun = scorefun_llt)\n\nresult\n\nMedDRA System Organ Class                             A: Drug X    B: Placebo    C: Combination   All Patients\n  MedDRA Preferred Term                                (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one event     122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%) \nTotal number of conditions                               609           622            703             1934    \ncl D                                                                                                          \n  Total number of patients with at least one event   96 (71.6%)    90 (67.2%)      98 (74.2%)     284 (71.0%) \n  Total number of conditions                             189           178            209             576     \n  trm D_3/3                                          47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%) \n  trm D_1/3                                          50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%) \n  trm D_2/3                                          48 (35.8%)    42 (31.3%)      50 (37.9%)     140 (35.0%) \ncl B                                                                                                          \n  Total number of patients with at least one event   96 (71.6%)    89 (66.4%)      97 (73.5%)     282 (70.5%) \n  Total number of conditions                             185           198            205             588     \n  trm B_3/3                                          48 (35.8%)    54 (40.3%)      51 (38.6%)     153 (38.2%) \n  trm B_2/3                                          49 (36.6%)    44 (32.8%)      52 (39.4%)     145 (36.2%) \n  trm B_1/3                                          47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%) \ncl A                                                                                                          \n  Total number of patients with at least one event   78 (58.2%)    75 (56.0%)      89 (67.4%)     242 (60.5%) \n  Total number of conditions                             132           130            160             422     \n  trm A_1/2                                          50 (37.3%)    45 (33.6%)      63 (47.7%)     158 (39.5%) \n  trm A_2/2                                          48 (35.8%)    48 (35.8%)      50 (37.9%)     146 (36.5%) \ncl C                                                                                                          \n  Total number of patients with at least one event   67 (50.0%)    75 (56.0%)      79 (59.8%)     221 (55.2%) \n  Total number of conditions                             103           116            129             348     \n  trm C_2/2                                          35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%) \n  trm C_1/2                                          43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n    filter(SAFFL == \"Y\")\n  ADMH &lt;- synthetic_cdisc_dataset(\"latest\", \"admh\") %&gt;%\n    filter(SAFFL == \"Y\" & MHBODSYS != \"\" & MHDECOD != \"\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADMH\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADMH &lt;- data[[\"ADMH\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Medical History Table\",\n      dataname = \"ADMH\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADMH, c(\"MHTERM\", \"MHDECOD\")),\n        selected = c(\"MHDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADMH, c(\"MHBODSYS\", \"MHSOC\")),\n        selected = \"MHBODSYS\"\n      ),\n      add_total = TRUE,\n      event_type = \"condition\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:50:18 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable of History  Prior to Study\nTable with Total Number  of Conditions Suppressed\nTable with Total Number of Conditions Per Body  System After The Summary of Patients\nTable Showing Additional  “All Patients” Column\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadmh &lt;- synthetic_cdisc_dataset(\"latest\", \"admh\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadmh &lt;- df_explicit_na(admh)\n\nadsl_f &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\") %&gt;%\n  select(USUBJID, ACTARM)\n\nadmh_f &lt;- admh %&gt;%\n  filter(SAFFL == \"Y\" & MHBODSYS != \"\" & MHDECOD != \"\") %&gt;%\n  var_relabel(\n    MHBODSYS = \"MedDRA System Organ Class\",\n    MHDECOD = \"MedDRA Preferred Term\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\"Total number of patients with at least one condition\", \"Total number of conditions\")\n  ) %&gt;%\n  split_rows_by(\n    var = \"MHBODSYS\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(admh_f$MHBODSYS)\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\"Total number of patients with at least one condition\", \"Total number of conditions\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"MHDECOD\") %&gt;%\n  append_varlabels(admh_f, \"MHDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, admh_f, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nMedDRA System Organ Class                                 A: Drug X    B: Placebo    C: Combination\n  MedDRA Preferred Term                                    (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one condition     122 (91.0%)   123 (91.8%)    120 (90.9%)  \nTotal number of conditions                                   609           622            703      \ncl A                                                                                               \n  Total number of patients with at least one condition   78 (58.2%)    75 (56.0%)      89 (67.4%)  \n  Total number of conditions                                 132           130            160      \n  trm A_1/2                                              50 (37.3%)    45 (33.6%)      63 (47.7%)  \n  trm A_2/2                                              48 (35.8%)    48 (35.8%)      50 (37.9%)  \ncl B                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    89 (66.4%)      97 (73.5%)  \n  Total number of conditions                                 185           198            205      \n  trm B_1/3                                              47 (35.1%)    49 (36.6%)      43 (32.6%)  \n  trm B_2/3                                              49 (36.6%)    44 (32.8%)      52 (39.4%)  \n  trm B_3/3                                              48 (35.8%)    54 (40.3%)      51 (38.6%)  \ncl C                                                                                               \n  Total number of patients with at least one condition   67 (50.0%)    75 (56.0%)      79 (59.8%)  \n  Total number of conditions                                 103           116            129      \n  trm C_1/2                                              43 (32.1%)    46 (34.3%)      43 (32.6%)  \n  trm C_2/2                                              35 (26.1%)    48 (35.8%)      55 (41.7%)  \ncl D                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    90 (67.2%)      98 (74.2%)  \n  Total number of conditions                                 189           178            209      \n  trm D_1/3                                              50 (37.3%)    42 (31.3%)      51 (38.6%)  \n  trm D_2/3                                              48 (35.8%)    42 (31.3%)      50 (37.9%)  \n  trm D_3/3                                              47 (35.1%)    58 (43.3%)      57 (43.2%)  \n\n\n\n\n\nCodeadmh_f_prior &lt;- admh_f %&gt;%\n  filter(ASTDY &lt;= 0)\n\nresult &lt;- build_table(lyt, admh_f_prior, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nMedDRA System Organ Class                                A: Drug X   B: Placebo   C: Combination\n  MedDRA Preferred Term                                   (N=134)     (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one condition         0           0           1 (0.8%)   \nTotal number of conditions                                   0           0              1       \ncl D                                                                                            \n  Total number of patients with at least one condition       0           0           1 (0.8%)   \n  Total number of conditions                                 0           0              1       \n  trm D_2/3                                                  0           0           1 (0.8%)   \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\"Total number of patients with at least one condition\")\n  ) %&gt;%\n  split_rows_by(\n    var = \"MHBODSYS\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(admh_f$MHBODSYS)\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\"Total number of patients with at least one condition\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"MHDECOD\") %&gt;%\n  append_varlabels(admh_f, \"MHDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, admh_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\nMedDRA System Organ Class                                 A: Drug X    B: Placebo    C: Combination\n  MedDRA Preferred Term                                    (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one condition     122 (91.0%)   123 (91.8%)    120 (90.9%)  \ncl A                                                                                               \n  Total number of patients with at least one condition   78 (58.2%)    75 (56.0%)      89 (67.4%)  \n  trm A_1/2                                              50 (37.3%)    45 (33.6%)      63 (47.7%)  \n  trm A_2/2                                              48 (35.8%)    48 (35.8%)      50 (37.9%)  \ncl B                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    89 (66.4%)      97 (73.5%)  \n  trm B_1/3                                              47 (35.1%)    49 (36.6%)      43 (32.6%)  \n  trm B_2/3                                              49 (36.6%)    44 (32.8%)      52 (39.4%)  \n  trm B_3/3                                              48 (35.8%)    54 (40.3%)      51 (38.6%)  \ncl C                                                                                               \n  Total number of patients with at least one condition   67 (50.0%)    75 (56.0%)      79 (59.8%)  \n  trm C_1/2                                              43 (32.1%)    46 (34.3%)      43 (32.6%)  \n  trm C_2/2                                              35 (26.1%)    48 (35.8%)      55 (41.7%)  \ncl D                                                                                               \n  Total number of patients with at least one condition   96 (71.6%)    90 (67.2%)      98 (74.2%)  \n  trm D_1/3                                              50 (37.3%)    42 (31.3%)      51 (38.6%)  \n  trm D_2/3                                              48 (35.8%)    42 (31.3%)      50 (37.9%)  \n  trm D_3/3                                              47 (35.1%)    58 (43.3%)      57 (43.2%)  \n\n\n\n\nNot currently supported by rtables. Users, please notify us if this variation is important to you.\n\n\nConditions are further sorted by decreasing high level terms and low level terms.\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_num_patients(\n    \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(unique = \"Total number of patients with at least one event\", nonunique = \"Total number of conditions\")\n  ) %&gt;%\n  split_rows_by(\n    var = \"MHBODSYS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    label_pos = \"topleft\",\n    split_label = obj_label(admh_f$MHBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(unique = \"Total number of patients with at least one event\", nonunique = \"Total number of conditions\")\n  ) %&gt;%\n  count_occurrences(vars = \"MHDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(admh_f, \"MHDECOD\", indent = 1L)\n\nscorefun_hlt &lt;- cont_n_allcols\nscorefun_llt &lt;- score_occurrences_cols(col_indices = nlevels(adsl_f$ACTARM) + 1)\n\nresult &lt;- build_table(lyt, admh_f, alt_counts_df = adsl_f) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(path = c(\"MHBODSYS\"), scorefun = scorefun_hlt) %&gt;%\n  sort_at_path(path = c(\"MHBODSYS\", \"*\", \"MHDECOD\"), scorefun = scorefun_llt)\n\nresult\n\nMedDRA System Organ Class                             A: Drug X    B: Placebo    C: Combination   All Patients\n  MedDRA Preferred Term                                (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one event     122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%) \nTotal number of conditions                               609           622            703             1934    \ncl D                                                                                                          \n  Total number of patients with at least one event   96 (71.6%)    90 (67.2%)      98 (74.2%)     284 (71.0%) \n  Total number of conditions                             189           178            209             576     \n  trm D_3/3                                          47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%) \n  trm D_1/3                                          50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%) \n  trm D_2/3                                          48 (35.8%)    42 (31.3%)      50 (37.9%)     140 (35.0%) \ncl B                                                                                                          \n  Total number of patients with at least one event   96 (71.6%)    89 (66.4%)      97 (73.5%)     282 (70.5%) \n  Total number of conditions                             185           198            205             588     \n  trm B_3/3                                          48 (35.8%)    54 (40.3%)      51 (38.6%)     153 (38.2%) \n  trm B_2/3                                          49 (36.6%)    44 (32.8%)      52 (39.4%)     145 (36.2%) \n  trm B_1/3                                          47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%) \ncl A                                                                                                          \n  Total number of patients with at least one event   78 (58.2%)    75 (56.0%)      89 (67.4%)     242 (60.5%) \n  Total number of conditions                             132           130            160             422     \n  trm A_1/2                                          50 (37.3%)    45 (33.6%)      63 (47.7%)     158 (39.5%) \n  trm A_2/2                                          48 (35.8%)    48 (35.8%)      50 (37.9%)     146 (36.5%) \ncl C                                                                                                          \n  Total number of patients with at least one event   67 (50.0%)    75 (56.0%)      79 (59.8%)     221 (55.2%) \n  Total number of conditions                             103           116            129             348     \n  trm C_2/2                                          35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%) \n  trm C_1/2                                          43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n    filter(SAFFL == \"Y\")\n  ADMH &lt;- synthetic_cdisc_dataset(\"latest\", \"admh\") %&gt;%\n    filter(SAFFL == \"Y\" & MHBODSYS != \"\" & MHDECOD != \"\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADMH\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADMH &lt;- data[[\"ADMH\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Medical History Table\",\n      dataname = \"ADMH\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADMH, c(\"MHTERM\", \"MHDECOD\")),\n        selected = c(\"MHDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADMH, c(\"MHBODSYS\", \"MHSOC\")),\n        selected = \"MHBODSYS\"\n      ),\n      add_total = TRUE,\n      event_type = \"condition\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:48:51 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Medical History",
@@ -504,7 +504,7 @@
     "href": "tables/vital-signs/vst02.html",
     "title": "VST02",
     "section": "",
-    "text": "Data Setup\nVST02_1 – Table of Abnormalities  Regardless of Abnormality at Baseline\nVST02_2 – Table of Abnormalities Among  Subjects Without Abnormality at Baseline\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\n# Ensure character variables are converted to factors and empty strings\n# and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadvs &lt;- df_explicit_na(advs)\n\n# Note:  We keep only post-baseline for analysis.\nadvs_f &lt;- advs %&gt;%\n  filter(ONTRTFL == \"Y\", ANRIND != \"&lt;Missing&gt;\") %&gt;%\n  var_relabel(\n    PARAM = \"Assessment\",\n    ANRIND = \"Abnormality\"\n  )\n\nsplit_fun &lt;- drop_split_levels\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(advs_f$PARAM)) %&gt;%\n  count_abnormal(\n    \"ANRIND\",\n    abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n    exclude_base_abn = FALSE\n  ) %&gt;%\n  append_varlabels(advs_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = advs_f, alt_counts_df = adsl)\n\nresult\n\nAssessment                    A: Drug X        B: Placebo      C: Combination     All Patients  \n  Abnormality                  (N=134)           (N=134)           (N=132)           (N=400)    \n————————————————————————————————————————————————————————————————————————————————————————————————\nDiastolic Blood Pressure                                                                        \n  Low                      66/134 (49.3%)    86/134 (64.2%)    78/132 (59.1%)    230/400 (57.5%)\n  High                     88/134 (65.7%)    70/134 (52.2%)    76/132 (57.6%)    234/400 (58.5%)\nPulse Rate                                                                                      \n  Low                      58/134 (43.3%)    32/134 (23.9%)    48/132 (36.4%)    138/400 (34.5%)\n  High                     53/134 (39.6%)    49/134 (36.6%)    44/132 (33.3%)    146/400 (36.5%)\nRespiratory Rate                                                                                \n  Low                      97/134 (72.4%)    84/134 (62.7%)    98/132 (74.2%)    279/400 (69.8%)\n  High                     96/134 (71.6%)    98/134 (73.1%)    98/132 (74.2%)     292/400 (73%) \nSystolic Blood Pressure                                                                         \n  Low                       71/134 (53%)     77/134 (57.5%)    73/132 (55.3%)    221/400 (55.2%)\n  High                     78/134 (58.2%)    74/134 (55.2%)    73/132 (55.3%)    225/400 (56.2%)\nTemperature                                                                                     \n  Low                      108/134 (80.6%)   110/134 (82.1%)   108/132 (81.8%)   326/400 (81.5%)\n  High                     112/134 (83.6%)   106/134 (79.1%)   107/132 (81.1%)   325/400 (81.2%)\nWeight                                                                                          \n  Low                      43/134 (32.1%)    39/134 (29.1%)    39/132 (29.5%)    121/400 (30.2%)\n  High                     46/134 (34.3%)    43/134 (32.1%)    44/132 (33.3%)    133/400 (33.2%)\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(advs_f$PARAM)) %&gt;%\n  count_abnormal(\n    \"ANRIND\",\n    abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n    exclude_base_abn = TRUE\n  ) %&gt;%\n  append_varlabels(advs_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = advs_f, alt_counts_df = adsl)\n\nresult\n\nAssessment                   A: Drug X        B: Placebo     C: Combination    All Patients  \n  Abnormality                 (N=134)          (N=134)          (N=132)           (N=400)    \n—————————————————————————————————————————————————————————————————————————————————————————————\nDiastolic Blood Pressure                                                                     \n  Low                       54/108 (50%)    76/116 (65.5%)   68/114 (59.6%)   198/338 (58.6%)\n  High                     74/114 (64.9%)   56/109 (51.4%)   62/106 (58.5%)   192/329 (58.4%)\nPulse Rate                                                                                   \n  Low                      53/121 (43.8%)    30/125 (24%)    42/119 (35.3%)   125/365 (34.2%)\n  High                      48/123 (39%)    45/116 (38.8%)   39/120 (32.5%)   132/359 (36.8%)\nRespiratory Rate                                                                             \n  Low                       72/100 (72%)    72/110 (65.5%)   79/102 (77.5%)   223/312 (71.5%)\n  High                      77/107 (72%)    78/108 (72.2%)   79/109 (72.5%)   234/324 (72.2%)\nSystolic Blood Pressure                                                                      \n  Low                       62/117 (53%)    63/114 (55.3%)   57/103 (55.3%)   182/334 (54.5%)\n  High                     64/110 (58.2%)   67/120 (55.8%)   68/115 (59.1%)   199/345 (57.7%)\nTemperature                                                                                  \n  Low                      77/96 (80.2%)     78/94 (83%)     74/90 (82.2%)    229/280 (81.8%)\n  High                     78/93 (83.9%)    73/92 (79.3%)     81/100 (81%)    232/285 (81.4%)\nWeight                                                                                       \n  Low                      42/129 (32.6%)   37/125 (29.6%)   37/123 (30.1%)   116/377 (30.8%)\n  High                     41/126 (32.5%)   41/124 (33.1%)   42/121 (34.7%)   124/371 (33.4%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADVS &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\") %&gt;%\n    mutate(ONTRTFL = ifelse(AVISIT %in% c(\"SCREENING\", \"BASELINE\"), \"\", \"Y\")) %&gt;%\n    col_relabel(ONTRTFL = \"On Treatment Record Flag\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADVS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADVS &lt;- data[[\"ADVS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADVS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADVS, subset = c(\"VSCAT\", \"PARAM\", \"AVISIT\")),\n        selected = c(\"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADVS, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n      exclude_base_abn = FALSE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:49:32 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nVST02_1 – Table of Abnormalities  Regardless of Abnormality at Baseline\nVST02_2 – Table of Abnormalities Among  Subjects Without Abnormality at Baseline\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\n# Ensure character variables are converted to factors and empty strings\n# and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadvs &lt;- df_explicit_na(advs)\n\n# Note:  We keep only post-baseline for analysis.\nadvs_f &lt;- advs %&gt;%\n  filter(ONTRTFL == \"Y\", ANRIND != \"&lt;Missing&gt;\") %&gt;%\n  var_relabel(\n    PARAM = \"Assessment\",\n    ANRIND = \"Abnormality\"\n  )\n\nsplit_fun &lt;- drop_split_levels\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(advs_f$PARAM)) %&gt;%\n  count_abnormal(\n    \"ANRIND\",\n    abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n    exclude_base_abn = FALSE\n  ) %&gt;%\n  append_varlabels(advs_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = advs_f, alt_counts_df = adsl)\n\nresult\n\nAssessment                    A: Drug X        B: Placebo      C: Combination     All Patients  \n  Abnormality                  (N=134)           (N=134)           (N=132)           (N=400)    \n————————————————————————————————————————————————————————————————————————————————————————————————\nDiastolic Blood Pressure                                                                        \n  Low                      66/134 (49.3%)    86/134 (64.2%)    78/132 (59.1%)    230/400 (57.5%)\n  High                     88/134 (65.7%)    70/134 (52.2%)    76/132 (57.6%)    234/400 (58.5%)\nPulse Rate                                                                                      \n  Low                      58/134 (43.3%)    32/134 (23.9%)    48/132 (36.4%)    138/400 (34.5%)\n  High                     53/134 (39.6%)    49/134 (36.6%)    44/132 (33.3%)    146/400 (36.5%)\nRespiratory Rate                                                                                \n  Low                      97/134 (72.4%)    84/134 (62.7%)    98/132 (74.2%)    279/400 (69.8%)\n  High                     96/134 (71.6%)    98/134 (73.1%)    98/132 (74.2%)     292/400 (73%) \nSystolic Blood Pressure                                                                         \n  Low                       71/134 (53%)     77/134 (57.5%)    73/132 (55.3%)    221/400 (55.2%)\n  High                     78/134 (58.2%)    74/134 (55.2%)    73/132 (55.3%)    225/400 (56.2%)\nTemperature                                                                                     \n  Low                      108/134 (80.6%)   110/134 (82.1%)   108/132 (81.8%)   326/400 (81.5%)\n  High                     112/134 (83.6%)   106/134 (79.1%)   107/132 (81.1%)   325/400 (81.2%)\nWeight                                                                                          \n  Low                      43/134 (32.1%)    39/134 (29.1%)    39/132 (29.5%)    121/400 (30.2%)\n  High                     46/134 (34.3%)    43/134 (32.1%)    44/132 (33.3%)    133/400 (33.2%)\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(advs_f$PARAM)) %&gt;%\n  count_abnormal(\n    \"ANRIND\",\n    abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n    exclude_base_abn = TRUE\n  ) %&gt;%\n  append_varlabels(advs_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = advs_f, alt_counts_df = adsl)\n\nresult\n\nAssessment                   A: Drug X        B: Placebo     C: Combination    All Patients  \n  Abnormality                 (N=134)          (N=134)          (N=132)           (N=400)    \n—————————————————————————————————————————————————————————————————————————————————————————————\nDiastolic Blood Pressure                                                                     \n  Low                       54/108 (50%)    76/116 (65.5%)   68/114 (59.6%)   198/338 (58.6%)\n  High                     74/114 (64.9%)   56/109 (51.4%)   62/106 (58.5%)   192/329 (58.4%)\nPulse Rate                                                                                   \n  Low                      53/121 (43.8%)    30/125 (24%)    42/119 (35.3%)   125/365 (34.2%)\n  High                      48/123 (39%)    45/116 (38.8%)   39/120 (32.5%)   132/359 (36.8%)\nRespiratory Rate                                                                             \n  Low                       72/100 (72%)    72/110 (65.5%)   79/102 (77.5%)   223/312 (71.5%)\n  High                      77/107 (72%)    78/108 (72.2%)   79/109 (72.5%)   234/324 (72.2%)\nSystolic Blood Pressure                                                                      \n  Low                       62/117 (53%)    63/114 (55.3%)   57/103 (55.3%)   182/334 (54.5%)\n  High                     64/110 (58.2%)   67/120 (55.8%)   68/115 (59.1%)   199/345 (57.7%)\nTemperature                                                                                  \n  Low                      77/96 (80.2%)     78/94 (83%)     74/90 (82.2%)    229/280 (81.8%)\n  High                     78/93 (83.9%)    73/92 (79.3%)     81/100 (81%)    232/285 (81.4%)\nWeight                                                                                       \n  Low                      42/129 (32.6%)   37/125 (29.6%)   37/123 (30.1%)   116/377 (30.8%)\n  High                     41/126 (32.5%)   41/124 (33.1%)   42/121 (34.7%)   124/371 (33.4%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADVS &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\") %&gt;%\n    mutate(ONTRTFL = ifelse(AVISIT %in% c(\"SCREENING\", \"BASELINE\"), \"\", \"Y\")) %&gt;%\n    col_relabel(ONTRTFL = \"On Treatment Record Flag\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADVS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADVS &lt;- data[[\"ADVS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADVS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADVS, subset = c(\"VSCAT\", \"PARAM\", \"AVISIT\")),\n        selected = c(\"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADVS, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")),\n      exclude_base_abn = FALSE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:48:06 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Vital Signs",
@@ -516,7 +516,7 @@
     "href": "tables/adverse-events/aet01_aesi.html",
     "title": "AET01_AESI",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with  Optional Lines\nTable For Studies  with Multiple Drugs\nTable of AEs  by SMQ\nteal App\nReproducibility\n\n\n\nTo illustrate, additional variables such as flags (TRUE/FALSE) for selected AEs of interest. Please consult your SAP on how to handle missing AE grades.\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl &lt;- filter(adsl, SAFFL == \"Y\")\nadae &lt;- filter(adae, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nnot_resolved &lt;- adae %&gt;%\n  filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n  distinct(USUBJID) %&gt;%\n  mutate(NOT_RESOLVED = \"Y\")\n\nadae &lt;- adae %&gt;%\n  left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    ALL_RESOLVED = with_label(\n      is.na(NOT_RESOLVED),\n      \"Total number of patients with all non-fatal AEs resolved\"\n    ),\n    NOT_RESOLVED = with_label(\n      !is.na(NOT_RESOLVED),\n      \"Total number of patients with at least one unresolved or ongoing non-fatal AE\"\n    )\n  )\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AEDECOD = as.character(AEDECOD),\n    WD = with_label(\n      AEACN == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug withdrawn due to AE\"\n    ),\n    DSM = with_label(\n      AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose modified/interrupted due to AE\"\n    ),\n    CONTRT = with_label(\n      AECONTRT == \"Y\",\n      \"Total number of patients with treatment received for AE\"\n    ),\n    SER = with_label(\n      AESER == \"Y\",\n      \"Total number of patients with at least one serious AE\"\n    ),\n    REL = with_label(\n      AEREL == \"Y\",\n      \"Total number of patients with at least one related AE\"\n    ),\n    ALL_RESOLVED_WD = with_label(\n      WD == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM = with_label(\n      DSM == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to resolved AE\"\n    ),\n    ALL_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with treatment received for resolved AE\"\n    ),\n    NOT_RESOLVED_WD = with_label(\n      WD == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM = with_label(\n      DSM == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with treatment received for unresolved or ongoing AE\"\n    ),\n    SERWD = with_label(\n      AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to serious AE\"\n    ),\n    SERCONTRT = with_label(\n      AECONTRT == \"Y\" & AESER == \"Y\",\n      \"No. of patients with dose modified/interrupted due to serious AE\"\n    ),\n    SERDSM = with_label(\n      AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with treatment received for serious AE\"\n    ),\n    RELWD = with_label(\n      AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to related AE\"\n    ),\n    RELDSM = with_label(\n      AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose modified/interrupted due to related AE\"\n    ),\n    RELCONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with treatment received for related AE\"\n    ),\n    RELSER = with_label(\n      AESER == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with serious, related AE\"\n    )\n  )\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AETOXGR = forcats::fct_recode(AETOXGR,\n      \"Grade 1\" = \"1\",\n      \"Grade 2\" = \"2\",\n      \"Grade 3\" = \"3\",\n      \"Grade 4\" = \"4\",\n      \"Grade 5 (fatal outcome)\" = \"5\"\n    )\n  )\n\n\n\n\n\nCodeaesi_vars &lt;- c(\"WD\", \"DSM\", \"CONTRT\", \"ALL_RESOLVED\", \"NOT_RESOLVED\", \"SER\", \"REL\")\n\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\"USUBJID\", flag_variables = aesi_vars, denom = \"N_col\")\n\nresult &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\nresult\n\n                                                                                 A: Drug X    B: Placebo   C: Combination\n                                                                                  (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                                   100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                                                 502          480            604      \nTotal number of patients with at least one AE by worst grade                                                             \n  Grade 1                                                                        5 (3.7%)      7 (5.2%)       4 (3.0%)   \n  Grade 2                                                                        5 (3.7%)      8 (6.0%)       6 (4.5%)   \n  Grade 3                                                                        13 (9.7%)    13 (9.7%)      14 (10.6%)  \n  Grade 4                                                                        13 (9.7%)    18 (13.4%)     15 (11.4%)  \n  Grade 5 (fatal outcome)                                                       64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with study drug withdrawn due to AE                    22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with dose modified/interrupted due to AE               55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with treatment received for AE                         80 (59.7%)    80 (59.7%)     89 (67.4%)  \nTotal number of patients with all non-fatal AEs resolved                        17 (12.7%)    12 (9.0%)      11 (8.3%)   \nTotal number of patients with at least one unresolved or ongoing non-fatal AE   83 (61.9%)    86 (64.2%)     92 (69.7%)  \nTotal number of patients with at least one serious AE                           85 (63.4%)    80 (59.7%)     87 (65.9%)  \nTotal number of patients with at least one related AE                           86 (64.2%)    85 (63.4%)     92 (69.7%)  \n\n\n\n\n\nCodeaesi_vars &lt;- c(\"WD\", \"DSM\", \"CONTRT\")\naesi_resolved &lt;- c(\"ALL_RESOLVED\", \"ALL_RESOLVED_WD\", \"ALL_RESOLVED_DSM\", \"ALL_RESOLVED_CONTRT\")\naesi_not_resolved &lt;- c(\"NOT_RESOLVED\", \"NOT_RESOLVED_WD\", \"NOT_RESOLVED_DSM\", \"NOT_RESOLVED_CONTRT\")\naesi_ser &lt;- c(\"SER\", \"SERWD\", \"SERDSM\", \"SERCONTRT\")\naesi_rel &lt;- c(\"REL\", \"RELWD\", \"RELDSM\", \"RELCONTRT\", \"RELSER\")\n\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(aesi_vars, aesi_resolved[1]), denom = \"N_col\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_resolved[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_res\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_resolved[1], denom = \"N_col\", table_names = \"fl_notres_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_resolved[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_notres\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[1], denom = \"N_col\", table_names = \"fl_ser_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_ser\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel[1], denom = \"N_col\", table_names = \"fl_rel_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_rel\"\n  )\n\nresult &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\nresult\n\n                                                                                    A: Drug X    B: Placebo   C: Combination\n                                                                                     (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                                      100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                                                    502          480            604      \nTotal number of patients with at least one AE by worst grade                                                                \n  Grade 1                                                                           5 (3.7%)      7 (5.2%)       4 (3.0%)   \n  Grade 2                                                                           5 (3.7%)      8 (6.0%)       6 (4.5%)   \n  Grade 3                                                                           13 (9.7%)    13 (9.7%)      14 (10.6%)  \n  Grade 4                                                                           13 (9.7%)    18 (13.4%)     15 (11.4%)  \n  Grade 5 (fatal outcome)                                                          64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with study drug withdrawn due to AE                       22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with dose modified/interrupted due to AE                  55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with treatment received for AE                            80 (59.7%)    80 (59.7%)     89 (67.4%)  \nTotal number of patients with all non-fatal AEs resolved                           17 (12.7%)    12 (9.0%)      11 (8.3%)   \n  No. of patients with study drug withdrawn due to resolved AE                          0         1 (0.7%)       2 (1.5%)   \n  No. of patients with dose modified/interrupted due to resolved AE                 3 (2.2%)      4 (3.0%)       2 (1.5%)   \n  No. of patients with treatment received for resolved AE                           9 (6.7%)      7 (5.2%)       7 (5.3%)   \nTotal number of patients with at least one unresolved or ongoing non-fatal AE      83 (61.9%)    86 (64.2%)     92 (69.7%)  \n  No. of patients with study drug withdrawn due to unresolved or ongoing AE        22 (16.4%)    20 (14.9%)     26 (19.7%)  \n  No. of patients with dose modified/interrupted due to unresolved or ongoing AE   52 (38.8%)    58 (43.3%)     62 (47.0%)  \n  No. of patients with treatment received for unresolved or ongoing AE             71 (53.0%)    73 (54.5%)     82 (62.1%)  \nTotal number of patients with at least one serious AE                              85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  No. of patients with study drug withdrawn due to serious AE                       9 (6.7%)      4 (3.0%)      10 (7.6%)   \n  No. of patients with treatment received for serious AE                           21 (15.7%)    22 (16.4%)     28 (21.2%)  \n  No. of patients with dose modified/interrupted due to serious AE                 59 (44.0%)    58 (43.3%)     53 (40.2%)  \nTotal number of patients with at least one related AE                              86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  No. of patients with study drug withdrawn due to related AE                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with dose modified/interrupted due to related AE                 24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with treatment received for related AE                           53 (39.6%)    58 (43.3%)     60 (45.5%)  \n  No. of patients with serious, related AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  \n\n\n\n\n\nCodeadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae_mult &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl &lt;- filter(adsl, SAFFL == \"Y\")\nadae_mult &lt;- filter(adae_mult, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\nadsl &lt;- df_explicit_na(adsl)\nadae_mult &lt;- df_explicit_na(adae_mult)\n\n# for illustration purposes only, create AEREL1, AEREL2, AEACN1, AEACN2 from respective variables\nadae_mult &lt;- adae_mult %&gt;%\n  mutate(\n    AEREL1 = AEREL,\n    AEREL2 = AEREL,\n    AEACN1 = AEACN,\n    AEACN2 = AEACN\n  )\n\nnot_resolved &lt;- adae_mult %&gt;%\n  filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n  distinct(USUBJID) %&gt;%\n  mutate(NOT_RESOLVED = \"Y\")\n\nadae_mult &lt;- adae_mult %&gt;%\n  left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    ALL_RESOLVED = with_label(\n      is.na(NOT_RESOLVED),\n      \"Total number of patients with all non-fatal AEs resolved\"\n    ),\n    NOT_RESOLVED = with_label(\n      !is.na(NOT_RESOLVED),\n      \"Total number of patients with at least one non-fatal unresolved or ongoing AE\"\n    )\n  )\n\nadae_mult &lt;- adae_mult %&gt;%\n  mutate(\n    AEDECOD = as.character(AEDECOD),\n    WD1 = with_label(\n      AEACN1 == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug 1 withdrawn due to AE\"\n    ),\n    WD2 = with_label(\n      AEACN2 == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug 2 withdrawn due to AE\"\n    ),\n    DSM1 = with_label(\n      AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose of study drug 1 modified/interrupted due to AE\"\n    ),\n    DSM2 = with_label(\n      AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose of study drug 2 modified/interrupted due to AE\"\n    ),\n    CONTRT = with_label(\n      AECONTRT == \"Y\",\n      \"Total number of patients with treatment received for AE\"\n    ),\n    SER = with_label(\n      AESER == \"Y\",\n      \"Total number of patients with at least one serious AE\"\n    ),\n    REL1 = with_label(\n      AEREL1 == \"Y\",\n      \"Total number of patients with at least one AE related to study drug 1\"\n    ),\n    REL2 = with_label(\n      AEREL2 == \"Y\",\n      \"Total number of patients with at least one AE related to study drug 2\"\n    ),\n    ALL_RESOLVED_WD1 = with_label(\n      WD1 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug 1 withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM1 = with_label(\n      DSM1 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 1 modified/interrupted due to resolved AE\"\n    ),\n    ALL_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with treatment received for resolved AE\"\n    ),\n    ALL_RESOLVED_WD2 = with_label(\n      WD2 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug 2 withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM2 = with_label(\n      DSM2 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 2 modified/interrupted due to resolved AE\"\n    ),\n    NOT_RESOLVED_WD1 = with_label(\n      WD1 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug 1 withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM1 = with_label(\n      DSM1 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 1 modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with treatment received for unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_WD2 = with_label(\n      WD2 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug 2 withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM2 = with_label(\n      DSM2 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 2 modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    SERWD1 = with_label(\n      AESER == \"Y\" & AEACN1 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 1 withdrawn due to serious AE\"\n    ),\n    SERWD2 = with_label(\n      AESER == \"Y\" & AEACN2 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 2 withdrawn due to serious AE\"\n    ),\n    SERCONTRT = with_label(\n      AECONTRT == \"Y\" & AESER == \"Y\",\n      \"No. of patients with treatment received for serious AE\"\n    ),\n    SERDSM1 = with_label(\n      AESER == \"Y\" & AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 1 modified/interrupted due to serious AE\"\n    ),\n    SERDSM2 = with_label(\n      AESER == \"Y\" & AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 2 modified/interrupted due to serious AE\"\n    ),\n    REL1WD1 = with_label(\n      AEREL1 == \"Y\" & AEACN1 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 1 withdrawn due to AE related to study drug 1\"\n    ),\n    REL1WD2 = with_label(\n      AEREL1 == \"Y\" & AEACN2 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 1 withdrawn due to AE related to study drug 2\"\n    ),\n    REL2WD1 = with_label(\n      AEREL1 == \"Y\" & AEACN1 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 2 withdrawn due to AE related to study drug 1\"\n    ),\n    REL2WD2 = with_label(\n      AEREL1 == \"Y\" & AEACN2 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 2 withdrawn due to AE related to study drug 2\"\n    ),\n    REL1DSM1 = with_label(\n      AEREL1 == \"Y\" & AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 1\"\n    ),\n    REL2DSM1 = with_label(\n      AEREL2 == \"Y\" & AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 1\"\n    ),\n    REL1DSM2 = with_label(\n      AEREL1 == \"Y\" & AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 2\"\n    ),\n    REL2DSM2 = with_label(\n      AEREL2 == \"Y\" & AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 2\"\n    ),\n    REL1CONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL1 == \"Y\",\n      \"No. of patients with treatment received for AE related to study drug 1\"\n    ),\n    REL2CONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL2 == \"Y\",\n      \"No. of patients with treatment received for AE related to study drug 2\"\n    ),\n    REL1SER = with_label(\n      AESER == \"Y\" & AEREL1 == \"Y\",\n      \"No. of patients with serious AE related to study drug 1\"\n    ),\n    REL2SER = with_label(\n      AESER == \"Y\" & AEREL2 == \"Y\",\n      \"No. of patients with serious AE related to study drug 2\"\n    )\n  )\n\nadae_mult &lt;- adae_mult %&gt;%\n  mutate(AETOXGR = forcats::fct_recode(\n    AETOXGR,\n    \"Grade 1\" = \"1\",\n    \"Grade 2\" = \"2\",\n    \"Grade 3\" = \"3\",\n    \"Grade 4\" = \"4\",\n    \"Grade 5 (fatal outcome)\" = \"5\"\n  ))\n\naesi_vars &lt;- c(\"WD1\", \"WD2\", \"DSM1\", \"DSM2\", \"CONTRT\")\naesi_res &lt;- c(\n  \"ALL_RESOLVED\",\n  \"ALL_RESOLVED_WD1\",\n  \"ALL_RESOLVED_WD2\",\n  \"ALL_RESOLVED_DSM1\",\n  \"ALL_RESOLVED_DSM2\",\n  \"ALL_RESOLVED_CONTRT\"\n)\naesi_not_res &lt;- c(\n  \"NOT_RESOLVED\",\n  \"NOT_RESOLVED_WD1\",\n  \"NOT_RESOLVED_WD2\",\n  \"NOT_RESOLVED_DSM1\",\n  \"NOT_RESOLVED_DSM2\",\n  \"NOT_RESOLVED_CONTRT\"\n)\naesi_ser &lt;- c(\"SER\", \"SERWD1\", \"SERWD2\", \"SERDSM1\", \"SERDSM2\", \"SERCONTRT\")\naesi_rel1 &lt;- c(\"REL1\", \"REL1WD1\", \"REL1WD2\", \"REL1DSM1\", \"REL1DSM2\", \"REL1CONTRT\", \"REL1SER\")\naesi_rel2 &lt;- c(\"REL2\", \"REL2WD1\", \"REL2WD2\", \"REL2DSM1\", \"REL2DSM2\", \"REL2CONTRT\", \"REL2SER\")\n\nlyt_adae_mult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(aesi_vars, aesi_res[1]), denom = \"N_col\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_res[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_res\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_res[1], denom = \"N_col\", table_names = \"fl_notres_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_res[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_notres\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[1], denom = \"N_col\", table_names = \"fl_ser_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_ser\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel1[1], denom = \"N_col\", table_names = \"fl_rel1_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel1[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_rel1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel2[1], denom = \"N_col\", table_names = \"fl_rel2_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel2[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_rel2\"\n  )\n\nresult &lt;- build_table(lyt_adae_mult, df = adae_mult, alt_counts_df = adsl)\nresult\n\n                                                                                                      A: Drug X    B: Placebo   C: Combination\n                                                                                                       (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                                                        100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                                                                      502          480            604      \nTotal number of patients with at least one AE by worst grade                                                                                  \n  Grade 1                                                                                             5 (3.7%)      7 (5.2%)       4 (3.0%)   \n  Grade 2                                                                                             5 (3.7%)      8 (6.0%)       6 (4.5%)   \n  Grade 3                                                                                             13 (9.7%)    13 (9.7%)      14 (10.6%)  \n  Grade 4                                                                                             13 (9.7%)    18 (13.4%)     15 (11.4%)  \n  Grade 5 (fatal outcome)                                                                            64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with study drug 1 withdrawn due to AE                                       22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with study drug 2 withdrawn due to AE                                       22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with dose of study drug 1 modified/interrupted due to AE                    55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with dose of study drug 2 modified/interrupted due to AE                    55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with treatment received for AE                                              80 (59.7%)    80 (59.7%)     89 (67.4%)  \nTotal number of patients with all non-fatal AEs resolved                                             17 (12.7%)    12 (9.0%)      11 (8.3%)   \n  No. of patients with study drug 1 withdrawn due to resolved AE                                          0         1 (0.7%)       2 (1.5%)   \n  No. of patients with study drug 2 withdrawn due to resolved AE                                          0         1 (0.7%)       2 (1.5%)   \n  No. of patients with dose of study drug 1 modified/interrupted due to resolved AE                   3 (2.2%)      4 (3.0%)       2 (1.5%)   \n  No. of patients with dose of study drug 2 modified/interrupted due to resolved AE                   3 (2.2%)      4 (3.0%)       2 (1.5%)   \n  No. of patients with treatment received for resolved AE                                             9 (6.7%)      7 (5.2%)       7 (5.3%)   \nTotal number of patients with at least one non-fatal unresolved or ongoing AE                        83 (61.9%)    86 (64.2%)     92 (69.7%)  \n  No. of patients with study drug 1 withdrawn due to unresolved or ongoing AE                        22 (16.4%)    20 (14.9%)     26 (19.7%)  \n  No. of patients with study drug 2 withdrawn due to unresolved or ongoing AE                        22 (16.4%)    20 (14.9%)     26 (19.7%)  \n  No. of patients with dose of study drug 1 modified/interrupted due to unresolved or ongoing AE     52 (38.8%)    58 (43.3%)     62 (47.0%)  \n  No. of patients with dose of study drug 2 modified/interrupted due to unresolved or ongoing AE     52 (38.8%)    58 (43.3%)     62 (47.0%)  \n  No. of patients with treatment received for unresolved or ongoing AE                               71 (53.0%)    73 (54.5%)     82 (62.1%)  \nTotal number of patients with at least one serious AE                                                85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  No. of patients with study drug 1 withdrawn due to serious AE                                       9 (6.7%)      4 (3.0%)      10 (7.6%)   \n  No. of patients with study drug 2 withdrawn due to serious AE                                       9 (6.7%)      4 (3.0%)      10 (7.6%)   \n  No. of patients with dose of study drug 1 modified/interrupted due to serious AE                   21 (15.7%)    22 (16.4%)     28 (21.2%)  \n  No. of patients with dose of study drug 2 modified/interrupted due to serious AE                   21 (15.7%)    22 (16.4%)     28 (21.2%)  \n  No. of patients with treatment received for serious AE                                             59 (44.0%)    58 (43.3%)     53 (40.2%)  \nTotal number of patients with at least one AE related to study drug 1                                86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  No. of patients with study drug 1 withdrawn due to AE related to study drug 1                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with study drug 1 withdrawn due to AE related to study drug 2                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 1   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 2   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with treatment received for AE related to study drug 1                             53 (39.6%)    58 (43.3%)     60 (45.5%)  \n  No. of patients with serious AE related to study drug 1                                            64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with at least one AE related to study drug 2                                86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  No. of patients with study drug 2 withdrawn due to AE related to study drug 1                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with study drug 2 withdrawn due to AE related to study drug 2                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 1   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 2   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with treatment received for AE related to study drug 2                             53 (39.6%)    58 (43.3%)     60 (45.5%)  \n  No. of patients with serious AE related to study drug 2                                            64 (47.8%)    52 (38.8%)     64 (48.5%)  \n\n\n\n\n\nCodeadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl &lt;- filter(adsl, SAFFL == \"Y\")\nadae &lt;- filter(adae, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nstack_adae_by_smq &lt;- function(adae, smq) {\n  adae_labels &lt;- c(var_labels(adae), \"Standardized MedDRA Query\")\n  l_df &lt;- lapply(smq, function(ae_grp) {\n    ae_scope &lt;- gsub(\"NAM\", \"SC\", ae_grp)\n    keep &lt;- adae[[ae_grp]] != \"&lt;Missing&gt;\"\n    df &lt;- adae[keep, ]\n    if (substr(ae_grp, 1, 3) == \"SMQ\") {\n      df[[\"SMQ\"]] &lt;- aesi_label(as.character(df[[ae_grp]]), scope = as.character(df[[ae_scope]]))\n    } else {\n      df[[\"SMQ\"]] &lt;- df[[ae_grp]]\n    }\n    df\n  })\n  result &lt;- do.call(rbind, l_df)\n  var_labels(result) &lt;- adae_labels\n  result\n}\n\nadae_smq &lt;- stack_adae_by_smq(adae, c(\"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\"))\n\nnot_resolved &lt;- adae_smq %&gt;%\n  filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n  distinct(USUBJID) %&gt;%\n  mutate(NOT_RESOLVED = \"Y\")\n\nadae_smq &lt;- adae_smq %&gt;%\n  left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    ALL_RESOLVED = with_label(\n      is.na(NOT_RESOLVED),\n      \"Total number of patients with all non-fatal AEs resolved\"\n    ),\n    NOT_RESOLVED = with_label(\n      !is.na(NOT_RESOLVED),\n      \"Total number of patients with at least one non-fatal unresolved or ongoing AE\"\n    )\n  )\n\nadae_smq &lt;- adae_smq %&gt;%\n  mutate(\n    AEDECOD = as.character(AEDECOD),\n    WD = with_label(\n      AEACN == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug withdrawn due to AE\"\n    ),\n    DSM = with_label(\n      AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose modified/interrupted due to AE\"\n    ),\n    CONTRT = with_label(\n      AECONTRT == \"Y\",\n      \"Total number of patients with treatment received for AE\"\n    ),\n    SER = with_label(\n      AESER == \"Y\",\n      \"Total number of patients with at least one serious AE\"\n    ),\n    REL = with_label(\n      AEREL == \"Y\",\n      \"Total number of patients with at least one related AE\"\n    ),\n    ALL_RESOLVED_WD = with_label(\n      WD == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM = with_label(\n      DSM == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to resolved AE\"\n    ),\n    ALL_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with treatment received for resolved AE\"\n    ),\n    NOT_RESOLVED_WD = with_label(\n      WD == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM = with_label(\n      DSM == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with treatment received for unresolved or ongoing AE\"\n    ),\n    SERWD = with_label(\n      AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to serious AE\"\n    ),\n    SERCONTRT = with_label(\n      AECONTRT == \"Y\" & AESER == \"Y\",\n      \"No. of patients with treatment received for serious AE\"\n    ),\n    SERDSM = with_label(\n      AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose modified/interrupted due to serious AE\"\n    ),\n    RELWD = with_label(\n      AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to related AE\"\n    ),\n    RELDSM = with_label(\n      AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose modified/interrupted due to related AE\"\n    ),\n    RELCONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with treatment received for related AE\"\n    ),\n    RELSER = with_label(\n      AESER == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with serious, related AE\"\n    )\n  )\n\nadae_smq &lt;- adae_smq %&gt;%\n  mutate(\n    AETOXGR = forcats::fct_recode(AETOXGR,\n      \"Grade 1\" = \"1\",\n      \"Grade 2\" = \"2\",\n      \"Grade 3\" = \"3\",\n      \"Grade 4\" = \"4\",\n      \"Grade 5 (fatal outcome)\" = \"5\"\n    )\n  )\n\nsplit_fun &lt;- remove_split_levels(\"&lt;Missing&gt;\")\naesi_vars &lt;- c(\"WD\", \"DSM\", \"CONTRT\", \"ALL_RESOLVED\", \"NOT_RESOLVED\", \"SER\", \"REL\")\n\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    split_fun = split_fun,\n    split_label = \"Standardized MedDRA Query\",\n    label_pos = \"topleft\"\n  ) %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    .show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\"USUBJID\", flag_variables = aesi_vars, denom = \"N_col\")\n\nresult &lt;- build_table(lyt_adae, df = adae_smq, alt_counts_df = adsl)\nresult\n\n                                                                                  A: Drug X    B: Placebo   C: Combination\nStandardized MedDRA Query                                                          (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nC.1.1.1.3/B.2.2.3.1 AESI (BROAD)                                                                                          \n  Total number of patients with at least one AE                                   58 (43.3%)   60 (44.8%)     66 (50.0%)  \n  Total number of AEs                                                                 97          106            122      \n  Total number of patients with at least one AE by worst grade                                                            \n    Grade 1                                                                       22 (16.4%)   26 (19.4%)     30 (22.7%)  \n    Grade 2                                                                           0            0              0       \n    Grade 3                                                                           0            0              0       \n    Grade 4                                                                       36 (26.9%)   34 (25.4%)     36 (27.3%)  \n    Grade 5 (fatal outcome)                                                           0            0              0       \n  Total number of patients with study drug withdrawn due to AE                     5 (3.7%)     6 (4.5%)       9 (6.8%)   \n  Total number of patients with dose modified/interrupted due to AE               21 (15.7%)   21 (15.7%)     24 (18.2%)  \n  Total number of patients with treatment received for AE                         32 (23.9%)   35 (26.1%)     44 (33.3%)  \n  Total number of patients with all non-fatal AEs resolved                         7 (5.2%)     9 (6.7%)       8 (6.1%)   \n  Total number of patients with at least one non-fatal unresolved or ongoing AE   51 (38.1%)   51 (38.1%)     58 (43.9%)  \n  Total number of patients with at least one serious AE                           38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  Total number of patients with at least one related AE                           36 (26.9%)   34 (25.4%)     36 (27.3%)  \nD.2.1.5.3/A.1.1.1.1 AESI                                                                                                  \n  Total number of patients with at least one AE                                   62 (46.3%)   61 (45.5%)     76 (57.6%)  \n  Total number of AEs                                                                106          100            139      \n  Total number of patients with at least one AE by worst grade                                                            \n    Grade 1                                                                       62 (46.3%)   61 (45.5%)     76 (57.6%)  \n    Grade 2                                                                           0            0              0       \n    Grade 3                                                                           0            0              0       \n    Grade 4                                                                           0            0              0       \n    Grade 5 (fatal outcome)                                                           0            0              0       \n  Total number of patients with study drug withdrawn due to AE                     6 (4.5%)     8 (6.0%)       4 (3.0%)   \n  Total number of patients with dose modified/interrupted due to AE               23 (17.2%)   20 (14.9%)     26 (19.7%)  \n  Total number of patients with treatment received for AE                         33 (24.6%)   37 (27.6%)     33 (25.0%)  \n  Total number of patients with all non-fatal AEs resolved                         4 (3.0%)     5 (3.7%)      14 (10.6%)  \n  Total number of patients with at least one non-fatal unresolved or ongoing AE   58 (43.3%)   56 (41.8%)     62 (47.0%)  \n  Total number of patients with at least one serious AE                               0            0              0       \n  Total number of patients with at least one related AE                           37 (27.6%)   46 (34.3%)     50 (37.9%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n  ADAE &lt;- filter(ADAE, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\n  not_resolved &lt;- ADAE %&gt;%\n    filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n    distinct(USUBJID) %&gt;%\n    mutate(NOT_RESOLVED = \"Y\")\n\n  ADAE &lt;- ADAE %&gt;%\n    left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n    mutate(\n      ALL_RESOLVED = with_label(is.na(NOT_RESOLVED), \"All non-fatal AEs resolved\"),\n      NOT_RESOLVED = with_label(!is.na(NOT_RESOLVED), \"At least one non-fatal unresolved or ongoing AE\")\n    )\n\n  ADAE &lt;- ADAE %&gt;%\n    mutate(\n      AEDECOD = as.character(AEDECOD),\n      WD = with_label(AEACN == \"DRUG WITHDRAWN\", \"AE that led to study drug withdrawal\"),\n      DSM = with_label(\n        AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n        \"AE that led to study drug dose modified/interrupted\"\n      ),\n      CONTRT = with_label(AECONTRT == \"Y\", \"AE that required treatment\"),\n      SER = with_label(AESER == \"Y\", \"Serious AE\"),\n      REL = with_label(AEREL == \"Y\", \"Related AE\"),\n      ALL_RESOLVED_WD = with_label(\n        WD == TRUE & ALL_RESOLVED == TRUE,\n        \"Resolved AE that led to study drug withdrawal\"\n      ),\n      ALL_RESOLVED_DSM = with_label(\n        DSM == TRUE & ALL_RESOLVED == TRUE,\n        \"Resolved AE that led to study drug dose modified/interrupted\"\n      ),\n      ALL_RESOLVED_CONTRT = with_label(\n        CONTRT == TRUE & ALL_RESOLVED == TRUE,\n        \"Resolved AE that required treatment\"\n      ),\n      NOT_RESOLVED_WD = with_label(\n        WD == TRUE & NOT_RESOLVED == TRUE,\n        \"Unresolved AE that led to study drug withdrawal\"\n      ),\n      NOT_RESOLVED_DSM = with_label(\n        DSM == TRUE & NOT_RESOLVED == TRUE,\n        \"Unresolved AE that led to study drug dose modified/interrupted\"\n      ),\n      NOT_RESOLVED_CONTRT = with_label(\n        CONTRT == TRUE & NOT_RESOLVED == TRUE,\n        \"Unresolved AE that required treatment\"\n      ),\n      SERWD = with_label(\n        AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n        \"Serious AE that led to study drug withdrawal\"\n      ),\n      SERCONTRT = with_label(\n        AECONTRT == \"Y\" & AESER == \"Y\",\n        \"Serious AE that required treatment\"\n      ),\n      SERDSM = with_label(\n        AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n        \"Serious AE that led to study drug dose modified/interrupted\"\n      ),\n      RELWD = with_label(\n        AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\", \"Related AE that led to study drug withdrawal\"\n      ),\n      RELDSM = with_label(\n        AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n        \"Related AE that led to study drug dose modified/interrupted\"\n      ),\n      RELCONTRT = with_label(AECONTRT == \"Y\" & AEREL == \"Y\", \"Related AE that required treatment\"),\n      RELSER = with_label(AESER == \"Y\" & AEREL == \"Y\", \"Serious related AE\")\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\naesi_vars &lt;- c(\n  \"WD\", \"DSM\", \"CONTRT\", \"ALL_RESOLVED_WD\", \"ALL_RESOLVED_DSM\", \"ALL_RESOLVED_CONTRT\",\n  \"NOT_RESOLVED_WD\", \"NOT_RESOLVED_DSM\", \"NOT_RESOLVED_CONTRT\", \"SER\", \"SERWD\", \"SERDSM\",\n  \"SERCONTRT\", \"REL\", \"RELWD\", \"RELDSM\", \"RELCONTRT\", \"RELSER\"\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_summary(\n      label = \"Adverse Events Summary\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(\"ADSL\", c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      flag_var_anl = choices_selected(\n        choices = variable_choices(\"ADAE\", aesi_vars),\n        selected = aesi_vars[1],\n        keep_order = TRUE,\n        fixed = FALSE\n      ),\n      add_total = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:48:58 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with  Optional Lines\nTable For Studies  with Multiple Drugs\nTable of AEs  by SMQ\nteal App\nReproducibility\n\n\n\nTo illustrate, additional variables such as flags (TRUE/FALSE) for selected AEs of interest. Please consult your SAP on how to handle missing AE grades.\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl &lt;- filter(adsl, SAFFL == \"Y\")\nadae &lt;- filter(adae, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nnot_resolved &lt;- adae %&gt;%\n  filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n  distinct(USUBJID) %&gt;%\n  mutate(NOT_RESOLVED = \"Y\")\n\nadae &lt;- adae %&gt;%\n  left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    ALL_RESOLVED = with_label(\n      is.na(NOT_RESOLVED),\n      \"Total number of patients with all non-fatal AEs resolved\"\n    ),\n    NOT_RESOLVED = with_label(\n      !is.na(NOT_RESOLVED),\n      \"Total number of patients with at least one unresolved or ongoing non-fatal AE\"\n    )\n  )\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AEDECOD = as.character(AEDECOD),\n    WD = with_label(\n      AEACN == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug withdrawn due to AE\"\n    ),\n    DSM = with_label(\n      AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose modified/interrupted due to AE\"\n    ),\n    CONTRT = with_label(\n      AECONTRT == \"Y\",\n      \"Total number of patients with treatment received for AE\"\n    ),\n    SER = with_label(\n      AESER == \"Y\",\n      \"Total number of patients with at least one serious AE\"\n    ),\n    REL = with_label(\n      AEREL == \"Y\",\n      \"Total number of patients with at least one related AE\"\n    ),\n    ALL_RESOLVED_WD = with_label(\n      WD == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM = with_label(\n      DSM == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to resolved AE\"\n    ),\n    ALL_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with treatment received for resolved AE\"\n    ),\n    NOT_RESOLVED_WD = with_label(\n      WD == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM = with_label(\n      DSM == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with treatment received for unresolved or ongoing AE\"\n    ),\n    SERWD = with_label(\n      AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to serious AE\"\n    ),\n    SERCONTRT = with_label(\n      AECONTRT == \"Y\" & AESER == \"Y\",\n      \"No. of patients with dose modified/interrupted due to serious AE\"\n    ),\n    SERDSM = with_label(\n      AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with treatment received for serious AE\"\n    ),\n    RELWD = with_label(\n      AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to related AE\"\n    ),\n    RELDSM = with_label(\n      AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose modified/interrupted due to related AE\"\n    ),\n    RELCONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with treatment received for related AE\"\n    ),\n    RELSER = with_label(\n      AESER == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with serious, related AE\"\n    )\n  )\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AETOXGR = forcats::fct_recode(AETOXGR,\n      \"Grade 1\" = \"1\",\n      \"Grade 2\" = \"2\",\n      \"Grade 3\" = \"3\",\n      \"Grade 4\" = \"4\",\n      \"Grade 5 (fatal outcome)\" = \"5\"\n    )\n  )\n\n\n\n\n\nCodeaesi_vars &lt;- c(\"WD\", \"DSM\", \"CONTRT\", \"ALL_RESOLVED\", \"NOT_RESOLVED\", \"SER\", \"REL\")\n\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\"USUBJID\", flag_variables = aesi_vars, denom = \"N_col\")\n\nresult &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\nresult\n\n                                                                                 A: Drug X    B: Placebo   C: Combination\n                                                                                  (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                                   100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                                                 502          480            604      \nTotal number of patients with at least one AE by worst grade                                                             \n  Grade 1                                                                        5 (3.7%)      7 (5.2%)       4 (3.0%)   \n  Grade 2                                                                        5 (3.7%)      8 (6.0%)       6 (4.5%)   \n  Grade 3                                                                        13 (9.7%)    13 (9.7%)      14 (10.6%)  \n  Grade 4                                                                        13 (9.7%)    18 (13.4%)     15 (11.4%)  \n  Grade 5 (fatal outcome)                                                       64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with study drug withdrawn due to AE                    22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with dose modified/interrupted due to AE               55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with treatment received for AE                         80 (59.7%)    80 (59.7%)     89 (67.4%)  \nTotal number of patients with all non-fatal AEs resolved                        17 (12.7%)    12 (9.0%)      11 (8.3%)   \nTotal number of patients with at least one unresolved or ongoing non-fatal AE   83 (61.9%)    86 (64.2%)     92 (69.7%)  \nTotal number of patients with at least one serious AE                           85 (63.4%)    80 (59.7%)     87 (65.9%)  \nTotal number of patients with at least one related AE                           86 (64.2%)    85 (63.4%)     92 (69.7%)  \n\n\n\n\n\nCodeaesi_vars &lt;- c(\"WD\", \"DSM\", \"CONTRT\")\naesi_resolved &lt;- c(\"ALL_RESOLVED\", \"ALL_RESOLVED_WD\", \"ALL_RESOLVED_DSM\", \"ALL_RESOLVED_CONTRT\")\naesi_not_resolved &lt;- c(\"NOT_RESOLVED\", \"NOT_RESOLVED_WD\", \"NOT_RESOLVED_DSM\", \"NOT_RESOLVED_CONTRT\")\naesi_ser &lt;- c(\"SER\", \"SERWD\", \"SERDSM\", \"SERCONTRT\")\naesi_rel &lt;- c(\"REL\", \"RELWD\", \"RELDSM\", \"RELCONTRT\", \"RELSER\")\n\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(aesi_vars, aesi_resolved[1]), denom = \"N_col\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_resolved[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_res\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_resolved[1], denom = \"N_col\", table_names = \"fl_notres_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_resolved[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_notres\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[1], denom = \"N_col\", table_names = \"fl_ser_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_ser\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel[1], denom = \"N_col\", table_names = \"fl_rel_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_rel\"\n  )\n\nresult &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\nresult\n\n                                                                                    A: Drug X    B: Placebo   C: Combination\n                                                                                     (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                                      100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                                                    502          480            604      \nTotal number of patients with at least one AE by worst grade                                                                \n  Grade 1                                                                           5 (3.7%)      7 (5.2%)       4 (3.0%)   \n  Grade 2                                                                           5 (3.7%)      8 (6.0%)       6 (4.5%)   \n  Grade 3                                                                           13 (9.7%)    13 (9.7%)      14 (10.6%)  \n  Grade 4                                                                           13 (9.7%)    18 (13.4%)     15 (11.4%)  \n  Grade 5 (fatal outcome)                                                          64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with study drug withdrawn due to AE                       22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with dose modified/interrupted due to AE                  55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with treatment received for AE                            80 (59.7%)    80 (59.7%)     89 (67.4%)  \nTotal number of patients with all non-fatal AEs resolved                           17 (12.7%)    12 (9.0%)      11 (8.3%)   \n  No. of patients with study drug withdrawn due to resolved AE                          0         1 (0.7%)       2 (1.5%)   \n  No. of patients with dose modified/interrupted due to resolved AE                 3 (2.2%)      4 (3.0%)       2 (1.5%)   \n  No. of patients with treatment received for resolved AE                           9 (6.7%)      7 (5.2%)       7 (5.3%)   \nTotal number of patients with at least one unresolved or ongoing non-fatal AE      83 (61.9%)    86 (64.2%)     92 (69.7%)  \n  No. of patients with study drug withdrawn due to unresolved or ongoing AE        22 (16.4%)    20 (14.9%)     26 (19.7%)  \n  No. of patients with dose modified/interrupted due to unresolved or ongoing AE   52 (38.8%)    58 (43.3%)     62 (47.0%)  \n  No. of patients with treatment received for unresolved or ongoing AE             71 (53.0%)    73 (54.5%)     82 (62.1%)  \nTotal number of patients with at least one serious AE                              85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  No. of patients with study drug withdrawn due to serious AE                       9 (6.7%)      4 (3.0%)      10 (7.6%)   \n  No. of patients with treatment received for serious AE                           21 (15.7%)    22 (16.4%)     28 (21.2%)  \n  No. of patients with dose modified/interrupted due to serious AE                 59 (44.0%)    58 (43.3%)     53 (40.2%)  \nTotal number of patients with at least one related AE                              86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  No. of patients with study drug withdrawn due to related AE                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with dose modified/interrupted due to related AE                 24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with treatment received for related AE                           53 (39.6%)    58 (43.3%)     60 (45.5%)  \n  No. of patients with serious, related AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  \n\n\n\n\n\nCodeadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae_mult &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl &lt;- filter(adsl, SAFFL == \"Y\")\nadae_mult &lt;- filter(adae_mult, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\nadsl &lt;- df_explicit_na(adsl)\nadae_mult &lt;- df_explicit_na(adae_mult)\n\n# for illustration purposes only, create AEREL1, AEREL2, AEACN1, AEACN2 from respective variables\nadae_mult &lt;- adae_mult %&gt;%\n  mutate(\n    AEREL1 = AEREL,\n    AEREL2 = AEREL,\n    AEACN1 = AEACN,\n    AEACN2 = AEACN\n  )\n\nnot_resolved &lt;- adae_mult %&gt;%\n  filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n  distinct(USUBJID) %&gt;%\n  mutate(NOT_RESOLVED = \"Y\")\n\nadae_mult &lt;- adae_mult %&gt;%\n  left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    ALL_RESOLVED = with_label(\n      is.na(NOT_RESOLVED),\n      \"Total number of patients with all non-fatal AEs resolved\"\n    ),\n    NOT_RESOLVED = with_label(\n      !is.na(NOT_RESOLVED),\n      \"Total number of patients with at least one non-fatal unresolved or ongoing AE\"\n    )\n  )\n\nadae_mult &lt;- adae_mult %&gt;%\n  mutate(\n    AEDECOD = as.character(AEDECOD),\n    WD1 = with_label(\n      AEACN1 == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug 1 withdrawn due to AE\"\n    ),\n    WD2 = with_label(\n      AEACN2 == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug 2 withdrawn due to AE\"\n    ),\n    DSM1 = with_label(\n      AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose of study drug 1 modified/interrupted due to AE\"\n    ),\n    DSM2 = with_label(\n      AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose of study drug 2 modified/interrupted due to AE\"\n    ),\n    CONTRT = with_label(\n      AECONTRT == \"Y\",\n      \"Total number of patients with treatment received for AE\"\n    ),\n    SER = with_label(\n      AESER == \"Y\",\n      \"Total number of patients with at least one serious AE\"\n    ),\n    REL1 = with_label(\n      AEREL1 == \"Y\",\n      \"Total number of patients with at least one AE related to study drug 1\"\n    ),\n    REL2 = with_label(\n      AEREL2 == \"Y\",\n      \"Total number of patients with at least one AE related to study drug 2\"\n    ),\n    ALL_RESOLVED_WD1 = with_label(\n      WD1 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug 1 withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM1 = with_label(\n      DSM1 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 1 modified/interrupted due to resolved AE\"\n    ),\n    ALL_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with treatment received for resolved AE\"\n    ),\n    ALL_RESOLVED_WD2 = with_label(\n      WD2 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug 2 withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM2 = with_label(\n      DSM2 == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 2 modified/interrupted due to resolved AE\"\n    ),\n    NOT_RESOLVED_WD1 = with_label(\n      WD1 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug 1 withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM1 = with_label(\n      DSM1 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 1 modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with treatment received for unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_WD2 = with_label(\n      WD2 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug 2 withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM2 = with_label(\n      DSM2 == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose of study drug 2 modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    SERWD1 = with_label(\n      AESER == \"Y\" & AEACN1 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 1 withdrawn due to serious AE\"\n    ),\n    SERWD2 = with_label(\n      AESER == \"Y\" & AEACN2 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 2 withdrawn due to serious AE\"\n    ),\n    SERCONTRT = with_label(\n      AECONTRT == \"Y\" & AESER == \"Y\",\n      \"No. of patients with treatment received for serious AE\"\n    ),\n    SERDSM1 = with_label(\n      AESER == \"Y\" & AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 1 modified/interrupted due to serious AE\"\n    ),\n    SERDSM2 = with_label(\n      AESER == \"Y\" & AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 2 modified/interrupted due to serious AE\"\n    ),\n    REL1WD1 = with_label(\n      AEREL1 == \"Y\" & AEACN1 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 1 withdrawn due to AE related to study drug 1\"\n    ),\n    REL1WD2 = with_label(\n      AEREL1 == \"Y\" & AEACN2 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 1 withdrawn due to AE related to study drug 2\"\n    ),\n    REL2WD1 = with_label(\n      AEREL1 == \"Y\" & AEACN1 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 2 withdrawn due to AE related to study drug 1\"\n    ),\n    REL2WD2 = with_label(\n      AEREL1 == \"Y\" & AEACN2 == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug 2 withdrawn due to AE related to study drug 2\"\n    ),\n    REL1DSM1 = with_label(\n      AEREL1 == \"Y\" & AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 1\"\n    ),\n    REL2DSM1 = with_label(\n      AEREL2 == \"Y\" & AEACN1 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 1\"\n    ),\n    REL1DSM2 = with_label(\n      AEREL1 == \"Y\" & AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 2\"\n    ),\n    REL2DSM2 = with_label(\n      AEREL2 == \"Y\" & AEACN2 %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 2\"\n    ),\n    REL1CONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL1 == \"Y\",\n      \"No. of patients with treatment received for AE related to study drug 1\"\n    ),\n    REL2CONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL2 == \"Y\",\n      \"No. of patients with treatment received for AE related to study drug 2\"\n    ),\n    REL1SER = with_label(\n      AESER == \"Y\" & AEREL1 == \"Y\",\n      \"No. of patients with serious AE related to study drug 1\"\n    ),\n    REL2SER = with_label(\n      AESER == \"Y\" & AEREL2 == \"Y\",\n      \"No. of patients with serious AE related to study drug 2\"\n    )\n  )\n\nadae_mult &lt;- adae_mult %&gt;%\n  mutate(AETOXGR = forcats::fct_recode(\n    AETOXGR,\n    \"Grade 1\" = \"1\",\n    \"Grade 2\" = \"2\",\n    \"Grade 3\" = \"3\",\n    \"Grade 4\" = \"4\",\n    \"Grade 5 (fatal outcome)\" = \"5\"\n  ))\n\naesi_vars &lt;- c(\"WD1\", \"WD2\", \"DSM1\", \"DSM2\", \"CONTRT\")\naesi_res &lt;- c(\n  \"ALL_RESOLVED\",\n  \"ALL_RESOLVED_WD1\",\n  \"ALL_RESOLVED_WD2\",\n  \"ALL_RESOLVED_DSM1\",\n  \"ALL_RESOLVED_DSM2\",\n  \"ALL_RESOLVED_CONTRT\"\n)\naesi_not_res &lt;- c(\n  \"NOT_RESOLVED\",\n  \"NOT_RESOLVED_WD1\",\n  \"NOT_RESOLVED_WD2\",\n  \"NOT_RESOLVED_DSM1\",\n  \"NOT_RESOLVED_DSM2\",\n  \"NOT_RESOLVED_CONTRT\"\n)\naesi_ser &lt;- c(\"SER\", \"SERWD1\", \"SERWD2\", \"SERDSM1\", \"SERDSM2\", \"SERCONTRT\")\naesi_rel1 &lt;- c(\"REL1\", \"REL1WD1\", \"REL1WD2\", \"REL1DSM1\", \"REL1DSM2\", \"REL1CONTRT\", \"REL1SER\")\naesi_rel2 &lt;- c(\"REL2\", \"REL2WD1\", \"REL2WD2\", \"REL2DSM1\", \"REL2DSM2\", \"REL2CONTRT\", \"REL2SER\")\n\nlyt_adae_mult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(aesi_vars, aesi_res[1]), denom = \"N_col\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_res[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_res\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_res[1], denom = \"N_col\", table_names = \"fl_notres_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_not_res[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_notres\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[1], denom = \"N_col\", table_names = \"fl_ser_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_ser[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_ser\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel1[1], denom = \"N_col\", table_names = \"fl_rel1_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel1[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_rel1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel2[1], denom = \"N_col\", table_names = \"fl_rel2_main\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_rel2[-1], denom = \"N_col\", .indent_mods = 1L, table_names = \"fl_rel2\"\n  )\n\nresult &lt;- build_table(lyt_adae_mult, df = adae_mult, alt_counts_df = adsl)\nresult\n\n                                                                                                      A: Drug X    B: Placebo   C: Combination\n                                                                                                       (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                                                        100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                                                                      502          480            604      \nTotal number of patients with at least one AE by worst grade                                                                                  \n  Grade 1                                                                                             5 (3.7%)      7 (5.2%)       4 (3.0%)   \n  Grade 2                                                                                             5 (3.7%)      8 (6.0%)       6 (4.5%)   \n  Grade 3                                                                                             13 (9.7%)    13 (9.7%)      14 (10.6%)  \n  Grade 4                                                                                             13 (9.7%)    18 (13.4%)     15 (11.4%)  \n  Grade 5 (fatal outcome)                                                                            64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with study drug 1 withdrawn due to AE                                       22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with study drug 2 withdrawn due to AE                                       22 (16.4%)    21 (15.7%)     28 (21.2%)  \nTotal number of patients with dose of study drug 1 modified/interrupted due to AE                    55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with dose of study drug 2 modified/interrupted due to AE                    55 (41.0%)    62 (46.3%)     64 (48.5%)  \nTotal number of patients with treatment received for AE                                              80 (59.7%)    80 (59.7%)     89 (67.4%)  \nTotal number of patients with all non-fatal AEs resolved                                             17 (12.7%)    12 (9.0%)      11 (8.3%)   \n  No. of patients with study drug 1 withdrawn due to resolved AE                                          0         1 (0.7%)       2 (1.5%)   \n  No. of patients with study drug 2 withdrawn due to resolved AE                                          0         1 (0.7%)       2 (1.5%)   \n  No. of patients with dose of study drug 1 modified/interrupted due to resolved AE                   3 (2.2%)      4 (3.0%)       2 (1.5%)   \n  No. of patients with dose of study drug 2 modified/interrupted due to resolved AE                   3 (2.2%)      4 (3.0%)       2 (1.5%)   \n  No. of patients with treatment received for resolved AE                                             9 (6.7%)      7 (5.2%)       7 (5.3%)   \nTotal number of patients with at least one non-fatal unresolved or ongoing AE                        83 (61.9%)    86 (64.2%)     92 (69.7%)  \n  No. of patients with study drug 1 withdrawn due to unresolved or ongoing AE                        22 (16.4%)    20 (14.9%)     26 (19.7%)  \n  No. of patients with study drug 2 withdrawn due to unresolved or ongoing AE                        22 (16.4%)    20 (14.9%)     26 (19.7%)  \n  No. of patients with dose of study drug 1 modified/interrupted due to unresolved or ongoing AE     52 (38.8%)    58 (43.3%)     62 (47.0%)  \n  No. of patients with dose of study drug 2 modified/interrupted due to unresolved or ongoing AE     52 (38.8%)    58 (43.3%)     62 (47.0%)  \n  No. of patients with treatment received for unresolved or ongoing AE                               71 (53.0%)    73 (54.5%)     82 (62.1%)  \nTotal number of patients with at least one serious AE                                                85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  No. of patients with study drug 1 withdrawn due to serious AE                                       9 (6.7%)      4 (3.0%)      10 (7.6%)   \n  No. of patients with study drug 2 withdrawn due to serious AE                                       9 (6.7%)      4 (3.0%)      10 (7.6%)   \n  No. of patients with dose of study drug 1 modified/interrupted due to serious AE                   21 (15.7%)    22 (16.4%)     28 (21.2%)  \n  No. of patients with dose of study drug 2 modified/interrupted due to serious AE                   21 (15.7%)    22 (16.4%)     28 (21.2%)  \n  No. of patients with treatment received for serious AE                                             59 (44.0%)    58 (43.3%)     53 (40.2%)  \nTotal number of patients with at least one AE related to study drug 1                                86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  No. of patients with study drug 1 withdrawn due to AE related to study drug 1                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with study drug 1 withdrawn due to AE related to study drug 2                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 1   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with dose of study drug 1 modified/interrupted due to AE related to study drug 2   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with treatment received for AE related to study drug 1                             53 (39.6%)    58 (43.3%)     60 (45.5%)  \n  No. of patients with serious AE related to study drug 1                                            64 (47.8%)    52 (38.8%)     64 (48.5%)  \nTotal number of patients with at least one AE related to study drug 2                                86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  No. of patients with study drug 2 withdrawn due to AE related to study drug 1                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with study drug 2 withdrawn due to AE related to study drug 2                       5 (3.7%)     10 (7.5%)       7 (5.3%)   \n  No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 1   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with dose of study drug 2 modified/interrupted due to AE related to study drug 2   24 (17.9%)    32 (23.9%)     34 (25.8%)  \n  No. of patients with treatment received for AE related to study drug 2                             53 (39.6%)    58 (43.3%)     60 (45.5%)  \n  No. of patients with serious AE related to study drug 2                                            64 (47.8%)    52 (38.8%)     64 (48.5%)  \n\n\n\n\n\nCodeadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl &lt;- filter(adsl, SAFFL == \"Y\")\nadae &lt;- filter(adae, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nstack_adae_by_smq &lt;- function(adae, smq) {\n  adae_labels &lt;- c(var_labels(adae), \"Standardized MedDRA Query\")\n  l_df &lt;- lapply(smq, function(ae_grp) {\n    ae_scope &lt;- gsub(\"NAM\", \"SC\", ae_grp)\n    keep &lt;- adae[[ae_grp]] != \"&lt;Missing&gt;\"\n    df &lt;- adae[keep, ]\n    if (substr(ae_grp, 1, 3) == \"SMQ\") {\n      df[[\"SMQ\"]] &lt;- aesi_label(as.character(df[[ae_grp]]), scope = as.character(df[[ae_scope]]))\n    } else {\n      df[[\"SMQ\"]] &lt;- df[[ae_grp]]\n    }\n    df\n  })\n  result &lt;- do.call(rbind, l_df)\n  var_labels(result) &lt;- adae_labels\n  result\n}\n\nadae_smq &lt;- stack_adae_by_smq(adae, c(\"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\"))\n\nnot_resolved &lt;- adae_smq %&gt;%\n  filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n  distinct(USUBJID) %&gt;%\n  mutate(NOT_RESOLVED = \"Y\")\n\nadae_smq &lt;- adae_smq %&gt;%\n  left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n  mutate(\n    ALL_RESOLVED = with_label(\n      is.na(NOT_RESOLVED),\n      \"Total number of patients with all non-fatal AEs resolved\"\n    ),\n    NOT_RESOLVED = with_label(\n      !is.na(NOT_RESOLVED),\n      \"Total number of patients with at least one non-fatal unresolved or ongoing AE\"\n    )\n  )\n\nadae_smq &lt;- adae_smq %&gt;%\n  mutate(\n    AEDECOD = as.character(AEDECOD),\n    WD = with_label(\n      AEACN == \"DRUG WITHDRAWN\",\n      \"Total number of patients with study drug withdrawn due to AE\"\n    ),\n    DSM = with_label(\n      AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Total number of patients with dose modified/interrupted due to AE\"\n    ),\n    CONTRT = with_label(\n      AECONTRT == \"Y\",\n      \"Total number of patients with treatment received for AE\"\n    ),\n    SER = with_label(\n      AESER == \"Y\",\n      \"Total number of patients with at least one serious AE\"\n    ),\n    REL = with_label(\n      AEREL == \"Y\",\n      \"Total number of patients with at least one related AE\"\n    ),\n    ALL_RESOLVED_WD = with_label(\n      WD == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to resolved AE\"\n    ),\n    ALL_RESOLVED_DSM = with_label(\n      DSM == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to resolved AE\"\n    ),\n    ALL_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & ALL_RESOLVED == TRUE,\n      \"No. of patients with treatment received for resolved AE\"\n    ),\n    NOT_RESOLVED_WD = with_label(\n      WD == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with study drug withdrawn due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_DSM = with_label(\n      DSM == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with dose modified/interrupted due to unresolved or ongoing AE\"\n    ),\n    NOT_RESOLVED_CONTRT = with_label(\n      CONTRT == TRUE & NOT_RESOLVED == TRUE,\n      \"No. of patients with treatment received for unresolved or ongoing AE\"\n    ),\n    SERWD = with_label(\n      AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to serious AE\"\n    ),\n    SERCONTRT = with_label(\n      AECONTRT == \"Y\" & AESER == \"Y\",\n      \"No. of patients with treatment received for serious AE\"\n    ),\n    SERDSM = with_label(\n      AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose modified/interrupted due to serious AE\"\n    ),\n    RELWD = with_label(\n      AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n      \"No. of patients with study drug withdrawn due to related AE\"\n    ),\n    RELDSM = with_label(\n      AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"No. of patients with dose modified/interrupted due to related AE\"\n    ),\n    RELCONTRT = with_label(\n      AECONTRT == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with treatment received for related AE\"\n    ),\n    RELSER = with_label(\n      AESER == \"Y\" & AEREL == \"Y\",\n      \"No. of patients with serious, related AE\"\n    )\n  )\n\nadae_smq &lt;- adae_smq %&gt;%\n  mutate(\n    AETOXGR = forcats::fct_recode(AETOXGR,\n      \"Grade 1\" = \"1\",\n      \"Grade 2\" = \"2\",\n      \"Grade 3\" = \"3\",\n      \"Grade 4\" = \"4\",\n      \"Grade 5 (fatal outcome)\" = \"5\"\n    )\n  )\n\nsplit_fun &lt;- remove_split_levels(\"&lt;Missing&gt;\")\naesi_vars &lt;- c(\"WD\", \"DSM\", \"CONTRT\", \"ALL_RESOLVED\", \"NOT_RESOLVED\", \"SER\", \"REL\")\n\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    split_fun = split_fun,\n    split_label = \"Standardized MedDRA Query\",\n    label_pos = \"topleft\"\n  ) %&gt;%\n  count_patients_with_event(\n    vars = \"USUBJID\",\n    filters = c(\"ANL01FL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients with at least one AE\")\n  ) %&gt;%\n  count_values(\n    \"ANL01FL\",\n    values = \"Y\",\n    .stats = \"count\",\n    .labels = c(count = \"Total number of AEs\"),\n    table_names = \"total_aes\"\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    var_labels = \"Total number of patients with at least one AE by worst grade\",\n    .show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\"USUBJID\", flag_variables = aesi_vars, denom = \"N_col\")\n\nresult &lt;- build_table(lyt_adae, df = adae_smq, alt_counts_df = adsl)\nresult\n\n                                                                                  A: Drug X    B: Placebo   C: Combination\nStandardized MedDRA Query                                                          (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nC.1.1.1.3/B.2.2.3.1 AESI (BROAD)                                                                                          \n  Total number of patients with at least one AE                                   58 (43.3%)   60 (44.8%)     66 (50.0%)  \n  Total number of AEs                                                                 97          106            122      \n  Total number of patients with at least one AE by worst grade                                                            \n    Grade 1                                                                       22 (16.4%)   26 (19.4%)     30 (22.7%)  \n    Grade 2                                                                           0            0              0       \n    Grade 3                                                                           0            0              0       \n    Grade 4                                                                       36 (26.9%)   34 (25.4%)     36 (27.3%)  \n    Grade 5 (fatal outcome)                                                           0            0              0       \n  Total number of patients with study drug withdrawn due to AE                     5 (3.7%)     6 (4.5%)       9 (6.8%)   \n  Total number of patients with dose modified/interrupted due to AE               21 (15.7%)   21 (15.7%)     24 (18.2%)  \n  Total number of patients with treatment received for AE                         32 (23.9%)   35 (26.1%)     44 (33.3%)  \n  Total number of patients with all non-fatal AEs resolved                         7 (5.2%)     9 (6.7%)       8 (6.1%)   \n  Total number of patients with at least one non-fatal unresolved or ongoing AE   51 (38.1%)   51 (38.1%)     58 (43.9%)  \n  Total number of patients with at least one serious AE                           38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  Total number of patients with at least one related AE                           36 (26.9%)   34 (25.4%)     36 (27.3%)  \nD.2.1.5.3/A.1.1.1.1 AESI                                                                                                  \n  Total number of patients with at least one AE                                   62 (46.3%)   61 (45.5%)     76 (57.6%)  \n  Total number of AEs                                                                106          100            139      \n  Total number of patients with at least one AE by worst grade                                                            \n    Grade 1                                                                       62 (46.3%)   61 (45.5%)     76 (57.6%)  \n    Grade 2                                                                           0            0              0       \n    Grade 3                                                                           0            0              0       \n    Grade 4                                                                           0            0              0       \n    Grade 5 (fatal outcome)                                                           0            0              0       \n  Total number of patients with study drug withdrawn due to AE                     6 (4.5%)     8 (6.0%)       4 (3.0%)   \n  Total number of patients with dose modified/interrupted due to AE               23 (17.2%)   20 (14.9%)     26 (19.7%)  \n  Total number of patients with treatment received for AE                         33 (24.6%)   37 (27.6%)     33 (25.0%)  \n  Total number of patients with all non-fatal AEs resolved                         4 (3.0%)     5 (3.7%)      14 (10.6%)  \n  Total number of patients with at least one non-fatal unresolved or ongoing AE   58 (43.3%)   56 (41.8%)     62 (47.0%)  \n  Total number of patients with at least one serious AE                               0            0              0       \n  Total number of patients with at least one related AE                           37 (27.6%)   46 (34.3%)     50 (37.9%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n  ADAE &lt;- filter(ADAE, ANL01FL == \"Y\" & SAFFL == \"Y\")\n\n  not_resolved &lt;- ADAE %&gt;%\n    filter(!(AEOUT %in% c(\"RECOVERED/RESOLVED\", \"FATAL\", \"RECOVERED/RESOLVED WITH SEQUELAE\"))) %&gt;%\n    distinct(USUBJID) %&gt;%\n    mutate(NOT_RESOLVED = \"Y\")\n\n  ADAE &lt;- ADAE %&gt;%\n    left_join(not_resolved, by = c(\"USUBJID\")) %&gt;%\n    mutate(\n      ALL_RESOLVED = with_label(is.na(NOT_RESOLVED), \"All non-fatal AEs resolved\"),\n      NOT_RESOLVED = with_label(!is.na(NOT_RESOLVED), \"At least one non-fatal unresolved or ongoing AE\")\n    )\n\n  ADAE &lt;- ADAE %&gt;%\n    mutate(\n      AEDECOD = as.character(AEDECOD),\n      WD = with_label(AEACN == \"DRUG WITHDRAWN\", \"AE that led to study drug withdrawal\"),\n      DSM = with_label(\n        AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n        \"AE that led to study drug dose modified/interrupted\"\n      ),\n      CONTRT = with_label(AECONTRT == \"Y\", \"AE that required treatment\"),\n      SER = with_label(AESER == \"Y\", \"Serious AE\"),\n      REL = with_label(AEREL == \"Y\", \"Related AE\"),\n      ALL_RESOLVED_WD = with_label(\n        WD == TRUE & ALL_RESOLVED == TRUE,\n        \"Resolved AE that led to study drug withdrawal\"\n      ),\n      ALL_RESOLVED_DSM = with_label(\n        DSM == TRUE & ALL_RESOLVED == TRUE,\n        \"Resolved AE that led to study drug dose modified/interrupted\"\n      ),\n      ALL_RESOLVED_CONTRT = with_label(\n        CONTRT == TRUE & ALL_RESOLVED == TRUE,\n        \"Resolved AE that required treatment\"\n      ),\n      NOT_RESOLVED_WD = with_label(\n        WD == TRUE & NOT_RESOLVED == TRUE,\n        \"Unresolved AE that led to study drug withdrawal\"\n      ),\n      NOT_RESOLVED_DSM = with_label(\n        DSM == TRUE & NOT_RESOLVED == TRUE,\n        \"Unresolved AE that led to study drug dose modified/interrupted\"\n      ),\n      NOT_RESOLVED_CONTRT = with_label(\n        CONTRT == TRUE & NOT_RESOLVED == TRUE,\n        \"Unresolved AE that required treatment\"\n      ),\n      SERWD = with_label(\n        AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\",\n        \"Serious AE that led to study drug withdrawal\"\n      ),\n      SERCONTRT = with_label(\n        AECONTRT == \"Y\" & AESER == \"Y\",\n        \"Serious AE that required treatment\"\n      ),\n      SERDSM = with_label(\n        AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n        \"Serious AE that led to study drug dose modified/interrupted\"\n      ),\n      RELWD = with_label(\n        AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\", \"Related AE that led to study drug withdrawal\"\n      ),\n      RELDSM = with_label(\n        AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n        \"Related AE that led to study drug dose modified/interrupted\"\n      ),\n      RELCONTRT = with_label(AECONTRT == \"Y\" & AEREL == \"Y\", \"Related AE that required treatment\"),\n      RELSER = with_label(AESER == \"Y\" & AEREL == \"Y\", \"Serious related AE\")\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\naesi_vars &lt;- c(\n  \"WD\", \"DSM\", \"CONTRT\", \"ALL_RESOLVED_WD\", \"ALL_RESOLVED_DSM\", \"ALL_RESOLVED_CONTRT\",\n  \"NOT_RESOLVED_WD\", \"NOT_RESOLVED_DSM\", \"NOT_RESOLVED_CONTRT\", \"SER\", \"SERWD\", \"SERDSM\",\n  \"SERCONTRT\", \"REL\", \"RELWD\", \"RELDSM\", \"RELCONTRT\", \"RELSER\"\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_summary(\n      label = \"Adverse Events Summary\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(\"ADSL\", c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      flag_var_anl = choices_selected(\n        choices = variable_choices(\"ADAE\", aesi_vars),\n        selected = aesi_vars[1],\n        keep_order = TRUE,\n        fixed = FALSE\n      ),\n      add_total = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:47:32 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -528,7 +528,7 @@
     "href": "tables/adverse-events/aet02_smq.html",
     "title": "AET02_SMQ",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Customized Queries\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(stringr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n# Simulate a random AAG dataset.\naag &lt;- data.frame(\n  NAMVAR = c(\"SMQ01NAM\", \"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\", \"CQ01NAM\"),\n  SRCVAR = rep(\"AEDECOD\", 5),\n  GRPTYPE = c(\"SMQ\", \"SMQ\", \"SMQ\", \"CUSTOM\", \"CUSTOM\"),\n  REFID = c(1, 1, 2, 3, 3),\n  REFNAME = c(rep(\"C.1.1.1.3/B.2.2.3.1 AESI\", 2), \"non observed SMQ02NAM level\", rep(\"D.2.1.5.3/A.1.1.1.1 AESI\", 2)),\n  SCOPE = c(\"BROAD\", \"BROAD\", \"BROAD\", \"\", \"\"),\n  REFTERM = c(\"C.1.1.1.3\", \"B.2.2.3.1\", \"Z.9.9.9.9\", \"D.2.1.5.3\", \"A.1.1.1.1\"),\n  stringsAsFactors = FALSE\n)\n\n# Create summary AAG dataset (used for labelling).\n# Note it's important to incorporate SCOPE into the basket names for SMQs so as to\n# match the flags present in ADAE.\naag_summary &lt;- aag %&gt;%\n  select(NAMVAR, REFNAME, SCOPE) %&gt;%\n  unique() %&gt;%\n  mutate(\n    REFNAME_SCOPE = ifelse(SCOPE == \"\", REFNAME, paste0(REFNAME, \"(\", SCOPE, \")\"))\n  ) %&gt;%\n  rename(basket = NAMVAR, basket_name = REFNAME_SCOPE) %&gt;%\n  select(basket, basket_name)\n\n# Make a summary of the full ADAE based on AAG by using h_stack_by_baskets helper function\nadae_smq_all &lt;- h_stack_by_baskets(\n  df = adae,\n  aag_summary = aag_summary,\n  keys = c(\"STUDYID\", \"USUBJID\", \"ACTARMCD\", \"AEDECOD\")\n)\n\n# Post-process adae_smq_all to keep only certain baskets of interest for variant 1\n# Not need to drop baskets for variant 2 as all baskets will be considered.\nbaskets_to_drop_variant &lt;- aag_summary$basket_name[!aag_summary$basket %in% c(\"SMQ01NAM\")]\n\n# Adjust the analysis dataset based on basket subset for variant 1.\nadae_smq_1 &lt;- adae_smq_all %&gt;%\n  filter(!adae_smq_all$SMQ %in% baskets_to_drop_variant) %&gt;%\n  mutate(\n    SMQ = tern::fct_discard(SMQ, discard = baskets_to_drop_variant)\n  )\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L,\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A        ARM B        ARM C   \n  Dictionary-Derived Term                                     (N=134)      (N=134)      (N=132)  \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     72 (53.7%)   79 (59.0%)   75 (56.8%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                  \n  Total number of patients with at least one adverse event   72 (53.7%)   79 (59.0%)   75 (56.8%)\n  Total number of events                                        119          139          141    \n  dcd B.2.2.3.1                                              48 (35.8%)   54 (40.3%)   51 (38.6%)\n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)   43 (32.6%)\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) {\n  !is(tr, \"ContentRow\") && all_zero_or_na(tr) && !grepl(\"Total number of\", obj_label(tr))\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nscore_ae_pts &lt;- function(tt) {\n  count_vals &lt;- cell_values(tree_children(tt)[[\"USUBJID\"]])[[1]]\n  sum(matrix(unlist(count_vals), nrow = length(count_vals), byrow = TRUE)[, 1])\n}\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_all,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = score_ae_pts) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A         ARM B         ARM C   \n  Dictionary-Derived Term                                     (N=134)       (N=134)       (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     95 (70.9%)   103 (76.9%)   108 (81.8%)\nD.2.1.5.3/A.1.1.1.1 AESI                                                                           \n  Total number of patients with at least one adverse event   74 (55.2%)   80 (59.7%)    87 (65.9%) \n  Total number of events                                        126           134           162    \n  dcd D.2.1.5.3                                              47 (35.1%)   58 (43.3%)    57 (43.2%) \n  dcd A.1.1.1.1                                              50 (37.3%)   45 (33.6%)    63 (47.7%) \nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                    \n  Total number of patients with at least one adverse event   72 (53.7%)   79 (59.0%)    75 (56.8%) \n  Total number of events                                        119           139           141    \n  dcd B.2.2.3.1                                              48 (35.8%)   54 (40.3%)    51 (38.6%) \n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)    43 (32.6%) \nnon observed SMQ02NAM level(BROAD)                                                                 \n  Total number of patients with at least one adverse event       0             0             0     \n  Total number of events                                         0             0             0     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\nnames_baskets &lt;- grep(\"^(SMQ|CQ).*NAM$\", names(ADAE), value = TRUE)\nnames_scopes &lt;- grep(\"^SMQ.*SC$\", names(ADAE), value = TRUE)\n\ncs_baskets &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_baskets),\n  selected = names_baskets\n)\ncs_scopes &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_scopes),\n  selected = names_scopes,\n  fixed = TRUE\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_smq(\n      label = \"Adverse events by `SMQ` Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"SEX\")),\n        selected = \"ARM\"\n      ),\n      add_total = FALSE,\n      baskets = cs_baskets,\n      scopes = cs_scopes,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, subset = c(\"AEDECOD\")),\n        selected = \"AEDECOD\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:48:17 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      stringr_1.5.1                   \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013            dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Customized Queries\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(stringr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n# Simulate a random AAG dataset.\naag &lt;- data.frame(\n  NAMVAR = c(\"SMQ01NAM\", \"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\", \"CQ01NAM\"),\n  SRCVAR = rep(\"AEDECOD\", 5),\n  GRPTYPE = c(\"SMQ\", \"SMQ\", \"SMQ\", \"CUSTOM\", \"CUSTOM\"),\n  REFID = c(1, 1, 2, 3, 3),\n  REFNAME = c(rep(\"C.1.1.1.3/B.2.2.3.1 AESI\", 2), \"non observed SMQ02NAM level\", rep(\"D.2.1.5.3/A.1.1.1.1 AESI\", 2)),\n  SCOPE = c(\"BROAD\", \"BROAD\", \"BROAD\", \"\", \"\"),\n  REFTERM = c(\"C.1.1.1.3\", \"B.2.2.3.1\", \"Z.9.9.9.9\", \"D.2.1.5.3\", \"A.1.1.1.1\"),\n  stringsAsFactors = FALSE\n)\n\n# Create summary AAG dataset (used for labelling).\n# Note it's important to incorporate SCOPE into the basket names for SMQs so as to\n# match the flags present in ADAE.\naag_summary &lt;- aag %&gt;%\n  select(NAMVAR, REFNAME, SCOPE) %&gt;%\n  unique() %&gt;%\n  mutate(\n    REFNAME_SCOPE = ifelse(SCOPE == \"\", REFNAME, paste0(REFNAME, \"(\", SCOPE, \")\"))\n  ) %&gt;%\n  rename(basket = NAMVAR, basket_name = REFNAME_SCOPE) %&gt;%\n  select(basket, basket_name)\n\n# Make a summary of the full ADAE based on AAG by using h_stack_by_baskets helper function\nadae_smq_all &lt;- h_stack_by_baskets(\n  df = adae,\n  aag_summary = aag_summary,\n  keys = c(\"STUDYID\", \"USUBJID\", \"ACTARMCD\", \"AEDECOD\")\n)\n\n# Post-process adae_smq_all to keep only certain baskets of interest for variant 1\n# Not need to drop baskets for variant 2 as all baskets will be considered.\nbaskets_to_drop_variant &lt;- aag_summary$basket_name[!aag_summary$basket %in% c(\"SMQ01NAM\")]\n\n# Adjust the analysis dataset based on basket subset for variant 1.\nadae_smq_1 &lt;- adae_smq_all %&gt;%\n  filter(!adae_smq_all$SMQ %in% baskets_to_drop_variant) %&gt;%\n  mutate(\n    SMQ = tern::fct_discard(SMQ, discard = baskets_to_drop_variant)\n  )\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L,\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A        ARM B        ARM C   \n  Dictionary-Derived Term                                     (N=134)      (N=134)      (N=132)  \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     72 (53.7%)   79 (59.0%)   75 (56.8%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                  \n  Total number of patients with at least one adverse event   72 (53.7%)   79 (59.0%)   75 (56.8%)\n  Total number of events                                        119          139          141    \n  dcd B.2.2.3.1                                              48 (35.8%)   54 (40.3%)   51 (38.6%)\n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)   43 (32.6%)\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) {\n  !is(tr, \"ContentRow\") && all_zero_or_na(tr) && !grepl(\"Total number of\", obj_label(tr))\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nscore_ae_pts &lt;- function(tt) {\n  count_vals &lt;- cell_values(tree_children(tt)[[\"USUBJID\"]])[[1]]\n  sum(matrix(unlist(count_vals), nrow = length(count_vals), byrow = TRUE)[, 1])\n}\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_all,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = score_ae_pts) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A         ARM B         ARM C   \n  Dictionary-Derived Term                                     (N=134)       (N=134)       (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     95 (70.9%)   103 (76.9%)   108 (81.8%)\nD.2.1.5.3/A.1.1.1.1 AESI                                                                           \n  Total number of patients with at least one adverse event   74 (55.2%)   80 (59.7%)    87 (65.9%) \n  Total number of events                                        126           134           162    \n  dcd D.2.1.5.3                                              47 (35.1%)   58 (43.3%)    57 (43.2%) \n  dcd A.1.1.1.1                                              50 (37.3%)   45 (33.6%)    63 (47.7%) \nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                    \n  Total number of patients with at least one adverse event   72 (53.7%)   79 (59.0%)    75 (56.8%) \n  Total number of events                                        119           139           141    \n  dcd B.2.2.3.1                                              48 (35.8%)   54 (40.3%)    51 (38.6%) \n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)    43 (32.6%) \nnon observed SMQ02NAM level(BROAD)                                                                 \n  Total number of patients with at least one adverse event       0             0             0     \n  Total number of events                                         0             0             0     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\nnames_baskets &lt;- grep(\"^(SMQ|CQ).*NAM$\", names(ADAE), value = TRUE)\nnames_scopes &lt;- grep(\"^SMQ.*SC$\", names(ADAE), value = TRUE)\n\ncs_baskets &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_baskets),\n  selected = names_baskets\n)\ncs_scopes &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_scopes),\n  selected = names_scopes,\n  fixed = TRUE\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_smq(\n      label = \"Adverse events by `SMQ` Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"SEX\")),\n        selected = \"ARM\"\n      ),\n      add_total = FALSE,\n      baskets = cs_baskets,\n      scopes = cs_scopes,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, subset = c(\"AEDECOD\")),\n        selected = \"AEDECOD\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:46:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      stringr_1.5.1                   \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015            dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -540,7 +540,7 @@
     "href": "tables/adverse-events/aet05.html",
     "title": "AET05",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Setting Type of Confidence Interval\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\nanl &lt;- adaette %&gt;%\n  dplyr::filter(PARAM == \"Time to first occurrence of any adverse event\") %&gt;%\n  dplyr::mutate(is_event = CNSR == 0) %&gt;%\n  dplyr::mutate(n_events = as.integer(is_event))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(num_pt_year = 100)\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\n\nresult\n\n                                      A: Drug X        B: Placebo      C: Combination\n                                       (N=134)           (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk             162.4             103.9            172.6     \nNumber of adverse events observed         78               104               67      \nAE rate per 100 patient-years           48.03            100.14            38.82     \n95% CI                              (37.37, 58.69)   (80.89, 119.38)   (29.53, 48.12)\n\n\n\n\nThe type of confidence interval can be specified through the conf_type argument.\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(\n      conf_level = 0.95,\n      conf_type = \"exact\",\n      num_pt_year = 100\n    )\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\n\nresult\n\n                                      A: Drug X        B: Placebo      C: Combination\n                                       (N=134)           (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk             162.4             103.9            172.6     \nNumber of adverse events observed         78               104               67      \nAE rate per 100 patient-years           48.03            100.14            38.82     \n95% CI                              (37.97, 59.94)   (81.82, 121.34)   (30.09, 49.30)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  ADAETTE &lt;- ADAETTE %&gt;%\n    filter(PARAMCD %in% c(\"AETTE1\", \"AETTE2\", \"AETTE3\")) %&gt;%\n    mutate(is_event = CNSR == 0) %&gt;%\n    mutate(n_events = as.integer(is_event))\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_patyear(\n      label = \"AE rate adjusted for patient-years at risk Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARMCD\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADAETTE, \"PARAMCD\", \"PARAM\"),\n        selected = \"AETTE1\"\n      ),\n      events_var = choices_selected(\n        choices = variable_choices(ADAETTE, \"n_events\"),\n        selected = \"n_events\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:47:44 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            scda_0.1.6.9017                 \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Setting Type of Confidence Interval\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\nanl &lt;- adaette %&gt;%\n  dplyr::filter(PARAM == \"Time to first occurrence of any adverse event\") %&gt;%\n  dplyr::mutate(is_event = CNSR == 0) %&gt;%\n  dplyr::mutate(n_events = as.integer(is_event))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(num_pt_year = 100)\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\n\nresult\n\n                                      A: Drug X        B: Placebo      C: Combination\n                                       (N=134)           (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk             162.4             103.9            172.6     \nNumber of adverse events observed         78               104               67      \nAE rate per 100 patient-years           48.03            100.14            38.82     \n95% CI                              (37.37, 58.69)   (80.89, 119.38)   (29.53, 48.12)\n\n\n\n\nThe type of confidence interval can be specified through the conf_type argument.\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(\n      conf_level = 0.95,\n      conf_type = \"exact\",\n      num_pt_year = 100\n    )\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\n\nresult\n\n                                      A: Drug X        B: Placebo      C: Combination\n                                       (N=134)           (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk             162.4             103.9            172.6     \nNumber of adverse events observed         78               104               67      \nAE rate per 100 patient-years           48.03            100.14            38.82     \n95% CI                              (37.97, 59.94)   (81.82, 121.34)   (30.09, 49.30)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  ADAETTE &lt;- ADAETTE %&gt;%\n    filter(PARAMCD %in% c(\"AETTE1\", \"AETTE2\", \"AETTE3\")) %&gt;%\n    mutate(is_event = CNSR == 0) %&gt;%\n    mutate(n_events = as.integer(is_event))\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_patyear(\n      label = \"AE rate adjusted for patient-years at risk Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARMCD\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADAETTE, \"PARAMCD\", \"PARAM\"),\n        selected = \"AETTE1\"\n      ),\n      events_var = choices_selected(\n        choices = variable_choices(ADAETTE, \"n_events\"),\n        selected = \"n_events\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:46:21 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            scda_0.1.6.9017                 \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -552,7 +552,7 @@
     "href": "tables/adverse-events/aet09.html",
     "title": "AET09",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Including High-Level Terms\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_labels &lt;- var_labels(adae)\n\nadae_f &lt;- adae %&gt;%\n  filter(AEREL == \"Y\")\n\nvar_labels(adae_f) &lt;- adae_labels\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Overall total number of events related to study drug\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_f$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Total number of events related to study drug\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae_f, \"AEDECOD\", indent = 1L)\n\ntbl1 &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table()\n\nresult &lt;- tbl1 %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nBody System or Organ Class                                                          A: Drug X    B: Placebo    C: Combination   All Patients\n  Dictionary-Derived Term                                                            (N=134)       (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event related to study drug     105 (78.4%)   108 (80.6%)    109 (82.6%)     322 (80.5%) \nOverall total number of events related to study drug                                   282           299            336             917     \ncl D.2                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%) \n  Total number of events related to study drug                                         62            72              74             208     \n  dcd D.2.1.5.3                                                                    47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%) \ncl D.1                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%) \n  Total number of events related to study drug                                         61            51              71             183     \n  dcd D.1.1.1.1                                                                    50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%) \ncl B.1                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%) \n  Total number of events related to study drug                                         56            60              62             178     \n  dcd B.1.1.1.1                                                                    47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%) \ncl C.2                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%) \n  Total number of events related to study drug                                         48            53              65             166     \n  dcd C.2.1.2.1                                                                    35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%) \ncl C.1                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%) \n  Total number of events related to study drug                                         55            63              64             182     \n  dcd C.1.1.1.3                                                                    43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Overall total number of events related to study drug\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = 1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_f$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Total number of events related to study drug\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_f$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Total number of events related to study drug\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae_f, c(\"AEDECOD\"), indent = 2L)\n\ntbl2 &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table()\n\nresult &lt;- tbl2 %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nBody System or Organ Class                                                                                                       \n  High Level Term                                                                       A: Drug X    B: Placebo    C: Combination\n    Dictionary-Derived Term                                                              (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event related to study drug         105 (78.4%)   108 (80.6%)    109 (82.6%)  \nOverall total number of events related to study drug                                       282           299            336      \n  cl D.2                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     47 (35.1%)    58 (43.3%)      57 (43.2%)  \n    Total number of events related to study drug                                           62            72              74      \n    hlt D.2.1.5                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   47 (35.1%)    58 (43.3%)      57 (43.2%)  \n      Total number of events related to study drug                                         62            72              74      \n      dcd D.2.1.5.3                                                                    47 (35.1%)    58 (43.3%)      57 (43.2%)  \n  cl D.1                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     50 (37.3%)    42 (31.3%)      51 (38.6%)  \n    Total number of events related to study drug                                           61            51              71      \n    hlt D.1.1.1                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   50 (37.3%)    42 (31.3%)      51 (38.6%)  \n      Total number of events related to study drug                                         61            51              71      \n      dcd D.1.1.1.1                                                                    50 (37.3%)    42 (31.3%)      51 (38.6%)  \n  cl B.1                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     47 (35.1%)    49 (36.6%)      43 (32.6%)  \n    Total number of events related to study drug                                           56            60              62      \n    hlt B.1.1.1                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   47 (35.1%)    49 (36.6%)      43 (32.6%)  \n      Total number of events related to study drug                                         56            60              62      \n      dcd B.1.1.1.1                                                                    47 (35.1%)    49 (36.6%)      43 (32.6%)  \n  cl C.2                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     35 (26.1%)    48 (35.8%)      55 (41.7%)  \n    Total number of events related to study drug                                           48            53              65      \n    hlt C.2.1.2                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   35 (26.1%)    48 (35.8%)      55 (41.7%)  \n      Total number of events related to study drug                                         48            53              65      \n      dcd C.2.1.2.1                                                                    35 (26.1%)    48 (35.8%)      55 (41.7%)  \n  cl C.1                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     43 (32.1%)    46 (34.3%)      43 (32.6%)  \n    Total number of events related to study drug                                           55            63              64      \n    hlt C.1.1.1                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   43 (32.1%)    46 (34.3%)      43 (32.6%)  \n      Total number of events related to study drug                                         55            63              64      \n      dcd C.1.1.1.3                                                                    43 (32.1%)    46 (34.3%)      43 (32.6%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Events Related to Study Drug\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AEHLT\")),\n        selected = c(\"AEBODSYS\")\n      ),\n      add_total = FALSE,\n      event_type = \"adverse event related to study drug\", # define event type here\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADAE\", \"AEREL\", selected = \"Y\")) # related AEs only\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:47:12 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Including High-Level Terms\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_labels &lt;- var_labels(adae)\n\nadae_f &lt;- adae %&gt;%\n  filter(AEREL == \"Y\")\n\nvar_labels(adae_f) &lt;- adae_labels\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Overall total number of events related to study drug\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_f$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Total number of events related to study drug\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae_f, \"AEDECOD\", indent = 1L)\n\ntbl1 &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table()\n\nresult &lt;- tbl1 %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nBody System or Organ Class                                                          A: Drug X    B: Placebo    C: Combination   All Patients\n  Dictionary-Derived Term                                                            (N=134)       (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event related to study drug     105 (78.4%)   108 (80.6%)    109 (82.6%)     322 (80.5%) \nOverall total number of events related to study drug                                   282           299            336             917     \ncl D.2                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%) \n  Total number of events related to study drug                                         62            72              74             208     \n  dcd D.2.1.5.3                                                                    47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%) \ncl D.1                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%) \n  Total number of events related to study drug                                         61            51              71             183     \n  dcd D.1.1.1.1                                                                    50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%) \ncl B.1                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%) \n  Total number of events related to study drug                                         56            60              62             178     \n  dcd B.1.1.1.1                                                                    47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%) \ncl C.2                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%) \n  Total number of events related to study drug                                         48            53              65             166     \n  dcd C.2.1.2.1                                                                    35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%) \ncl C.1                                                                                                                                      \n  Total number of patients with at least one adverse event related to study drug   43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%) \n  Total number of events related to study drug                                         55            63              64             182     \n  dcd C.1.1.1.3                                                                    43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Overall total number of events related to study drug\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = 1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_f$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Total number of events related to study drug\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_f$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event related to study drug\",\n      nonunique = \"Total number of events related to study drug\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae_f, c(\"AEDECOD\"), indent = 2L)\n\ntbl2 &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table()\n\nresult &lt;- tbl2 %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nBody System or Organ Class                                                                                                       \n  High Level Term                                                                       A: Drug X    B: Placebo    C: Combination\n    Dictionary-Derived Term                                                              (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event related to study drug         105 (78.4%)   108 (80.6%)    109 (82.6%)  \nOverall total number of events related to study drug                                       282           299            336      \n  cl D.2                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     47 (35.1%)    58 (43.3%)      57 (43.2%)  \n    Total number of events related to study drug                                           62            72              74      \n    hlt D.2.1.5                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   47 (35.1%)    58 (43.3%)      57 (43.2%)  \n      Total number of events related to study drug                                         62            72              74      \n      dcd D.2.1.5.3                                                                    47 (35.1%)    58 (43.3%)      57 (43.2%)  \n  cl D.1                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     50 (37.3%)    42 (31.3%)      51 (38.6%)  \n    Total number of events related to study drug                                           61            51              71      \n    hlt D.1.1.1                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   50 (37.3%)    42 (31.3%)      51 (38.6%)  \n      Total number of events related to study drug                                         61            51              71      \n      dcd D.1.1.1.1                                                                    50 (37.3%)    42 (31.3%)      51 (38.6%)  \n  cl B.1                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     47 (35.1%)    49 (36.6%)      43 (32.6%)  \n    Total number of events related to study drug                                           56            60              62      \n    hlt B.1.1.1                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   47 (35.1%)    49 (36.6%)      43 (32.6%)  \n      Total number of events related to study drug                                         56            60              62      \n      dcd B.1.1.1.1                                                                    47 (35.1%)    49 (36.6%)      43 (32.6%)  \n  cl C.2                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     35 (26.1%)    48 (35.8%)      55 (41.7%)  \n    Total number of events related to study drug                                           48            53              65      \n    hlt C.2.1.2                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   35 (26.1%)    48 (35.8%)      55 (41.7%)  \n      Total number of events related to study drug                                         48            53              65      \n      dcd C.2.1.2.1                                                                    35 (26.1%)    48 (35.8%)      55 (41.7%)  \n  cl C.1                                                                                                                         \n    Total number of patients with at least one adverse event related to study drug     43 (32.1%)    46 (34.3%)      43 (32.6%)  \n    Total number of events related to study drug                                           55            63              64      \n    hlt C.1.1.1                                                                                                                  \n      Total number of patients with at least one adverse event related to study drug   43 (32.1%)    46 (34.3%)      43 (32.6%)  \n      Total number of events related to study drug                                         55            63              64      \n      dcd C.1.1.1.3                                                                    43 (32.1%)    46 (34.3%)      43 (32.6%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Events Related to Study Drug\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AEHLT\")),\n        selected = c(\"AEBODSYS\")\n      ),\n      add_total = FALSE,\n      event_type = \"adverse event related to study drug\", # define event type here\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADAE\", \"AEREL\", selected = \"Y\")) # related AEs only\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:45:49 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -564,7 +564,7 @@
     "href": "tables/adverse-events/aet06.html",
     "title": "AET06",
     "section": "",
-    "text": "Data Setup\nAdverse Events by Sex\nAdverse Events by Other Baseline  Characteristics (e.g. Biomarker Group)\nAdverse Events by Baseline Characteristic  from ADSUB (e.g. BMI Category)\nAdverse Events by Regrouped Baseline  Characteristics (e.g. Race)\nAdverse Events by Baseline Characteristics  (e.g. Sex) Including High-Level Terms\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\nadsub &lt;- synthetic_cdisc_dataset(\"latest\", \"adsub\")\n\nadsub_bmi &lt;- adsub %&gt;%\n  filter(PARAMCD == \"BBMISI\") %&gt;%\n  select(STUDYID, USUBJID, AVALCAT1) %&gt;%\n  mutate(\n    AVALCAT1 = factor(AVALCAT1, levels = c(\"&lt;18.5\", \"18.5 - 24.9\", \"25 - 29.9\", \"&gt;30\"))\n  )\n\nadsl &lt;- adsl %&gt;%\n  mutate(\n    RACE1 = case_when(\n      RACE == \"WHITE\" ~ \"WHITE\",\n      TRUE ~ \"NON-WHITE\"\n    ),\n    RACE1 = factor(\n      RACE1,\n      levels = c(\"WHITE\", \"NON-WHITE\")\n    )\n  ) %&gt;%\n  left_join(\n    y = adsub_bmi,\n    by = c(\"STUDYID\", \"USUBJID\")\n  )\n\nadae_labels &lt;- var_labels(adae)\n\nadae &lt;- adae %&gt;%\n  mutate(\n    RACE1 = case_when(\n      RACE == \"WHITE\" ~ \"WHITE\",\n      TRUE ~ \"NON-WHITE\"\n    ),\n    RACE1 = factor(\n      RACE1,\n      levels = c(\"WHITE\", \"NON-WHITE\")\n    )\n  ) %&gt;%\n  left_join(\n    y = adsub_bmi,\n    by = c(\"STUDYID\", \"USUBJID\")\n  )\n\nvar_labels(adae) &lt;- c(adae_labels, \"RACE1\" = \"RACE WHITE/NON-WHITE\", \"AVALCAT1\" = \"Baseline BMI Category\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\nadsub_bmi &lt;- df_explicit_na(adsub_bmi)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                    A: Drug X                B: Placebo              C: Combination     \nBody System or Organ Class                                       F            M            F            M            F            M     \n  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     72 (91.1%)   50 (90.9%)   77 (93.9%)   46 (88.5%)   65 (92.9%)   55 (88.7%)\nOverall total number of events                                  377          232          419          203          378          325    \ncl A.1                                                                                                                                  \n  Total number of patients with at least one adverse event   53 (67.1%)   25 (45.5%)   51 (62.2%)   24 (46.2%)   43 (61.4%)   46 (74.2%)\n  Total number of events                                         85           47           93           37           86           74    \n  dcd A.1.1.1.1                                              34 (43.0%)   16 (29.1%)   31 (37.8%)   14 (26.9%)   33 (47.1%)   30 (48.4%)\n  dcd A.1.1.1.2                                              32 (40.5%)   16 (29.1%)   33 (40.2%)   15 (28.8%)   24 (34.3%)   26 (41.9%)\ncl B.2                                                                                                                                  \n  Total number of patients with at least one adverse event   46 (58.2%)   33 (60.0%)   45 (54.9%)   29 (55.8%)   44 (62.9%)   41 (66.1%)\n  Total number of events                                         81           48           86           52           64           79    \n  dcd B.2.2.3.1                                              30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n  dcd B.2.1.2.1                                              29 (36.7%)   20 (36.4%)   30 (36.6%)   14 (26.9%)   22 (31.4%)   30 (48.4%)\ncl D.1                                                                                                                                  \n  Total number of patients with at least one adverse event   45 (57.0%)   34 (61.8%)   40 (48.8%)   27 (51.9%)   41 (58.6%)   39 (62.9%)\n  Total number of events                                         72           55           64           42           73           62    \n  dcd D.1.1.1.1                                              25 (31.6%)   25 (45.5%)   29 (35.4%)   13 (25.0%)   27 (38.6%)   24 (38.7%)\n  dcd D.1.1.4.2                                              30 (38.0%)   18 (32.7%)   22 (26.8%)   20 (38.5%)   27 (38.6%)   23 (37.1%)\ncl D.2                                                                                                                                  \n  Total number of patients with at least one adverse event   26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n  Total number of events                                         35           27           49           23           43           31    \n  dcd D.2.1.5.3                                              26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\ncl B.1                                                                                                                                  \n  Total number of patients with at least one adverse event   28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\n  Total number of events                                         33           23           36           24           35           27    \n  dcd B.1.1.1.1                                              28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\ncl C.2                                                                                                                                  \n  Total number of patients with at least one adverse event   23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\n  Total number of events                                         32           16           39           14           33           32    \n  dcd C.2.1.2.1                                              23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\ncl C.1                                                                                                                                  \n  Total number of patients with at least one adverse event   30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n  Total number of events                                         39           16           52           11           44           20    \n  dcd C.1.1.1.3                                              30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"BMRKR2\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                          A: Drug X                              B: Placebo                           C: Combination           \nBody System or Organ Class                                      LOW         MEDIUM        HIGH         LOW         MEDIUM        HIGH         LOW         MEDIUM        HIGH   \n  Dictionary-Derived Term                                      (N=50)       (N=37)       (N=47)       (N=45)       (N=56)       (N=33)       (N=40)       (N=42)       (N=50)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     46 (92.0%)   34 (91.9%)   42 (89.4%)   40 (88.9%)   53 (94.6%)   30 (90.9%)   36 (90.0%)   39 (92.9%)   45 (90.0%)\nOverall total number of events                                  248          166          195          196          261          165          220          258          225    \ncl A.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   31 (62.0%)   19 (51.4%)   28 (59.6%)   25 (55.6%)   31 (55.4%)   19 (57.6%)   28 (70.0%)   30 (71.4%)   31 (62.0%)\n  Total number of events                                         53           34           45           46           54           30           61           51           48    \n  dcd A.1.1.1.1                                              22 (44.0%)   10 (27.0%)   18 (38.3%)   14 (31.1%)   20 (35.7%)   11 (33.3%)   21 (52.5%)   24 (57.1%)   18 (36.0%)\n  dcd A.1.1.1.2                                              17 (34.0%)   13 (35.1%)   18 (38.3%)   18 (40.0%)   18 (32.1%)   12 (36.4%)   18 (45.0%)   14 (33.3%)   18 (36.0%)\ncl B.2                                                                                                                                                                         \n  Total number of patients with at least one adverse event   34 (68.0%)   21 (56.8%)   24 (51.1%)   23 (51.1%)   33 (58.9%)   18 (54.5%)   24 (60.0%)   27 (64.3%)   34 (68.0%)\n  Total number of events                                         53           37           39           40           61           37           40           47           56    \n  dcd B.2.2.3.1                                              21 (42.0%)   13 (35.1%)   14 (29.8%)   18 (40.0%)   22 (39.3%)   14 (42.4%)   16 (40.0%)   15 (35.7%)   20 (40.0%)\n  dcd B.2.1.2.1                                              21 (42.0%)   11 (29.7%)   17 (36.2%)   11 (24.4%)   21 (37.5%)   12 (36.4%)   13 (32.5%)   17 (40.5%)   22 (44.0%)\ncl D.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   34 (68.0%)   21 (56.8%)   24 (51.1%)   18 (40.0%)   33 (58.9%)   16 (48.5%)   25 (62.5%)   31 (73.8%)   24 (48.0%)\n  Total number of events                                         51           35           41           27           49           30           45           53           37    \n  dcd D.1.1.1.1                                              19 (38.0%)   16 (43.2%)   15 (31.9%)   13 (28.9%)   19 (33.9%)   10 (30.3%)   14 (35.0%)   22 (52.4%)   15 (30.0%)\n  dcd D.1.1.4.2                                              21 (42.0%)   9 (24.3%)    18 (38.3%)   9 (20.0%)    20 (35.7%)   13 (39.4%)   17 (42.5%)   18 (42.9%)   15 (30.0%)\ncl D.2                                                                                                                                                                         \n  Total number of patients with at least one adverse event   20 (40.0%)   12 (32.4%)   15 (31.9%)   19 (42.2%)   22 (39.3%)   17 (51.5%)   13 (32.5%)   23 (54.8%)   21 (42.0%)\n  Total number of events                                         27           13           22           23           26           23           17           33           24    \n  dcd D.2.1.5.3                                              20 (40.0%)   12 (32.4%)   15 (31.9%)   19 (42.2%)   22 (39.3%)   17 (51.5%)   13 (32.5%)   23 (54.8%)   21 (42.0%)\ncl B.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   14 (28.0%)   14 (37.8%)   19 (40.4%)   15 (33.3%)   24 (42.9%)   10 (30.3%)   14 (35.0%)   15 (35.7%)   14 (28.0%)\n  Total number of events                                         16           19           21           18           25           17           19           25           18    \n  dcd B.1.1.1.1                                              14 (28.0%)   14 (37.8%)   19 (40.4%)   15 (33.3%)   24 (42.9%)   10 (30.3%)   14 (35.0%)   15 (35.7%)   14 (28.0%)\ncl C.2                                                                                                                                                                         \n  Total number of patients with at least one adverse event   18 (36.0%)   8 (21.6%)    9 (19.1%)    15 (33.3%)   22 (39.3%)   11 (33.3%)   18 (45.0%)   17 (40.5%)   20 (40.0%)\n  Total number of events                                         27           9            12           18           23           12           20           20           25    \n  dcd C.2.1.2.1                                              18 (36.0%)   8 (21.6%)    9 (19.1%)    15 (33.3%)   22 (39.3%)   11 (33.3%)   18 (45.0%)   17 (40.5%)   20 (40.0%)\ncl C.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   17 (34.0%)   14 (37.8%)   12 (25.5%)   17 (37.8%)   17 (30.4%)   12 (36.4%)   12 (30.0%)   17 (40.5%)   14 (28.0%)\n  Total number of events                                         21           19           15           24           23           16           18           29           17    \n  dcd C.1.1.1.3                                              17 (34.0%)   14 (37.8%)   12 (25.5%)   17 (37.8%)   17 (30.4%)   12 (36.4%)   12 (30.0%)   17 (40.5%)   14 (28.0%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"AVALCAT1\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                                 A: Drug X                                          B: Placebo                                         C: Combination                  \nBody System or Organ Class                                     &lt;18.5      18.5 - 24.9   25 - 29.9      &gt;30         &lt;18.5      18.5 - 24.9   25 - 29.9      &gt;30         &lt;18.5      18.5 - 24.9   25 - 29.9       &gt;30    \n  Dictionary-Derived Term                                      (N=44)       (N=17)       (N=11)       (N=62)       (N=37)       (N=18)       (N=10)       (N=69)       (N=28)       (N=20)        (N=18)       (N=66)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     41 (93.2%)   14 (82.4%)    11 (100%)   56 (90.3%)   35 (94.6%)   16 (88.9%)    9 (90.0%)   63 (91.3%)   25 (89.3%)   19 (95.0%)    15 (83.3%)   61 (92.4%)\nOverall total number of events                                  186           80           66          277          174           89           47          312          137           129          100          337    \ncl A.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   23 (52.3%)   12 (70.6%)    7 (63.6%)   36 (58.1%)   19 (51.4%)   10 (55.6%)    6 (60.0%)   40 (58.0%)   16 (57.1%)   13 (65.0%)    14 (77.8%)   46 (69.7%)\n  Total number of events                                         38           22           13           59           35           22            6           67           30           23            30           77    \n  dcd A.1.1.1.1                                              14 (31.8%)    7 (41.2%)    3 (27.3%)   26 (41.9%)   12 (32.4%)    6 (33.3%)    4 (40.0%)   23 (33.3%)   12 (42.9%)    7 (35.0%)    13 (72.2%)   31 (47.0%)\n  dcd A.1.1.1.2                                              15 (34.1%)    8 (47.1%)    5 (45.5%)   20 (32.3%)   12 (32.4%)    8 (44.4%)    2 (20.0%)   26 (37.7%)   9 (32.1%)     7 (35.0%)    7 (38.9%)    27 (40.9%)\ncl B.2                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   23 (52.3%)   10 (58.8%)    8 (72.7%)   38 (61.3%)   21 (56.8%)    8 (44.4%)    7 (70.0%)   38 (55.1%)   16 (57.1%)   16 (80.0%)    12 (66.7%)   41 (62.1%)\n  Total number of events                                         37           19           12           61           41           18           12           67           28           28            18           69    \n  dcd B.2.2.3.1                                              13 (29.5%)    7 (41.2%)    6 (54.5%)   22 (35.5%)   14 (37.8%)    7 (38.9%)    6 (60.0%)   27 (39.1%)   7 (25.0%)    13 (65.0%)    5 (27.8%)    26 (39.4%)\n  dcd B.2.1.2.1                                              14 (31.8%)    8 (47.1%)    4 (36.4%)   23 (37.1%)   14 (37.8%)    6 (33.3%)    4 (40.0%)   20 (29.0%)   13 (46.4%)    8 (40.0%)    8 (44.4%)    23 (34.8%)\ncl D.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   24 (54.5%)    9 (52.9%)    8 (72.7%)   38 (61.3%)   18 (48.6%)    5 (27.8%)    7 (70.0%)   37 (53.6%)   15 (53.6%)   12 (60.0%)    11 (61.1%)   42 (63.6%)\n  Total number of events                                         38           11           17           61           30           11           14           51           30           23            16           66    \n  dcd D.1.1.1.1                                              13 (29.5%)    7 (41.2%)    7 (63.6%)   23 (37.1%)   13 (35.1%)    3 (16.7%)    4 (40.0%)   22 (31.9%)   12 (42.9%)    6 (30.0%)    8 (44.4%)    25 (37.9%)\n  dcd D.1.1.4.2                                              16 (36.4%)    4 (23.5%)    6 (54.5%)   22 (35.5%)   10 (27.0%)    4 (22.2%)    5 (50.0%)   23 (33.3%)   10 (35.7%)    9 (45.0%)    6 (33.3%)    25 (37.9%)\ncl D.2                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   14 (31.8%)    7 (41.2%)    5 (45.5%)   21 (33.9%)   14 (37.8%)   10 (55.6%)    4 (40.0%)   30 (43.5%)   13 (46.4%)   10 (50.0%)    7 (38.9%)    27 (40.9%)\n  Total number of events                                         20            7           10           25           17           14            5           36           18           15            10           31    \n  dcd D.2.1.5.3                                              14 (31.8%)    7 (41.2%)    5 (45.5%)   21 (33.9%)   14 (37.8%)   10 (55.6%)    4 (40.0%)   30 (43.5%)   13 (46.4%)   10 (50.0%)    7 (38.9%)    27 (40.9%)\ncl B.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   17 (38.6%)    5 (29.4%)    7 (63.6%)   18 (29.0%)   14 (37.8%)    8 (44.4%)    4 (40.0%)   23 (33.3%)   7 (25.0%)    10 (50.0%)    7 (38.9%)    19 (28.8%)\n  Total number of events                                         22            5            7           22           16           11            4           29           11           12            8            31    \n  dcd B.1.1.1.1                                              17 (38.6%)    5 (29.4%)    7 (63.6%)   18 (29.0%)   14 (37.8%)    8 (44.4%)    4 (40.0%)   23 (33.3%)   7 (25.0%)    10 (50.0%)    7 (38.9%)    19 (28.8%)\ncl C.2                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   14 (31.8%)    4 (23.5%)    2 (18.2%)   15 (24.2%)   15 (40.5%)    5 (27.8%)    4 (40.0%)   24 (34.8%)   10 (35.7%)   13 (65.0%)    8 (44.4%)    24 (36.4%)\n  Total number of events                                         17            9            3           19           15            6            5           27           12           16            9            28    \n  dcd C.2.1.2.1                                              14 (31.8%)    4 (23.5%)    2 (18.2%)   15 (24.2%)   15 (40.5%)    5 (27.8%)    4 (40.0%)   24 (34.8%)   10 (35.7%)   13 (65.0%)    8 (44.4%)    24 (36.4%)\ncl C.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   11 (25.0%)    4 (23.5%)    4 (36.4%)   24 (38.7%)   13 (35.1%)    6 (33.3%)    1 (10.0%)   26 (37.7%)   6 (21.4%)     7 (35.0%)    6 (33.3%)    24 (36.4%)\n  Total number of events                                         14            7            4           30           20            7            1           35           8            12            9            35    \n  dcd C.1.1.1.3                                              11 (25.0%)    4 (23.5%)    4 (36.4%)   24 (38.7%)   13 (35.1%)    6 (33.3%)    1 (10.0%)   26 (37.7%)   6 (21.4%)     7 (35.0%)    6 (33.3%)    24 (36.4%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"RACE1\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                    A: Drug X                B: Placebo              C: Combination     \nBody System or Organ Class                                     WHITE      NON-WHITE      WHITE      NON-WHITE      WHITE      NON-WHITE \n  Dictionary-Derived Term                                      (N=27)      (N=107)       (N=26)      (N=108)       (N=21)      (N=111)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     26 (96.3%)   96 (89.7%)   25 (96.2%)   98 (90.7%)   21 (100%)    99 (89.2%)\nOverall total number of events                                  109          500          144          478          114          589    \ncl A.1                                                                                                                                  \n  Total number of patients with at least one adverse event   16 (59.3%)   62 (57.9%)   21 (80.8%)   54 (50.0%)   15 (71.4%)   74 (66.7%)\n  Total number of events                                         30          102           37           93           25          135    \n  dcd A.1.1.1.1                                              9 (33.3%)    41 (38.3%)   14 (53.8%)   31 (28.7%)   10 (47.6%)   53 (47.7%)\n  dcd A.1.1.1.2                                              11 (40.7%)   37 (34.6%)   13 (50.0%)   35 (32.4%)   9 (42.9%)    41 (36.9%)\ncl B.2                                                                                                                                  \n  Total number of patients with at least one adverse event   15 (55.6%)   64 (59.8%)   16 (61.5%)   58 (53.7%)   12 (57.1%)   73 (65.8%)\n  Total number of events                                         18          111           28          110           17          126    \n  dcd B.2.2.3.1                                              4 (14.8%)    44 (41.1%)   10 (38.5%)   44 (40.7%)   5 (23.8%)    46 (41.4%)\n  dcd B.2.1.2.1                                              12 (44.4%)   37 (34.6%)   9 (34.6%)    35 (32.4%)   7 (33.3%)    45 (40.5%)\ncl D.1                                                                                                                                  \n  Total number of patients with at least one adverse event   18 (66.7%)   61 (57.0%)   15 (57.7%)   52 (48.1%)   14 (66.7%)   66 (59.5%)\n  Total number of events                                         27          100           23           83           27          108    \n  dcd D.1.1.1.1                                              11 (40.7%)   39 (36.4%)   14 (53.8%)   28 (25.9%)   12 (57.1%)   39 (35.1%)\n  dcd D.1.1.4.2                                              11 (40.7%)   37 (34.6%)   7 (26.9%)    35 (32.4%)   9 (42.9%)    41 (36.9%)\ncl D.2                                                                                                                                  \n  Total number of patients with at least one adverse event   6 (22.2%)    41 (38.3%)   15 (57.7%)   43 (39.8%)   9 (42.9%)    48 (43.2%)\n  Total number of events                                         6            56           18           54           12           62    \n  dcd D.2.1.5.3                                              6 (22.2%)    41 (38.3%)   15 (57.7%)   43 (39.8%)   9 (42.9%)    48 (43.2%)\ncl B.1                                                                                                                                  \n  Total number of patients with at least one adverse event   12 (44.4%)   35 (32.7%)   7 (26.9%)    42 (38.9%)   6 (28.6%)    37 (33.3%)\n  Total number of events                                         15           41           8            52           8            54    \n  dcd B.1.1.1.1                                              12 (44.4%)   35 (32.7%)   7 (26.9%)    42 (38.9%)   6 (28.6%)    37 (33.3%)\ncl C.2                                                                                                                                  \n  Total number of patients with at least one adverse event   5 (18.5%)    30 (28.0%)   11 (42.3%)   37 (34.3%)   11 (52.4%)   44 (39.6%)\n  Total number of events                                         8            40           12           41           12           53    \n  dcd C.2.1.2.1                                              5 (18.5%)    30 (28.0%)   11 (42.3%)   37 (34.3%)   11 (52.4%)   44 (39.6%)\ncl C.1                                                                                                                                  \n  Total number of patients with at least one adverse event   5 (18.5%)    38 (35.5%)   13 (50.0%)   33 (30.6%)   8 (38.1%)    35 (31.5%)\n  Total number of events                                         5            50           18           45           13           51    \n  dcd C.1.1.1.3                                              5 (18.5%)    38 (35.5%)   13 (50.0%)   33 (30.6%)   8 (38.1%)    35 (31.5%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 2L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class, High-Level Term and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nBody System or Organ Class                                            A: Drug X                B: Placebo              C: Combination     \n  High Level Term                                                  F            M            F            M            F            M     \n    Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event       72 (91.1%)   50 (90.9%)   77 (93.9%)   46 (88.5%)   65 (92.9%)   55 (88.7%)\nOverall total number of events                                    377          232          419          203          378          325    \ncl A.1                                                                                                                                    \n  Total number of patients with at least one adverse event     53 (67.1%)   25 (45.5%)   51 (62.2%)   24 (46.2%)   43 (61.4%)   46 (74.2%)\n  Total number of events                                           85           47           93           37           86           74    \n  hlt A.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   53 (67.1%)   25 (45.5%)   51 (62.2%)   24 (46.2%)   43 (61.4%)   46 (74.2%)\n    Total number of events                                         85           47           93           37           86           74    \n    dcd A.1.1.1.1                                              34 (43.0%)   16 (29.1%)   31 (37.8%)   14 (26.9%)   33 (47.1%)   30 (48.4%)\n    dcd A.1.1.1.2                                              32 (40.5%)   16 (29.1%)   33 (40.2%)   15 (28.8%)   24 (34.3%)   26 (41.9%)\ncl B.2                                                                                                                                    \n  Total number of patients with at least one adverse event     46 (58.2%)   33 (60.0%)   45 (54.9%)   29 (55.8%)   44 (62.9%)   41 (66.1%)\n  Total number of events                                           81           48           86           52           64           79    \n  hlt B.2.2.3                                                                                                                             \n    Total number of patients with at least one adverse event   30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n    Total number of events                                         40           24           43           33           37           40    \n    dcd B.2.2.3.1                                              30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n  hlt B.2.1.2                                                                                                                             \n    Total number of patients with at least one adverse event   29 (36.7%)   20 (36.4%)   30 (36.6%)   14 (26.9%)   22 (31.4%)   30 (48.4%)\n    Total number of events                                         41           24           43           19           27           39    \n    dcd B.2.1.2.1                                              29 (36.7%)   20 (36.4%)   30 (36.6%)   14 (26.9%)   22 (31.4%)   30 (48.4%)\ncl D.1                                                                                                                                    \n  Total number of patients with at least one adverse event     45 (57.0%)   34 (61.8%)   40 (48.8%)   27 (51.9%)   41 (58.6%)   39 (62.9%)\n  Total number of events                                           72           55           64           42           73           62    \n  hlt D.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   25 (31.6%)   25 (45.5%)   29 (35.4%)   13 (25.0%)   27 (38.6%)   24 (38.7%)\n    Total number of events                                         32           29           36           15           39           32    \n    dcd D.1.1.1.1                                              25 (31.6%)   25 (45.5%)   29 (35.4%)   13 (25.0%)   27 (38.6%)   24 (38.7%)\n  hlt D.1.1.4                                                                                                                             \n    Total number of patients with at least one adverse event   30 (38.0%)   18 (32.7%)   22 (26.8%)   20 (38.5%)   27 (38.6%)   23 (37.1%)\n    Total number of events                                         40           26           28           27           34           30    \n    dcd D.1.1.4.2                                              30 (38.0%)   18 (32.7%)   22 (26.8%)   20 (38.5%)   27 (38.6%)   23 (37.1%)\ncl D.2                                                                                                                                    \n  Total number of patients with at least one adverse event     26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n  Total number of events                                           35           27           49           23           43           31    \n  hlt D.2.1.5                                                                                                                             \n    Total number of patients with at least one adverse event   26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n    Total number of events                                         35           27           49           23           43           31    \n    dcd D.2.1.5.3                                              26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\ncl B.1                                                                                                                                    \n  Total number of patients with at least one adverse event     28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\n  Total number of events                                           33           23           36           24           35           27    \n  hlt B.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\n    Total number of events                                         33           23           36           24           35           27    \n    dcd B.1.1.1.1                                              28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\ncl C.2                                                                                                                                    \n  Total number of patients with at least one adverse event     23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\n  Total number of events                                           32           16           39           14           33           32    \n  hlt C.2.1.2                                                                                                                             \n    Total number of patients with at least one adverse event   23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\n    Total number of events                                         32           16           39           14           33           32    \n    dcd C.2.1.2.1                                              23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\ncl C.1                                                                                                                                    \n  Total number of patients with at least one adverse event     30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n  Total number of events                                           39           16           52           11           44           20    \n  hlt C.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n    Total number of events                                         39           16           52           11           44           20    \n    dcd C.1.1.1.3                                              30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\", \"SEX\"), c(\"ARM\", \"SEX\")),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      add_total = TRUE,\n      event_type = \"adverse event\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:46:41 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nAdverse Events by Sex\nAdverse Events by Other Baseline  Characteristics (e.g. Biomarker Group)\nAdverse Events by Baseline Characteristic  from ADSUB (e.g. BMI Category)\nAdverse Events by Regrouped Baseline  Characteristics (e.g. Race)\nAdverse Events by Baseline Characteristics  (e.g. Sex) Including High-Level Terms\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\nadsub &lt;- synthetic_cdisc_dataset(\"latest\", \"adsub\")\n\nadsub_bmi &lt;- adsub %&gt;%\n  filter(PARAMCD == \"BBMISI\") %&gt;%\n  select(STUDYID, USUBJID, AVALCAT1) %&gt;%\n  mutate(\n    AVALCAT1 = factor(AVALCAT1, levels = c(\"&lt;18.5\", \"18.5 - 24.9\", \"25 - 29.9\", \"&gt;30\"))\n  )\n\nadsl &lt;- adsl %&gt;%\n  mutate(\n    RACE1 = case_when(\n      RACE == \"WHITE\" ~ \"WHITE\",\n      TRUE ~ \"NON-WHITE\"\n    ),\n    RACE1 = factor(\n      RACE1,\n      levels = c(\"WHITE\", \"NON-WHITE\")\n    )\n  ) %&gt;%\n  left_join(\n    y = adsub_bmi,\n    by = c(\"STUDYID\", \"USUBJID\")\n  )\n\nadae_labels &lt;- var_labels(adae)\n\nadae &lt;- adae %&gt;%\n  mutate(\n    RACE1 = case_when(\n      RACE == \"WHITE\" ~ \"WHITE\",\n      TRUE ~ \"NON-WHITE\"\n    ),\n    RACE1 = factor(\n      RACE1,\n      levels = c(\"WHITE\", \"NON-WHITE\")\n    )\n  ) %&gt;%\n  left_join(\n    y = adsub_bmi,\n    by = c(\"STUDYID\", \"USUBJID\")\n  )\n\nvar_labels(adae) &lt;- c(adae_labels, \"RACE1\" = \"RACE WHITE/NON-WHITE\", \"AVALCAT1\" = \"Baseline BMI Category\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\nadsub_bmi &lt;- df_explicit_na(adsub_bmi)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                    A: Drug X                B: Placebo              C: Combination     \nBody System or Organ Class                                       F            M            F            M            F            M     \n  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     72 (91.1%)   50 (90.9%)   77 (93.9%)   46 (88.5%)   65 (92.9%)   55 (88.7%)\nOverall total number of events                                  377          232          419          203          378          325    \ncl A.1                                                                                                                                  \n  Total number of patients with at least one adverse event   53 (67.1%)   25 (45.5%)   51 (62.2%)   24 (46.2%)   43 (61.4%)   46 (74.2%)\n  Total number of events                                         85           47           93           37           86           74    \n  dcd A.1.1.1.1                                              34 (43.0%)   16 (29.1%)   31 (37.8%)   14 (26.9%)   33 (47.1%)   30 (48.4%)\n  dcd A.1.1.1.2                                              32 (40.5%)   16 (29.1%)   33 (40.2%)   15 (28.8%)   24 (34.3%)   26 (41.9%)\ncl B.2                                                                                                                                  \n  Total number of patients with at least one adverse event   46 (58.2%)   33 (60.0%)   45 (54.9%)   29 (55.8%)   44 (62.9%)   41 (66.1%)\n  Total number of events                                         81           48           86           52           64           79    \n  dcd B.2.2.3.1                                              30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n  dcd B.2.1.2.1                                              29 (36.7%)   20 (36.4%)   30 (36.6%)   14 (26.9%)   22 (31.4%)   30 (48.4%)\ncl D.1                                                                                                                                  \n  Total number of patients with at least one adverse event   45 (57.0%)   34 (61.8%)   40 (48.8%)   27 (51.9%)   41 (58.6%)   39 (62.9%)\n  Total number of events                                         72           55           64           42           73           62    \n  dcd D.1.1.1.1                                              25 (31.6%)   25 (45.5%)   29 (35.4%)   13 (25.0%)   27 (38.6%)   24 (38.7%)\n  dcd D.1.1.4.2                                              30 (38.0%)   18 (32.7%)   22 (26.8%)   20 (38.5%)   27 (38.6%)   23 (37.1%)\ncl D.2                                                                                                                                  \n  Total number of patients with at least one adverse event   26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n  Total number of events                                         35           27           49           23           43           31    \n  dcd D.2.1.5.3                                              26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\ncl B.1                                                                                                                                  \n  Total number of patients with at least one adverse event   28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\n  Total number of events                                         33           23           36           24           35           27    \n  dcd B.1.1.1.1                                              28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\ncl C.2                                                                                                                                  \n  Total number of patients with at least one adverse event   23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\n  Total number of events                                         32           16           39           14           33           32    \n  dcd C.2.1.2.1                                              23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\ncl C.1                                                                                                                                  \n  Total number of patients with at least one adverse event   30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n  Total number of events                                         39           16           52           11           44           20    \n  dcd C.1.1.1.3                                              30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"BMRKR2\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                          A: Drug X                              B: Placebo                           C: Combination           \nBody System or Organ Class                                      LOW         MEDIUM        HIGH         LOW         MEDIUM        HIGH         LOW         MEDIUM        HIGH   \n  Dictionary-Derived Term                                      (N=50)       (N=37)       (N=47)       (N=45)       (N=56)       (N=33)       (N=40)       (N=42)       (N=50)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     46 (92.0%)   34 (91.9%)   42 (89.4%)   40 (88.9%)   53 (94.6%)   30 (90.9%)   36 (90.0%)   39 (92.9%)   45 (90.0%)\nOverall total number of events                                  248          166          195          196          261          165          220          258          225    \ncl A.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   31 (62.0%)   19 (51.4%)   28 (59.6%)   25 (55.6%)   31 (55.4%)   19 (57.6%)   28 (70.0%)   30 (71.4%)   31 (62.0%)\n  Total number of events                                         53           34           45           46           54           30           61           51           48    \n  dcd A.1.1.1.1                                              22 (44.0%)   10 (27.0%)   18 (38.3%)   14 (31.1%)   20 (35.7%)   11 (33.3%)   21 (52.5%)   24 (57.1%)   18 (36.0%)\n  dcd A.1.1.1.2                                              17 (34.0%)   13 (35.1%)   18 (38.3%)   18 (40.0%)   18 (32.1%)   12 (36.4%)   18 (45.0%)   14 (33.3%)   18 (36.0%)\ncl B.2                                                                                                                                                                         \n  Total number of patients with at least one adverse event   34 (68.0%)   21 (56.8%)   24 (51.1%)   23 (51.1%)   33 (58.9%)   18 (54.5%)   24 (60.0%)   27 (64.3%)   34 (68.0%)\n  Total number of events                                         53           37           39           40           61           37           40           47           56    \n  dcd B.2.2.3.1                                              21 (42.0%)   13 (35.1%)   14 (29.8%)   18 (40.0%)   22 (39.3%)   14 (42.4%)   16 (40.0%)   15 (35.7%)   20 (40.0%)\n  dcd B.2.1.2.1                                              21 (42.0%)   11 (29.7%)   17 (36.2%)   11 (24.4%)   21 (37.5%)   12 (36.4%)   13 (32.5%)   17 (40.5%)   22 (44.0%)\ncl D.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   34 (68.0%)   21 (56.8%)   24 (51.1%)   18 (40.0%)   33 (58.9%)   16 (48.5%)   25 (62.5%)   31 (73.8%)   24 (48.0%)\n  Total number of events                                         51           35           41           27           49           30           45           53           37    \n  dcd D.1.1.1.1                                              19 (38.0%)   16 (43.2%)   15 (31.9%)   13 (28.9%)   19 (33.9%)   10 (30.3%)   14 (35.0%)   22 (52.4%)   15 (30.0%)\n  dcd D.1.1.4.2                                              21 (42.0%)   9 (24.3%)    18 (38.3%)   9 (20.0%)    20 (35.7%)   13 (39.4%)   17 (42.5%)   18 (42.9%)   15 (30.0%)\ncl D.2                                                                                                                                                                         \n  Total number of patients with at least one adverse event   20 (40.0%)   12 (32.4%)   15 (31.9%)   19 (42.2%)   22 (39.3%)   17 (51.5%)   13 (32.5%)   23 (54.8%)   21 (42.0%)\n  Total number of events                                         27           13           22           23           26           23           17           33           24    \n  dcd D.2.1.5.3                                              20 (40.0%)   12 (32.4%)   15 (31.9%)   19 (42.2%)   22 (39.3%)   17 (51.5%)   13 (32.5%)   23 (54.8%)   21 (42.0%)\ncl B.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   14 (28.0%)   14 (37.8%)   19 (40.4%)   15 (33.3%)   24 (42.9%)   10 (30.3%)   14 (35.0%)   15 (35.7%)   14 (28.0%)\n  Total number of events                                         16           19           21           18           25           17           19           25           18    \n  dcd B.1.1.1.1                                              14 (28.0%)   14 (37.8%)   19 (40.4%)   15 (33.3%)   24 (42.9%)   10 (30.3%)   14 (35.0%)   15 (35.7%)   14 (28.0%)\ncl C.2                                                                                                                                                                         \n  Total number of patients with at least one adverse event   18 (36.0%)   8 (21.6%)    9 (19.1%)    15 (33.3%)   22 (39.3%)   11 (33.3%)   18 (45.0%)   17 (40.5%)   20 (40.0%)\n  Total number of events                                         27           9            12           18           23           12           20           20           25    \n  dcd C.2.1.2.1                                              18 (36.0%)   8 (21.6%)    9 (19.1%)    15 (33.3%)   22 (39.3%)   11 (33.3%)   18 (45.0%)   17 (40.5%)   20 (40.0%)\ncl C.1                                                                                                                                                                         \n  Total number of patients with at least one adverse event   17 (34.0%)   14 (37.8%)   12 (25.5%)   17 (37.8%)   17 (30.4%)   12 (36.4%)   12 (30.0%)   17 (40.5%)   14 (28.0%)\n  Total number of events                                         21           19           15           24           23           16           18           29           17    \n  dcd C.1.1.1.3                                              17 (34.0%)   14 (37.8%)   12 (25.5%)   17 (37.8%)   17 (30.4%)   12 (36.4%)   12 (30.0%)   17 (40.5%)   14 (28.0%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"AVALCAT1\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                                 A: Drug X                                          B: Placebo                                         C: Combination                  \nBody System or Organ Class                                     &lt;18.5      18.5 - 24.9   25 - 29.9      &gt;30         &lt;18.5      18.5 - 24.9   25 - 29.9      &gt;30         &lt;18.5      18.5 - 24.9   25 - 29.9       &gt;30    \n  Dictionary-Derived Term                                      (N=44)       (N=17)       (N=11)       (N=62)       (N=37)       (N=18)       (N=10)       (N=69)       (N=28)       (N=20)        (N=18)       (N=66)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     41 (93.2%)   14 (82.4%)    11 (100%)   56 (90.3%)   35 (94.6%)   16 (88.9%)    9 (90.0%)   63 (91.3%)   25 (89.3%)   19 (95.0%)    15 (83.3%)   61 (92.4%)\nOverall total number of events                                  186           80           66          277          174           89           47          312          137           129          100          337    \ncl A.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   23 (52.3%)   12 (70.6%)    7 (63.6%)   36 (58.1%)   19 (51.4%)   10 (55.6%)    6 (60.0%)   40 (58.0%)   16 (57.1%)   13 (65.0%)    14 (77.8%)   46 (69.7%)\n  Total number of events                                         38           22           13           59           35           22            6           67           30           23            30           77    \n  dcd A.1.1.1.1                                              14 (31.8%)    7 (41.2%)    3 (27.3%)   26 (41.9%)   12 (32.4%)    6 (33.3%)    4 (40.0%)   23 (33.3%)   12 (42.9%)    7 (35.0%)    13 (72.2%)   31 (47.0%)\n  dcd A.1.1.1.2                                              15 (34.1%)    8 (47.1%)    5 (45.5%)   20 (32.3%)   12 (32.4%)    8 (44.4%)    2 (20.0%)   26 (37.7%)   9 (32.1%)     7 (35.0%)    7 (38.9%)    27 (40.9%)\ncl B.2                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   23 (52.3%)   10 (58.8%)    8 (72.7%)   38 (61.3%)   21 (56.8%)    8 (44.4%)    7 (70.0%)   38 (55.1%)   16 (57.1%)   16 (80.0%)    12 (66.7%)   41 (62.1%)\n  Total number of events                                         37           19           12           61           41           18           12           67           28           28            18           69    \n  dcd B.2.2.3.1                                              13 (29.5%)    7 (41.2%)    6 (54.5%)   22 (35.5%)   14 (37.8%)    7 (38.9%)    6 (60.0%)   27 (39.1%)   7 (25.0%)    13 (65.0%)    5 (27.8%)    26 (39.4%)\n  dcd B.2.1.2.1                                              14 (31.8%)    8 (47.1%)    4 (36.4%)   23 (37.1%)   14 (37.8%)    6 (33.3%)    4 (40.0%)   20 (29.0%)   13 (46.4%)    8 (40.0%)    8 (44.4%)    23 (34.8%)\ncl D.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   24 (54.5%)    9 (52.9%)    8 (72.7%)   38 (61.3%)   18 (48.6%)    5 (27.8%)    7 (70.0%)   37 (53.6%)   15 (53.6%)   12 (60.0%)    11 (61.1%)   42 (63.6%)\n  Total number of events                                         38           11           17           61           30           11           14           51           30           23            16           66    \n  dcd D.1.1.1.1                                              13 (29.5%)    7 (41.2%)    7 (63.6%)   23 (37.1%)   13 (35.1%)    3 (16.7%)    4 (40.0%)   22 (31.9%)   12 (42.9%)    6 (30.0%)    8 (44.4%)    25 (37.9%)\n  dcd D.1.1.4.2                                              16 (36.4%)    4 (23.5%)    6 (54.5%)   22 (35.5%)   10 (27.0%)    4 (22.2%)    5 (50.0%)   23 (33.3%)   10 (35.7%)    9 (45.0%)    6 (33.3%)    25 (37.9%)\ncl D.2                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   14 (31.8%)    7 (41.2%)    5 (45.5%)   21 (33.9%)   14 (37.8%)   10 (55.6%)    4 (40.0%)   30 (43.5%)   13 (46.4%)   10 (50.0%)    7 (38.9%)    27 (40.9%)\n  Total number of events                                         20            7           10           25           17           14            5           36           18           15            10           31    \n  dcd D.2.1.5.3                                              14 (31.8%)    7 (41.2%)    5 (45.5%)   21 (33.9%)   14 (37.8%)   10 (55.6%)    4 (40.0%)   30 (43.5%)   13 (46.4%)   10 (50.0%)    7 (38.9%)    27 (40.9%)\ncl B.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   17 (38.6%)    5 (29.4%)    7 (63.6%)   18 (29.0%)   14 (37.8%)    8 (44.4%)    4 (40.0%)   23 (33.3%)   7 (25.0%)    10 (50.0%)    7 (38.9%)    19 (28.8%)\n  Total number of events                                         22            5            7           22           16           11            4           29           11           12            8            31    \n  dcd B.1.1.1.1                                              17 (38.6%)    5 (29.4%)    7 (63.6%)   18 (29.0%)   14 (37.8%)    8 (44.4%)    4 (40.0%)   23 (33.3%)   7 (25.0%)    10 (50.0%)    7 (38.9%)    19 (28.8%)\ncl C.2                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   14 (31.8%)    4 (23.5%)    2 (18.2%)   15 (24.2%)   15 (40.5%)    5 (27.8%)    4 (40.0%)   24 (34.8%)   10 (35.7%)   13 (65.0%)    8 (44.4%)    24 (36.4%)\n  Total number of events                                         17            9            3           19           15            6            5           27           12           16            9            28    \n  dcd C.2.1.2.1                                              14 (31.8%)    4 (23.5%)    2 (18.2%)   15 (24.2%)   15 (40.5%)    5 (27.8%)    4 (40.0%)   24 (34.8%)   10 (35.7%)   13 (65.0%)    8 (44.4%)    24 (36.4%)\ncl C.1                                                                                                                                                                                                                 \n  Total number of patients with at least one adverse event   11 (25.0%)    4 (23.5%)    4 (36.4%)   24 (38.7%)   13 (35.1%)    6 (33.3%)    1 (10.0%)   26 (37.7%)   6 (21.4%)     7 (35.0%)    6 (33.3%)    24 (36.4%)\n  Total number of events                                         14            7            4           30           20            7            1           35           8            12            9            35    \n  dcd C.1.1.1.3                                              11 (25.0%)    4 (23.5%)    4 (36.4%)   24 (38.7%)   13 (35.1%)    6 (33.3%)    1 (10.0%)   26 (37.7%)   6 (21.4%)     7 (35.0%)    6 (33.3%)    24 (36.4%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"RACE1\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                                    A: Drug X                B: Placebo              C: Combination     \nBody System or Organ Class                                     WHITE      NON-WHITE      WHITE      NON-WHITE      WHITE      NON-WHITE \n  Dictionary-Derived Term                                      (N=27)      (N=107)       (N=26)      (N=108)       (N=21)      (N=111)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     26 (96.3%)   96 (89.7%)   25 (96.2%)   98 (90.7%)   21 (100%)    99 (89.2%)\nOverall total number of events                                  109          500          144          478          114          589    \ncl A.1                                                                                                                                  \n  Total number of patients with at least one adverse event   16 (59.3%)   62 (57.9%)   21 (80.8%)   54 (50.0%)   15 (71.4%)   74 (66.7%)\n  Total number of events                                         30          102           37           93           25          135    \n  dcd A.1.1.1.1                                              9 (33.3%)    41 (38.3%)   14 (53.8%)   31 (28.7%)   10 (47.6%)   53 (47.7%)\n  dcd A.1.1.1.2                                              11 (40.7%)   37 (34.6%)   13 (50.0%)   35 (32.4%)   9 (42.9%)    41 (36.9%)\ncl B.2                                                                                                                                  \n  Total number of patients with at least one adverse event   15 (55.6%)   64 (59.8%)   16 (61.5%)   58 (53.7%)   12 (57.1%)   73 (65.8%)\n  Total number of events                                         18          111           28          110           17          126    \n  dcd B.2.2.3.1                                              4 (14.8%)    44 (41.1%)   10 (38.5%)   44 (40.7%)   5 (23.8%)    46 (41.4%)\n  dcd B.2.1.2.1                                              12 (44.4%)   37 (34.6%)   9 (34.6%)    35 (32.4%)   7 (33.3%)    45 (40.5%)\ncl D.1                                                                                                                                  \n  Total number of patients with at least one adverse event   18 (66.7%)   61 (57.0%)   15 (57.7%)   52 (48.1%)   14 (66.7%)   66 (59.5%)\n  Total number of events                                         27          100           23           83           27          108    \n  dcd D.1.1.1.1                                              11 (40.7%)   39 (36.4%)   14 (53.8%)   28 (25.9%)   12 (57.1%)   39 (35.1%)\n  dcd D.1.1.4.2                                              11 (40.7%)   37 (34.6%)   7 (26.9%)    35 (32.4%)   9 (42.9%)    41 (36.9%)\ncl D.2                                                                                                                                  \n  Total number of patients with at least one adverse event   6 (22.2%)    41 (38.3%)   15 (57.7%)   43 (39.8%)   9 (42.9%)    48 (43.2%)\n  Total number of events                                         6            56           18           54           12           62    \n  dcd D.2.1.5.3                                              6 (22.2%)    41 (38.3%)   15 (57.7%)   43 (39.8%)   9 (42.9%)    48 (43.2%)\ncl B.1                                                                                                                                  \n  Total number of patients with at least one adverse event   12 (44.4%)   35 (32.7%)   7 (26.9%)    42 (38.9%)   6 (28.6%)    37 (33.3%)\n  Total number of events                                         15           41           8            52           8            54    \n  dcd B.1.1.1.1                                              12 (44.4%)   35 (32.7%)   7 (26.9%)    42 (38.9%)   6 (28.6%)    37 (33.3%)\ncl C.2                                                                                                                                  \n  Total number of patients with at least one adverse event   5 (18.5%)    30 (28.0%)   11 (42.3%)   37 (34.3%)   11 (52.4%)   44 (39.6%)\n  Total number of events                                         8            40           12           41           12           53    \n  dcd C.2.1.2.1                                              5 (18.5%)    30 (28.0%)   11 (42.3%)   37 (34.3%)   11 (52.4%)   44 (39.6%)\ncl C.1                                                                                                                                  \n  Total number of patients with at least one adverse event   5 (18.5%)    38 (35.5%)   13 (50.0%)   33 (30.6%)   8 (38.1%)    35 (31.5%)\n  Total number of events                                         5            50           18           45           13           51    \n  dcd C.1.1.1.3                                              5 (18.5%)    38 (35.5%)   13 (50.0%)   33 (30.6%)   8 (38.1%)    35 (31.5%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 2L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sorted by decreasing frequency across all groups by System Organ Class, High-Level Term and Preferred Term.\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nBody System or Organ Class                                            A: Drug X                B: Placebo              C: Combination     \n  High Level Term                                                  F            M            F            M            F            M     \n    Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event       72 (91.1%)   50 (90.9%)   77 (93.9%)   46 (88.5%)   65 (92.9%)   55 (88.7%)\nOverall total number of events                                    377          232          419          203          378          325    \ncl A.1                                                                                                                                    \n  Total number of patients with at least one adverse event     53 (67.1%)   25 (45.5%)   51 (62.2%)   24 (46.2%)   43 (61.4%)   46 (74.2%)\n  Total number of events                                           85           47           93           37           86           74    \n  hlt A.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   53 (67.1%)   25 (45.5%)   51 (62.2%)   24 (46.2%)   43 (61.4%)   46 (74.2%)\n    Total number of events                                         85           47           93           37           86           74    \n    dcd A.1.1.1.1                                              34 (43.0%)   16 (29.1%)   31 (37.8%)   14 (26.9%)   33 (47.1%)   30 (48.4%)\n    dcd A.1.1.1.2                                              32 (40.5%)   16 (29.1%)   33 (40.2%)   15 (28.8%)   24 (34.3%)   26 (41.9%)\ncl B.2                                                                                                                                    \n  Total number of patients with at least one adverse event     46 (58.2%)   33 (60.0%)   45 (54.9%)   29 (55.8%)   44 (62.9%)   41 (66.1%)\n  Total number of events                                           81           48           86           52           64           79    \n  hlt B.2.2.3                                                                                                                             \n    Total number of patients with at least one adverse event   30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n    Total number of events                                         40           24           43           33           37           40    \n    dcd B.2.2.3.1                                              30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n  hlt B.2.1.2                                                                                                                             \n    Total number of patients with at least one adverse event   29 (36.7%)   20 (36.4%)   30 (36.6%)   14 (26.9%)   22 (31.4%)   30 (48.4%)\n    Total number of events                                         41           24           43           19           27           39    \n    dcd B.2.1.2.1                                              29 (36.7%)   20 (36.4%)   30 (36.6%)   14 (26.9%)   22 (31.4%)   30 (48.4%)\ncl D.1                                                                                                                                    \n  Total number of patients with at least one adverse event     45 (57.0%)   34 (61.8%)   40 (48.8%)   27 (51.9%)   41 (58.6%)   39 (62.9%)\n  Total number of events                                           72           55           64           42           73           62    \n  hlt D.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   25 (31.6%)   25 (45.5%)   29 (35.4%)   13 (25.0%)   27 (38.6%)   24 (38.7%)\n    Total number of events                                         32           29           36           15           39           32    \n    dcd D.1.1.1.1                                              25 (31.6%)   25 (45.5%)   29 (35.4%)   13 (25.0%)   27 (38.6%)   24 (38.7%)\n  hlt D.1.1.4                                                                                                                             \n    Total number of patients with at least one adverse event   30 (38.0%)   18 (32.7%)   22 (26.8%)   20 (38.5%)   27 (38.6%)   23 (37.1%)\n    Total number of events                                         40           26           28           27           34           30    \n    dcd D.1.1.4.2                                              30 (38.0%)   18 (32.7%)   22 (26.8%)   20 (38.5%)   27 (38.6%)   23 (37.1%)\ncl D.2                                                                                                                                    \n  Total number of patients with at least one adverse event     26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n  Total number of events                                           35           27           49           23           43           31    \n  hlt D.2.1.5                                                                                                                             \n    Total number of patients with at least one adverse event   26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n    Total number of events                                         35           27           49           23           43           31    \n    dcd D.2.1.5.3                                              26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\ncl B.1                                                                                                                                    \n  Total number of patients with at least one adverse event     28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\n  Total number of events                                           33           23           36           24           35           27    \n  hlt B.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\n    Total number of events                                         33           23           36           24           35           27    \n    dcd B.1.1.1.1                                              28 (35.4%)   19 (34.5%)   33 (40.2%)   16 (30.8%)   24 (34.3%)   19 (30.6%)\ncl C.2                                                                                                                                    \n  Total number of patients with at least one adverse event     23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\n  Total number of events                                           32           16           39           14           33           32    \n  hlt C.2.1.2                                                                                                                             \n    Total number of patients with at least one adverse event   23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\n    Total number of events                                         32           16           39           14           33           32    \n    dcd C.2.1.2.1                                              23 (29.1%)   12 (21.8%)   36 (43.9%)   12 (23.1%)   30 (42.9%)   25 (40.3%)\ncl C.1                                                                                                                                    \n  Total number of patients with at least one adverse event     30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n  Total number of events                                           39           16           52           11           44           20    \n  hlt C.1.1.1                                                                                                                             \n    Total number of patients with at least one adverse event   30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n    Total number of events                                         39           16           52           11           44           20    \n    dcd C.1.1.1.3                                              30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\", \"SEX\"), c(\"ARM\", \"SEX\")),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      add_total = TRUE,\n      event_type = \"adverse event\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:45:18 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -576,7 +576,7 @@
     "href": "tables/adverse-events/aet09_smq.html",
     "title": "AET09_SMQ",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Customized Queries\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(stringr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing\n# levels. For details, refer to Teal and Study Data article.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n# AEs are filtered for relatedness to study drug.\nadae_rel &lt;- adae %&gt;% filter(AEREL == \"Y\")\n\n# Simulate a random AAG dataset.\naag &lt;- data.frame(\n  NAMVAR = c(\"SMQ01NAM\", \"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\", \"CQ01NAM\"),\n  SRCVAR = rep(\"AEDECOD\", 5),\n  GRPTYPE = c(\"SMQ\", \"SMQ\", \"SMQ\", \"CUSTOM\", \"CUSTOM\"),\n  REFID = c(1, 1, 2, 3, 3),\n  REFNAME = c(rep(\"C.1.1.1.3/B.2.2.3.1 AESI\", 2), \"non observed SMQ02NAM level\", rep(\"D.2.1.5.3/A.1.1.1.1 AESI\", 2)),\n  SCOPE = c(\"BROAD\", \"BROAD\", \"BROAD\", \"\", \"\"),\n  REFTERM = c(\"C.1.1.1.3\", \"B.2.2.3.1\", \"Z.9.9.9.9\", \"D.2.1.5.3\", \"A.1.1.1.1\"),\n  stringsAsFactors = FALSE\n)\n\n# Create summary AAG dataset (used for labelling).\n# Note it's important to incorporate SCOPE into the basket names for SMQs so as to\n# match the flags present in ADAE.\naag_summary &lt;- aag %&gt;%\n  select(NAMVAR, REFNAME, SCOPE) %&gt;%\n  unique() %&gt;%\n  mutate(\n    REFNAME_SCOPE = ifelse(SCOPE == \"\", REFNAME, paste0(REFNAME, \"(\", SCOPE, \")\"))\n  ) %&gt;%\n  rename(basket = NAMVAR, basket_name = REFNAME_SCOPE) %&gt;%\n  select(basket, basket_name)\n\n# Make a summary of the full ADAE based on AAG by using h_stack_by_baskets helper function\nadae_smq_all &lt;- h_stack_by_baskets(\n  df = adae_rel,\n  aag_summary = aag_summary,\n  keys = c(\"STUDYID\", \"USUBJID\", \"ACTARMCD\", \"AEDECOD\")\n)\n\n# Post-process adae_smq_all to keep only certain baskets of interest for variant 1\n# Not need to drop baskets for variant 2 as all baskets will be considered.\nbaskets_to_drop_variant &lt;- aag_summary$basket_name[!aag_summary$basket %in% c(\"SMQ01NAM\")]\n\n# Adjust the analysis dataset based on basket subset for variant 1.\nadae_smq_1 &lt;- adae_smq_all %&gt;%\n  filter(!adae_smq_all$SMQ %in% baskets_to_drop_variant) %&gt;%\n  mutate(\n    SMQ = tern::fct_discard(SMQ, discard = baskets_to_drop_variant)\n  )\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L,\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A        ARM B        ARM C   \n  Dictionary-Derived Term                                     (N=134)      (N=134)      (N=132)  \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     43 (32.1%)   46 (34.3%)   43 (32.6%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                  \n  Total number of patients with at least one adverse event   43 (32.1%)   46 (34.3%)   43 (32.6%)\n  Total number of events                                         55           63           64    \n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)   43 (32.6%)\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) {\n  !is(tr, \"ContentRow\") && all_zero_or_na(tr) && !grepl(\"Total number of\", obj_label(tr))\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nscore_ae_pts &lt;- function(tt) {\n  count_vals &lt;- cell_values(tree_children(tt)[[\"USUBJID\"]])[[1]]\n  sum(matrix(unlist(count_vals), nrow = length(count_vals), byrow = TRUE)[, 1])\n}\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_all,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = score_ae_pts) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A        ARM B        ARM C   \n  Dictionary-Derived Term                                     (N=134)      (N=134)      (N=132)  \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     69 (51.5%)   78 (58.2%)   77 (58.3%)\nD.2.1.5.3/A.1.1.1.1 AESI                                                                         \n  Total number of patients with at least one adverse event   47 (35.1%)   58 (43.3%)   57 (43.2%)\n  Total number of events                                         62           72           74    \n  dcd D.2.1.5.3                                              47 (35.1%)   58 (43.3%)   57 (43.2%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                  \n  Total number of patients with at least one adverse event   43 (32.1%)   46 (34.3%)   43 (32.6%)\n  Total number of events                                         55           63           64    \n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)   43 (32.6%)\nnon observed SMQ02NAM level(BROAD)                                                               \n  Total number of patients with at least one adverse event       0            0            0     \n  Total number of events                                         0            0            0     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nNote that filtering AEs for relatedness to study drug is not a necessary data pre-processing step for the module. It can be achieved using the teal module filter panel. The example here pre-sets the filters using AEREL = \"Y\" in adae.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\n\nnames_baskets &lt;- grep(\"^(SMQ|CQ).*NAM$\", names(ADAE), value = TRUE)\nnames_scopes &lt;- grep(\"^SMQ.*SC$\", names(ADAE), value = TRUE)\n\ncs_baskets &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_baskets),\n  selected = names_baskets\n)\ncs_scopes &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_scopes),\n  selected = names_scopes,\n  fixed = TRUE\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_smq(\n      label = \"Adverse Events by SMQ Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"SEX\")),\n        selected = \"ARM\"\n      ),\n      add_total = FALSE,\n      baskets = cs_baskets,\n      scopes = cs_scopes,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, subset = c(\"AEDECOD\")),\n        selected = \"AEDECOD\"\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADAE\", \"AEREL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:45:51 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      stringr_1.5.1                   \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013            dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Customized Queries\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(stringr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing\n# levels. For details, refer to Teal and Study Data article.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n# AEs are filtered for relatedness to study drug.\nadae_rel &lt;- adae %&gt;% filter(AEREL == \"Y\")\n\n# Simulate a random AAG dataset.\naag &lt;- data.frame(\n  NAMVAR = c(\"SMQ01NAM\", \"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\", \"CQ01NAM\"),\n  SRCVAR = rep(\"AEDECOD\", 5),\n  GRPTYPE = c(\"SMQ\", \"SMQ\", \"SMQ\", \"CUSTOM\", \"CUSTOM\"),\n  REFID = c(1, 1, 2, 3, 3),\n  REFNAME = c(rep(\"C.1.1.1.3/B.2.2.3.1 AESI\", 2), \"non observed SMQ02NAM level\", rep(\"D.2.1.5.3/A.1.1.1.1 AESI\", 2)),\n  SCOPE = c(\"BROAD\", \"BROAD\", \"BROAD\", \"\", \"\"),\n  REFTERM = c(\"C.1.1.1.3\", \"B.2.2.3.1\", \"Z.9.9.9.9\", \"D.2.1.5.3\", \"A.1.1.1.1\"),\n  stringsAsFactors = FALSE\n)\n\n# Create summary AAG dataset (used for labelling).\n# Note it's important to incorporate SCOPE into the basket names for SMQs so as to\n# match the flags present in ADAE.\naag_summary &lt;- aag %&gt;%\n  select(NAMVAR, REFNAME, SCOPE) %&gt;%\n  unique() %&gt;%\n  mutate(\n    REFNAME_SCOPE = ifelse(SCOPE == \"\", REFNAME, paste0(REFNAME, \"(\", SCOPE, \")\"))\n  ) %&gt;%\n  rename(basket = NAMVAR, basket_name = REFNAME_SCOPE) %&gt;%\n  select(basket, basket_name)\n\n# Make a summary of the full ADAE based on AAG by using h_stack_by_baskets helper function\nadae_smq_all &lt;- h_stack_by_baskets(\n  df = adae_rel,\n  aag_summary = aag_summary,\n  keys = c(\"STUDYID\", \"USUBJID\", \"ACTARMCD\", \"AEDECOD\")\n)\n\n# Post-process adae_smq_all to keep only certain baskets of interest for variant 1\n# Not need to drop baskets for variant 2 as all baskets will be considered.\nbaskets_to_drop_variant &lt;- aag_summary$basket_name[!aag_summary$basket %in% c(\"SMQ01NAM\")]\n\n# Adjust the analysis dataset based on basket subset for variant 1.\nadae_smq_1 &lt;- adae_smq_all %&gt;%\n  filter(!adae_smq_all$SMQ %in% baskets_to_drop_variant) %&gt;%\n  mutate(\n    SMQ = tern::fct_discard(SMQ, discard = baskets_to_drop_variant)\n  )\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L,\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A        ARM B        ARM C   \n  Dictionary-Derived Term                                     (N=134)      (N=134)      (N=132)  \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     43 (32.1%)   46 (34.3%)   43 (32.6%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                  \n  Total number of patients with at least one adverse event   43 (32.1%)   46 (34.3%)   43 (32.6%)\n  Total number of events                                         55           63           64    \n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)   43 (32.6%)\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) {\n  !is(tr, \"ContentRow\") && all_zero_or_na(tr) && !grepl(\"Total number of\", obj_label(tr))\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    drop = FALSE\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nscore_ae_pts &lt;- function(tt) {\n  count_vals &lt;- cell_values(tree_children(tt)[[\"USUBJID\"]])[[1]]\n  sum(matrix(unlist(count_vals), nrow = length(count_vals), byrow = TRUE)[, 1])\n}\n\nresult &lt;- build_table(\n  lyt,\n  df = adae_smq_all,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = score_ae_pts) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\nStandardized MedDRA Query                                      ARM A        ARM B        ARM C   \n  Dictionary-Derived Term                                     (N=134)      (N=134)      (N=132)  \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     69 (51.5%)   78 (58.2%)   77 (58.3%)\nD.2.1.5.3/A.1.1.1.1 AESI                                                                         \n  Total number of patients with at least one adverse event   47 (35.1%)   58 (43.3%)   57 (43.2%)\n  Total number of events                                         62           72           74    \n  dcd D.2.1.5.3                                              47 (35.1%)   58 (43.3%)   57 (43.2%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                  \n  Total number of patients with at least one adverse event   43 (32.1%)   46 (34.3%)   43 (32.6%)\n  Total number of events                                         55           63           64    \n  dcd C.1.1.1.3                                              43 (32.1%)   46 (34.3%)   43 (32.6%)\nnon observed SMQ02NAM level(BROAD)                                                               \n  Total number of patients with at least one adverse event       0            0            0     \n  Total number of events                                         0            0            0     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nNote that filtering AEs for relatedness to study drug is not a necessary data pre-processing step for the module. It can be achieved using the teal module filter panel. The example here pre-sets the filters using AEREL = \"Y\" in adae.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\n\nnames_baskets &lt;- grep(\"^(SMQ|CQ).*NAM$\", names(ADAE), value = TRUE)\nnames_scopes &lt;- grep(\"^SMQ.*SC$\", names(ADAE), value = TRUE)\n\ncs_baskets &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_baskets),\n  selected = names_baskets\n)\ncs_scopes &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_scopes),\n  selected = names_scopes,\n  fixed = TRUE\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_smq(\n      label = \"Adverse Events by SMQ Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"SEX\")),\n        selected = \"ARM\"\n      ),\n      add_total = FALSE,\n      baskets = cs_baskets,\n      scopes = cs_scopes,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, subset = c(\"AEDECOD\")),\n        selected = \"AEDECOD\"\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADAE\", \"AEREL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:44:29 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      stringr_1.5.1                   \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015            dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -588,7 +588,7 @@
     "href": "tables/adverse-events/aet02.html",
     "title": "AET02",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with  Event Totals\nTable with  High-Level Term\nTable with Preferred  Terms Only\nTable with Fill-In of  Treatment Groups\nTable of AEs with an Incidence Rate \\(\\geq\\) 5% in Any Treatment Group (subsetting  preferred terms based on frequency)\nTable of AEs with an Incidence Rate \\(\\geq\\) 5% in  Any Treatment Group (subsetting preferred terms  based on frequency with high-level terms)\nTable of AEs with an Incidence Rate \\(\\geq\\) 10%  in Any Treatment Group (subsetting  preferred terms based on frequency)\nTable of AEs with an Incidence Rate \\(\\geq\\) 3  Patients in Any Treatment Group (subsetting  preferred terms based on number of patients)\nTable of AEs with a Difference in Incidence Rate \\(\\geq\\) 5%  Between Any Treatment (subsetting preferred terms based  on difference in percentage between treatment groups)\nTable of AEs with an Incidence Rate \\(\\geq\\) 5%  in B: Placebo (subsetting preferred terms based  on frequency for a particular treatment group)\nTable of AEs with a Difference in Incidence Rate \\(\\geq\\) 5% Between  Arm A and Arm B or Arm C (displaying preferred terms with a  difference of at least x% between selected treatment groups)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae) %&gt;%\n  var_relabel(\n    AEBODSYS = \"MedDRA System Organ Class\",\n    AEDECOD = \"MedDRA Preferred Term\"\n  ) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl)\n\n\nThe variable result corresponds to the adverse events table. However, it includes many empty rows accounting for events which were not reported. The table can be post-processed to prune empty rows and to sort rows, for example by occurrence.\n\nCoderesult &lt;- result %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination   All Patients\n  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)    103 (78.0%)     301 (75.2%) \nOverall total number of events                                   502          480            604             1586    \ncl A.1                                                                                                               \n  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)     202 (50.5%) \n  Total number of events                                         115           99            137             351     \n  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)     128 (32.0%) \n  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)     122 (30.5%) \ncl B.2                                                                                                               \n  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)     74 (56.1%)     192 (48.0%) \n  Total number of events                                         102          106            127             335     \n  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)     123 (30.8%) \n  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)     119 (29.8%) \ncl D.1                                                                                                               \n  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)     68 (51.5%)     186 (46.5%) \n  Total number of events                                         106           84            114             304     \n  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)     120 (30.0%) \n  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)     112 (28.0%) \ncl D.2                                                                                                               \n  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \n  Total number of events                                         49            57             65             171     \n  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \ncl C.2                                                                                                               \n  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \n  Total number of events                                         39            40             57             136     \n  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \ncl B.1                                                                                                               \n  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \n  Total number of events                                         44            43             50             137     \n  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \ncl C.1                                                                                                               \n  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n  Total number of events                                         47            51             54             152     \n  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L) %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of events\"),\n    .indent_mods = c(count = -1L)\n  )\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  )\n\nresult\n\nMedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination   All Patients\n  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)    103 (78.0%)     301 (75.2%) \nOverall total number of events                                   502          480            604             1586    \ncl A.1                                                                                                               \n  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)     202 (50.5%) \n  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)     128 (32.0%) \n  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)     122 (30.5%) \n  Total number of events                                         115           99            137             351     \ncl B.2                                                                                                               \n  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)     74 (56.1%)     192 (48.0%) \n  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)     123 (30.8%) \n  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)     119 (29.8%) \n  Total number of events                                         102          106            127             335     \ncl D.1                                                                                                               \n  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)     68 (51.5%)     186 (46.5%) \n  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)     120 (30.0%) \n  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)     112 (28.0%) \n  Total number of events                                         106           84            114             304     \ncl D.2                                                                                                               \n  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \n  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \n  Total number of events                                         49            57             65             171     \ncl C.2                                                                                                               \n  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \n  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \n  Total number of events                                         39            40             57             136     \ncl B.1                                                                                                               \n  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \n  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \n  Total number of events                                         44            43             50             137     \ncl C.1                                                                                                               \n  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n  Total number of events                                         47            51             54             152     \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, c(\"AEDECOD\"), indent = 2L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedDRA System Organ Class                                                                               \n  High Level Term                                               A: Drug X    B: Placebo   C: Combination\n    MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event       100 (74.6%)   98 (73.1%)    103 (78.0%)  \nOverall total number of events                                     502          480            604      \ncl A.1                                                                                                  \n  Total number of patients with at least one adverse event     68 (50.7%)    58 (43.3%)     76 (57.6%)  \n  Total number of events                                           115           99            137      \n  hlt A.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n    Total number of events                                         115           99            137      \n    dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)  \n    dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                                                  \n  Total number of patients with at least one adverse event     62 (46.3%)    56 (41.8%)     74 (56.1%)  \n  Total number of events                                           102          106            127      \n  hlt B.2.2.3                                                                                           \n    Total number of patients with at least one adverse event   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n    Total number of events                                         50            55             68      \n    dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  hlt B.2.1.2                                                                                           \n    Total number of patients with at least one adverse event   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n    Total number of events                                         52            51             59      \n    dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                                                  \n  Total number of patients with at least one adverse event     64 (47.8%)    54 (40.3%)     68 (51.5%)  \n  Total number of events                                           106           84            114      \n  hlt D.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n    Total number of events                                         52            40             64      \n    dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  hlt D.1.1.4                                                                                           \n    Total number of patients with at least one adverse event   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n    Total number of events                                         54            44             50      \n    dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                                                  \n  Total number of patients with at least one adverse event     37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  Total number of events                                           49            57             65      \n  hlt D.2.1.5                                                                                           \n    Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n    Total number of events                                         49            57             65      \n    dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                                                  \n  Total number of patients with at least one adverse event     28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  Total number of events                                           39            40             57      \n  hlt C.2.1.2                                                                                           \n    Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n    Total number of events                                         39            40             57      \n    dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                                                  \n  Total number of patients with at least one adverse event     38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  Total number of events                                           44            43             50      \n  hlt B.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n    Total number of events                                         44            43             50      \n    dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                                                  \n  Total number of patients with at least one adverse event     36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  Total number of events                                           47            51             54      \n  hlt C.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n    Total number of events                                         47            51             54      \n    dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\") %&gt;%\n  append_varlabels(adae, \"AEDECOD\")\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  sort_at_path(\n    path = c(\"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                            A: Drug X    B: Placebo   C: Combination\nMedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event   100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of events                                         502          480            604      \ndcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)  \ndcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)  \ndcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)  \ndcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)  \ndcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)  \ndcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)  \ndcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)  \ndcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)  \ndcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)  \ndcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodeadae_5 &lt;- adae %&gt;% dplyr::filter(ACTARM != \"C: Combination\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae_5, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)         0       \nOverall total number of events                                   502          480             0       \ncl A.1                                                                                                \n  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)         0       \n  Total number of events                                         115           99             0       \n  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)         0       \n  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)         0       \ncl B.2                                                                                                \n  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)         0       \n  Total number of events                                         102          106             0       \n  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)         0       \n  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)         0       \ncl D.1                                                                                                \n  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)         0       \n  Total number of events                                         106           84             0       \n  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)         0       \n  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)         0       \ncl D.2                                                                                                \n  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)         0       \n  Total number of events                                         49            57             0       \n  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)         0       \ncl B.1                                                                                                \n  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)         0       \n  Total number of events                                         44            43             0       \n  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)         0       \ncl C.1                                                                                                \n  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)         0       \n  Total number of events                                         47            51             0       \n  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)         0       \ncl C.2                                                                                                \n  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)         0       \n  Total number of events                                         39            40             0       \n  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)         0       \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_any_col(\n  atleast = 0.05,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\") %&gt;%\n  append_varlabels(adae, c(\"AEDECOD\"), indent = 2L)\n\nresult &lt;- build_table(lyt, adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_any_col(\n  atleast = 0.05,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class                                           \n  High Level Term           A: Drug X    B: Placebo   C: Combination\n    MedDRA Preferred Term    (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  hlt A.1.1.1                                                       \n    dcd A.1.1.1.1           45 (33.6%)   31 (23.1%)     52 (39.4%)  \n    dcd A.1.1.1.2           41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  hlt B.2.2.3                                                       \n    dcd B.2.2.3.1           38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  hlt B.2.1.2                                                       \n    dcd B.2.1.2.1           39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  hlt D.1.1.1                                                       \n    dcd D.1.1.1.1           42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  hlt D.1.1.4                                                       \n    dcd D.1.1.4.2           38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  hlt D.2.1.5                                                       \n    dcd D.2.1.5.3           37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  hlt C.2.1.2                                                       \n    dcd C.2.1.2.1           28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  hlt B.1.1.1                                                       \n    dcd B.1.1.1.1           38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  hlt C.1.1.1                                                       \n    dcd C.1.1.1.3           36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_any_col(\n  atleast = 0.10,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    split_label = obj_label(adae$AEBODSYS),\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_count_in_any_col(\n  atleast = 3,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fractions_difference(\n  atleast = 0.05,\n  col_names = levels(adsl$ACTARM)\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_cols(\n  atleast = 0.05,\n  col_names = c(\"B: Placebo\")\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition1 &lt;- has_fractions_difference(atleast = 0.05, col_names = c(\"A: Drug X\", \"B: Placebo\"))\nrow_condition2 &lt;- has_fractions_difference(atleast = 0.05, col_names = c(\"A: Drug X\", \"C: Combination\"))\nrow_condition &lt;- row_condition1 | row_condition2\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      add_total = TRUE,\n      event_type = \"adverse event\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:45:19 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with  Event Totals\nTable with  High-Level Term\nTable with Preferred  Terms Only\nTable with Fill-In of  Treatment Groups\nTable of AEs with an Incidence Rate \\(\\geq\\) 5% in Any Treatment Group (subsetting  preferred terms based on frequency)\nTable of AEs with an Incidence Rate \\(\\geq\\) 5% in  Any Treatment Group (subsetting preferred terms  based on frequency with high-level terms)\nTable of AEs with an Incidence Rate \\(\\geq\\) 10%  in Any Treatment Group (subsetting  preferred terms based on frequency)\nTable of AEs with an Incidence Rate \\(\\geq\\) 3  Patients in Any Treatment Group (subsetting  preferred terms based on number of patients)\nTable of AEs with a Difference in Incidence Rate \\(\\geq\\) 5%  Between Any Treatment (subsetting preferred terms based  on difference in percentage between treatment groups)\nTable of AEs with an Incidence Rate \\(\\geq\\) 5%  in B: Placebo (subsetting preferred terms based  on frequency for a particular treatment group)\nTable of AEs with a Difference in Incidence Rate \\(\\geq\\) 5% Between  Arm A and Arm B or Arm C (displaying preferred terms with a  difference of at least x% between selected treatment groups)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae) %&gt;%\n  var_relabel(\n    AEBODSYS = \"MedDRA System Organ Class\",\n    AEDECOD = \"MedDRA Preferred Term\"\n  ) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl)\n\n\nThe variable result corresponds to the adverse events table. However, it includes many empty rows accounting for events which were not reported. The table can be post-processed to prune empty rows and to sort rows, for example by occurrence.\n\nCoderesult &lt;- result %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination   All Patients\n  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)    103 (78.0%)     301 (75.2%) \nOverall total number of events                                   502          480            604             1586    \ncl A.1                                                                                                               \n  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)     202 (50.5%) \n  Total number of events                                         115           99            137             351     \n  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)     128 (32.0%) \n  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)     122 (30.5%) \ncl B.2                                                                                                               \n  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)     74 (56.1%)     192 (48.0%) \n  Total number of events                                         102          106            127             335     \n  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)     123 (30.8%) \n  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)     119 (29.8%) \ncl D.1                                                                                                               \n  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)     68 (51.5%)     186 (46.5%) \n  Total number of events                                         106           84            114             304     \n  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)     120 (30.0%) \n  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)     112 (28.0%) \ncl D.2                                                                                                               \n  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \n  Total number of events                                         49            57             65             171     \n  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \ncl C.2                                                                                                               \n  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \n  Total number of events                                         39            40             57             136     \n  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \ncl B.1                                                                                                               \n  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \n  Total number of events                                         44            43             50             137     \n  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \ncl C.1                                                                                                               \n  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n  Total number of events                                         47            51             54             152     \n  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L) %&gt;%\n  count_values(\n    \"STUDYID\",\n    values = as.character(unique(adae$STUDYID)),\n    .stats = \"count\",\n    .labels = c(count = \"Total number of events\"),\n    .indent_mods = c(count = -1L)\n  )\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  )\n\nresult\n\nMedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination   All Patients\n  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)    103 (78.0%)     301 (75.2%) \nOverall total number of events                                   502          480            604             1586    \ncl A.1                                                                                                               \n  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)     202 (50.5%) \n  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)     128 (32.0%) \n  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)     122 (30.5%) \n  Total number of events                                         115           99            137             351     \ncl B.2                                                                                                               \n  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)     74 (56.1%)     192 (48.0%) \n  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)     123 (30.8%) \n  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)     119 (29.8%) \n  Total number of events                                         102          106            127             335     \ncl D.1                                                                                                               \n  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)     68 (51.5%)     186 (46.5%) \n  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)     120 (30.0%) \n  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)     112 (28.0%) \n  Total number of events                                         106           84            114             304     \ncl D.2                                                                                                               \n  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \n  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) \n  Total number of events                                         49            57             65             171     \ncl C.2                                                                                                               \n  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \n  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) \n  Total number of events                                         39            40             57             136     \ncl B.1                                                                                                               \n  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \n  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) \n  Total number of events                                         44            43             50             137     \ncl C.1                                                                                                               \n  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) \n  Total number of events                                         47            51             54             152     \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, c(\"AEDECOD\"), indent = 2L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedDRA System Organ Class                                                                               \n  High Level Term                                               A: Drug X    B: Placebo   C: Combination\n    MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event       100 (74.6%)   98 (73.1%)    103 (78.0%)  \nOverall total number of events                                     502          480            604      \ncl A.1                                                                                                  \n  Total number of patients with at least one adverse event     68 (50.7%)    58 (43.3%)     76 (57.6%)  \n  Total number of events                                           115           99            137      \n  hlt A.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n    Total number of events                                         115           99            137      \n    dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)  \n    dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                                                  \n  Total number of patients with at least one adverse event     62 (46.3%)    56 (41.8%)     74 (56.1%)  \n  Total number of events                                           102          106            127      \n  hlt B.2.2.3                                                                                           \n    Total number of patients with at least one adverse event   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n    Total number of events                                         50            55             68      \n    dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  hlt B.2.1.2                                                                                           \n    Total number of patients with at least one adverse event   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n    Total number of events                                         52            51             59      \n    dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                                                  \n  Total number of patients with at least one adverse event     64 (47.8%)    54 (40.3%)     68 (51.5%)  \n  Total number of events                                           106           84            114      \n  hlt D.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n    Total number of events                                         52            40             64      \n    dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  hlt D.1.1.4                                                                                           \n    Total number of patients with at least one adverse event   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n    Total number of events                                         54            44             50      \n    dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                                                  \n  Total number of patients with at least one adverse event     37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  Total number of events                                           49            57             65      \n  hlt D.2.1.5                                                                                           \n    Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n    Total number of events                                         49            57             65      \n    dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                                                  \n  Total number of patients with at least one adverse event     28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  Total number of events                                           39            40             57      \n  hlt C.2.1.2                                                                                           \n    Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n    Total number of events                                         39            40             57      \n    dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                                                  \n  Total number of patients with at least one adverse event     38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  Total number of events                                           44            43             50      \n  hlt B.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n    Total number of events                                         44            43             50      \n    dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                                                  \n  Total number of patients with at least one adverse event     36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  Total number of events                                           47            51             54      \n  hlt C.1.1.1                                                                                           \n    Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n    Total number of events                                         47            51             54      \n    dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\") %&gt;%\n  append_varlabels(adae, \"AEDECOD\")\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  sort_at_path(\n    path = c(\"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                            A: Drug X    B: Placebo   C: Combination\nMedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event   100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of events                                         502          480            604      \ndcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)  \ndcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)  \ndcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)  \ndcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)  \ndcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)  \ndcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)  \ndcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)  \ndcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)  \ndcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)  \ndcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodeadae_5 &lt;- adae %&gt;% dplyr::filter(ACTARM != \"C: Combination\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Overall total number of events\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae_5, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)         0       \nOverall total number of events                                   502          480             0       \ncl A.1                                                                                                \n  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)         0       \n  Total number of events                                         115           99             0       \n  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)         0       \n  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)         0       \ncl B.2                                                                                                \n  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)         0       \n  Total number of events                                         102          106             0       \n  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)         0       \n  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)         0       \ncl D.1                                                                                                \n  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)         0       \n  Total number of events                                         106           84             0       \n  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)         0       \n  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)         0       \ncl D.2                                                                                                \n  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)         0       \n  Total number of events                                         49            57             0       \n  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)         0       \ncl B.1                                                                                                \n  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)         0       \n  Total number of events                                         44            43             0       \n  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)         0       \ncl C.1                                                                                                \n  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)         0       \n  Total number of events                                         47            51             0       \n  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)         0       \ncl C.2                                                                                                \n  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)         0       \n  Total number of events                                         39            40             0       \n  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)         0       \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_any_col(\n  atleast = 0.05,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEHLT\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEHLT)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\") %&gt;%\n  append_varlabels(adae, c(\"AEDECOD\"), indent = 2L)\n\nresult &lt;- build_table(lyt, adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEHLT\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_any_col(\n  atleast = 0.05,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class                                           \n  High Level Term           A: Drug X    B: Placebo   C: Combination\n    MedDRA Preferred Term    (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  hlt A.1.1.1                                                       \n    dcd A.1.1.1.1           45 (33.6%)   31 (23.1%)     52 (39.4%)  \n    dcd A.1.1.1.2           41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  hlt B.2.2.3                                                       \n    dcd B.2.2.3.1           38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  hlt B.2.1.2                                                       \n    dcd B.2.1.2.1           39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  hlt D.1.1.1                                                       \n    dcd D.1.1.1.1           42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  hlt D.1.1.4                                                       \n    dcd D.1.1.4.2           38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  hlt D.2.1.5                                                       \n    dcd D.2.1.5.3           37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  hlt C.2.1.2                                                       \n    dcd C.2.1.2.1           28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  hlt B.1.1.1                                                       \n    dcd B.1.1.1.1           38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  hlt C.1.1.1                                                       \n    dcd C.1.1.1.3           36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_any_col(\n  atleast = 0.10,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    split_label = obj_label(adae$AEBODSYS),\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_count_in_any_col(\n  atleast = 3,\n  col_names = names(table(adsl$ACTARM))\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fractions_difference(\n  atleast = 0.05,\n  col_names = levels(adsl$ACTARM)\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition &lt;- has_fraction_in_cols(\n  atleast = 0.05,\n  col_names = c(\"B: Placebo\")\n)\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \ncl B.1                                                              \n  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  \ncl C.1                                                              \n  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"AEDECOD\",\n    .indent_mods = 1L\n  ) %&gt;%\n  append_varlabels(adae, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt, df = adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = cont_n_allcols\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences\n  )\n\ncriteria_fun &lt;- function(tr) is(tr, \"ContentRow\")\nresult &lt;- trim_rows(result, criteria = criteria_fun)\n\nrow_condition1 &lt;- has_fractions_difference(atleast = 0.05, col_names = c(\"A: Drug X\", \"B: Placebo\"))\nrow_condition2 &lt;- has_fractions_difference(atleast = 0.05, col_names = c(\"A: Drug X\", \"C: Combination\"))\nrow_condition &lt;- row_condition1 | row_condition2\nresult &lt;- prune_table(result, keep_rows(row_condition))\n\nresult\n\nMedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination\n  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————\ncl A.1                                                              \n  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  \ncl B.2                                                              \n  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  \ncl D.1                                                              \n  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  \ncl D.2                                                              \n  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  \ncl C.2                                                              \n  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      add_total = TRUE,\n      event_type = \"adverse event\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:43:57 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -600,7 +600,7 @@
     "href": "tables/adverse-events/aet10.html",
     "title": "AET10",
     "section": "",
-    "text": "Data Setup\nStandard Table\nMost Common (\\(\\geq\\) 35%) Adverse Events (setting threshold)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n\n\n\nThe “All Patients” column is not required in this table but for illustrative purposes we include it to show correct sorting.\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    var = \"ARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\")\n\ntbl1 &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n)\n\ntbl2 &lt;- prune_table(\n  tt = tbl1,\n  prune_func = keep_rows(\n    has_fraction_in_any_col(\n      atleast = 0.05, # specify threshold\n      col_names = levels(adsl$ARM)\n    )\n  )\n)\n\nresult &lt;- sort_at_path(\n  tbl2,\n  path = c(\"AEDECOD\"),\n  scorefun = score_occurrences\n)\n\nresult\n\n                A: Drug X    B: Placebo   C: Combination   All Patients\n                 (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————\ndcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) \ndcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%) \ndcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%) \ndcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%) \ndcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%) \ndcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%) \ndcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%) \ndcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%) \ndcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%) \ndcd C.1.1.1.3   43 (32.1%)   46 (34.3%)     43 (32.6%)     132 (33.0%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    var = \"ARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\")\n\ntbl1 &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n)\n\ntbl2 &lt;- prune_table(\n  tt = tbl1,\n  prune_func = keep_rows(\n    has_fraction_in_any_col(\n      atleast = 0.35, # specify threshold\n      col_names = levels(adsl$ARM)\n    )\n  )\n)\n\nresult &lt;- sort_at_path(\n  tbl2,\n  path = c(\"AEDECOD\"),\n  scorefun = score_occurrences\n)\n\nresult\n\n                A: Drug X    B: Placebo   C: Combination   All Patients\n                 (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————\ndcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) \ndcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%) \ndcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%) \ndcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%) \ndcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%) \ndcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%) \ndcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%) \ndcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%) \ndcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nThe desired frequency is specified in prune_freq. For illustration, frequency is set to \\(\\geq\\) 35%.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = NULL\n      ),\n      add_total = TRUE,\n      event_type = \"adverse event\",\n      prune_freq = 35,\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:44:41 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nMost Common (\\(\\geq\\) 35%) Adverse Events (setting threshold)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n\n\n\nThe “All Patients” column is not required in this table but for illustrative purposes we include it to show correct sorting.\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    var = \"ARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\")\n\ntbl1 &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n)\n\ntbl2 &lt;- prune_table(\n  tt = tbl1,\n  prune_func = keep_rows(\n    has_fraction_in_any_col(\n      atleast = 0.05, # specify threshold\n      col_names = levels(adsl$ARM)\n    )\n  )\n)\n\nresult &lt;- sort_at_path(\n  tbl2,\n  path = c(\"AEDECOD\"),\n  scorefun = score_occurrences\n)\n\nresult\n\n                A: Drug X    B: Placebo   C: Combination   All Patients\n                 (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————\ndcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) \ndcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%) \ndcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%) \ndcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%) \ndcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%) \ndcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%) \ndcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%) \ndcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%) \ndcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%) \ndcd C.1.1.1.3   43 (32.1%)   46 (34.3%)     43 (32.6%)     132 (33.0%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    var = \"ARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\")\n\ntbl1 &lt;- build_table(\n  lyt = lyt,\n  df = adae,\n  alt_counts_df = adsl\n)\n\ntbl2 &lt;- prune_table(\n  tt = tbl1,\n  prune_func = keep_rows(\n    has_fraction_in_any_col(\n      atleast = 0.35, # specify threshold\n      col_names = levels(adsl$ARM)\n    )\n  )\n)\n\nresult &lt;- sort_at_path(\n  tbl2,\n  path = c(\"AEDECOD\"),\n  scorefun = score_occurrences\n)\n\nresult\n\n                A: Drug X    B: Placebo   C: Combination   All Patients\n                 (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————\ndcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) \ndcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%) \ndcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%) \ndcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%) \ndcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%) \ndcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%) \ndcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%) \ndcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%) \ndcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nThe desired frequency is specified in prune_freq. For illustration, frequency is set to \\(\\geq\\) 35%.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = NULL\n      ),\n      add_total = TRUE,\n      event_type = \"adverse event\",\n      prune_freq = 35,\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:43:20 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -612,7 +612,7 @@
     "href": "tables/disposition/pdt02.html",
     "title": "PDT02",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\naddv &lt;- synthetic_cdisc_dataset(\"latest\", \"addv\")\n\nadsl &lt;- df_explicit_na(adsl)\naddv &lt;- df_explicit_na(addv)\n\naddv_pan &lt;- addv %&gt;%\n  filter(AEPRELFL == \"Y\" & DVCAT == \"MAJOR\") %&gt;%\n  var_relabel(\n    DVREAS = \"Primary Reason\",\n    DVTERM = \"Description\"\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one major protocol deviation related to epidemic/pandemic\",\n      nonunique = \"Total number of major protocol deviations related to epidemic/pandemic\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"DVREAS\",\n    split_fun = drop_split_levels,\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(addv_pan$DVREAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = \"Site action due to epidemic/pandemic\"\n  ) %&gt;%\n  count_occurrences(vars = \"DVTERM\") %&gt;%\n  append_varlabels(addv_pan, \"DVTERM\", indent = 1L)\n\nresult &lt;- build_table(lyt, addv_pan, alt_counts_df = adsl)\nresult\n\nPrimary Reason                                                                                     A: Drug X   B: Placebo   C: Combination\n  Description                                                                                       (N=134)     (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one major protocol deviation related to epidemic/pandemic   8 (6.0%)     4 (3.0%)       4 (3.0%)   \nTotal number of major protocol deviations related to epidemic/pandemic                                 8           5              4       \nSite action due to epidemic/pandemic                                                               8 (6.0%)     4 (3.0%)       4 (3.0%)   \n  Dose missed or significantly out of window                                                       2 (1.5%)        0           1 (0.8%)   \n  Failure to sign updated ICF within two visits                                                    2 (1.5%)     1 (0.7%)       1 (0.8%)   \n  Missed 2 or more efficacy assessments                                                            1 (0.7%)     2 (1.5%)          0       \n  Significant deviation from planned dose                                                          3 (2.2%)     1 (0.7%)       2 (1.5%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:44:11 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\naddv &lt;- synthetic_cdisc_dataset(\"latest\", \"addv\")\n\nadsl &lt;- df_explicit_na(adsl)\naddv &lt;- df_explicit_na(addv)\n\naddv_pan &lt;- addv %&gt;%\n  filter(AEPRELFL == \"Y\" & DVCAT == \"MAJOR\") %&gt;%\n  var_relabel(\n    DVREAS = \"Primary Reason\",\n    DVTERM = \"Description\"\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one major protocol deviation related to epidemic/pandemic\",\n      nonunique = \"Total number of major protocol deviations related to epidemic/pandemic\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"DVREAS\",\n    split_fun = drop_split_levels,\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(addv_pan$DVREAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = \"Site action due to epidemic/pandemic\"\n  ) %&gt;%\n  count_occurrences(vars = \"DVTERM\") %&gt;%\n  append_varlabels(addv_pan, \"DVTERM\", indent = 1L)\n\nresult &lt;- build_table(lyt, addv_pan, alt_counts_df = adsl)\nresult\n\nPrimary Reason                                                                                     A: Drug X   B: Placebo   C: Combination\n  Description                                                                                       (N=134)     (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one major protocol deviation related to epidemic/pandemic   8 (6.0%)     4 (3.0%)       4 (3.0%)   \nTotal number of major protocol deviations related to epidemic/pandemic                                 8           5              4       \nSite action due to epidemic/pandemic                                                               8 (6.0%)     4 (3.0%)       4 (3.0%)   \n  Dose missed or significantly out of window                                                       2 (1.5%)        0           1 (0.8%)   \n  Failure to sign updated ICF within two visits                                                    2 (1.5%)     1 (0.7%)       1 (0.8%)   \n  Missed 2 or more efficacy assessments                                                            1 (0.7%)     2 (1.5%)          0       \n  Significant deviation from planned dose                                                          3 (2.2%)     1 (0.7%)       2 (1.5%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:51 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Disposition",
@@ -624,7 +624,7 @@
     "href": "tables/ADA/adat02.html",
     "title": "ADAT02",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadab &lt;- df_explicit_na(adab) %&gt;%\n  mutate(ADPBLPFL = \"Y\") %&gt;% # temp fix\n  filter(\n    ADPBLPFL == \"Y\",\n    !PARAM %in% c(\n      \"NAB interpreted per sample result\",\n      \"NAB Status of a patient\",\n      \"Treatment enhanced ADA\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"Treatment induced ADA\",\n        \"Transient ADA\",\n        \"Persistent ADA\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADPBLPFL = ADPBLPFL == \"Y\",\n    TI_ADA = if (exists(\"Treatment induced ADA\", where = .)) `Treatment induced ADA` else FALSE\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients\",\n    TI_ADA = \"Treatment-induced ADA patients\"\n  )\n\nadab_ti &lt;- adab %&gt;%\n  filter(TI_ADA)\n\n\n\n\n\nCode# Layout for post-baseline evaluable patient variables from adab dataset.\nlyt_adab &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = drop_split_levels\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"ADPBLPFL\",\n    .stats = \"count\",\n    table_names = \"post_baseline\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TI_ADA\"\n  )\n\n# Layout for treatment-induced patient variables from adab dataset.\nlyt_adab_ti &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = keep_split_levels(c(\"A: Drug X\", \"C: Combination\", \"&lt;Missing&gt;\")) # temp fix\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"Transient ADA\", \"Persistent ADA\"),\n    var_labels = \"Treatment-induced ADA patients with\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  analyze_vars(\n    \"Time to onset of ADA\",\n    .stats = \"median\",\n    nested = FALSE,\n    .labels = c(median = \"Median time to onset of ADA (weeks)\")\n  ) %&gt;%\n  analyze_vars(\n    \"Antibody titer units\",\n    .stats = \"range\",\n    nested = FALSE,\n    .labels = c(range = \"ADA titer range (min - max)\")\n  )\n\nresult_adab &lt;- build_table(lyt_adab, df = adab, alt_counts_df = adsl)\nresult_adab_ti &lt;- build_table(lyt_adab_ti, df = adab_ti, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adab) &lt;- col_info(result_adab_ti)\nresult &lt;- rbind(\n  result_adab,\n  result_adab_ti\n)\n\nmain_title(result) &lt;- paste(\n  \"Summary of Patients with Treatment-Induced ADA, PK Population\"\n)\nsubtitles(result) &lt;- paste(\"Protocol:\", unique(adab$PARCAT1)[1])\nmain_footer(result) &lt;- paste(\n  \"ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic\n  Antibodies)\n  Treatment-induced ADA = negative or missing baseline.\n  ADA result(s) and at least one positive post-baseline ADA result.\n  Transient ADA = ADA positive result detected (a) at only one post-baseline\n  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints\n  during treatment where the first and last ADA positive samples are separated\n  by a period of &lt; 16 weeks, irrespective of any negative samples in between.\n  Persistent ADA = ADA positive result detected (a) at the last post-baseline\n  sampling timepoint, OR (b) at 2 or more time points during treatment where\n  the first and last ADA positive samples are separated by a period ≥ 16\n  weeks, irrespective of any negative samples in between.\"\n)\nresult\n\nSummary of Patients with Treatment-Induced ADA, PK Population\nProtocol: A: Drug X Antibody\n\n—————————————————————————————————————————————————————————————————————————————\n                                      A: Drug X   C: Combination   &lt;Missing&gt; \n                                       (N=134)       (N=132)         (N=0)   \n—————————————————————————————————————————————————————————————————————————————\nPost-baseline evaluable patients         134           132            266    \nTreatment-induced ADA patients            0             0          64 (24.1%)\nTreatment-induced ADA patients with                                          \n  Transient ADA                           0             0              0     \n  Persistent ADA                          0             0              0     \nMedian time to onset of ADA (weeks)      NA             NA            0.1    \nADA titer range (min - max)              NA             NA             NA    \n—————————————————————————————————————————————————————————————————————————————\n\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic\n  Antibodies)\n  Treatment-induced ADA = negative or missing baseline.\n  ADA result(s) and at least one positive post-baseline ADA result.\n  Transient ADA = ADA positive result detected (a) at only one post-baseline\n  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints\n  during treatment where the first and last ADA positive samples are separated\n  by a period of &lt; 16 weeks, irrespective of any negative samples in between.\n  Persistent ADA = ADA positive result detected (a) at the last post-baseline\n  sampling timepoint, OR (b) at 2 or more time points during treatment where\n  the first and last ADA positive samples are separated by a period ≥ 16\n  weeks, irrespective of any negative samples in between.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:43:50 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       dplyr_1.1.4           tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadab &lt;- df_explicit_na(adab) %&gt;%\n  mutate(ADPBLPFL = \"Y\") %&gt;% # temp fix\n  filter(\n    ADPBLPFL == \"Y\",\n    !PARAM %in% c(\n      \"NAB interpreted per sample result\",\n      \"NAB Status of a patient\",\n      \"Treatment enhanced ADA\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"Treatment induced ADA\",\n        \"Transient ADA\",\n        \"Persistent ADA\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADPBLPFL = ADPBLPFL == \"Y\",\n    TI_ADA = if (exists(\"Treatment induced ADA\", where = .)) `Treatment induced ADA` else FALSE\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients\",\n    TI_ADA = \"Treatment-induced ADA patients\"\n  )\n\nadab_ti &lt;- adab %&gt;%\n  filter(TI_ADA)\n\n\n\n\n\nCode# Layout for post-baseline evaluable patient variables from adab dataset.\nlyt_adab &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = drop_split_levels\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"ADPBLPFL\",\n    .stats = \"count\",\n    table_names = \"post_baseline\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TI_ADA\"\n  )\n\n# Layout for treatment-induced patient variables from adab dataset.\nlyt_adab_ti &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = keep_split_levels(c(\"A: Drug X\", \"C: Combination\", \"&lt;Missing&gt;\")) # temp fix\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"Transient ADA\", \"Persistent ADA\"),\n    var_labels = \"Treatment-induced ADA patients with\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  analyze_vars(\n    \"Time to onset of ADA\",\n    .stats = \"median\",\n    nested = FALSE,\n    .labels = c(median = \"Median time to onset of ADA (weeks)\")\n  ) %&gt;%\n  analyze_vars(\n    \"Antibody titer units\",\n    .stats = \"range\",\n    nested = FALSE,\n    .labels = c(range = \"ADA titer range (min - max)\")\n  )\n\nresult_adab &lt;- build_table(lyt_adab, df = adab, alt_counts_df = adsl)\nresult_adab_ti &lt;- build_table(lyt_adab_ti, df = adab_ti, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adab) &lt;- col_info(result_adab_ti)\nresult &lt;- rbind(\n  result_adab,\n  result_adab_ti\n)\n\nmain_title(result) &lt;- paste(\n  \"Summary of Patients with Treatment-Induced ADA, PK Population\"\n)\nsubtitles(result) &lt;- paste(\"Protocol:\", unique(adab$PARCAT1)[1])\nmain_footer(result) &lt;- paste(\n  \"ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic\n  Antibodies)\n  Treatment-induced ADA = negative or missing baseline.\n  ADA result(s) and at least one positive post-baseline ADA result.\n  Transient ADA = ADA positive result detected (a) at only one post-baseline\n  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints\n  during treatment where the first and last ADA positive samples are separated\n  by a period of &lt; 16 weeks, irrespective of any negative samples in between.\n  Persistent ADA = ADA positive result detected (a) at the last post-baseline\n  sampling timepoint, OR (b) at 2 or more time points during treatment where\n  the first and last ADA positive samples are separated by a period ≥ 16\n  weeks, irrespective of any negative samples in between.\"\n)\nresult\n\nSummary of Patients with Treatment-Induced ADA, PK Population\nProtocol: A: Drug X Antibody\n\n—————————————————————————————————————————————————————————————————————————————\n                                      A: Drug X   C: Combination   &lt;Missing&gt; \n                                       (N=134)       (N=132)         (N=0)   \n—————————————————————————————————————————————————————————————————————————————\nPost-baseline evaluable patients         134           132            266    \nTreatment-induced ADA patients            0             0          64 (24.1%)\nTreatment-induced ADA patients with                                          \n  Transient ADA                           0             0              0     \n  Persistent ADA                          0             0              0     \nMedian time to onset of ADA (weeks)      NA             NA            0.1    \nADA titer range (min - max)              NA             NA             NA    \n—————————————————————————————————————————————————————————————————————————————\n\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic\n  Antibodies)\n  Treatment-induced ADA = negative or missing baseline.\n  ADA result(s) and at least one positive post-baseline ADA result.\n  Transient ADA = ADA positive result detected (a) at only one post-baseline\n  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints\n  during treatment where the first and last ADA positive samples are separated\n  by a period of &lt; 16 weeks, irrespective of any negative samples in between.\n  Persistent ADA = ADA positive result detected (a) at the last post-baseline\n  sampling timepoint, OR (b) at 2 or more time points during treatment where\n  the first and last ADA positive samples are separated by a period ≥ 16\n  weeks, irrespective of any negative samples in between.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       dplyr_1.1.4           tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ADA",
@@ -636,7 +636,7 @@
     "href": "tables/ADA/adat01.html",
     "title": "ADAT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(tibble)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\ncombodf &lt;- tribble(\n  ~valname, ~label, ~levelcombo, ~exargs,\n  \"all_X\", \"All Drug X\", c(\"A: Drug X\", \"C: Combination\"), list(),\n  \"all_pt\", \"All Patients\", c(\"A: Drug X\", \"B: Placebo\", \"C: Combination\"), list()\n)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadab &lt;- adab %&gt;%\n  filter(PARCAT1 == \"A: Drug X Antibody\") %&gt;%\n  select(-ARRLT, -NRRLT)\n\n# Baseline Pts\nadab_b &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL == \"Y\",\n    ADABLPFL == \"Y\",\n    PARAM %in% c(\"ADA interpreted per sample result\")\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate_at(\n    c(\"ADA interpreted per sample result\"),\n    as.logical\n  ) %&gt;%\n  mutate(\n    ADABLPFL = ADABLPFL == \"Y\",\n    PADABLPFL = `ADA interpreted per sample result` == \"TRUE\",\n    NADABLPFL = `ADA interpreted per sample result` == \"FALSE\"\n  ) %&gt;%\n  var_relabel(\n    ADABLPFL = \"Baseline evaluable patients\",\n    PADABLPFL = \"Patient with a positive sample at baseline\",\n    NADABLPFL = \"Patient with no positive samples at baseline\"\n  )\n\n# Post Baseline Treatment Enhanced NAb positive Pts\nadab_pb &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    ADPBLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"Treatment Emergent - Positive\",\n      \"Treatment induced ADA\",\n      \"Treatment enhanced ADA\",\n      \"Treatment Emergent - Negative\",\n      \"Treatment unaffected\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\", \"Treatment Emergent - Positive\",\n        \"Treatment induced ADA\", \"Treatment enhanced ADA\",\n        \"Treatment Emergent - Negative\", \"Treatment unaffected\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADPBLPFL = ADPBLPFL == \"Y\",\n    PTEFL = if (exists(\"Treatment Emergent - Positive\", where = .)) {\n      `Treatment Emergent - Positive` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TIFL = if (exists(\"Treatment induced ADA\", where = .)) {\n      `Treatment induced ADA` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TEFL = if (exists(\"Treatment enhanced ADA\", where = .)) {\n      `Treatment enhanced ADA` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NTEFL = if (exists(\"Treatment Emergent - Negative\", where = .)) {\n      `Treatment Emergent - Negative` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TUFL = if (exists(\"Treatment unaffected\", where = .)) {\n      `Treatment unaffected` == \"TRUE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients\",\n    PTEFL = \"Patient positive for Treatment Emergent ADA\",\n    TIFL = \"Treatment-induced ADA\",\n    TEFL = \"Treatment-enhanced ADA\",\n    NTEFL = \"Patient negative for Treatment Emergent ADA\",\n    TUFL = \"Treatment unaffected\"\n  )\n\n\n\n\n\n\nCode# Layout for Baseline Prevalence of NAbs\nlyt_bl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"ADABLPFL\",\n    .stats = \"count\",\n    var_labels = \"Baseline Prevalence of ADAs\",\n    show_labels = \"visible\",\n    table_names = \"t1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PADABLPFL\",\n    table_names = \"t2\",\n    .indent_mods = 1L,\n    var_labels = \"a\",\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NADABLPFL\",\n    .stats = \"count\",\n    show_labels = \"hidden\",\n    .indent_mods = 1L,\n    table_names = \"t3\"\n  )\n\n# Layout for incidence of NAbs\nlyt_pb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"ADPBLPFL\",\n    .stats = \"count\",\n    var_labels = \"Incidence of Treatment Emergent ADAs\",\n    show_labels = \"visible\",\n    table_names = \"tb1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PTEFL\",\n    table_names = \"tb2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"TIFL\", \"TEFL\"),\n    .stats = \"count\",\n    table_names = \"tb3\",\n    .indent_mods = 2L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NTEFL\",\n    table_names = \"tb4\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TUFL\",\n    .stats = \"count\",\n    table_names = \"tb5\",\n    .indent_mods = 2L,\n    show_labels = \"hidden\"\n  )\n\nresult_1 &lt;- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)\nresult_2 &lt;- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)\n\n# Combine tables.\nresult_1@col_info &lt;- result_2@col_info\nresult &lt;- rbind(result_1, result_2)\n\n# Change the column order.\nresult &lt;- cbind_rtables(result[, 1], result[, 3]) %&gt;%\n  cbind_rtables(result[, 4]) %&gt;%\n  cbind_rtables(result[, 2]) %&gt;%\n  cbind_rtables(result[, 5])\n\nmain_title(result) &lt;- paste(\n  \"Baseline Prevalence and Incidence of Treatment Emergent ADA\"\n)\n\nmain_footer(result) &lt;- \"ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)\n  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent\n  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\n  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\n  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\n  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.\n  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.\n  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\"\n\nresult\n\nBaseline Prevalence and Incidence of Treatment Emergent ADA\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                               A: Drug X    C: Combination   &lt;Missing&gt;   B: Placebo   All Drug X \n                                                (N=134)        (N=132)         (N=0)      (N=134)       (N=266)  \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBaseline Prevalence of ADAs                                                                                      \n  Baseline evaluable patients                     134            132             0           0            266    \nPatient with a positive sample at baseline     63 (47.0%)     64 (48.5%)         0           0        127 (47.7%)\nPatient with no positive samples at baseline       71             68             0           0            139    \nIncidence of Treatment Emergent ADAs                                                                             \n  Post-baseline evaluable patients                134            132             0           0            266    \nPatient positive for Treatment Emergent ADA        0              0              0           0             0     \nTreatment-induced ADA                              0              0              0           0             0     \nTreatment-enhanced ADA                             0              0              0           0             0     \nPatient negative for Treatment Emergent ADA        0              0              0           0             0     \nTreatment unaffected                               0              0              0           0             0     \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)\n  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent\n  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\n  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\n  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\n  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.\n  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.\n  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:43:37 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tibble_3.2.1          scda_0.1.6.9017       dplyr_1.1.4          \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(tibble)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\ncombodf &lt;- tribble(\n  ~valname, ~label, ~levelcombo, ~exargs,\n  \"all_X\", \"All Drug X\", c(\"A: Drug X\", \"C: Combination\"), list(),\n  \"all_pt\", \"All Patients\", c(\"A: Drug X\", \"B: Placebo\", \"C: Combination\"), list()\n)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadab &lt;- adab %&gt;%\n  filter(PARCAT1 == \"A: Drug X Antibody\") %&gt;%\n  select(-ARRLT, -NRRLT)\n\n# Baseline Pts\nadab_b &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL == \"Y\",\n    ADABLPFL == \"Y\",\n    PARAM %in% c(\"ADA interpreted per sample result\")\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate_at(\n    c(\"ADA interpreted per sample result\"),\n    as.logical\n  ) %&gt;%\n  mutate(\n    ADABLPFL = ADABLPFL == \"Y\",\n    PADABLPFL = `ADA interpreted per sample result` == \"TRUE\",\n    NADABLPFL = `ADA interpreted per sample result` == \"FALSE\"\n  ) %&gt;%\n  var_relabel(\n    ADABLPFL = \"Baseline evaluable patients\",\n    PADABLPFL = \"Patient with a positive sample at baseline\",\n    NADABLPFL = \"Patient with no positive samples at baseline\"\n  )\n\n# Post Baseline Treatment Enhanced NAb positive Pts\nadab_pb &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    ADPBLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"Treatment Emergent - Positive\",\n      \"Treatment induced ADA\",\n      \"Treatment enhanced ADA\",\n      \"Treatment Emergent - Negative\",\n      \"Treatment unaffected\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\", \"Treatment Emergent - Positive\",\n        \"Treatment induced ADA\", \"Treatment enhanced ADA\",\n        \"Treatment Emergent - Negative\", \"Treatment unaffected\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADPBLPFL = ADPBLPFL == \"Y\",\n    PTEFL = if (exists(\"Treatment Emergent - Positive\", where = .)) {\n      `Treatment Emergent - Positive` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TIFL = if (exists(\"Treatment induced ADA\", where = .)) {\n      `Treatment induced ADA` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TEFL = if (exists(\"Treatment enhanced ADA\", where = .)) {\n      `Treatment enhanced ADA` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NTEFL = if (exists(\"Treatment Emergent - Negative\", where = .)) {\n      `Treatment Emergent - Negative` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TUFL = if (exists(\"Treatment unaffected\", where = .)) {\n      `Treatment unaffected` == \"TRUE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients\",\n    PTEFL = \"Patient positive for Treatment Emergent ADA\",\n    TIFL = \"Treatment-induced ADA\",\n    TEFL = \"Treatment-enhanced ADA\",\n    NTEFL = \"Patient negative for Treatment Emergent ADA\",\n    TUFL = \"Treatment unaffected\"\n  )\n\n\n\n\n\n\nCode# Layout for Baseline Prevalence of NAbs\nlyt_bl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"ADABLPFL\",\n    .stats = \"count\",\n    var_labels = \"Baseline Prevalence of ADAs\",\n    show_labels = \"visible\",\n    table_names = \"t1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PADABLPFL\",\n    table_names = \"t2\",\n    .indent_mods = 1L,\n    var_labels = \"a\",\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NADABLPFL\",\n    .stats = \"count\",\n    show_labels = \"hidden\",\n    .indent_mods = 1L,\n    table_names = \"t3\"\n  )\n\n# Layout for incidence of NAbs\nlyt_pb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"ADPBLPFL\",\n    .stats = \"count\",\n    var_labels = \"Incidence of Treatment Emergent ADAs\",\n    show_labels = \"visible\",\n    table_names = \"tb1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PTEFL\",\n    table_names = \"tb2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"TIFL\", \"TEFL\"),\n    .stats = \"count\",\n    table_names = \"tb3\",\n    .indent_mods = 2L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NTEFL\",\n    table_names = \"tb4\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TUFL\",\n    .stats = \"count\",\n    table_names = \"tb5\",\n    .indent_mods = 2L,\n    show_labels = \"hidden\"\n  )\n\nresult_1 &lt;- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)\nresult_2 &lt;- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)\n\n# Combine tables.\nresult_1@col_info &lt;- result_2@col_info\nresult &lt;- rbind(result_1, result_2)\n\n# Change the column order.\nresult &lt;- cbind_rtables(result[, 1], result[, 3]) %&gt;%\n  cbind_rtables(result[, 4]) %&gt;%\n  cbind_rtables(result[, 2]) %&gt;%\n  cbind_rtables(result[, 5])\n\nmain_title(result) &lt;- paste(\n  \"Baseline Prevalence and Incidence of Treatment Emergent ADA\"\n)\n\nmain_footer(result) &lt;- \"ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)\n  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent\n  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\n  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\n  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\n  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.\n  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.\n  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\"\n\nresult\n\nBaseline Prevalence and Incidence of Treatment Emergent ADA\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                               A: Drug X    C: Combination   &lt;Missing&gt;   B: Placebo   All Drug X \n                                                (N=134)        (N=132)         (N=0)      (N=134)       (N=266)  \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBaseline Prevalence of ADAs                                                                                      \n  Baseline evaluable patients                     134            132             0           0            266    \nPatient with a positive sample at baseline     63 (47.0%)     64 (48.5%)         0           0        127 (47.7%)\nPatient with no positive samples at baseline       71             68             0           0            139    \nIncidence of Treatment Emergent ADAs                                                                             \n  Post-baseline evaluable patients                134            132             0           0            266    \nPatient positive for Treatment Emergent ADA        0              0              0           0             0     \nTreatment-induced ADA                              0              0              0           0             0     \nTreatment-enhanced ADA                             0              0              0           0             0     \nPatient negative for Treatment Emergent ADA        0              0              0           0             0     \nTreatment unaffected                               0              0              0           0             0     \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)\n  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent\n  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\n  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\n  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\n  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.\n  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.\n  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:19 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tibble_3.2.1          scda_0.1.6.9017       dplyr_1.1.4          \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ADA",
@@ -648,7 +648,7 @@
     "href": "tables/ADA/adat04b.html",
     "title": "ADAT04B",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(tibble)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\ncombodf &lt;- tribble(\n  ~valname, ~label, ~levelcombo, ~exargs,\n  \"all_X\", \"All Drug X\", c(\"A: Drug X\", \"C: Combination\"), list(),\n  \"all_pt\", \"All Patients\", c(\"A: Drug X\", \"B: Placebo\", \"C: Combination\"), list()\n)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\n# Baseline Pts\nadab_b &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL == \"Y\",\n    ADABLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"NAB interpreted per sample result\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\",\n        \"NAB interpreted per sample result\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADABLPFL = ADABLPFL == \"Y\",\n    PADABLPFL = if (exists(\"ADA interpreted per sample result\", where = .)) {\n      `ADA interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    PNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"FALSE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADABLPFL = \"Baseline evaluable patients for ADA\",\n    PADABLPFL = \"Patients with a positive ADA sample at baseline\",\n    PNABBLFL = \"Patients with a positive NAb sample at baseline\",\n    NNABBLFL = \"Patients with no positive NAb sample at baseline\"\n  )\n\n# Post-Baseline Evaluable Pts\nadab_pb_ada &lt;- df_explicit_na(adab) %&gt;%\n  filter(ADPBLPFL == \"Y\") %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, ADPBLPFL) %&gt;%\n  mutate(ADPBLPFL = ADPBLPFL == \"Y\") %&gt;%\n  distinct()\n\n# Post-Baseline ADA Positive Pts\nadab_pb_adap &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    PARAM == \"ADA interpreted per sample result\",\n    AVALC == \"POSITIVE\"\n  ) %&gt;%\n  mutate(ADAPFL = AVALC == \"POSITIVE\") %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, ADAPFL) %&gt;%\n  distinct()\n\n# Post-Baseline NAb Positive Pts\nadab_pb_nabp &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    PARAM == \"NAb interpreted per sample result\",\n    AVALC == \"POSITIVE\"\n  ) %&gt;%\n  mutate(NABPFL = AVALC == \"POSITIVE\") %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, NABPFL) %&gt;%\n  distinct()\n\n# Post-Baseline NAb Negative Pts\nadab_pb_nabn &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    PARAM == \"NAb interpreted per sample result\",\n    AVALC == \"NEGATIVE\"\n  ) %&gt;%\n  rename(NABNFL = AVALC) %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, NABNFL) %&gt;%\n  distinct()\n\nmergecol &lt;- c(\"STUDYID\", \"USUBJID\", \"ARM\", \"ACTARM\")\n\nadab_pb &lt;- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %&gt;%\n  left_join(adab_pb_nabp, by = mergecol) %&gt;%\n  mutate(\n    NABNFL = ifelse(is.na(NABPFL), \"TRUE\", \"FALSE\"),\n    NABNFL = NABNFL == \"TRUE\"\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients for ADA\",\n    ADAPFL = \"Patients positive for ADA\",\n    NABPFL = \"Patients positive for NAb\",\n    NABNFL = \"Patients negative for NAb\"\n  )\n\n\n\n\n\n\nCode# Layout for Baseline Prevalence of NAbs\nlyt_bl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADABLPFL\", \"PADABLPFL\"),\n    .stats = \"count\",\n    var_labels = \"Baseline Prevalence of NAbs\",\n    show_labels = \"visible\",\n    table_names = \"t1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PNABBLFL\",\n    table_names = \"t2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NNABBLFL\",\n    .stats = \"count\",\n    table_names = \"t3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\n# Layout for incidence of NAbs\nlyt_pb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADPBLPFL\", \"ADAPFL\"),\n    .stats = \"count\",\n    var_labels = \"Incidence of NAbs\",\n    show_labels = \"visible\",\n    table_names = \"tb1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NABPFL\",\n    table_names = \"tb2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NABNFL\",\n    .stats = \"count\",\n    table_names = \"tb3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\nresult_1 &lt;- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)\nresult_2 &lt;- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)\n\n# Combine tables.\nresult &lt;- rbind(result_1, result_2)\n\n# Change the column order.\nresult &lt;- cbind_rtables(result[, 1], result[, 3]) %&gt;%\n  cbind_rtables(result[, 4]) %&gt;%\n  cbind_rtables(result[, 2]) %&gt;%\n  cbind_rtables(result[, 5])\n\nmain_title(result) &lt;- paste(\n  \"Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)\"\n)\nsubtitles(result) &lt;- paste(\"Protocol:\", unique(adab$PARCAT1)[1])\n\nmain_footer(result) &lt;- \"NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results.\"\n\nresult\n\nBaseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)\nProtocol: A: Drug X Antibody\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                    A: Drug X   C: Combination   &lt;Missing&gt;   B: Placebo   All Drug X\n                                                     (N=134)       (N=132)         (N=0)      (N=134)      (N=266)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBaseline Prevalence of NAbs                                                                                         \n  Baseline evaluable patients for ADA                  134           132             0           0           266    \n  Patients with a positive ADA sample at baseline      63             64             0           0           127    \nPatients with a positive NAb sample at baseline         0             0              0           0            0     \nPatients with no positive NAb sample at baseline        0             0              0           0            0     \nIncidence of NAbs                                                                                                   \n  Post-baseline evaluable patients for ADA             134           132             0           0           266    \n  Patients positive for ADA                            66             59             0           0           125    \nPatients positive for NAb                               0             0              0           0            0     \nPatients negative for NAb                              134           132             0           0           266    \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:43:25 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tibble_3.2.1          scda_0.1.6.9017       dplyr_1.1.4          \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(tibble)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\ncombodf &lt;- tribble(\n  ~valname, ~label, ~levelcombo, ~exargs,\n  \"all_X\", \"All Drug X\", c(\"A: Drug X\", \"C: Combination\"), list(),\n  \"all_pt\", \"All Patients\", c(\"A: Drug X\", \"B: Placebo\", \"C: Combination\"), list()\n)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\n# Baseline Pts\nadab_b &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL == \"Y\",\n    ADABLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"NAB interpreted per sample result\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\",\n        \"NAB interpreted per sample result\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADABLPFL = ADABLPFL == \"Y\",\n    PADABLPFL = if (exists(\"ADA interpreted per sample result\", where = .)) {\n      `ADA interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    PNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"FALSE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADABLPFL = \"Baseline evaluable patients for ADA\",\n    PADABLPFL = \"Patients with a positive ADA sample at baseline\",\n    PNABBLFL = \"Patients with a positive NAb sample at baseline\",\n    NNABBLFL = \"Patients with no positive NAb sample at baseline\"\n  )\n\n# Post-Baseline Evaluable Pts\nadab_pb_ada &lt;- df_explicit_na(adab) %&gt;%\n  filter(ADPBLPFL == \"Y\") %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, ADPBLPFL) %&gt;%\n  mutate(ADPBLPFL = ADPBLPFL == \"Y\") %&gt;%\n  distinct()\n\n# Post-Baseline ADA Positive Pts\nadab_pb_adap &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    PARAM == \"ADA interpreted per sample result\",\n    AVALC == \"POSITIVE\"\n  ) %&gt;%\n  mutate(ADAPFL = AVALC == \"POSITIVE\") %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, ADAPFL) %&gt;%\n  distinct()\n\n# Post-Baseline NAb Positive Pts\nadab_pb_nabp &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    PARAM == \"NAb interpreted per sample result\",\n    AVALC == \"POSITIVE\"\n  ) %&gt;%\n  mutate(NABPFL = AVALC == \"POSITIVE\") %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, NABPFL) %&gt;%\n  distinct()\n\n# Post-Baseline NAb Negative Pts\nadab_pb_nabn &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    PARAM == \"NAb interpreted per sample result\",\n    AVALC == \"NEGATIVE\"\n  ) %&gt;%\n  rename(NABNFL = AVALC) %&gt;%\n  select(STUDYID, USUBJID, ARM, ACTARM, NABNFL) %&gt;%\n  distinct()\n\nmergecol &lt;- c(\"STUDYID\", \"USUBJID\", \"ARM\", \"ACTARM\")\n\nadab_pb &lt;- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %&gt;%\n  left_join(adab_pb_nabp, by = mergecol) %&gt;%\n  mutate(\n    NABNFL = ifelse(is.na(NABPFL), \"TRUE\", \"FALSE\"),\n    NABNFL = NABNFL == \"TRUE\"\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients for ADA\",\n    ADAPFL = \"Patients positive for ADA\",\n    NABPFL = \"Patients positive for NAb\",\n    NABNFL = \"Patients negative for NAb\"\n  )\n\n\n\n\n\n\nCode# Layout for Baseline Prevalence of NAbs\nlyt_bl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADABLPFL\", \"PADABLPFL\"),\n    .stats = \"count\",\n    var_labels = \"Baseline Prevalence of NAbs\",\n    show_labels = \"visible\",\n    table_names = \"t1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PNABBLFL\",\n    table_names = \"t2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NNABBLFL\",\n    .stats = \"count\",\n    table_names = \"t3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\n# Layout for incidence of NAbs\nlyt_pb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADPBLPFL\", \"ADAPFL\"),\n    .stats = \"count\",\n    var_labels = \"Incidence of NAbs\",\n    show_labels = \"visible\",\n    table_names = \"tb1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NABPFL\",\n    table_names = \"tb2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NABNFL\",\n    .stats = \"count\",\n    table_names = \"tb3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\nresult_1 &lt;- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)\nresult_2 &lt;- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)\n\n# Combine tables.\nresult &lt;- rbind(result_1, result_2)\n\n# Change the column order.\nresult &lt;- cbind_rtables(result[, 1], result[, 3]) %&gt;%\n  cbind_rtables(result[, 4]) %&gt;%\n  cbind_rtables(result[, 2]) %&gt;%\n  cbind_rtables(result[, 5])\n\nmain_title(result) &lt;- paste(\n  \"Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)\"\n)\nsubtitles(result) &lt;- paste(\"Protocol:\", unique(adab$PARCAT1)[1])\n\nmain_footer(result) &lt;- \"NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results.\"\n\nresult\n\nBaseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)\nProtocol: A: Drug X Antibody\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                    A: Drug X   C: Combination   &lt;Missing&gt;   B: Placebo   All Drug X\n                                                     (N=134)       (N=132)         (N=0)      (N=134)      (N=266)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBaseline Prevalence of NAbs                                                                                         \n  Baseline evaluable patients for ADA                  134           132             0           0           266    \n  Patients with a positive ADA sample at baseline      63             64             0           0           127    \nPatients with a positive NAb sample at baseline         0             0              0           0            0     \nPatients with no positive NAb sample at baseline        0             0              0           0            0     \nIncidence of NAbs                                                                                                   \n  Post-baseline evaluable patients for ADA             134           132             0           0           266    \n  Patients positive for ADA                            66             59             0           0           125    \nPatients positive for NAb                               0             0              0           0            0     \nPatients negative for NAb                              134           132             0           0           266    \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:07 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tibble_3.2.1          scda_0.1.6.9017       dplyr_1.1.4          \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ADA",
@@ -660,7 +660,7 @@
     "href": "tables/ECG/egt01.html",
     "title": "EGT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\n# Data should be filtered for the studied Parameter (`PARAM`) and the\n# Analysis Visit (`AVISIT`). According to the GDSR template, the values for\n# the `AVISIT` reported in the EGT01 standard may be:\n# 'POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST'.\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg)\n\nadeg_f &lt;- adeg %&gt;%\n  filter(ANL01FL == \"Y\") %&gt;%\n  filter(PARAM %in% c(\"Heart Rate\", \"QT Duration\", \"RR Duration\"))\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f$PARAM)) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f$AVISIT)) %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt, adeg_f, alt_counts_df = adsl)\nresult\n\n                                 A: Drug X                              B: Placebo                            C: Combination            \n                                         Change from                             Change from                             Change from    \nParameter           Value at Visit         Baseline         Value at Visit         Baseline         Value at Visit         Baseline     \n  Analysis Visit       (N=134)             (N=134)             (N=134)             (N=134)             (N=132)             (N=132)      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nHeart Rate                                                                                                                              \n  BASELINE                                                                                                                              \n    n                    134                                     134                                     132                            \n    Mean (SD)       71.45 (17.93)                           69.83 (20.72)                           69.34 (20.95)                       \n    Median              72.70                                   73.35                                   71.96                           \n    Min - Max       9.09 - 106.91                           13.49 - 115.52                          11.63 - 115.49                      \n  WEEK 1 DAY 8                                                                                                                          \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       70.19 (20.27)       -1.26 (25.97)       69.43 (20.80)       -0.40 (29.73)       68.65 (18.06)       -0.69 (27.81)   \n    Median              70.70               -2.20               70.46               -0.52               68.57               -0.76       \n    Min - Max       8.53 - 127.50       -50.97 - 89.16      16.85 - 129.14      -68.18 - 88.38      16.87 - 115.60      -68.25 - 67.20  \n  WEEK 2 DAY 15                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       70.32 (19.56)       -1.13 (26.26)       71.11 (20.56)        1.28 (31.35)       69.19 (18.06)       -0.15 (28.61)   \n    Median              70.88               -1.10               72.52               -0.31               69.30               -1.55       \n    Min - Max       17.14 - 116.32      -85.03 - 67.52      9.22 - 120.54       -73.07 - 81.44      29.62 - 120.50      -67.75 - 66.29  \n  WEEK 3 DAY 22                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       68.25 (20.17)       -3.20 (28.66)       68.95 (20.56)       -0.88 (30.14)       70.88 (20.28)        1.54 (27.28)   \n    Median              68.79               -2.17               67.80                1.19               70.12                0.14       \n    Min - Max       13.33 - 131.73      -81.20 - 72.57      23.98 - 130.41     -73.03 - 103.31      20.91 - 116.79      -64.51 - 72.52  \n  WEEK 4 DAY 29                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       70.06 (18.41)       -1.38 (26.59)       71.60 (20.34)        1.77 (30.26)       71.02 (20.59)        1.68 (29.60)   \n    Median              69.33               -4.54               72.21               -0.88               70.52                3.81       \n    Min - Max       22.30 - 116.51      -58.07 - 77.32      17.53 - 129.06      -64.32 - 85.34      10.35 - 117.30     -65.91 - 105.67  \n  WEEK 5 DAY 36                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       66.41 (19.74)       -5.04 (27.11)       71.25 (18.92)        1.42 (27.08)       71.26 (18.56)        1.92 (29.59)   \n    Median              65.40               -6.83               70.86                0.64               73.29                1.74       \n    Min - Max       23.89 - 110.38      -73.26 - 57.24      25.86 - 125.73      -71.15 - 67.37      23.89 - 117.19      -58.23 - 68.86  \nQT Duration                                                                                                                             \n  BASELINE                                                                                                                              \n    n                    134                                     134                                     132                            \n    Mean (SD)      336.83 (117.96)                          351.00 (98.44)                         352.60 (105.11)                      \n    Median              344.41                                  351.48                                  347.96                          \n    Min - Max       86.98 - 665.40                          98.90 - 641.92                         105.68 - 628.14                      \n  WEEK 1 DAY 8                                                                                                                          \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      342.57 (101.09)      5.74 (159.94)      363.95 (102.34)      12.94 (140.73)     359.42 (105.16)      6.83 (147.13)   \n    Median              347.26              -1.78               356.54              13.73               363.84               8.69       \n    Min - Max       91.63 - 591.42     -346.44 - 452.75    114.92 - 656.45     -317.53 - 416.35     51.91 - 611.88     -473.19 - 358.00 \n  WEEK 2 DAY 15                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       353.24 (93.93)      16.41 (162.10)      345.93 (96.78)      -5.08 (152.51)      335.69 (98.61)     -16.91 (138.45)  \n    Median              351.10              16.72               346.83              -9.68               320.96              -22.27      \n    Min - Max      138.01 - 587.30     -414.07 - 389.16    146.42 - 556.07     -440.28 - 364.76    104.91 - 562.34     -326.55 - 325.27 \n  WEEK 3 DAY 22                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      370.12 (101.57)      33.28 (152.78)     343.41 (102.58)      -7.59 (138.80)      346.95 (94.96)      -5.65 (144.65)  \n    Median              378.21              39.59               335.12              -15.89              352.15               9.11       \n    Min - Max      118.14 - 615.18     -391.72 - 520.09     63.37 - 566.51     -311.28 - 293.76    126.09 - 580.81     -412.11 - 410.01 \n  WEEK 4 DAY 29                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       345.77 (95.97)      8.94 (145.65)      354.38 (108.29)      3.37 (142.39)      341.36 (106.75)     -11.24 (145.65)  \n    Median              340.13               5.58               346.70              -17.13              352.30              -11.87      \n    Min - Max      110.12 - 616.58     -393.34 - 456.04     80.82 - 687.69     -439.90 - 364.80     4.95 - 570.61      -480.94 - 330.67 \n  WEEK 5 DAY 36                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       358.90 (97.69)      22.07 (155.68)      338.65 (99.26)     -12.36 (130.46)      349.30 (95.77)      -3.30 (136.00)  \n    Median              351.96               5.89               344.17               0.40               350.32              12.68       \n    Min - Max       88.38 - 661.12     -353.30 - 539.84     31.25 - 563.90     -338.85 - 352.75    119.02 - 581.83     -311.45 - 295.53 \nRR Duration                                                                                                                             \n  BASELINE                                                                                                                              \n    n                    134                                     134                                     132                            \n    Mean (SD)      1028.42 (286.38)                        1027.48 (324.00)                        1074.62 (277.33)                     \n    Median             1041.86                                 1047.27                                 1080.87                          \n    Min - Max      34.33 - 1783.71                          5.29 - 1877.19                         289.60 - 1617.06                     \n  WEEK 1 DAY 8                                                                                                                          \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      990.15 (318.74)     -38.26 (446.40)     1061.59 (269.14)     34.10 (401.76)     1039.64 (284.39)    -34.98 (406.04)  \n    Median              963.88              -86.50             1061.50              66.91              1014.78              -97.83      \n    Min - Max      110.82 - 2014.56   -1014.82 - 1389.40   276.83 - 1711.99    -942.16 - 993.82    498.21 - 1937.47    -958.61 - 908.90 \n  WEEK 2 DAY 15                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1013.37 (304.13)    -15.05 (425.81)     1109.40 (318.23)     81.92 (453.11)     1045.21 (277.51)    -29.41 (380.84)  \n    Median             1040.69              40.49              1117.76              69.27              1034.64              -15.33      \n    Min - Max      164.19 - 1677.10   -1216.10 - 1053.15   160.73 - 2048.73   -1127.10 - 1148.61   252.84 - 1722.33    -859.27 - 871.54 \n  WEEK 3 DAY 22                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1013.99 (304.99)    -14.43 (406.53)     1118.35 (296.53)     90.86 (409.57)     1036.59 (268.52)    -38.03 (421.48)  \n    Median             1027.23              -50.54             1134.60              52.03              1030.72              -60.72      \n    Min - Max      357.04 - 1798.65   -882.94 - 1080.26    97.14 - 1825.43    -887.06 - 1166.15    446.02 - 1713.38    -984.79 - 902.37 \n  WEEK 4 DAY 29                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1033.31 (313.92)     4.89 (483.15)      1079.76 (298.94)     52.28 (429.25)     1027.55 (295.86)    -47.07 (378.23)  \n    Median             1050.40               5.60              1051.61              69.55              1023.36              -49.52      \n    Min - Max      54.33 - 1979.43    -1345.93 - 1195.41   347.94 - 1762.04   -826.43 - 1191.83    341.62 - 2144.86   -1002.03 - 1048.66\n  WEEK 5 DAY 36                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1072.33 (282.90)     43.92 (379.05)     1058.45 (271.01)     30.97 (436.03)     1029.04 (271.21)    -45.58 (405.53)  \n    Median             1067.33              55.87              1068.95              33.61              1065.26              -34.40      \n    Min - Max      352.97 - 2000.56   -1028.79 - 1418.57   208.83 - 1794.73   -978.97 - 1365.13    436.28 - 1794.07   -962.18 - 1329.88 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADEG &lt;- df_explicit_na(ADEG)\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"ECG Results and Change from Baseline by Visit\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEG, \"PARAMCD\", \"PARAM\"),\n        selected = \"HR\"\n      ),\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADEG\", \"AVAL\", selected = NULL))\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADEG-filter-ADEG_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:42:58 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\n# Data should be filtered for the studied Parameter (`PARAM`) and the\n# Analysis Visit (`AVISIT`). According to the GDSR template, the values for\n# the `AVISIT` reported in the EGT01 standard may be:\n# 'POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST'.\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg)\n\nadeg_f &lt;- adeg %&gt;%\n  filter(ANL01FL == \"Y\") %&gt;%\n  filter(PARAM %in% c(\"Heart Rate\", \"QT Duration\", \"RR Duration\"))\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f$PARAM)) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f$AVISIT)) %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt, adeg_f, alt_counts_df = adsl)\nresult\n\n                                 A: Drug X                              B: Placebo                            C: Combination            \n                                         Change from                             Change from                             Change from    \nParameter           Value at Visit         Baseline         Value at Visit         Baseline         Value at Visit         Baseline     \n  Analysis Visit       (N=134)             (N=134)             (N=134)             (N=134)             (N=132)             (N=132)      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nHeart Rate                                                                                                                              \n  BASELINE                                                                                                                              \n    n                    134                                     134                                     132                            \n    Mean (SD)       71.45 (17.93)                           69.83 (20.72)                           69.34 (20.95)                       \n    Median              72.70                                   73.35                                   71.96                           \n    Min - Max       9.09 - 106.91                           13.49 - 115.52                          11.63 - 115.49                      \n  WEEK 1 DAY 8                                                                                                                          \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       70.19 (20.27)       -1.26 (25.97)       69.43 (20.80)       -0.40 (29.73)       68.65 (18.06)       -0.69 (27.81)   \n    Median              70.70               -2.20               70.46               -0.52               68.57               -0.76       \n    Min - Max       8.53 - 127.50       -50.97 - 89.16      16.85 - 129.14      -68.18 - 88.38      16.87 - 115.60      -68.25 - 67.20  \n  WEEK 2 DAY 15                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       70.32 (19.56)       -1.13 (26.26)       71.11 (20.56)        1.28 (31.35)       69.19 (18.06)       -0.15 (28.61)   \n    Median              70.88               -1.10               72.52               -0.31               69.30               -1.55       \n    Min - Max       17.14 - 116.32      -85.03 - 67.52      9.22 - 120.54       -73.07 - 81.44      29.62 - 120.50      -67.75 - 66.29  \n  WEEK 3 DAY 22                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       68.25 (20.17)       -3.20 (28.66)       68.95 (20.56)       -0.88 (30.14)       70.88 (20.28)        1.54 (27.28)   \n    Median              68.79               -2.17               67.80                1.19               70.12                0.14       \n    Min - Max       13.33 - 131.73      -81.20 - 72.57      23.98 - 130.41     -73.03 - 103.31      20.91 - 116.79      -64.51 - 72.52  \n  WEEK 4 DAY 29                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       70.06 (18.41)       -1.38 (26.59)       71.60 (20.34)        1.77 (30.26)       71.02 (20.59)        1.68 (29.60)   \n    Median              69.33               -4.54               72.21               -0.88               70.52                3.81       \n    Min - Max       22.30 - 116.51      -58.07 - 77.32      17.53 - 129.06      -64.32 - 85.34      10.35 - 117.30     -65.91 - 105.67  \n  WEEK 5 DAY 36                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       66.41 (19.74)       -5.04 (27.11)       71.25 (18.92)        1.42 (27.08)       71.26 (18.56)        1.92 (29.59)   \n    Median              65.40               -6.83               70.86                0.64               73.29                1.74       \n    Min - Max       23.89 - 110.38      -73.26 - 57.24      25.86 - 125.73      -71.15 - 67.37      23.89 - 117.19      -58.23 - 68.86  \nQT Duration                                                                                                                             \n  BASELINE                                                                                                                              \n    n                    134                                     134                                     132                            \n    Mean (SD)      336.83 (117.96)                          351.00 (98.44)                         352.60 (105.11)                      \n    Median              344.41                                  351.48                                  347.96                          \n    Min - Max       86.98 - 665.40                          98.90 - 641.92                         105.68 - 628.14                      \n  WEEK 1 DAY 8                                                                                                                          \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      342.57 (101.09)      5.74 (159.94)      363.95 (102.34)      12.94 (140.73)     359.42 (105.16)      6.83 (147.13)   \n    Median              347.26              -1.78               356.54              13.73               363.84               8.69       \n    Min - Max       91.63 - 591.42     -346.44 - 452.75    114.92 - 656.45     -317.53 - 416.35     51.91 - 611.88     -473.19 - 358.00 \n  WEEK 2 DAY 15                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       353.24 (93.93)      16.41 (162.10)      345.93 (96.78)      -5.08 (152.51)      335.69 (98.61)     -16.91 (138.45)  \n    Median              351.10              16.72               346.83              -9.68               320.96              -22.27      \n    Min - Max      138.01 - 587.30     -414.07 - 389.16    146.42 - 556.07     -440.28 - 364.76    104.91 - 562.34     -326.55 - 325.27 \n  WEEK 3 DAY 22                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      370.12 (101.57)      33.28 (152.78)     343.41 (102.58)      -7.59 (138.80)      346.95 (94.96)      -5.65 (144.65)  \n    Median              378.21              39.59               335.12              -15.89              352.15               9.11       \n    Min - Max      118.14 - 615.18     -391.72 - 520.09     63.37 - 566.51     -311.28 - 293.76    126.09 - 580.81     -412.11 - 410.01 \n  WEEK 4 DAY 29                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       345.77 (95.97)      8.94 (145.65)      354.38 (108.29)      3.37 (142.39)      341.36 (106.75)     -11.24 (145.65)  \n    Median              340.13               5.58               346.70              -17.13              352.30              -11.87      \n    Min - Max      110.12 - 616.58     -393.34 - 456.04     80.82 - 687.69     -439.90 - 364.80     4.95 - 570.61      -480.94 - 330.67 \n  WEEK 5 DAY 36                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)       358.90 (97.69)      22.07 (155.68)      338.65 (99.26)     -12.36 (130.46)      349.30 (95.77)      -3.30 (136.00)  \n    Median              351.96               5.89               344.17               0.40               350.32              12.68       \n    Min - Max       88.38 - 661.12     -353.30 - 539.84     31.25 - 563.90     -338.85 - 352.75    119.02 - 581.83     -311.45 - 295.53 \nRR Duration                                                                                                                             \n  BASELINE                                                                                                                              \n    n                    134                                     134                                     132                            \n    Mean (SD)      1028.42 (286.38)                        1027.48 (324.00)                        1074.62 (277.33)                     \n    Median             1041.86                                 1047.27                                 1080.87                          \n    Min - Max      34.33 - 1783.71                          5.29 - 1877.19                         289.60 - 1617.06                     \n  WEEK 1 DAY 8                                                                                                                          \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      990.15 (318.74)     -38.26 (446.40)     1061.59 (269.14)     34.10 (401.76)     1039.64 (284.39)    -34.98 (406.04)  \n    Median              963.88              -86.50             1061.50              66.91              1014.78              -97.83      \n    Min - Max      110.82 - 2014.56   -1014.82 - 1389.40   276.83 - 1711.99    -942.16 - 993.82    498.21 - 1937.47    -958.61 - 908.90 \n  WEEK 2 DAY 15                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1013.37 (304.13)    -15.05 (425.81)     1109.40 (318.23)     81.92 (453.11)     1045.21 (277.51)    -29.41 (380.84)  \n    Median             1040.69              40.49              1117.76              69.27              1034.64              -15.33      \n    Min - Max      164.19 - 1677.10   -1216.10 - 1053.15   160.73 - 2048.73   -1127.10 - 1148.61   252.84 - 1722.33    -859.27 - 871.54 \n  WEEK 3 DAY 22                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1013.99 (304.99)    -14.43 (406.53)     1118.35 (296.53)     90.86 (409.57)     1036.59 (268.52)    -38.03 (421.48)  \n    Median             1027.23              -50.54             1134.60              52.03              1030.72              -60.72      \n    Min - Max      357.04 - 1798.65   -882.94 - 1080.26    97.14 - 1825.43    -887.06 - 1166.15    446.02 - 1713.38    -984.79 - 902.37 \n  WEEK 4 DAY 29                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1033.31 (313.92)     4.89 (483.15)      1079.76 (298.94)     52.28 (429.25)     1027.55 (295.86)    -47.07 (378.23)  \n    Median             1050.40               5.60              1051.61              69.55              1023.36              -49.52      \n    Min - Max      54.33 - 1979.43    -1345.93 - 1195.41   347.94 - 1762.04   -826.43 - 1191.83    341.62 - 2144.86   -1002.03 - 1048.66\n  WEEK 5 DAY 36                                                                                                                         \n    n                    134                 134                 134                 134                 132                 132        \n    Mean (SD)      1072.33 (282.90)     43.92 (379.05)     1058.45 (271.01)     30.97 (436.03)     1029.04 (271.21)    -45.58 (405.53)  \n    Median             1067.33              55.87              1068.95              33.61              1065.26              -34.40      \n    Min - Max      352.97 - 2000.56   -1028.79 - 1418.57   208.83 - 1794.73   -978.97 - 1365.13    436.28 - 1794.07   -962.18 - 1329.88 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADEG &lt;- df_explicit_na(ADEG)\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"ECG Results and Change from Baseline by Visit\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEG, \"PARAMCD\", \"PARAM\"),\n        selected = \"HR\"\n      ),\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADEG\", \"AVAL\", selected = NULL))\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADEG-filter-ADEG_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:41:40 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ECG",
@@ -672,7 +672,7 @@
     "href": "tables/ECG/egt05_qtcat.html",
     "title": "EGT05_QTCAT",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg)\n\nadeg_labels &lt;- var_labels(adeg)\nadeg_f &lt;- adeg %&gt;%\n  filter(\n    PARAMCD == \"QT\",\n    ANL01FL == \"Y\"\n  ) %&gt;%\n  mutate(\n    AVALCAT1 = case_when(\n      AVAL &lt;= 450 ~ \"&lt;=450 msec\",\n      AVAL &lt;= 480 ~ \"&gt;450 to &lt;=480 msec\",\n      AVAL &lt;= 500 ~ \"&gt;480 to &lt;= 500 msec\",\n      AVAL &gt; 500 ~ \"&gt;500 msec\",\n      is.na(AVAL) ~ \"&lt;Missing&gt;\"\n    ),\n    CHGCAT1 = case_when(\n      CHG &lt;= 30 ~ \"&lt;=30 msec\",\n      CHG &lt;= 60 ~ \"&gt;30 to &lt;=60 msec\",\n      CHG &gt; 60 ~ \"&gt;60 msec\",\n      is.na(CHG) ~ \"&lt;Missing&gt;\"\n    )\n  ) %&gt;%\n  mutate(\n    AVALCAT1 = factor(\n      AVALCAT1,\n      levels = c(\n        \"&lt;=450 msec\",\n        \"&gt;450 to &lt;=480 msec\",\n        \"&gt;480 to &lt;= 500 msec\",\n        \"&gt;500 msec\",\n        \"&lt;Missing&gt;\"\n      )\n    ),\n    CHGCAT1 = factor(\n      CHGCAT1,\n      levels = c(\n        \"&lt;=30 msec\",\n        \"&gt;30 to &lt;=60 msec\",\n        \"&gt;60 msec\",\n        \"&lt;Missing&gt;\"\n      )\n    )\n  ) %&gt;%\n  var_relabel(\n    AVALCAT1 = \"Value at Visit\",\n    CHGCAT1 = \"Change from Baseline\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_label = obj_label(adeg_f$PARAM),\n    split_fun = split_fun,\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_label = obj_label(adeg_f$AVISIT),\n    split_fun = split_fun,\n    label_pos = \"topleft\"\n  ) %&gt;%\n  analyze_vars(\n    vars = c(\"AVALCAT1\", \"CHGCAT1\"),\n    var_labels = c(\"Value at Visit\", \"Change from Baseline\")\n  ) %&gt;%\n  append_topleft(\"    Category\")\n\nresult &lt;- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                             \n  Analysis Visit             A: Drug X    B: Placebo    C: Combination\n    Category                  (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————\nQT Duration                                                           \n  BASELINE                                                            \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            115 (85.8%)   117 (87.3%)    104 (78.8%)  \n      &gt;450 to &lt;=480 msec     6 (4.5%)      10 (7.5%)       9 (6.8%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)       3 (2.2%)        6 (4.5%)   \n      &gt;500 msec              9 (6.7%)       4 (3%)        13 (9.8%)   \n  WEEK 1 DAY 8                                                        \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            113 (84.3%)   106 (79.1%)    106 (80.3%)  \n      &gt;450 to &lt;=480 msec     10 (7.5%)     10 (7.5%)      11 (8.3%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)        4 (3%)         3 (2.3%)   \n      &gt;500 msec              7 (5.2%)     14 (10.4%)      12 (9.1%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec             76 (56.7%)     75 (56%)       75 (56.8%)  \n      &gt;30 to &lt;=60 msec       7 (5.2%)      13 (9.7%)      11 (8.3%)   \n      &gt;60 msec              51 (38.1%)    46 (34.3%)      46 (34.8%)  \n  WEEK 2 DAY 15                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            111 (82.8%)   114 (85.1%)    112 (84.8%)  \n      &gt;450 to &lt;=480 msec     10 (7.5%)     9 (6.7%)        9 (6.8%)   \n      &gt;480 to &lt;= 500 msec    7 (5.2%)      2 (1.5%)        5 (3.8%)   \n      &gt;500 msec              6 (4.5%)      9 (6.7%)        6 (4.5%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec              71 (53%)     87 (64.9%)      89 (67.4%)  \n      &gt;30 to &lt;=60 msec       11 (8.2%)     9 (6.7%)        9 (6.8%)   \n      &gt;60 msec              52 (38.8%)    38 (28.4%)      34 (25.8%)  \n  WEEK 3 DAY 22                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            106 (79.1%)   112 (83.6%)    118 (89.4%)  \n      &gt;450 to &lt;=480 msec     13 (9.7%)     7 (5.2%)        3 (2.3%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)       5 (3.7%)        2 (1.5%)   \n      &gt;500 msec              11 (8.2%)     10 (7.5%)       9 (6.8%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec              63 (47%)     80 (59.7%)      81 (61.4%)  \n      &gt;30 to &lt;=60 msec      14 (10.4%)      8 (6%)        11 (8.3%)   \n      &gt;60 msec              57 (42.5%)    46 (34.3%)      40 (30.3%)  \n  WEEK 4 DAY 29                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            117 (87.3%)   103 (76.9%)    114 (86.4%)  \n      &gt;450 to &lt;=480 msec     7 (5.2%)     14 (10.4%)       6 (4.5%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)       7 (5.2%)        3 (2.3%)   \n      &gt;500 msec              6 (4.5%)      10 (7.5%)       9 (6.8%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec              79 (59%)     80 (59.7%)      79 (59.8%)  \n      &gt;30 to &lt;=60 msec       11 (8.2%)     7 (5.2%)       10 (7.6%)   \n      &gt;60 msec              44 (32.8%)    47 (35.1%)      43 (32.6%)  \n  WEEK 5 DAY 36                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            107 (79.9%)   117 (87.3%)    112 (84.8%)  \n      &gt;450 to &lt;=480 msec    16 (11.9%)     5 (3.7%)       13 (9.8%)   \n      &gt;480 to &lt;= 500 msec    5 (3.7%)      9 (6.7%)        3 (2.3%)   \n      &gt;500 msec              6 (4.5%)      3 (2.2%)         4 (3%)    \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec             72 (53.7%)    82 (61.2%)      73 (55.3%)  \n      &gt;30 to &lt;=60 msec       10 (7.5%)     11 (8.2%)      11 (8.3%)   \n      &gt;60 msec              52 (38.8%)    41 (30.6%)      48 (36.4%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADEG &lt;- df_explicit_na(ADEG)\n  adeg_labels &lt;- col_labels(ADEG)\n\n  ADEG &lt;- ADEG %&gt;%\n    filter(\n      ANL01FL == \"Y\" # no need to filter for PARAMCD here\n    ) %&gt;%\n    mutate(\n      AVALCAT1 = case_when(\n        AVAL &lt;= 450 ~ \"&lt;=450 msec\",\n        AVAL &lt;= 480 ~ \"&gt;450 to &lt;=480 msec\",\n        AVAL &lt;= 500 ~ \"&gt;480 to &lt;= 500 msec\",\n        AVAL &gt; 500 ~ \"&gt;500 msec\",\n        is.na(AVAL) ~ \"&lt;Missing&gt;\"\n      ),\n      CHGCAT1 = case_when(\n        CHG &lt;= 30 ~ \"&lt;=30 msec\",\n        CHG &lt;= 60 ~ \"&gt;30 to &lt;=60 msec\",\n        CHG &gt; 60 ~ \"&gt;60 msec\",\n        is.na(CHG) ~ \"&lt;Missing&gt;\"\n      )\n    ) %&gt;%\n    mutate(\n      AVALCAT1 = factor(\n        AVALCAT1,\n        levels = c(\n          \"&lt;=450 msec\",\n          \"&gt;450 to &lt;=480 msec\",\n          \"&gt;480 to &lt;= 500 msec\",\n          \"&gt;500 msec\",\n          \"&lt;Missing&gt;\"\n        )\n      ),\n      CHGCAT1 = factor(\n        CHGCAT1,\n        levels = c(\n          \"&lt;=30 msec\",\n          \"&gt;30 to &lt;=60 msec\",\n          \"&gt;60 msec\",\n          \"&lt;Missing&gt;\"\n        )\n      )\n    )\n  col_labels(ADEG) &lt;- c(\n    adeg_labels,\n    \"AVALCAT1\" = \"Value at Visit\",\n    \"CHGCAT1\" = \"Change from Baseline\"\n  )\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"ECG Actual Values and Changes from Baseline by Visit\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"AVALCAT1\", \"CHGCAT1\")),\n        selected = c(\"AVALCAT1\", \"CHGCAT1\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEG, \"PARAMCD\", \"PARAM\"),\n        selected = \"QT\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:42:25 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg)\n\nadeg_labels &lt;- var_labels(adeg)\nadeg_f &lt;- adeg %&gt;%\n  filter(\n    PARAMCD == \"QT\",\n    ANL01FL == \"Y\"\n  ) %&gt;%\n  mutate(\n    AVALCAT1 = case_when(\n      AVAL &lt;= 450 ~ \"&lt;=450 msec\",\n      AVAL &lt;= 480 ~ \"&gt;450 to &lt;=480 msec\",\n      AVAL &lt;= 500 ~ \"&gt;480 to &lt;= 500 msec\",\n      AVAL &gt; 500 ~ \"&gt;500 msec\",\n      is.na(AVAL) ~ \"&lt;Missing&gt;\"\n    ),\n    CHGCAT1 = case_when(\n      CHG &lt;= 30 ~ \"&lt;=30 msec\",\n      CHG &lt;= 60 ~ \"&gt;30 to &lt;=60 msec\",\n      CHG &gt; 60 ~ \"&gt;60 msec\",\n      is.na(CHG) ~ \"&lt;Missing&gt;\"\n    )\n  ) %&gt;%\n  mutate(\n    AVALCAT1 = factor(\n      AVALCAT1,\n      levels = c(\n        \"&lt;=450 msec\",\n        \"&gt;450 to &lt;=480 msec\",\n        \"&gt;480 to &lt;= 500 msec\",\n        \"&gt;500 msec\",\n        \"&lt;Missing&gt;\"\n      )\n    ),\n    CHGCAT1 = factor(\n      CHGCAT1,\n      levels = c(\n        \"&lt;=30 msec\",\n        \"&gt;30 to &lt;=60 msec\",\n        \"&gt;60 msec\",\n        \"&lt;Missing&gt;\"\n      )\n    )\n  ) %&gt;%\n  var_relabel(\n    AVALCAT1 = \"Value at Visit\",\n    CHGCAT1 = \"Change from Baseline\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_label = obj_label(adeg_f$PARAM),\n    split_fun = split_fun,\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_label = obj_label(adeg_f$AVISIT),\n    split_fun = split_fun,\n    label_pos = \"topleft\"\n  ) %&gt;%\n  analyze_vars(\n    vars = c(\"AVALCAT1\", \"CHGCAT1\"),\n    var_labels = c(\"Value at Visit\", \"Change from Baseline\")\n  ) %&gt;%\n  append_topleft(\"    Category\")\n\nresult &lt;- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                             \n  Analysis Visit             A: Drug X    B: Placebo    C: Combination\n    Category                  (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————\nQT Duration                                                           \n  BASELINE                                                            \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            115 (85.8%)   117 (87.3%)    104 (78.8%)  \n      &gt;450 to &lt;=480 msec     6 (4.5%)      10 (7.5%)       9 (6.8%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)       3 (2.2%)        6 (4.5%)   \n      &gt;500 msec              9 (6.7%)       4 (3%)        13 (9.8%)   \n  WEEK 1 DAY 8                                                        \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            113 (84.3%)   106 (79.1%)    106 (80.3%)  \n      &gt;450 to &lt;=480 msec     10 (7.5%)     10 (7.5%)      11 (8.3%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)        4 (3%)         3 (2.3%)   \n      &gt;500 msec              7 (5.2%)     14 (10.4%)      12 (9.1%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec             76 (56.7%)     75 (56%)       75 (56.8%)  \n      &gt;30 to &lt;=60 msec       7 (5.2%)      13 (9.7%)      11 (8.3%)   \n      &gt;60 msec              51 (38.1%)    46 (34.3%)      46 (34.8%)  \n  WEEK 2 DAY 15                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            111 (82.8%)   114 (85.1%)    112 (84.8%)  \n      &gt;450 to &lt;=480 msec     10 (7.5%)     9 (6.7%)        9 (6.8%)   \n      &gt;480 to &lt;= 500 msec    7 (5.2%)      2 (1.5%)        5 (3.8%)   \n      &gt;500 msec              6 (4.5%)      9 (6.7%)        6 (4.5%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec              71 (53%)     87 (64.9%)      89 (67.4%)  \n      &gt;30 to &lt;=60 msec       11 (8.2%)     9 (6.7%)        9 (6.8%)   \n      &gt;60 msec              52 (38.8%)    38 (28.4%)      34 (25.8%)  \n  WEEK 3 DAY 22                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            106 (79.1%)   112 (83.6%)    118 (89.4%)  \n      &gt;450 to &lt;=480 msec     13 (9.7%)     7 (5.2%)        3 (2.3%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)       5 (3.7%)        2 (1.5%)   \n      &gt;500 msec              11 (8.2%)     10 (7.5%)       9 (6.8%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec              63 (47%)     80 (59.7%)      81 (61.4%)  \n      &gt;30 to &lt;=60 msec      14 (10.4%)      8 (6%)        11 (8.3%)   \n      &gt;60 msec              57 (42.5%)    46 (34.3%)      40 (30.3%)  \n  WEEK 4 DAY 29                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            117 (87.3%)   103 (76.9%)    114 (86.4%)  \n      &gt;450 to &lt;=480 msec     7 (5.2%)     14 (10.4%)       6 (4.5%)   \n      &gt;480 to &lt;= 500 msec     4 (3%)       7 (5.2%)        3 (2.3%)   \n      &gt;500 msec              6 (4.5%)      10 (7.5%)       9 (6.8%)   \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec              79 (59%)     80 (59.7%)      79 (59.8%)  \n      &gt;30 to &lt;=60 msec       11 (8.2%)     7 (5.2%)       10 (7.6%)   \n      &gt;60 msec              44 (32.8%)    47 (35.1%)      43 (32.6%)  \n  WEEK 5 DAY 36                                                       \n    Value at Visit                                                    \n      n                         134           134            132      \n      &lt;=450 msec            107 (79.9%)   117 (87.3%)    112 (84.8%)  \n      &gt;450 to &lt;=480 msec    16 (11.9%)     5 (3.7%)       13 (9.8%)   \n      &gt;480 to &lt;= 500 msec    5 (3.7%)      9 (6.7%)        3 (2.3%)   \n      &gt;500 msec              6 (4.5%)      3 (2.2%)         4 (3%)    \n    Change from Baseline                                              \n      n                         134           134            132      \n      &lt;=30 msec             72 (53.7%)    82 (61.2%)      73 (55.3%)  \n      &gt;30 to &lt;=60 msec       10 (7.5%)     11 (8.2%)      11 (8.3%)   \n      &gt;60 msec              52 (38.8%)    41 (30.6%)      48 (36.4%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADEG &lt;- df_explicit_na(ADEG)\n  adeg_labels &lt;- col_labels(ADEG)\n\n  ADEG &lt;- ADEG %&gt;%\n    filter(\n      ANL01FL == \"Y\" # no need to filter for PARAMCD here\n    ) %&gt;%\n    mutate(\n      AVALCAT1 = case_when(\n        AVAL &lt;= 450 ~ \"&lt;=450 msec\",\n        AVAL &lt;= 480 ~ \"&gt;450 to &lt;=480 msec\",\n        AVAL &lt;= 500 ~ \"&gt;480 to &lt;= 500 msec\",\n        AVAL &gt; 500 ~ \"&gt;500 msec\",\n        is.na(AVAL) ~ \"&lt;Missing&gt;\"\n      ),\n      CHGCAT1 = case_when(\n        CHG &lt;= 30 ~ \"&lt;=30 msec\",\n        CHG &lt;= 60 ~ \"&gt;30 to &lt;=60 msec\",\n        CHG &gt; 60 ~ \"&gt;60 msec\",\n        is.na(CHG) ~ \"&lt;Missing&gt;\"\n      )\n    ) %&gt;%\n    mutate(\n      AVALCAT1 = factor(\n        AVALCAT1,\n        levels = c(\n          \"&lt;=450 msec\",\n          \"&gt;450 to &lt;=480 msec\",\n          \"&gt;480 to &lt;= 500 msec\",\n          \"&gt;500 msec\",\n          \"&lt;Missing&gt;\"\n        )\n      ),\n      CHGCAT1 = factor(\n        CHGCAT1,\n        levels = c(\n          \"&lt;=30 msec\",\n          \"&gt;30 to &lt;=60 msec\",\n          \"&gt;60 msec\",\n          \"&lt;Missing&gt;\"\n        )\n      )\n    )\n  col_labels(ADEG) &lt;- c(\n    adeg_labels,\n    \"AVALCAT1\" = \"Value at Visit\",\n    \"CHGCAT1\" = \"Change from Baseline\"\n  )\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"ECG Actual Values and Changes from Baseline by Visit\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADEG, c(\"AVALCAT1\", \"CHGCAT1\")),\n        selected = c(\"AVALCAT1\", \"CHGCAT1\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEG, \"PARAMCD\", \"PARAM\"),\n        selected = \"QT\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:41:08 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ECG",
@@ -684,7 +684,7 @@
     "href": "tables/concomitant-medications/cmt01a.html",
     "title": "CMT01A",
     "section": "",
-    "text": "Data Setup\nStandard Table,  Safety-Evaluable Patients\nTable Changing  ATC Class Level\nTable with Classes  Sorted by Frequency\nTable with Total Number of Treatments  per Medication Class Suppressed\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nadcm$CMSEQ &lt;- as.factor(adcm$CMSEQ)\n\nadsl &lt;- df_explicit_na(adsl)\nadcm &lt;- df_explicit_na(adcm)\n\n# Keep only safety-evaluable patients and concomitant medications\nadsl &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\")\n\nadcm &lt;- adcm %&gt;%\n  filter(SAFFL == \"Y\" & ATIREL == \"CONCOMITANT\") %&gt;%\n  var_relabel(CMDECOD = \"Other Treatment\")\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC2\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS2 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  Total number of treatments                                 134           137            143      \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  Total number of treatments                                 58            66              64      \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  Total number of treatments                                 141           137            162      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS2 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  Total number of treatments                                 69            73              80      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC1\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC1)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC1\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 1 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS1 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  Total number of treatments                                 134           137            143      \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  Total number of treatments                                 58            66              64      \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  Total number of treatments                                 141           137            162      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS1 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  Total number of treatments                                 69            73              80      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(path = c(\"ATC2\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"ATC2\", \"*\", \"CMDECOD\"), scorefun = score_occurrences)\n\nresult\n\nATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS2 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  Total number of treatments                                 141           137            162      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS2 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  Total number of treatments                                 134           137            143      \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  Total number of treatments                                 69            73              80      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \nATCCLAS2 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  Total number of treatments                                 58            66              64      \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC2\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS2 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS2 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\nadcm_keys &lt;- c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"CMSEQ\", \"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n  ADCM$CMSEQ &lt;- as.integer(ADCM$CMSEQ)\n\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\njoin_keys(data)[\"ADCM\", \"ADCM\"] &lt;- adcm_keys\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_mult_events(\n      label = \"Concomitant Medications by Medication Class and Preferred Name\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      seq_var = choices_selected(\"CMSEQ\", selected = \"CMSEQ\", fixed = TRUE),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")),\n        selected = c(\"ATC2\")\n      ),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, c(\"CMDECOD\")),\n        selected = c(\"CMDECOD\")\n      ),\n      add_total = FALSE,\n      event_type = \"treatment\"\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n    teal_slice(\"ADCM\", \"ATIREL\", selected = \"CONCOMITANT\")\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:42:02 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table,  Safety-Evaluable Patients\nTable Changing  ATC Class Level\nTable with Classes  Sorted by Frequency\nTable with Total Number of Treatments  per Medication Class Suppressed\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nadcm$CMSEQ &lt;- as.factor(adcm$CMSEQ)\n\nadsl &lt;- df_explicit_na(adsl)\nadcm &lt;- df_explicit_na(adcm)\n\n# Keep only safety-evaluable patients and concomitant medications\nadsl &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\")\n\nadcm &lt;- adcm %&gt;%\n  filter(SAFFL == \"Y\" & ATIREL == \"CONCOMITANT\") %&gt;%\n  var_relabel(CMDECOD = \"Other Treatment\")\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC2\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS2 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  Total number of treatments                                 134           137            143      \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  Total number of treatments                                 58            66              64      \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  Total number of treatments                                 141           137            162      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS2 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  Total number of treatments                                 69            73              80      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC1\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC1)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC1\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 1 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS1 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  Total number of treatments                                 134           137            143      \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  Total number of treatments                                 58            66              64      \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  Total number of treatments                                 141           137            162      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS1 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  Total number of treatments                                 69            73              80      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(path = c(\"ATC2\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"ATC2\", \"*\", \"CMDECOD\"), scorefun = score_occurrences)\n\nresult\n\nATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS2 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  Total number of treatments                                 140           140            155      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  Total number of treatments                                 141           137            162      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS2 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  Total number of treatments                                 134           137            143      \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  Total number of treatments                                 69            73              80      \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \nATCCLAS2 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  Total number of treatments                                 75            82              83      \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  Total number of treatments                                 58            66              64      \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC2\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n  Other Treatment                                          (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nATCCLAS2 A                                                                                         \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 A p2                                                                                      \n  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS2 B                                                                                         \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS2 B p2                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 B p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS2 C                                                                                         \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p2                                                                                      \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS2 C p3                                                                                      \n  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\nadcm_keys &lt;- c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"CMSEQ\", \"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n  ADCM$CMSEQ &lt;- as.integer(ADCM$CMSEQ)\n\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\njoin_keys(data)[\"ADCM\", \"ADCM\"] &lt;- adcm_keys\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_mult_events(\n      label = \"Concomitant Medications by Medication Class and Preferred Name\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      seq_var = choices_selected(\"CMSEQ\", selected = \"CMSEQ\", fixed = TRUE),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")),\n        selected = c(\"ATC2\")\n      ),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, c(\"CMDECOD\")),\n        selected = c(\"CMDECOD\")\n      ),\n      add_total = FALSE,\n      event_type = \"treatment\"\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n    teal_slice(\"ADCM\", \"ATIREL\", selected = \"CONCOMITANT\")\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:40:46 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Concomitant Medications",
@@ -696,7 +696,7 @@
     "href": "tables/concomitant-medications/cmt01b.html",
     "title": "CMT01B",
     "section": "",
-    "text": "Data Setup\nStandard Table with  &gt; 1 ATC Class Level\nTable Changing  Class Levels\nTable with Classes  Sorted by Frequency\nTable with Total Number of  Treatments per Medication  Class Suppressed\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nadcm$CMSEQ &lt;- as.factor(adcm$CMSEQ)\n\nadsl &lt;- df_explicit_na(adsl)\nadcm &lt;- df_explicit_na(adcm)\n\n# Keep only safety-evaluable patients and concomitant medications\nadsl &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\")\n\nadcm &lt;- adcm %&gt;%\n  filter(SAFFL == \"Y\" & ATIREL == \"CONCOMITANT\")\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC1\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC1)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC3\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC3)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC4\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC4)\n  ) %&gt;%\n  append_topleft(\"        Other Treatment\") %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC1\", \"*\", \"ATC2\", \"*\", \"ATC3\", \"*\", \"ATC4\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 1 Text                                                                                         \n  ATC Level 2 Text                                                                                       \n    ATC Level 3 Text                                                                                     \n      ATC Level 4 Text                                          A: Drug X    B: Placebo    C: Combination\n        Other Treatment                                          (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment           117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                         415           414            460      \nATCCLAS1 A                                                                                               \n  ATCCLAS2 A                                                                                             \n    ATCCLAS3 A                                                                                           \n      ATCCLAS4 A                                                                                         \n        Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n        Total number of treatments                                 134           137            143      \n        medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n        medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 A p2                                                                                            \n  ATCCLAS2 A p2                                                                                          \n    ATCCLAS3 A p2                                                                                        \n      ATCCLAS4 A p2                                                                                      \n        Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n        Total number of treatments                                 58            66              64      \n        medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 B                                                                                               \n  ATCCLAS2 B                                                                                             \n    ATCCLAS3 B                                                                                           \n      ATCCLAS4 B                                                                                         \n        Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n        Total number of treatments                                 141           137            162      \n        medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n        medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS1 B p2                                                                                            \n  ATCCLAS2 B p2                                                                                          \n    ATCCLAS3 B p2                                                                                        \n      ATCCLAS4 B p2                                                                                      \n        Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n        Total number of treatments                                 75            82              83      \n        medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 B p3                                                                                            \n  ATCCLAS2 B p3                                                                                          \n    ATCCLAS3 B p3                                                                                        \n      ATCCLAS4 B p3                                                                                      \n        Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n        Total number of treatments                                 75            82              83      \n        medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 C                                                                                               \n  ATCCLAS2 C                                                                                             \n    ATCCLAS3 C                                                                                           \n      ATCCLAS4 C                                                                                         \n        Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n        Total number of treatments                                 140           140            155      \n        medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p2                                                                                            \n  ATCCLAS2 C p2                                                                                          \n    ATCCLAS3 C p2                                                                                        \n      ATCCLAS4 C p2                                                                                      \n        Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n        Total number of treatments                                 140           140            155      \n        medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p3                                                                                            \n  ATCCLAS2 C p3                                                                                          \n    ATCCLAS3 C p3                                                                                        \n      ATCCLAS4 C p3                                                                                      \n        Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n        Total number of treatments                                 69            73              80      \n        medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC3\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC3)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_topleft(\"    Other Treatment\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC3\", \"*\", \"ATC2\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 3 Text                                                                                     \n  ATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n    Other Treatment                                          (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment       117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                     415           414            460      \nATCCLAS3 A                                                                                           \n  ATCCLAS2 A                                                                                         \n    Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n    Total number of treatments                                 134           137            143      \n    medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n    medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS3 A p2                                                                                        \n  ATCCLAS2 A p2                                                                                      \n    Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n    Total number of treatments                                 58            66              64      \n    medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS3 B                                                                                           \n  ATCCLAS2 B                                                                                         \n    Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n    Total number of treatments                                 141           137            162      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS3 B p2                                                                                        \n  ATCCLAS2 B p2                                                                                      \n    Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    Total number of treatments                                 75            82              83      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS3 B p3                                                                                        \n  ATCCLAS2 B p3                                                                                      \n    Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    Total number of treatments                                 75            82              83      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS3 C                                                                                           \n  ATCCLAS2 C                                                                                         \n    Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n    Total number of treatments                                 140           140            155      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS3 C p2                                                                                        \n  ATCCLAS2 C p2                                                                                      \n    Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n    Total number of treatments                                 140           140            155      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS3 C p3                                                                                        \n  ATCCLAS2 C p3                                                                                      \n    Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    Total number of treatments                                 69            73              80      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCode# In progress\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\n    \"ATC1\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC1)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC3\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC3)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC4\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC4)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_topleft(\"        Other Treatment\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC1\", \"*\", \"ATC2\", \"*\", \"ATC3\", \"*\", \"ATC4\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 1 Text                                                                                       \n  ATC Level 2 Text                                                                                     \n    ATC Level 3 Text                                                                                   \n      ATC Level 4 Text                                         A: Drug X    B: Placebo   C: Combination\n        Other Treatment                                         (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————\nATCCLAS1 A                                                                                             \n  ATCCLAS2 A                                                                                           \n    ATCCLAS3 A                                                                                         \n      Total number of patients with at least one treatment     75 (56.0%)   79 (59.0%)     81 (61.4%)  \n      Total number of treatments                                  134          137            143      \n      ATCCLAS4 A                                                                                       \n        Total number of patients with at least one treatment   75 (56.0%)   79 (59.0%)     81 (61.4%)  \n        medname A_2/3                                          53 (39.6%)   50 (37.3%)     56 (42.4%)  \n        medname A_3/3                                          45 (33.6%)   54 (40.3%)     48 (36.4%)  \nATCCLAS1 A p2                                                                                          \n  ATCCLAS2 A p2                                                                                        \n    ATCCLAS3 A p2                                                                                      \n      Total number of patients with at least one treatment     45 (33.6%)   54 (40.3%)     48 (36.4%)  \n      Total number of treatments                                   58           66             64      \n      ATCCLAS4 A p2                                                                                    \n        Total number of patients with at least one treatment   45 (33.6%)   54 (40.3%)     48 (36.4%)  \n        medname A_3/3                                          45 (33.6%)   54 (40.3%)     48 (36.4%)  \nATCCLAS1 B                                                                                             \n  ATCCLAS2 B                                                                                           \n    ATCCLAS3 B                                                                                         \n      Total number of patients with at least one treatment     83 (61.9%)   74 (55.2%)     88 (66.7%)  \n      Total number of treatments                                  141          137            162      \n      ATCCLAS4 B                                                                                       \n        Total number of patients with at least one treatment   83 (61.9%)   74 (55.2%)     88 (66.7%)  \n        medname B_1/4                                          52 (38.8%)   57 (42.5%)     59 (44.7%)  \n        medname B_4/4                                          50 (37.3%)   45 (33.6%)     55 (41.7%)  \nATCCLAS1 B p2                                                                                          \n  ATCCLAS2 B p2                                                                                        \n    ATCCLAS3 B p2                                                                                      \n      Total number of patients with at least one treatment     52 (38.8%)   57 (42.5%)     59 (44.7%)  \n      Total number of treatments                                   75           82             83      \n      ATCCLAS4 B p2                                                                                    \n        Total number of patients with at least one treatment   52 (38.8%)   57 (42.5%)     59 (44.7%)  \n        medname B_1/4                                          52 (38.8%)   57 (42.5%)     59 (44.7%)  \nATCCLAS1 B p3                                                                                          \n  ATCCLAS2 B p3                                                                                        \n    ATCCLAS3 B p3                                                                                      \n      Total number of patients with at least one treatment     52 (38.8%)   57 (42.5%)     59 (44.7%)  \n      Total number of treatments                                   75           82             83      \n      ATCCLAS4 B p3                                                                                    \n        Total number of patients with at least one treatment   52 (38.8%)   57 (42.5%)     59 (44.7%)  \n        medname B_1/4                                          52 (38.8%)   57 (42.5%)     59 (44.7%)  \nATCCLAS1 C                                                                                             \n  ATCCLAS2 C                                                                                           \n    ATCCLAS3 C                                                                                         \n      Total number of patients with at least one treatment     82 (61.2%)   84 (62.7%)     89 (67.4%)  \n      Total number of treatments                                  140          140            155      \n      ATCCLAS4 C                                                                                       \n        Total number of patients with at least one treatment   82 (61.2%)   84 (62.7%)     89 (67.4%)  \n        medname C_2/2                                          52 (38.8%)   58 (43.3%)     60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)   50 (37.3%)     56 (42.4%)  \nATCCLAS1 C p2                                                                                          \n  ATCCLAS2 C p2                                                                                        \n    ATCCLAS3 C p2                                                                                      \n      Total number of patients with at least one treatment     82 (61.2%)   84 (62.7%)     89 (67.4%)  \n      Total number of treatments                                  140          140            155      \n      ATCCLAS4 C p2                                                                                    \n        Total number of patients with at least one treatment   82 (61.2%)   84 (62.7%)     89 (67.4%)  \n        medname C_2/2                                          52 (38.8%)   58 (43.3%)     60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)   50 (37.3%)     56 (42.4%)  \nATCCLAS1 C p3                                                                                          \n  ATCCLAS2 C p3                                                                                        \n    ATCCLAS3 C p3                                                                                      \n      Total number of patients with at least one treatment     52 (38.8%)   58 (43.3%)     60 (45.5%)  \n      Total number of treatments                                   69           73             80      \n      ATCCLAS4 C p3                                                                                    \n        Total number of patients with at least one treatment   52 (38.8%)   58 (43.3%)     60 (45.5%)  \n        medname C_2/2                                          52 (38.8%)   58 (43.3%)     60 (45.5%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\nadcm_keys &lt;- c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"CMSEQ\", \"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n  ADCM$CMSEQ &lt;- as.integer(ADCM$CMSEQ)\n\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\njoin_keys(data)[\"ADCM\", \"ADCM\"] &lt;- adcm_keys\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_mult_events(\n      label = \"Concomitant Medications by Medication Class and Preferred Name\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      seq_var = choices_selected(\"CMSEQ\", selected = \"CMSEQ\", fixed = TRUE),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")),\n        selected = c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n      ),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, c(\"CMDECOD\")),\n        selected = c(\"CMDECOD\")\n      ),\n      add_total = FALSE,\n      event_type = \"treatment\"\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n    teal_slice(\"ADCM\", \"ATIREL\", selected = \"CONCOMITANT\")\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:41:28 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table with  &gt; 1 ATC Class Level\nTable Changing  Class Levels\nTable with Classes  Sorted by Frequency\nTable with Total Number of  Treatments per Medication  Class Suppressed\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nadcm$CMSEQ &lt;- as.factor(adcm$CMSEQ)\n\nadsl &lt;- df_explicit_na(adsl)\nadcm &lt;- df_explicit_na(adcm)\n\n# Keep only safety-evaluable patients and concomitant medications\nadsl &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\")\n\nadcm &lt;- adcm %&gt;%\n  filter(SAFFL == \"Y\" & ATIREL == \"CONCOMITANT\")\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC1\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC1)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC3\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC3)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC4\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC4)\n  ) %&gt;%\n  append_topleft(\"        Other Treatment\") %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC1\", \"*\", \"ATC2\", \"*\", \"ATC3\", \"*\", \"ATC4\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 1 Text                                                                                         \n  ATC Level 2 Text                                                                                       \n    ATC Level 3 Text                                                                                     \n      ATC Level 4 Text                                          A: Drug X    B: Placebo    C: Combination\n        Other Treatment                                          (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment           117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                         415           414            460      \nATCCLAS1 A                                                                                               \n  ATCCLAS2 A                                                                                             \n    ATCCLAS3 A                                                                                           \n      ATCCLAS4 A                                                                                         \n        Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n        Total number of treatments                                 134           137            143      \n        medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n        medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 A p2                                                                                            \n  ATCCLAS2 A p2                                                                                          \n    ATCCLAS3 A p2                                                                                        \n      ATCCLAS4 A p2                                                                                      \n        Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n        Total number of treatments                                 58            66              64      \n        medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS1 B                                                                                               \n  ATCCLAS2 B                                                                                             \n    ATCCLAS3 B                                                                                           \n      ATCCLAS4 B                                                                                         \n        Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n        Total number of treatments                                 141           137            162      \n        medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n        medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS1 B p2                                                                                            \n  ATCCLAS2 B p2                                                                                          \n    ATCCLAS3 B p2                                                                                        \n      ATCCLAS4 B p2                                                                                      \n        Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n        Total number of treatments                                 75            82              83      \n        medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 B p3                                                                                            \n  ATCCLAS2 B p3                                                                                          \n    ATCCLAS3 B p3                                                                                        \n      ATCCLAS4 B p3                                                                                      \n        Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n        Total number of treatments                                 75            82              83      \n        medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS1 C                                                                                               \n  ATCCLAS2 C                                                                                             \n    ATCCLAS3 C                                                                                           \n      ATCCLAS4 C                                                                                         \n        Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n        Total number of treatments                                 140           140            155      \n        medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p2                                                                                            \n  ATCCLAS2 C p2                                                                                          \n    ATCCLAS3 C p2                                                                                        \n      ATCCLAS4 C p2                                                                                      \n        Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n        Total number of treatments                                 140           140            155      \n        medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS1 C p3                                                                                            \n  ATCCLAS2 C p3                                                                                          \n    ATCCLAS3 C p3                                                                                        \n      ATCCLAS4 C p3                                                                                      \n        Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n        Total number of treatments                                 69            73              80      \n        medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC3\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC3)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_topleft(\"    Other Treatment\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC3\", \"*\", \"ATC2\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 3 Text                                                                                     \n  ATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination\n    Other Treatment                                          (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment       117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                     415           414            460      \nATCCLAS3 A                                                                                           \n  ATCCLAS2 A                                                                                         \n    Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n    Total number of treatments                                 134           137            143      \n    medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n    medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS3 A p2                                                                                        \n  ATCCLAS2 A p2                                                                                      \n    Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  \n    Total number of treatments                                 58            66              64      \n    medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nATCCLAS3 B                                                                                           \n  ATCCLAS2 B                                                                                         \n    Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n    Total number of treatments                                 141           137            162      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nATCCLAS3 B p2                                                                                        \n  ATCCLAS2 B p2                                                                                      \n    Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    Total number of treatments                                 75            82              83      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS3 B p3                                                                                        \n  ATCCLAS2 B p3                                                                                      \n    Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    Total number of treatments                                 75            82              83      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \nATCCLAS3 C                                                                                           \n  ATCCLAS2 C                                                                                         \n    Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n    Total number of treatments                                 140           140            155      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS3 C p2                                                                                        \n  ATCCLAS2 C p2                                                                                      \n    Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n    Total number of treatments                                 140           140            155      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \nATCCLAS3 C p3                                                                                        \n  ATCCLAS2 C p3                                                                                      \n    Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    Total number of treatments                                 69            73              80      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n\n\n\n\n\nCode# In progress\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\n    \"ATC1\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC1)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC2\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC2)\n  ) %&gt;%\n  split_rows_by(\n    \"ATC3\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC3)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"ATC4\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    indent_mod = -1L,\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm$ATC4)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_topleft(\"        Other Treatment\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  # Sort lowest level terms by descending frequency.\n  sort_at_path(\n    path = c(\"ATC1\", \"*\", \"ATC2\", \"*\", \"ATC3\", \"*\", \"ATC4\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nATC Level 1 Text                                                                                       \n  ATC Level 2 Text                                                                                     \n    ATC Level 3 Text                                                                                   \n      ATC Level 4 Text                                         A: Drug X    B: Placebo   C: Combination\n        Other Treatment                                         (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————\nATCCLAS1 A                                                                                             \n  ATCCLAS2 A                                                                                           \n    ATCCLAS3 A                                                                                         \n      Total number of patients with at least one treatment     75 (56.0%)   79 (59.0%)     81 (61.4%)  \n      Total number of treatments                                  134          137            143      \n      ATCCLAS4 A                                                                                       \n        Total number of patients with at least one treatment   75 (56.0%)   79 (59.0%)     81 (61.4%)  \n        medname A_2/3                                          53 (39.6%)   50 (37.3%)     56 (42.4%)  \n        medname A_3/3                                          45 (33.6%)   54 (40.3%)     48 (36.4%)  \nATCCLAS1 A p2                                                                                          \n  ATCCLAS2 A p2                                                                                        \n    ATCCLAS3 A p2                                                                                      \n      Total number of patients with at least one treatment     45 (33.6%)   54 (40.3%)     48 (36.4%)  \n      Total number of treatments                                   58           66             64      \n      ATCCLAS4 A p2                                                                                    \n        Total number of patients with at least one treatment   45 (33.6%)   54 (40.3%)     48 (36.4%)  \n        medname A_3/3                                          45 (33.6%)   54 (40.3%)     48 (36.4%)  \nATCCLAS1 B                                                                                             \n  ATCCLAS2 B                                                                                           \n    ATCCLAS3 B                                                                                         \n      Total number of patients with at least one treatment     83 (61.9%)   74 (55.2%)     88 (66.7%)  \n      Total number of treatments                                  141          137            162      \n      ATCCLAS4 B                                                                                       \n        Total number of patients with at least one treatment   83 (61.9%)   74 (55.2%)     88 (66.7%)  \n        medname B_1/4                                          52 (38.8%)   57 (42.5%)     59 (44.7%)  \n        medname B_4/4                                          50 (37.3%)   45 (33.6%)     55 (41.7%)  \nATCCLAS1 B p2                                                                                          \n  ATCCLAS2 B p2                                                                                        \n    ATCCLAS3 B p2                                                                                      \n      Total number of patients with at least one treatment     52 (38.8%)   57 (42.5%)     59 (44.7%)  \n      Total number of treatments                                   75           82             83      \n      ATCCLAS4 B p2                                                                                    \n        Total number of patients with at least one treatment   52 (38.8%)   57 (42.5%)     59 (44.7%)  \n        medname B_1/4                                          52 (38.8%)   57 (42.5%)     59 (44.7%)  \nATCCLAS1 B p3                                                                                          \n  ATCCLAS2 B p3                                                                                        \n    ATCCLAS3 B p3                                                                                      \n      Total number of patients with at least one treatment     52 (38.8%)   57 (42.5%)     59 (44.7%)  \n      Total number of treatments                                   75           82             83      \n      ATCCLAS4 B p3                                                                                    \n        Total number of patients with at least one treatment   52 (38.8%)   57 (42.5%)     59 (44.7%)  \n        medname B_1/4                                          52 (38.8%)   57 (42.5%)     59 (44.7%)  \nATCCLAS1 C                                                                                             \n  ATCCLAS2 C                                                                                           \n    ATCCLAS3 C                                                                                         \n      Total number of patients with at least one treatment     82 (61.2%)   84 (62.7%)     89 (67.4%)  \n      Total number of treatments                                  140          140            155      \n      ATCCLAS4 C                                                                                       \n        Total number of patients with at least one treatment   82 (61.2%)   84 (62.7%)     89 (67.4%)  \n        medname C_2/2                                          52 (38.8%)   58 (43.3%)     60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)   50 (37.3%)     56 (42.4%)  \nATCCLAS1 C p2                                                                                          \n  ATCCLAS2 C p2                                                                                        \n    ATCCLAS3 C p2                                                                                      \n      Total number of patients with at least one treatment     82 (61.2%)   84 (62.7%)     89 (67.4%)  \n      Total number of treatments                                  140          140            155      \n      ATCCLAS4 C p2                                                                                    \n        Total number of patients with at least one treatment   82 (61.2%)   84 (62.7%)     89 (67.4%)  \n        medname C_2/2                                          52 (38.8%)   58 (43.3%)     60 (45.5%)  \n        medname C_1/2                                          51 (38.1%)   50 (37.3%)     56 (42.4%)  \nATCCLAS1 C p3                                                                                          \n  ATCCLAS2 C p3                                                                                        \n    ATCCLAS3 C p3                                                                                      \n      Total number of patients with at least one treatment     52 (38.8%)   58 (43.3%)     60 (45.5%)  \n      Total number of treatments                                   69           73             80      \n      ATCCLAS4 C p3                                                                                    \n        Total number of patients with at least one treatment   52 (38.8%)   58 (43.3%)     60 (45.5%)  \n        medname C_2/2                                          52 (38.8%)   58 (43.3%)     60 (45.5%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\nadcm_keys &lt;- c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"CMSEQ\", \"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n  ADCM$CMSEQ &lt;- as.integer(ADCM$CMSEQ)\n\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\njoin_keys(data)[\"ADCM\", \"ADCM\"] &lt;- adcm_keys\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_mult_events(\n      label = \"Concomitant Medications by Medication Class and Preferred Name\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      seq_var = choices_selected(\"CMSEQ\", selected = \"CMSEQ\", fixed = TRUE),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")),\n        selected = c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n      ),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, c(\"CMDECOD\")),\n        selected = c(\"CMDECOD\")\n      ),\n      add_total = FALSE,\n      event_type = \"treatment\"\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n    teal_slice(\"ADCM\", \"ATIREL\", selected = \"CONCOMITANT\")\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:40:14 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Concomitant Medications",
@@ -708,7 +708,7 @@
     "href": "tables/efficacy/dort01.html",
     "title": "DORT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable Modifying Analysis Details  like Conf. Type and Alpha Level\nTable Modifying Time Point for  the “XX Months duration” Analysis\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadtte &lt;- df_explicit_na(adtte)\n\nadtte_f &lt;- adtte %&gt;%\n  filter(PARAMCD == \"CRSD\" & BMEASIFL == \"Y\") %&gt;%\n  dplyr::mutate(\n    AVAL = day2month(AVAL),\n    is_event = CNSR == 0,\n    is_not_event = CNSR == 1,\n    EVNT1 = factor(\n      case_when(\n        is_event == TRUE ~ \"Responders with subsequent event (%)\",\n        is_event == FALSE ~ \"Responders without subsequent event (%)\"\n      ),\n      levels = c(\"Responders with subsequent event (%)\", \"Responders without subsequent event (%)\")\n    ),\n    EVNTDESC = factor(EVNTDESC)\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months duration\",\n    is_event = \"is_event\",\n    time_point = 12\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                            A: Drug X    B: Placebo   C: Combination\n                                             (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————\nResponders                                      68           73             62      \n  Responders with subsequent event (%)      46 (67.6%)   39 (53.4%)     32 (51.6%)  \n    Earliest contributing event                                                     \n      Death                                     26           24             16      \n      Disease Progression                       20           15             16      \n  Responders without subsequent event (%)   22 (32.4%)   34 (46.6%)     30 (48.4%)  \nDuration of response (Months)                                                       \n  Median                                       5.3          6.2            5.3      \n    95% CI                                  (4.6, 5.8)   (5.4, 6.3)     (4.6, 5.8)  \n  25% and 75%-ile                            3.8, 6.3     4.6, 6.4       4.0, 6.1   \n  Range                                     0.5 to 6.4   0.9 to 6.5     0.6 to 6.6  \n12 Months duration                                                                  \n  Patients remaining at risk                    NA           NA             NA      \n  Event Free Rate (%)                           NA           NA             NA      \n  95% CI                                        NA           NA             NA      \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\",\n    table_names = \"surv_time\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"cox_pair\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                            A: Drug X     B: Placebo    C: Combination\n                                             (N=134)       (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nResponders                                      68            73              62      \n  Responders with subsequent event (%)      46 (67.6%)    39 (53.4%)      32 (51.6%)  \n    Earliest contributing event                                                       \n      Death                                     26            24              16      \n      Disease Progression                       20            15              16      \n  Responders without subsequent event (%)   22 (32.4%)    34 (46.6%)      30 (48.4%)  \nDuration of response (Months)                                                         \n  Median                                       5.3           6.2             5.3      \n    95% CI                                  (4.6, 5.8)    (5.4, 6.3)      (4.6, 5.8)  \n  25% and 75%-ile                            3.8, 6.3      4.6, 6.4        4.0, 6.1   \n  Range                                     0.5 to 6.4    0.9 to 6.5      0.6 to 6.6  \nUnstratified Analysis                                                                 \n  p-value (log-rank)                                        0.0223          0.6659    \n  Hazard Ratio                                               0.60            0.90     \n  95% CI                                                 (0.39, 0.93)    (0.57, 1.44) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\",\n    control = control_surv_time(conf_level = 0.90, conf_type = \"log-log\")\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months duration\",\n    is_event = \"is_event\",\n    time_point = 12,\n    control = control_surv_timepoint(conf_level = 0.975)\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                            A: Drug X    B: Placebo   C: Combination\n                                             (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————\nResponders                                      68           73             62      \n  Responders with subsequent event (%)      46 (67.6%)   39 (53.4%)     32 (51.6%)  \n    Earliest contributing event                                                     \n      Death                                     26           24             16      \n      Disease Progression                       20           15             16      \n  Responders without subsequent event (%)   22 (32.4%)   34 (46.6%)     30 (48.4%)  \nDuration of response (Months)                                                       \n  Median                                       5.3          6.2            5.3      \n    90% CI                                  (4.6, 5.8)   (5.5, 6.3)     (4.6, 5.7)  \n  25% and 75%-ile                            3.8, 6.3     4.6, 6.4       4.0, 6.1   \n  Range                                     0.5 to 6.4   0.9 to 6.5     0.6 to 6.6  \n12 Months duration                                                                  \n  Patients remaining at risk                    NA           NA             NA      \n  Event Free Rate (%)                           NA           NA             NA      \n  97.5% CI                                      NA           NA             NA      \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months duration\",\n    is_event = \"is_event\",\n    time_point = 6\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                              A: Drug X        B: Placebo     C: Combination\n                                               (N=134)          (N=134)          (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————\nResponders                                        68               73               62      \n  Responders with subsequent event (%)        46 (67.6%)       39 (53.4%)       32 (51.6%)  \n    Earliest contributing event                                                             \n      Death                                       26               24               16      \n      Disease Progression                         20               15               16      \n  Responders without subsequent event (%)     22 (32.4%)       34 (46.6%)       30 (48.4%)  \nDuration of response (Months)                                                               \n  Median                                         5.3              6.2              5.3      \n    95% CI                                    (4.6, 5.8)       (5.4, 6.3)       (4.6, 5.8)  \n  25% and 75%-ile                              3.8, 6.3         4.6, 6.4         4.0, 6.1   \n  Range                                       0.5 to 6.4       0.9 to 6.5       0.6 to 6.6  \n6 Months duration                                                                           \n  Patients remaining at risk                      10               18               7       \n  Event Free Rate (%)                           33.46            50.23            30.56     \n  95% CI                                    (20.88, 46.05)   (36.54, 63.92)   (13.56, 47.57)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(tern)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADTTE &lt;- df_explicit_na(ADTTE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To Event Table\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"),\n        \"CRSD\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        \"SEX\"\n      ),\n      time_points = choices_selected(c(6, 8), 6),\n      event_desc_var = choices_selected(\n        variable_choices(ADTTE, \"EVNTDESC\"),\n        \"EVNTDESC\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:40:52 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable Modifying Analysis Details  like Conf. Type and Alpha Level\nTable Modifying Time Point for  the “XX Months duration” Analysis\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadtte &lt;- df_explicit_na(adtte)\n\nadtte_f &lt;- adtte %&gt;%\n  filter(PARAMCD == \"CRSD\" & BMEASIFL == \"Y\") %&gt;%\n  dplyr::mutate(\n    AVAL = day2month(AVAL),\n    is_event = CNSR == 0,\n    is_not_event = CNSR == 1,\n    EVNT1 = factor(\n      case_when(\n        is_event == TRUE ~ \"Responders with subsequent event (%)\",\n        is_event == FALSE ~ \"Responders without subsequent event (%)\"\n      ),\n      levels = c(\"Responders with subsequent event (%)\", \"Responders without subsequent event (%)\")\n    ),\n    EVNTDESC = factor(EVNTDESC)\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months duration\",\n    is_event = \"is_event\",\n    time_point = 12\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                            A: Drug X    B: Placebo   C: Combination\n                                             (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————\nResponders                                      68           73             62      \n  Responders with subsequent event (%)      46 (67.6%)   39 (53.4%)     32 (51.6%)  \n    Earliest contributing event                                                     \n      Death                                     26           24             16      \n      Disease Progression                       20           15             16      \n  Responders without subsequent event (%)   22 (32.4%)   34 (46.6%)     30 (48.4%)  \nDuration of response (Months)                                                       \n  Median                                       5.3          6.2            5.3      \n    95% CI                                  (4.6, 5.8)   (5.4, 6.3)     (4.6, 5.8)  \n  25% and 75%-ile                            3.8, 6.3     4.6, 6.4       4.0, 6.1   \n  Range                                     0.5 to 6.4   0.9 to 6.5     0.6 to 6.6  \n12 Months duration                                                                  \n  Patients remaining at risk                    NA           NA             NA      \n  Event Free Rate (%)                           NA           NA             NA      \n  95% CI                                        NA           NA             NA      \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\",\n    table_names = \"surv_time\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"cox_pair\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                            A: Drug X     B: Placebo    C: Combination\n                                             (N=134)       (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nResponders                                      68            73              62      \n  Responders with subsequent event (%)      46 (67.6%)    39 (53.4%)      32 (51.6%)  \n    Earliest contributing event                                                       \n      Death                                     26            24              16      \n      Disease Progression                       20            15              16      \n  Responders without subsequent event (%)   22 (32.4%)    34 (46.6%)      30 (48.4%)  \nDuration of response (Months)                                                         \n  Median                                       5.3           6.2             5.3      \n    95% CI                                  (4.6, 5.8)    (5.4, 6.3)      (4.6, 5.8)  \n  25% and 75%-ile                            3.8, 6.3      4.6, 6.4        4.0, 6.1   \n  Range                                     0.5 to 6.4    0.9 to 6.5      0.6 to 6.6  \nUnstratified Analysis                                                                 \n  p-value (log-rank)                                        0.0223          0.6659    \n  Hazard Ratio                                               0.60            0.90     \n  95% CI                                                 (0.39, 0.93)    (0.57, 1.44) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\",\n    control = control_surv_time(conf_level = 0.90, conf_type = \"log-log\")\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months duration\",\n    is_event = \"is_event\",\n    time_point = 12,\n    control = control_surv_timepoint(conf_level = 0.975)\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                            A: Drug X    B: Placebo   C: Combination\n                                             (N=134)      (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————\nResponders                                      68           73             62      \n  Responders with subsequent event (%)      46 (67.6%)   39 (53.4%)     32 (51.6%)  \n    Earliest contributing event                                                     \n      Death                                     26           24             16      \n      Disease Progression                       20           15             16      \n  Responders without subsequent event (%)   22 (32.4%)   34 (46.6%)     30 (48.4%)  \nDuration of response (Months)                                                       \n  Median                                       5.3          6.2            5.3      \n    90% CI                                  (4.6, 5.8)   (5.5, 6.3)     (4.6, 5.7)  \n  25% and 75%-ile                            3.8, 6.3     4.6, 6.4       4.0, 6.1   \n  Range                                     0.5 to 6.4   0.9 to 6.5     0.6 to 6.6  \n12 Months duration                                                                  \n  Patients remaining at risk                    NA           NA             NA      \n  Event Free Rate (%)                           NA           NA             NA      \n  97.5% CI                                      NA           NA             NA      \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  count_values(\n    vars = \"USUBJID\",\n    values = unique(adtte$USUBJID),\n    .labels = \"Responders\",\n    .stats = \"count\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders with subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    show_labels = \"hidden\",\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Responders with subsequent event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 2L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Responders without subsequent event (%)\"),\n    .indent_mods = c(count_fraction = 1L),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Duration of response (Months)\",\n    is_event = \"is_event\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months duration\",\n    is_event = \"is_event\",\n    time_point = 6\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table(prune_func = prune_zeros_only)\n\nresult\n\n                                              A: Drug X        B: Placebo     C: Combination\n                                               (N=134)          (N=134)          (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————————\nResponders                                        68               73               62      \n  Responders with subsequent event (%)        46 (67.6%)       39 (53.4%)       32 (51.6%)  \n    Earliest contributing event                                                             \n      Death                                       26               24               16      \n      Disease Progression                         20               15               16      \n  Responders without subsequent event (%)     22 (32.4%)       34 (46.6%)       30 (48.4%)  \nDuration of response (Months)                                                               \n  Median                                         5.3              6.2              5.3      \n    95% CI                                    (4.6, 5.8)       (5.4, 6.3)       (4.6, 5.8)  \n  25% and 75%-ile                              3.8, 6.3         4.6, 6.4         4.0, 6.1   \n  Range                                       0.5 to 6.4       0.9 to 6.5       0.6 to 6.6  \n6 Months duration                                                                           \n  Patients remaining at risk                      10               18               7       \n  Event Free Rate (%)                           33.46            50.23            30.56     \n  95% CI                                    (20.88, 46.05)   (36.54, 63.92)   (13.56, 47.57)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(tern)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADTTE &lt;- df_explicit_na(ADTTE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To Event Table\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"),\n        \"CRSD\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        \"SEX\"\n      ),\n      time_points = choices_selected(c(6, 8), 6),\n      event_desc_var = choices_selected(\n        variable_choices(ADTTE, \"EVNTDESC\"),\n        \"EVNTDESC\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:39:39 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -720,7 +720,7 @@
     "href": "tables/efficacy/rspt01.html",
     "title": "RSPT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable Modifying Analysis Details like Type of  Confidence Interval, Alpha Level, Test Statistic\nTable with  Stratified Analysis\nTable Modifying the Definition  of Overall Response\nTable Defining New  Sections to Display\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadrs &lt;- df_explicit_na(adrs)\n\nanl_adrs &lt;- adrs %&gt;%\n  filter(PARAMCD == \"INVET\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVISIT, AVALC)\nanl_adsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ARM, STRATA1)\n\n\nData pre-processing is done to label the analysis value (AVALC) so as to improve the readability of the output table. In addition, the response is binary defined (is_rsp, responder yes/no) depending on the analysis value. Finally, for comparison purposes, the reference arm is specified.\n\nCode# Pre-Processing\n\nanl &lt;- anl_adsl %&gt;%\n  left_join(anl_adrs, by = c(\"STUDYID\", \"USUBJID\")) %&gt;%\n  mutate(rsp_lab = d_onco_rsp_label(AVALC)) %&gt;%\n  mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\")\n\n\n\n\nThe tabulation layout is built in layers for the analysis of overall response and applied to the pre-processed dataset.\n\nCodelyt_01 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    table_names = \"test_prop_diff\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    table_names = \"est_or\"\n  ) %&gt;%\n  estimate_multinomial_response(var = \"rsp_lab\")\n\nresult &lt;- build_table(lyt = lyt_01, df = anl)\nresult\n\n                                       A: Drug X          B: Placebo         C: Combination  \n                                        (N=134)            (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————\nResponders                            100 (74.6%)         84 (62.7%)           81 (61.4%)    \n95% CI (Wald, with correction)        (66.9, 82.4)       (54.1, 71.2)         (52.7, 70.0)   \nUnstratified Analysis                                                                        \n  Difference in Response rate (%)                           -11.9                -13.3       \n    95% CI (Wald, with correction)                      (-23.7, -0.2)        (-25.1, -1.4)   \n  p-value (Chi-Squared Test)                                0.0351               0.0204      \nOdds Ratio (95% CI)                                   0.57 (0.34 - 0.96)   0.54 (0.32 - 0.91)\nComplete Response (CR)                 60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  95% CI (Wald, with correction)     (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  \nPartial Response (PR)                  40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  95% CI (Wald, with correction)     (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  \nStable Disease (SD)                     9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  95% CI (Wald, with correction)     (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   \nProgressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNot Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\n\nRemove (or add) rows of results by removing/adding the corresponding layers from the layout. For instance, the odds-ratio row is removed by simply removing the estimate_odds_ratio call:\n\nCodelyt_02 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    table_names = \"test_prop\"\n  ) %&gt;%\n  estimate_multinomial_response(var = \"rsp_lab\")\n\nresult &lt;- build_table(lyt = lyt_02, df = anl)\nresult\n\n                                       A: Drug X        B: Placebo     C: Combination\n                                        (N=134)          (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nResponders                            100 (74.6%)       84 (62.7%)       81 (61.4%)  \n95% CI (Wald, with correction)        (66.9, 82.4)     (54.1, 71.2)     (52.7, 70.0) \nUnstratified Analysis                                                                \n  Difference in Response rate (%)                         -11.9            -13.3     \n    95% CI (Wald, with correction)                    (-23.7, -0.2)    (-25.1, -1.4) \n  p-value (Chi-Squared Test)                              0.0351           0.0204    \nComplete Response (CR)                 60 (44.8%)       47 (35.1%)       57 (43.2%)  \n  95% CI (Wald, with correction)     (35.98, 53.57)   (26.62, 43.53)   (34.35, 52.01)\nPartial Response (PR)                  40 (29.9%)       37 (27.6%)       24 (18.2%)  \n  95% CI (Wald, with correction)     (21.73, 37.97)   (19.67, 35.55)   (11.22, 25.14)\nStable Disease (SD)                     9 (6.7%)        22 (16.4%)       13 (9.8%)   \n  95% CI (Wald, with correction)     (2.11, 11.33)    (9.77, 23.06)    (4.39, 15.31) \nProgressive Disease (PD)               24 (17.9%)       16 (11.9%)       33 (25.0%)  \n  95% CI (Wald, with correction)     (11.05, 24.78)   (6.08, 17.80)    (17.23, 32.77)\nNot Evaluable (NE)                      1 (0.7%)        12 (9.0%)         5 (3.8%)   \n  95% CI (Wald, with correction)      (0.00, 2.58)    (3.75, 14.16)     (0.15, 7.42) \n\n\n\n\nThe confidence level is controlled by the conf_level parameter to the estimation functions. Similarly, the methods for tests and confidence interval can be modified (see ?estimate_proportion_diff).\n\nCodeconf_level &lt;- 0.90\nlyt_03 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    conf_level = conf_level,\n    method = \"clopper-pearson\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    conf_level = conf_level,\n    method = \"ha\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"fisher\",\n    table_names = \"test_prop\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    conf_level = conf_level,\n    table_names = \"est_or\"\n  ) %&gt;%\n  estimate_multinomial_response(\n    var = \"rsp_lab\",\n    conf_level = conf_level,\n    method = \"clopper-pearson\"\n  )\n\nresult &lt;- build_table(lyt = lyt_03, df = anl)\nresult\n\n                                      A: Drug X          B: Placebo         C: Combination  \n                                       (N=134)            (N=134)              (N=132)      \n————————————————————————————————————————————————————————————————————————————————————————————\nResponders                           100 (74.6%)         84 (62.7%)           81 (61.4%)    \n90% CI (Clopper-Pearson)             (67.7, 80.7)       (55.3, 69.7)         (53.9, 68.5)   \nUnstratified Analysis                                                                       \n  Difference in Response rate (%)                          -11.9                -13.3       \n    90% CI (Anderson-Hauck)                            (-21.6, -2.3)        (-23.0, -3.5)   \n  p-value (Fisher's Exact Test)                            0.0479               0.0253      \nOdds Ratio (90% CI)                                  0.57 (0.37 - 0.89)   0.54 (0.35 - 0.84)\nComplete Response (CR)                60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  90% CI (Clopper-Pearson)          (37.48, 52.25)     (28.22, 42.43)       (35.88, 50.71)  \nPartial Response (PR)                 40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  90% CI (Clopper-Pearson)          (23.36, 37.02)     (21.31, 34.67)       (12.87, 24.61)  \nStable Disease (SD)                    9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  90% CI (Clopper-Pearson)          (3.55, 11.43)      (11.38, 22.61)       (5.92, 15.20)   \nProgressive Disease (PD)              24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  90% CI (Clopper-Pearson)          (12.67, 24.25)     (7.63, 17.57)        (18.90, 31.97)  \nNot Evaluable (NE)                     1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  90% CI (Clopper-Pearson)           (0.04, 3.49)      (5.25, 14.11)         (1.50, 7.80)   \n\n\n\n\nThe stratified analysis section can be added by defining the analyses needed with control_binary_comparison for the argument strat_analysis and identifying the stratification variables to use.\n\n\nCodestrata &lt;- \"STRATA1\"\nlyt_04 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    table_names = \"test_prop\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    table_names = \"est_or\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Stratified Analysis\",\n    method = \"cmh\",\n    variables = list(strata = strata),\n    table_names = \"est_prop_diff_strat\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"cmh\",\n    variables = list(strata = strata),\n    table_names = \"test_prop_strat\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    variables = list(strata = strata, arm = \"ARM\"),\n    table_names = \"est_or_strat\"\n  ) %&gt;%\n  estimate_multinomial_response(var = \"rsp_lab\")\n\nresult &lt;- build_table(lyt = lyt_04, df = anl)\nresult\n\n                                             A: Drug X          B: Placebo         C: Combination  \n                                              (N=134)            (N=134)              (N=132)      \n———————————————————————————————————————————————————————————————————————————————————————————————————\nResponders                                  100 (74.6%)         84 (62.7%)           81 (61.4%)    \n95% CI (Wald, with correction)              (66.9, 82.4)       (54.1, 71.2)         (52.7, 70.0)   \nUnstratified Analysis                                                                              \n  Difference in Response rate (%)                                 -11.9                -13.3       \n    95% CI (Wald, with correction)                            (-23.7, -0.2)        (-25.1, -1.4)   \n  p-value (Chi-Squared Test)                                      0.0351               0.0204      \nOdds Ratio (95% CI)                                         0.57 (0.34 - 0.96)   0.54 (0.32 - 0.91)\nStratified Analysis                                                                                \n  Difference in Response rate (%)                                 -11.9                -13.5       \n    95% CI (CMH, without correction)                          (-22.7, -1.0)        (-24.5, -2.5)   \n  p-value (Cochran-Mantel-Haenszel Test)                          0.0366               0.0180      \nOdds Ratio (95% CI)                                         0.57 (0.34 - 0.96)   0.54 (0.32 - 0.90)\nComplete Response (CR)                       60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  95% CI (Wald, with correction)           (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  \nPartial Response (PR)                        40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  95% CI (Wald, with correction)           (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  \nStable Disease (SD)                           9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  95% CI (Wald, with correction)           (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   \nProgressive Disease (PD)                     24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)           (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNot Evaluable (NE)                            1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)            (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\n\nThe definition of responders is realized during the pre-processing step. The layout does not need to be modified and can be reused.\n\nCodeanl &lt;- anl_adsl %&gt;%\n  left_join(anl_adrs, by = c(\"STUDYID\", \"USUBJID\")) %&gt;%\n  mutate(rsp_lab = d_onco_rsp_label(AVALC)) %&gt;%\n  mutate(is_rsp = AVALC == \"CR\") %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\")\n\nresult &lt;- build_table(lyt = lyt_01, df = anl)\nresult\n\n                                       A: Drug X          B: Placebo         C: Combination  \n                                        (N=134)            (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————\nResponders                             60 (44.8%)         47 (35.1%)           57 (43.2%)    \n95% CI (Wald, with correction)        (36.0, 53.6)       (26.6, 43.5)         (34.4, 52.0)   \nUnstratified Analysis                                                                        \n  Difference in Response rate (%)                            -9.7                 -1.6       \n    95% CI (Wald, with correction)                       (-22.1, 2.7)        (-14.3, 11.1)   \n  p-value (Chi-Squared Test)                                0.1049               0.7934      \nOdds Ratio (95% CI)                                   0.67 (0.41 - 1.09)   0.94 (0.58 - 1.52)\nComplete Response (CR)                 60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  95% CI (Wald, with correction)     (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  \nPartial Response (PR)                  40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  95% CI (Wald, with correction)     (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  \nStable Disease (SD)                     9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  95% CI (Wald, with correction)     (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   \nProgressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNot Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\n\nSimilarly to in the previous tab, redefinition or relabeling of the result is a pre-processing step and the original table layout can be reused.\n\nCodeanl &lt;- anl_adsl %&gt;%\n  left_join(anl_adrs, by = c(\"STUDYID\", \"USUBJID\")) %&gt;%\n  mutate(rsp_lab = as.character(d_onco_rsp_label(AVALC))) %&gt;%\n  mutate(\n    rsp_lab = case_when(\n      rsp_lab == \"Complete Response (CR)\" ~ \"No Progression\",\n      rsp_lab == \"Partial Response (PR)\" ~ \"No Progression\",\n      rsp_lab == \"Stable Disease (SD)\" ~ \"No Progression\",\n      TRUE ~ rsp_lab\n    )\n  ) %&gt;%\n  mutate(is_rsp = rsp_lab %in% \"No Progression\") %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\")\n\nresult &lt;- build_table(lyt = lyt_01, df = anl)\nresult\n\n                                       A: Drug X          B: Placebo         C: Combination  \n                                        (N=134)            (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————\nResponders                            109 (81.3%)        106 (79.1%)           94 (71.2%)    \n95% CI (Wald, with correction)        (74.4, 88.3)       (71.8, 86.4)         (63.1, 79.3)   \nUnstratified Analysis                                                                        \n  Difference in Response rate (%)                            -2.2                -10.1       \n    95% CI (Wald, with correction)                       (-12.5, 8.0)         (-21.0, 0.8)   \n  p-value (Chi-Squared Test)                                0.6455               0.0520      \nOdds Ratio (95% CI)                                   0.87 (0.48 - 1.59)   0.57 (0.32 - 1.01)\nProgressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNo Progression                        109 (81.3%)        106 (79.1%)           94 (71.2%)    \n  95% CI (Wald, with correction)     (74.37, 88.31)     (71.85, 86.36)       (63.11, 79.31)  \nNot Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADRS &lt;- df_explicit_na(ADRS)\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(Dum_ARM = factor(rep(\"Single ARM\", nrow(.))))\n  ADRS &lt;- ADRS %&gt;%\n    mutate(Dum_ARM = factor(rep(\"Single ARM\", nrow(.))))\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_binary_outcome(\n      label = \"Responders\",\n      dataname = \"ADRS\",\n      paramcd = choices_selected(\n        choices = value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BESRSPI\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"ARM\", \"ARMCD\", \"ACTARMCD\", \"Dum_ARM\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      strata_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"SEX\", \"BMRKR2\")),\n        select = NULL\n      ),\n      rsp_table = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:40:10 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable Modifying Analysis Details like Type of  Confidence Interval, Alpha Level, Test Statistic\nTable with  Stratified Analysis\nTable Modifying the Definition  of Overall Response\nTable Defining New  Sections to Display\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadrs &lt;- df_explicit_na(adrs)\n\nanl_adrs &lt;- adrs %&gt;%\n  filter(PARAMCD == \"INVET\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVISIT, AVALC)\nanl_adsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ARM, STRATA1)\n\n\nData pre-processing is done to label the analysis value (AVALC) so as to improve the readability of the output table. In addition, the response is binary defined (is_rsp, responder yes/no) depending on the analysis value. Finally, for comparison purposes, the reference arm is specified.\n\nCode# Pre-Processing\n\nanl &lt;- anl_adsl %&gt;%\n  left_join(anl_adrs, by = c(\"STUDYID\", \"USUBJID\")) %&gt;%\n  mutate(rsp_lab = d_onco_rsp_label(AVALC)) %&gt;%\n  mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\")\n\n\n\n\nThe tabulation layout is built in layers for the analysis of overall response and applied to the pre-processed dataset.\n\nCodelyt_01 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    table_names = \"test_prop_diff\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    table_names = \"est_or\"\n  ) %&gt;%\n  estimate_multinomial_response(var = \"rsp_lab\")\n\nresult &lt;- build_table(lyt = lyt_01, df = anl)\nresult\n\n                                       A: Drug X          B: Placebo         C: Combination  \n                                        (N=134)            (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————\nResponders                            100 (74.6%)         84 (62.7%)           81 (61.4%)    \n95% CI (Wald, with correction)        (66.9, 82.4)       (54.1, 71.2)         (52.7, 70.0)   \nUnstratified Analysis                                                                        \n  Difference in Response rate (%)                           -11.9                -13.3       \n    95% CI (Wald, with correction)                      (-23.7, -0.2)        (-25.1, -1.4)   \n  p-value (Chi-Squared Test)                                0.0351               0.0204      \nOdds Ratio (95% CI)                                   0.57 (0.34 - 0.96)   0.54 (0.32 - 0.91)\nComplete Response (CR)                 60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  95% CI (Wald, with correction)     (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  \nPartial Response (PR)                  40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  95% CI (Wald, with correction)     (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  \nStable Disease (SD)                     9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  95% CI (Wald, with correction)     (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   \nProgressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNot Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\n\nRemove (or add) rows of results by removing/adding the corresponding layers from the layout. For instance, the odds-ratio row is removed by simply removing the estimate_odds_ratio call:\n\nCodelyt_02 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    table_names = \"test_prop\"\n  ) %&gt;%\n  estimate_multinomial_response(var = \"rsp_lab\")\n\nresult &lt;- build_table(lyt = lyt_02, df = anl)\nresult\n\n                                       A: Drug X        B: Placebo     C: Combination\n                                        (N=134)          (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nResponders                            100 (74.6%)       84 (62.7%)       81 (61.4%)  \n95% CI (Wald, with correction)        (66.9, 82.4)     (54.1, 71.2)     (52.7, 70.0) \nUnstratified Analysis                                                                \n  Difference in Response rate (%)                         -11.9            -13.3     \n    95% CI (Wald, with correction)                    (-23.7, -0.2)    (-25.1, -1.4) \n  p-value (Chi-Squared Test)                              0.0351           0.0204    \nComplete Response (CR)                 60 (44.8%)       47 (35.1%)       57 (43.2%)  \n  95% CI (Wald, with correction)     (35.98, 53.57)   (26.62, 43.53)   (34.35, 52.01)\nPartial Response (PR)                  40 (29.9%)       37 (27.6%)       24 (18.2%)  \n  95% CI (Wald, with correction)     (21.73, 37.97)   (19.67, 35.55)   (11.22, 25.14)\nStable Disease (SD)                     9 (6.7%)        22 (16.4%)       13 (9.8%)   \n  95% CI (Wald, with correction)     (2.11, 11.33)    (9.77, 23.06)    (4.39, 15.31) \nProgressive Disease (PD)               24 (17.9%)       16 (11.9%)       33 (25.0%)  \n  95% CI (Wald, with correction)     (11.05, 24.78)   (6.08, 17.80)    (17.23, 32.77)\nNot Evaluable (NE)                      1 (0.7%)        12 (9.0%)         5 (3.8%)   \n  95% CI (Wald, with correction)      (0.00, 2.58)    (3.75, 14.16)     (0.15, 7.42) \n\n\n\n\nThe confidence level is controlled by the conf_level parameter to the estimation functions. Similarly, the methods for tests and confidence interval can be modified (see ?estimate_proportion_diff).\n\nCodeconf_level &lt;- 0.90\nlyt_03 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    conf_level = conf_level,\n    method = \"clopper-pearson\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    conf_level = conf_level,\n    method = \"ha\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"fisher\",\n    table_names = \"test_prop\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    conf_level = conf_level,\n    table_names = \"est_or\"\n  ) %&gt;%\n  estimate_multinomial_response(\n    var = \"rsp_lab\",\n    conf_level = conf_level,\n    method = \"clopper-pearson\"\n  )\n\nresult &lt;- build_table(lyt = lyt_03, df = anl)\nresult\n\n                                      A: Drug X          B: Placebo         C: Combination  \n                                       (N=134)            (N=134)              (N=132)      \n————————————————————————————————————————————————————————————————————————————————————————————\nResponders                           100 (74.6%)         84 (62.7%)           81 (61.4%)    \n90% CI (Clopper-Pearson)             (67.7, 80.7)       (55.3, 69.7)         (53.9, 68.5)   \nUnstratified Analysis                                                                       \n  Difference in Response rate (%)                          -11.9                -13.3       \n    90% CI (Anderson-Hauck)                            (-21.6, -2.3)        (-23.0, -3.5)   \n  p-value (Fisher's Exact Test)                            0.0479               0.0253      \nOdds Ratio (90% CI)                                  0.57 (0.37 - 0.89)   0.54 (0.35 - 0.84)\nComplete Response (CR)                60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  90% CI (Clopper-Pearson)          (37.48, 52.25)     (28.22, 42.43)       (35.88, 50.71)  \nPartial Response (PR)                 40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  90% CI (Clopper-Pearson)          (23.36, 37.02)     (21.31, 34.67)       (12.87, 24.61)  \nStable Disease (SD)                    9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  90% CI (Clopper-Pearson)          (3.55, 11.43)      (11.38, 22.61)       (5.92, 15.20)   \nProgressive Disease (PD)              24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  90% CI (Clopper-Pearson)          (12.67, 24.25)     (7.63, 17.57)        (18.90, 31.97)  \nNot Evaluable (NE)                     1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  90% CI (Clopper-Pearson)           (0.04, 3.49)      (5.25, 14.11)         (1.50, 7.80)   \n\n\n\n\nThe stratified analysis section can be added by defining the analyses needed with control_binary_comparison for the argument strat_analysis and identifying the stratification variables to use.\n\n\nCodestrata &lt;- \"STRATA1\"\nlyt_04 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(\n    vars = \"is_rsp\",\n    table_names = \"est_prop\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    table_names = \"test_prop\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    table_names = \"est_or\"\n  ) %&gt;%\n  estimate_proportion_diff(\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    var_labels = \"Stratified Analysis\",\n    method = \"cmh\",\n    variables = list(strata = strata),\n    table_names = \"est_prop_diff_strat\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"cmh\",\n    variables = list(strata = strata),\n    table_names = \"test_prop_strat\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    variables = list(strata = strata, arm = \"ARM\"),\n    table_names = \"est_or_strat\"\n  ) %&gt;%\n  estimate_multinomial_response(var = \"rsp_lab\")\n\nresult &lt;- build_table(lyt = lyt_04, df = anl)\nresult\n\n                                             A: Drug X          B: Placebo         C: Combination  \n                                              (N=134)            (N=134)              (N=132)      \n———————————————————————————————————————————————————————————————————————————————————————————————————\nResponders                                  100 (74.6%)         84 (62.7%)           81 (61.4%)    \n95% CI (Wald, with correction)              (66.9, 82.4)       (54.1, 71.2)         (52.7, 70.0)   \nUnstratified Analysis                                                                              \n  Difference in Response rate (%)                                 -11.9                -13.3       \n    95% CI (Wald, with correction)                            (-23.7, -0.2)        (-25.1, -1.4)   \n  p-value (Chi-Squared Test)                                      0.0351               0.0204      \nOdds Ratio (95% CI)                                         0.57 (0.34 - 0.96)   0.54 (0.32 - 0.91)\nStratified Analysis                                                                                \n  Difference in Response rate (%)                                 -11.9                -13.5       \n    95% CI (CMH, without correction)                          (-22.7, -1.0)        (-24.5, -2.5)   \n  p-value (Cochran-Mantel-Haenszel Test)                          0.0366               0.0180      \nOdds Ratio (95% CI)                                         0.57 (0.34 - 0.96)   0.54 (0.32 - 0.90)\nComplete Response (CR)                       60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  95% CI (Wald, with correction)           (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  \nPartial Response (PR)                        40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  95% CI (Wald, with correction)           (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  \nStable Disease (SD)                           9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  95% CI (Wald, with correction)           (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   \nProgressive Disease (PD)                     24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)           (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNot Evaluable (NE)                            1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)            (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\n\nThe definition of responders is realized during the pre-processing step. The layout does not need to be modified and can be reused.\n\nCodeanl &lt;- anl_adsl %&gt;%\n  left_join(anl_adrs, by = c(\"STUDYID\", \"USUBJID\")) %&gt;%\n  mutate(rsp_lab = d_onco_rsp_label(AVALC)) %&gt;%\n  mutate(is_rsp = AVALC == \"CR\") %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\")\n\nresult &lt;- build_table(lyt = lyt_01, df = anl)\nresult\n\n                                       A: Drug X          B: Placebo         C: Combination  \n                                        (N=134)            (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————\nResponders                             60 (44.8%)         47 (35.1%)           57 (43.2%)    \n95% CI (Wald, with correction)        (36.0, 53.6)       (26.6, 43.5)         (34.4, 52.0)   \nUnstratified Analysis                                                                        \n  Difference in Response rate (%)                            -9.7                 -1.6       \n    95% CI (Wald, with correction)                       (-22.1, 2.7)        (-14.3, 11.1)   \n  p-value (Chi-Squared Test)                                0.1049               0.7934      \nOdds Ratio (95% CI)                                   0.67 (0.41 - 1.09)   0.94 (0.58 - 1.52)\nComplete Response (CR)                 60 (44.8%)         47 (35.1%)           57 (43.2%)    \n  95% CI (Wald, with correction)     (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  \nPartial Response (PR)                  40 (29.9%)         37 (27.6%)           24 (18.2%)    \n  95% CI (Wald, with correction)     (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  \nStable Disease (SD)                     9 (6.7%)          22 (16.4%)           13 (9.8%)     \n  95% CI (Wald, with correction)     (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   \nProgressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNot Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\n\nSimilarly to in the previous tab, redefinition or relabeling of the result is a pre-processing step and the original table layout can be reused.\n\nCodeanl &lt;- anl_adsl %&gt;%\n  left_join(anl_adrs, by = c(\"STUDYID\", \"USUBJID\")) %&gt;%\n  mutate(rsp_lab = as.character(d_onco_rsp_label(AVALC))) %&gt;%\n  mutate(\n    rsp_lab = case_when(\n      rsp_lab == \"Complete Response (CR)\" ~ \"No Progression\",\n      rsp_lab == \"Partial Response (PR)\" ~ \"No Progression\",\n      rsp_lab == \"Stable Disease (SD)\" ~ \"No Progression\",\n      TRUE ~ rsp_lab\n    )\n  ) %&gt;%\n  mutate(is_rsp = rsp_lab %in% \"No Progression\") %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\")\n\nresult &lt;- build_table(lyt = lyt_01, df = anl)\nresult\n\n                                       A: Drug X          B: Placebo         C: Combination  \n                                        (N=134)            (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————\nResponders                            109 (81.3%)        106 (79.1%)           94 (71.2%)    \n95% CI (Wald, with correction)        (74.4, 88.3)       (71.8, 86.4)         (63.1, 79.3)   \nUnstratified Analysis                                                                        \n  Difference in Response rate (%)                            -2.2                -10.1       \n    95% CI (Wald, with correction)                       (-12.5, 8.0)         (-21.0, 0.8)   \n  p-value (Chi-Squared Test)                                0.6455               0.0520      \nOdds Ratio (95% CI)                                   0.87 (0.48 - 1.59)   0.57 (0.32 - 1.01)\nProgressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    \n  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  \nNo Progression                        109 (81.3%)        106 (79.1%)           94 (71.2%)    \n  95% CI (Wald, with correction)     (74.37, 88.31)     (71.85, 86.36)       (63.11, 79.31)  \nNot Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     \n  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADRS &lt;- df_explicit_na(ADRS)\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(Dum_ARM = factor(rep(\"Single ARM\", nrow(.))))\n  ADRS &lt;- ADRS %&gt;%\n    mutate(Dum_ARM = factor(rep(\"Single ARM\", nrow(.))))\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_binary_outcome(\n      label = \"Responders\",\n      dataname = \"ADRS\",\n      paramcd = choices_selected(\n        choices = value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BESRSPI\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"ARM\", \"ARMCD\", \"ACTARMCD\", \"Dum_ARM\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      strata_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"SEX\", \"BMRKR2\")),\n        select = NULL\n      ),\n      rsp_table = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:38:59 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -732,7 +732,7 @@
     "href": "tables/efficacy/rbmit01.html",
     "title": "RBMIT01",
     "section": "",
-    "text": "Data Setup\nDraws\nImpute\nAnalyze\nPool\nCreate Output\nReproducibility\n\n\n\nWe use a publicly available example dataset from an antidepressant clinical trial of an active drug versus placebo from the rbmi package. The relevant endpoint is the Hamilton 17-item depression rating scale (HAMD17) which was assessed at baseline and at weeks 1, 2, 4, and 6. Study drug discontinuation occurred in 24% of subjects from the active drug and 26% of subjects from placebo. All data after study drug discontinuation are missing and there is a single additional intermittent missing observation.\n\nCodelibrary(tern.rbmi)\nlibrary(dplyr)\nset.seed(123)\n\ndata &lt;- antidepressant_data\nlevels(data$THERAPY) &lt;- c(\"PLACEBO\", \"DRUG\") # This is important! The order defines the computation order later\n\nmissing_var &lt;- \"CHANGE\"\nvars &lt;- list(\n  id = \"PATIENT\",\n  visit = \"VISIT\",\n  expand_vars = c(\"BASVAL\", \"THERAPY\"),\n  group = \"THERAPY\"\n)\ncovariates &lt;- list(\n  draws = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\"),\n  analyse = c(\"BASVAL\")\n)\n\ndata &lt;- data %&gt;%\n  dplyr::select(PATIENT, THERAPY, VISIT, BASVAL, THERAPY, CHANGE) %&gt;%\n  dplyr::mutate(dplyr::across(.cols = vars$id, ~ as.factor(.x))) %&gt;%\n  dplyr::arrange(dplyr::across(.cols = c(vars$id, vars$visit)))\n\n# Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset\ndata_full &lt;- do.call(\n  expand_locf,\n  args = list(\n    data = data,\n    vars = c(vars$expand_vars, vars$group),\n    group = vars$id,\n    order = c(vars$id, vars$visit)\n  ) %&gt;%\n    append(lapply(data[c(vars$id, vars$visit)], levels))\n)\n\ndata_full &lt;- data_full %&gt;%\n  dplyr::group_by(dplyr::across(vars$id)) %&gt;%\n  dplyr::mutate(!!vars$group := Filter(Negate(is.na), .data[[vars$group]])[1])\n\n# there are duplicates - use first value\ndata_full &lt;- data_full %&gt;%\n  dplyr::group_by(dplyr::across(c(vars$id, vars$group, vars$visit))) %&gt;%\n  dplyr::slice(1) %&gt;%\n  dplyr::ungroup()\n# need to have a single ID column\ndata_full &lt;- data_full %&gt;%\n  tidyr::unite(\"TMP_ID\", dplyr::all_of(vars$id), sep = \"_#_\", remove = FALSE) %&gt;%\n  dplyr::mutate(TMP_ID = as.factor(TMP_ID))\n\n\nCreating Intercurrent Event Data\nSet the imputation strategy to \"MAR\" for each patient with at least one missing observation.\n\nCodedata_ice &lt;- data_full %&gt;%\n  dplyr::arrange(dplyr::across(.cols = c(\"TMP_ID\", vars$visit))) %&gt;%\n  dplyr::filter(is.na(.data[[missing_var]])) %&gt;%\n  dplyr::group_by(TMP_ID) %&gt;%\n  dplyr::slice(1) %&gt;%\n  dplyr::ungroup() %&gt;%\n  dplyr::select(all_of(c(\"TMP_ID\", vars$visit))) %&gt;%\n  dplyr::mutate(strategy = \"MAR\")\n\n\n\n\n\nThe rbmi::draws() function fits the imputation models and stores the corresponding parameter estimates or Bayesian posterior parameter draws. The three main inputs to the rbmi::draws() function are:\n\n\ndata - The primary longitudinal data.frame containing the outcome variable and all covariates.\n\ndata_ice - A data.frame which specifies the first visit affected by an intercurrent event (ICE) and the imputation strategy for handling missing outcome data after the ICE. At most one ICE which is to be imputed by a non-MAR strategy is allowed per subject.\n\nmethod - The statistical method used to fit the imputation models and to create imputed datasets.\n\nDefine Key Variables\nDefine the names of key variables in our dataset and the covariates included in the imputation model using rbmi::set_vars(). Note that the covariates argument can also include interaction terms.\n\nCodedebug_mode &lt;- FALSE\n\ndraws_vars &lt;- rbmi::set_vars(\n  outcome = missing_var,\n  visit = vars$visit,\n  group = vars$group,\n  covariates = covariates$draws\n)\ndraws_vars$subjid &lt;- \"TMP_ID\"\n\n\nDefine which imputation method to use, then create samples for the imputation parameters by running the draws() function.\n\n\nCodedraws_method &lt;- method_bayes()\n\ndraws_obj &lt;- rbmi::draws(\n  data = data_full,\n  data_ice = data_ice,\n  vars = draws_vars,\n  method = draws_method\n)\n\n\nSAMPLING FOR MODEL 'MMRM' NOW (CHAIN 1).\nChain 1: \nChain 1: Gradient evaluation took 0.000367 seconds\nChain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.67 seconds.\nChain 1: Adjust your expectations accordingly!\nChain 1: \nChain 1: \nChain 1: Iteration:    1 / 1200 [  0%]  (Warmup)\nChain 1: Iteration:  120 / 1200 [ 10%]  (Warmup)\nChain 1: Iteration:  201 / 1200 [ 16%]  (Sampling)\nChain 1: Iteration:  320 / 1200 [ 26%]  (Sampling)\nChain 1: Iteration:  440 / 1200 [ 36%]  (Sampling)\nChain 1: Iteration:  560 / 1200 [ 46%]  (Sampling)\nChain 1: Iteration:  680 / 1200 [ 56%]  (Sampling)\nChain 1: Iteration:  800 / 1200 [ 66%]  (Sampling)\nChain 1: Iteration:  920 / 1200 [ 76%]  (Sampling)\nChain 1: Iteration: 1040 / 1200 [ 86%]  (Sampling)\nChain 1: Iteration: 1160 / 1200 [ 96%]  (Sampling)\nChain 1: Iteration: 1200 / 1200 [100%]  (Sampling)\nChain 1: \nChain 1:  Elapsed Time: 0.559 seconds (Warm-up)\nChain 1:                1.791 seconds (Sampling)\nChain 1:                2.35 seconds (Total)\nChain 1: \n\n\n\n\n\nThe next step is to use the parameters from the imputation model to generate the imputed datasets. This is done via the rbmi::impute() function. The function only has two key inputs: the imputation model output from rbmi::draws() and the reference groups relevant to reference-based imputation methods. It’s usage is thus:\n\nCodeimpute_references &lt;- c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\")\n\nimpute_obj &lt;- rbmi::impute(\n  draws_obj,\n  references = impute_references\n)\n\n\n\n\nThe next step is to run the analysis model on each imputed dataset. This is done by defining an analysis function and then calling rbmi::analyse() to apply this function to each imputed dataset.\n\n\nCode# Define analysis model\nanalyse_fun &lt;- ancova\n\nref_levels &lt;- levels(impute_obj$data$group[[1]])\nnames(ref_levels) &lt;- c(\"ref\", \"alt\")\n\nanalyse_obj &lt;- rbmi::analyse(\n  imputations = impute_obj,\n  fun = analyse_fun,\n  vars = rbmi::set_vars(\n    subjid = \"TMP_ID\",\n    outcome = missing_var,\n    visit = vars$visit,\n    group = vars$group,\n    covariates = covariates$analyse\n  )\n)\n\n\n\n\nThe rbmi::pool() function can be used to summarize the analysis results across multiple imputed datasets to provide an overall statistic with a standard error, confidence intervals and a p-value for the hypothesis test of the null hypothesis that the effect is equal to 0. Using the broom::tidy() function the rbmi final results are reshaped.\n\n\nCodelibrary(broom)\n\npool_obj &lt;- rbmi::pool(\n  results = analyse_obj,\n  conf.level = 0.95,\n  alternative = c(\"two.sided\", \"less\", \"greater\"),\n  type = c(\"percentile\", \"normal\")\n)\n\ndf &lt;- tidy(pool_obj)\ndf\n\n  group       est    se_est lower_cl_est upper_cl_est   est_contr  se_contr\n1   ref -1.615820 0.4862316    -2.575771   -0.6558685          NA        NA\n2   alt -1.707626 0.4749573    -2.645319   -0.7699335 -0.09180645 0.6826279\n3   ref -4.250814 0.6466452    -5.527714   -2.9739143          NA        NA\n4   alt -2.804837 0.6438255    -4.076779   -1.5328939  1.44597772 0.9136660\n5   ref -6.365807 0.7214451    -7.792725   -4.9388895          NA        NA\n6   alt -4.095430 0.6958248    -5.470969   -2.7198908  2.27037720 0.9961376\n7   ref -7.688935 0.7965136    -9.265897   -6.1119740          NA        NA\n8   alt -4.775673 0.7516059    -6.261450   -3.2898970  2.91326216 1.0733820\n  lower_cl_contr upper_cl_contr     p_value relative_reduc visit conf_level\n1             NA             NA          NA             NA     4       0.95\n2     -1.4394968       1.255884 0.893177243     0.05681725     4       0.95\n3             NA             NA          NA             NA     5       0.95\n4     -0.3584507       3.250406 0.115488900    -0.34016488     5       0.95\n5             NA             NA          NA             NA     6       0.95\n6      0.3014522       4.239302 0.024124977    -0.35665190     6       0.95\n7             NA             NA          NA             NA     7       0.95\n8      0.7918874       5.034637 0.007446276    -0.37889019     7       0.95\n\n\n\n\nFinally, use functions from the rtables and tern packages to generate a nicely formatted rtable object.\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"group\", ref_group = levels(df$group)[1]) %&gt;%\n  split_rows_by(\"visit\", split_label = \"Visit\", label_pos = \"topleft\") %&gt;%\n  summarize_rbmi() %&gt;%\n  build_table(df)\nresult\n\nVisit                                       ref                alt       \n—————————————————————————————————————————————————————————————————————————\n4                                                                        \n  Adjusted Mean (SE)                   -1.616 (0.486)     -1.708 (0.475) \n    95% CI                            (-2.576, -0.656)   (-2.645, -0.770)\n  Difference in Adjusted Means (SE)                       -0.092 (0.683) \n    95% CI                                               (-1.439, 1.256) \n    Relative Reduction (%)                                     5.7%      \n    p-value (RBMI)                                            0.8932     \n5                                                                        \n  Adjusted Mean (SE)                   -4.251 (0.647)     -2.805 (0.644) \n    95% CI                            (-5.528, -2.974)   (-4.077, -1.533)\n  Difference in Adjusted Means (SE)                       1.446 (0.914)  \n    95% CI                                               (-0.358, 3.250) \n    Relative Reduction (%)                                    -34.0%     \n    p-value (RBMI)                                            0.1155     \n6                                                                        \n  Adjusted Mean (SE)                   -6.366 (0.721)     -4.095 (0.696) \n    95% CI                            (-7.793, -4.939)   (-5.471, -2.720)\n  Difference in Adjusted Means (SE)                       2.270 (0.996)  \n    95% CI                                                (0.301, 4.239) \n    Relative Reduction (%)                                    -35.7%     \n    p-value (RBMI)                                            0.0241     \n7                                                                        \n  Adjusted Mean (SE)                   -7.689 (0.797)     -4.776 (0.752) \n    95% CI                            (-9.266, -6.112)   (-6.261, -3.290)\n  Difference in Adjusted Means (SE)                       2.913 (1.073)  \n    95% CI                                                (0.792, 5.035) \n    Relative Reduction (%)                                    -37.9%     \n    p-value (RBMI)                                            0.0074     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:39:33 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] broom_1.0.5           dplyr_1.1.4           tern.rbmi_0.1.2.9012 \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9013 rbmi_1.2.6           \n\nloaded via a namespace (and not attached):\n [1] gtable_0.3.4       TMB_1.9.10         xfun_0.42          ggplot2_3.5.0     \n [5] QuickJSR_1.1.3     htmlwidgets_1.6.4  inline_0.3.19      lattice_0.22-5    \n [9] vctrs_0.6.5        tools_4.3.3        Rdpack_2.6         generics_0.1.3    \n[13] stats4_4.3.3       curl_5.2.1         parallel_4.3.3     tibble_3.2.1      \n[17] mmrm_0.3.11        fansi_1.0.6        pkgconfig_2.0.3    Matrix_1.6-5      \n[21] checkmate_2.3.1    RcppParallel_5.1.7 assertthat_0.2.1   lifecycle_1.0.4   \n[25] compiler_4.3.3     stringr_1.5.1      brio_1.1.4         munsell_0.5.0     \n[29] codetools_0.2-19   htmltools_0.5.7    yaml_2.3.8         pillar_1.9.0      \n[33] tidyr_1.3.1        StanHeaders_2.32.6 nlme_3.1-164       rstan_2.32.6      \n[37] tidyselect_1.2.0   digest_0.6.34      stringi_1.8.3      purrr_1.0.2       \n[41] splines_4.3.3      fastmap_1.1.1      grid_4.3.3         colorspace_2.1-0  \n[45] cli_3.6.2          loo_2.7.0          survival_3.5-8     pkgbuild_1.4.3    \n[49] utf8_1.2.4         withr_3.0.0        scales_1.3.0       backports_1.4.1   \n[53] rmarkdown_2.26     matrixStats_1.2.0  gridExtra_2.3      evaluate_0.23     \n[57] knitr_1.45         rbibutils_2.2.16   testthat_3.2.1     V8_4.4.2          \n[61] rlang_1.1.3        Rcpp_1.0.12        glue_1.7.0         jsonlite_1.8.8    \n[65] R6_2.5.1          \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nDraws\nImpute\nAnalyze\nPool\nCreate Output\nReproducibility\n\n\n\nWe use a publicly available example dataset from an antidepressant clinical trial of an active drug versus placebo from the rbmi package. The relevant endpoint is the Hamilton 17-item depression rating scale (HAMD17) which was assessed at baseline and at weeks 1, 2, 4, and 6. Study drug discontinuation occurred in 24% of subjects from the active drug and 26% of subjects from placebo. All data after study drug discontinuation are missing and there is a single additional intermittent missing observation.\n\nCodelibrary(tern.rbmi)\nlibrary(dplyr)\nset.seed(123)\n\ndata &lt;- antidepressant_data\nlevels(data$THERAPY) &lt;- c(\"PLACEBO\", \"DRUG\") # This is important! The order defines the computation order later\n\nmissing_var &lt;- \"CHANGE\"\nvars &lt;- list(\n  id = \"PATIENT\",\n  visit = \"VISIT\",\n  expand_vars = c(\"BASVAL\", \"THERAPY\"),\n  group = \"THERAPY\"\n)\ncovariates &lt;- list(\n  draws = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\"),\n  analyse = c(\"BASVAL\")\n)\n\ndata &lt;- data %&gt;%\n  dplyr::select(PATIENT, THERAPY, VISIT, BASVAL, THERAPY, CHANGE) %&gt;%\n  dplyr::mutate(dplyr::across(.cols = vars$id, ~ as.factor(.x))) %&gt;%\n  dplyr::arrange(dplyr::across(.cols = c(vars$id, vars$visit)))\n\n# Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset\ndata_full &lt;- do.call(\n  expand_locf,\n  args = list(\n    data = data,\n    vars = c(vars$expand_vars, vars$group),\n    group = vars$id,\n    order = c(vars$id, vars$visit)\n  ) %&gt;%\n    append(lapply(data[c(vars$id, vars$visit)], levels))\n)\n\ndata_full &lt;- data_full %&gt;%\n  dplyr::group_by(dplyr::across(vars$id)) %&gt;%\n  dplyr::mutate(!!vars$group := Filter(Negate(is.na), .data[[vars$group]])[1])\n\n# there are duplicates - use first value\ndata_full &lt;- data_full %&gt;%\n  dplyr::group_by(dplyr::across(c(vars$id, vars$group, vars$visit))) %&gt;%\n  dplyr::slice(1) %&gt;%\n  dplyr::ungroup()\n# need to have a single ID column\ndata_full &lt;- data_full %&gt;%\n  tidyr::unite(\"TMP_ID\", dplyr::all_of(vars$id), sep = \"_#_\", remove = FALSE) %&gt;%\n  dplyr::mutate(TMP_ID = as.factor(TMP_ID))\n\n\nCreating Intercurrent Event Data\nSet the imputation strategy to \"MAR\" for each patient with at least one missing observation.\n\nCodedata_ice &lt;- data_full %&gt;%\n  dplyr::arrange(dplyr::across(.cols = c(\"TMP_ID\", vars$visit))) %&gt;%\n  dplyr::filter(is.na(.data[[missing_var]])) %&gt;%\n  dplyr::group_by(TMP_ID) %&gt;%\n  dplyr::slice(1) %&gt;%\n  dplyr::ungroup() %&gt;%\n  dplyr::select(all_of(c(\"TMP_ID\", vars$visit))) %&gt;%\n  dplyr::mutate(strategy = \"MAR\")\n\n\n\n\n\nThe rbmi::draws() function fits the imputation models and stores the corresponding parameter estimates or Bayesian posterior parameter draws. The three main inputs to the rbmi::draws() function are:\n\n\ndata - The primary longitudinal data.frame containing the outcome variable and all covariates.\n\ndata_ice - A data.frame which specifies the first visit affected by an intercurrent event (ICE) and the imputation strategy for handling missing outcome data after the ICE. At most one ICE which is to be imputed by a non-MAR strategy is allowed per subject.\n\nmethod - The statistical method used to fit the imputation models and to create imputed datasets.\n\nDefine Key Variables\nDefine the names of key variables in our dataset and the covariates included in the imputation model using rbmi::set_vars(). Note that the covariates argument can also include interaction terms.\n\nCodedebug_mode &lt;- FALSE\n\ndraws_vars &lt;- rbmi::set_vars(\n  outcome = missing_var,\n  visit = vars$visit,\n  group = vars$group,\n  covariates = covariates$draws\n)\ndraws_vars$subjid &lt;- \"TMP_ID\"\n\n\nDefine which imputation method to use, then create samples for the imputation parameters by running the draws() function.\n\n\nCodedraws_method &lt;- method_bayes()\n\ndraws_obj &lt;- rbmi::draws(\n  data = data_full,\n  data_ice = data_ice,\n  vars = draws_vars,\n  method = draws_method\n)\n\n\nSAMPLING FOR MODEL 'MMRM' NOW (CHAIN 1).\nChain 1: \nChain 1: Gradient evaluation took 0.000349 seconds\nChain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.49 seconds.\nChain 1: Adjust your expectations accordingly!\nChain 1: \nChain 1: \nChain 1: Iteration:    1 / 1200 [  0%]  (Warmup)\nChain 1: Iteration:  120 / 1200 [ 10%]  (Warmup)\nChain 1: Iteration:  201 / 1200 [ 16%]  (Sampling)\nChain 1: Iteration:  320 / 1200 [ 26%]  (Sampling)\nChain 1: Iteration:  440 / 1200 [ 36%]  (Sampling)\nChain 1: Iteration:  560 / 1200 [ 46%]  (Sampling)\nChain 1: Iteration:  680 / 1200 [ 56%]  (Sampling)\nChain 1: Iteration:  800 / 1200 [ 66%]  (Sampling)\nChain 1: Iteration:  920 / 1200 [ 76%]  (Sampling)\nChain 1: Iteration: 1040 / 1200 [ 86%]  (Sampling)\nChain 1: Iteration: 1160 / 1200 [ 96%]  (Sampling)\nChain 1: Iteration: 1200 / 1200 [100%]  (Sampling)\nChain 1: \nChain 1:  Elapsed Time: 0.557 seconds (Warm-up)\nChain 1:                1.78 seconds (Sampling)\nChain 1:                2.337 seconds (Total)\nChain 1: \n\n\n\n\n\nThe next step is to use the parameters from the imputation model to generate the imputed datasets. This is done via the rbmi::impute() function. The function only has two key inputs: the imputation model output from rbmi::draws() and the reference groups relevant to reference-based imputation methods. It’s usage is thus:\n\nCodeimpute_references &lt;- c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\")\n\nimpute_obj &lt;- rbmi::impute(\n  draws_obj,\n  references = impute_references\n)\n\n\n\n\nThe next step is to run the analysis model on each imputed dataset. This is done by defining an analysis function and then calling rbmi::analyse() to apply this function to each imputed dataset.\n\n\nCode# Define analysis model\nanalyse_fun &lt;- ancova\n\nref_levels &lt;- levels(impute_obj$data$group[[1]])\nnames(ref_levels) &lt;- c(\"ref\", \"alt\")\n\nanalyse_obj &lt;- rbmi::analyse(\n  imputations = impute_obj,\n  fun = analyse_fun,\n  vars = rbmi::set_vars(\n    subjid = \"TMP_ID\",\n    outcome = missing_var,\n    visit = vars$visit,\n    group = vars$group,\n    covariates = covariates$analyse\n  )\n)\n\n\n\n\nThe rbmi::pool() function can be used to summarize the analysis results across multiple imputed datasets to provide an overall statistic with a standard error, confidence intervals and a p-value for the hypothesis test of the null hypothesis that the effect is equal to 0. Using the broom::tidy() function the rbmi final results are reshaped.\n\n\nCodelibrary(broom)\n\npool_obj &lt;- rbmi::pool(\n  results = analyse_obj,\n  conf.level = 0.95,\n  alternative = c(\"two.sided\", \"less\", \"greater\"),\n  type = c(\"percentile\", \"normal\")\n)\n\ndf &lt;- tidy(pool_obj)\ndf\n\n  group       est    se_est lower_cl_est upper_cl_est   est_contr  se_contr\n1   ref -1.615820 0.4862316    -2.575771   -0.6558685          NA        NA\n2   alt -1.707626 0.4749573    -2.645319   -0.7699335 -0.09180645 0.6826279\n3   ref -4.250814 0.6466452    -5.527714   -2.9739143          NA        NA\n4   alt -2.804837 0.6438255    -4.076779   -1.5328939  1.44597772 0.9136660\n5   ref -6.365807 0.7214451    -7.792725   -4.9388895          NA        NA\n6   alt -4.095430 0.6958248    -5.470969   -2.7198908  2.27037720 0.9961376\n7   ref -7.688935 0.7965136    -9.265897   -6.1119740          NA        NA\n8   alt -4.775673 0.7516059    -6.261450   -3.2898970  2.91326216 1.0733820\n  lower_cl_contr upper_cl_contr     p_value relative_reduc visit conf_level\n1             NA             NA          NA             NA     4       0.95\n2     -1.4394968       1.255884 0.893177243     0.05681725     4       0.95\n3             NA             NA          NA             NA     5       0.95\n4     -0.3584507       3.250406 0.115488900    -0.34016488     5       0.95\n5             NA             NA          NA             NA     6       0.95\n6      0.3014522       4.239302 0.024124977    -0.35665190     6       0.95\n7             NA             NA          NA             NA     7       0.95\n8      0.7918874       5.034637 0.007446276    -0.37889019     7       0.95\n\n\n\n\nFinally, use functions from the rtables and tern packages to generate a nicely formatted rtable object.\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"group\", ref_group = levels(df$group)[1]) %&gt;%\n  split_rows_by(\"visit\", split_label = \"Visit\", label_pos = \"topleft\") %&gt;%\n  summarize_rbmi() %&gt;%\n  build_table(df)\nresult\n\nVisit                                       ref                alt       \n—————————————————————————————————————————————————————————————————————————\n4                                                                        \n  Adjusted Mean (SE)                   -1.616 (0.486)     -1.708 (0.475) \n    95% CI                            (-2.576, -0.656)   (-2.645, -0.770)\n  Difference in Adjusted Means (SE)                       -0.092 (0.683) \n    95% CI                                               (-1.439, 1.256) \n    Relative Reduction (%)                                     5.7%      \n    p-value (RBMI)                                            0.8932     \n5                                                                        \n  Adjusted Mean (SE)                   -4.251 (0.647)     -2.805 (0.644) \n    95% CI                            (-5.528, -2.974)   (-4.077, -1.533)\n  Difference in Adjusted Means (SE)                       1.446 (0.914)  \n    95% CI                                               (-0.358, 3.250) \n    Relative Reduction (%)                                    -34.0%     \n    p-value (RBMI)                                            0.1155     \n6                                                                        \n  Adjusted Mean (SE)                   -6.366 (0.721)     -4.095 (0.696) \n    95% CI                            (-7.793, -4.939)   (-5.471, -2.720)\n  Difference in Adjusted Means (SE)                       2.270 (0.996)  \n    95% CI                                                (0.301, 4.239) \n    Relative Reduction (%)                                    -35.7%     \n    p-value (RBMI)                                            0.0241     \n7                                                                        \n  Adjusted Mean (SE)                   -7.689 (0.797)     -4.776 (0.752) \n    95% CI                            (-9.266, -6.112)   (-6.261, -3.290)\n  Difference in Adjusted Means (SE)                       2.913 (1.073)  \n    95% CI                                                (0.792, 5.035) \n    Relative Reduction (%)                                    -37.9%     \n    p-value (RBMI)                                            0.0074     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:38:24 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] broom_1.0.5           dplyr_1.1.4           tern.rbmi_0.1.2.9012 \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9015 rbmi_1.2.6           \n\nloaded via a namespace (and not attached):\n [1] gtable_0.3.4       TMB_1.9.10         xfun_0.42          ggplot2_3.5.0     \n [5] QuickJSR_1.1.3     htmlwidgets_1.6.4  inline_0.3.19      lattice_0.22-5    \n [9] vctrs_0.6.5        tools_4.3.3        Rdpack_2.6         generics_0.1.3    \n[13] stats4_4.3.3       curl_5.2.1         parallel_4.3.3     tibble_3.2.1      \n[17] mmrm_0.3.11        fansi_1.0.6        pkgconfig_2.0.3    Matrix_1.6-5      \n[21] checkmate_2.3.1    RcppParallel_5.1.7 assertthat_0.2.1   lifecycle_1.0.4   \n[25] compiler_4.3.3     stringr_1.5.1      brio_1.1.4         munsell_0.5.0     \n[29] codetools_0.2-19   htmltools_0.5.7    yaml_2.3.8         pillar_1.9.0      \n[33] tidyr_1.3.1        StanHeaders_2.32.6 nlme_3.1-164       rstan_2.32.6      \n[37] tidyselect_1.2.0   digest_0.6.34      stringi_1.8.3      purrr_1.0.2       \n[41] splines_4.3.3      fastmap_1.1.1      grid_4.3.3         colorspace_2.1-0  \n[45] cli_3.6.2          loo_2.7.0          survival_3.5-8     pkgbuild_1.4.3    \n[49] utf8_1.2.4         withr_3.0.0        scales_1.3.0       backports_1.4.1   \n[53] rmarkdown_2.26     matrixStats_1.2.0  gridExtra_2.3      evaluate_0.23     \n[57] knitr_1.45         rbibutils_2.2.16   testthat_3.2.1     V8_4.4.2          \n[61] rlang_1.1.3        Rcpp_1.0.12        glue_1.7.0         jsonlite_1.8.8    \n[65] R6_2.5.1          \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -744,7 +744,7 @@
     "href": "tables/efficacy/aovt02.html",
     "title": "AOVT02",
     "section": "",
-    "text": "This example will focus on data from a single visit “WEEK 1 DAY 8” and a single endpoint “FKSI-FWB”. Only evaluable populations will be analyzed.\n\n\nData Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\nadqs_single &lt;- adqs %&gt;%\n  filter(\n    AVISIT == \"WEEK 1 DAY 8\",\n    PARAMCD == \"FKSI-FWB\"\n  ) %&gt;%\n  mutate(CHG = ifelse(BMEASIFL == \"Y\", CHG, NA))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  append_varlabels(adqs_single, \"PARAM\") %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = NULL),\n    conf_level = 0.95,\n    var_labels = \"Unadjusted comparison\",\n    .labels = c(lsmean = \"Mean\", lsmean_diff = \"Difference in Means\"),\n    table_names = \"unadjusted\"\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"STRATA1\")),\n    conf_level = 0.95,\n    var_labels = \"Adjusted comparison (covariates BASE and STRATA1)\",\n    table_names = \"adjusted\"\n  )\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adqs_single,\n  alt_counts_df = adsl\n)\n\nresult\n\n                                                     ARM A        ARM B           ARM C    \nParameter                                           (N=134)      (N=134)         (N=132)   \n———————————————————————————————————————————————————————————————————————————————————————————\nUnadjusted comparison                                                                      \n  n                                                   68           73              62      \n  Mean                                               3.68         5.07            3.09     \n  Difference in Means                                             1.38            -0.59    \n    95% CI                                                    (-2.76, 5.53)   (-4.91, 3.73)\n    p-value                                                      0.5113          0.7873    \nAdjusted comparison (covariates BASE and STRATA1)                                          \n  n                                                   68           73              62      \n  Adjusted Mean                                      4.06         3.57            3.34     \n  Difference in Adjusted Means                                    -0.49           -0.72    \n    95% CI                                                    (-3.28, 2.29)   (-3.57, 2.12)\n    p-value                                                      0.7277          0.6165    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADQS &lt;- df_explicit_na(ADQS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADQS &lt;- data[[\"ADQS\"]]\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_ancova(\n      label = \"ANCOVA table\",\n      dataname = \"ADQS\",\n      avisit = choices_selected(\n        choices = value_choices(ADQS, \"AVISIT\"),\n        selected = \"WEEK 1 DAY 8\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ACTARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      aval_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"CHG\", \"AVAL\")),\n        selected = \"CHG\"\n      ),\n      cov_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"BASE\", \"STRATA1\", \"SEX\")),\n        selected = \"STRATA1\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"FKSI-FWB\"\n      )\n    )\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:39:06 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "This example will focus on data from a single visit “WEEK 1 DAY 8” and a single endpoint “FKSI-FWB”. Only evaluable populations will be analyzed.\n\n\nData Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\nadqs_single &lt;- adqs %&gt;%\n  filter(\n    AVISIT == \"WEEK 1 DAY 8\",\n    PARAMCD == \"FKSI-FWB\"\n  ) %&gt;%\n  mutate(CHG = ifelse(BMEASIFL == \"Y\", CHG, NA))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  append_varlabels(adqs_single, \"PARAM\") %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = NULL),\n    conf_level = 0.95,\n    var_labels = \"Unadjusted comparison\",\n    .labels = c(lsmean = \"Mean\", lsmean_diff = \"Difference in Means\"),\n    table_names = \"unadjusted\"\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"STRATA1\")),\n    conf_level = 0.95,\n    var_labels = \"Adjusted comparison (covariates BASE and STRATA1)\",\n    table_names = \"adjusted\"\n  )\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adqs_single,\n  alt_counts_df = adsl\n)\n\nresult\n\n                                                     ARM A        ARM B           ARM C    \nParameter                                           (N=134)      (N=134)         (N=132)   \n———————————————————————————————————————————————————————————————————————————————————————————\nUnadjusted comparison                                                                      \n  n                                                   68           73              62      \n  Mean                                               3.68         5.07            3.09     \n  Difference in Means                                             1.38            -0.59    \n    95% CI                                                    (-2.76, 5.53)   (-4.91, 3.73)\n    p-value                                                      0.5113          0.7873    \nAdjusted comparison (covariates BASE and STRATA1)                                          \n  n                                                   68           73              62      \n  Adjusted Mean                                      4.06         3.57            3.34     \n  Difference in Adjusted Means                                    -0.49           -0.72    \n    95% CI                                                    (-3.28, 2.29)   (-3.57, 2.12)\n    p-value                                                      0.7277          0.6165    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADQS &lt;- df_explicit_na(ADQS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADQS &lt;- data[[\"ADQS\"]]\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_ancova(\n      label = \"ANCOVA table\",\n      dataname = \"ADQS\",\n      avisit = choices_selected(\n        choices = value_choices(ADQS, \"AVISIT\"),\n        selected = \"WEEK 1 DAY 8\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ACTARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      aval_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"CHG\", \"AVAL\")),\n        selected = \"CHG\"\n      ),\n      cov_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"BASE\", \"STRATA1\", \"SEX\")),\n        selected = \"STRATA1\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"FKSI-FWB\"\n      )\n    )\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:37:58 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -756,7 +756,7 @@
     "href": "tables/efficacy/coxt02.html",
     "title": "COXT02",
     "section": "",
-    "text": "Analysis based on multivariable Cox models is usually not performed for the Clinical Study Report (CSR) or regulatory documents, serving exploratory purposes only (e.g. for publication). In practice, the model usually includes only the main effects (without interaction terms). It produces the estimates for each of the covariates included in the model. The analysis follows the same principles (i.e. stratified vs. unstratified analysis and tie handling) as the general Cox model analysis also used in COXT01. Since there is usually no pre-specified hypothesis testing for such analysis, the p-values must be interpreted with caution.\n\n\nData Setup\nMultivariable Cox Regression\nMultivariable Cox Regression  with Interaction Term\nMultivariable Cox Regression  Specifying Covariates\nMultivariable Cox Regression  Specifying Covariates from ADSUB\nMultivariable Cox Regression  Setting Strata, Ties, Alpha Level, Statistics\nMultivariable Cox Regression  with Selection Process for Covariates\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(broom)\n\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadtte &lt;- df_explicit_na(adtte)\n\nanl &lt;- adtte %&gt;%\n  filter(\n    PARAMCD == \"OS\",\n    SEX %in% c(\"F\", \"M\"),\n    RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\")\n  ) %&gt;%\n  mutate(\n    ARM = droplevels(relevel(ARM, \"B: Placebo\")),\n    SEX = droplevels(SEX),\n    RACE = droplevels(RACE)\n  ) %&gt;%\n  mutate(EVENT = 1 - CNSR) %&gt;%\n  var_relabel(\n    ARM = \"Planned Arm\",\n    SEX = \"Sex\",\n    RACE = \"Race\",\n    AGE = \"Age\"\n  )\n\n\n\n\nThe summarize_coxreg function fits, tidies and arranges a Cox regression model in a table layout using the rtables framework. For a multivariable Cox regression model, argument multivar must be set to TRUE. Arguments variables and control can be specified to set up the model (see ?summarize_coxreg for more details and customization options). All variables specified within variables must be present in the data used when building the table.\nTo see the same model as a data.frame object, these two arguments (as well as the data) can be passed to the fit_coxreg_multivar function, and the resulting list tidied using broom::tidy().\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"SEX\", \"AGE\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_coxreg(variables = variables, multivar = TRUE) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\nEffect/Covariate Included in the Model   Hazard Ratio      95% CI      p-value\n——————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                    \n  Planned Arm (reference = B: Placebo)                                 0.2643 \n    A: Drug X                                0.96       (0.66, 1.42)   0.8536 \n    C: Combination                           1.27       (0.88, 1.83)   0.2010 \nCovariate:                                                                    \n  Sex (reference = F)                                                         \n    M                                        1.09       (0.80, 1.48)   0.5987 \n  Age                                                                         \n    All                                      0.99       (0.97, 1.01)   0.5104 \n\n\n\n\nThe estimation of interaction terms is not supported.\nInteraction terms are not included in the GDSR. For this reason and because we must take precautions when fitting such models, this functionality has not been translated in fit_coxreg_multivar. Please remove interaction terms or, if required by the study, refer to the survival::coxph function. Aside from this, using tern the developer must add the necessary variables to the analysis dataset during pre-processing based on ADVS or ADSUB. An example can be found in DMT01.\n\n\nThis option is not supported.\nSee the Multivariable Cox Regression with Interaction Term tab for more details.\n\n\nThis option is not supported.\nSee the Multivariable Cox Regression with Interaction Term tab for more details.\n\n\nAdditional controls can be customized using control_coxreg (see ?control_coxreg) such as the ties calculation method and the confidence level. Stratification variables can be added via the strata element of the variables list.\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARMCD\",\n  covariates = c(\"SEX\", \"AGE\"),\n  strata = \"RACE\"\n)\n\ncontrol &lt;- control_coxreg(\n  conf_level = 0.9,\n  ties = \"efron\"\n)\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control,\n    multivar = TRUE,\n    .stats = c(\"hr\", \"ci\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\nEffect/Covariate Included in the Model   Hazard Ratio      90% CI   \n————————————————————————————————————————————————————————————————————\nTreatment:                                                          \n  Planned Arm Code (reference = ARM A)                              \n    ARM B                                    1.03       (0.74, 1.42)\n    ARM C                                    1.30       (0.96, 1.77)\nCovariate:                                                          \n  Sex (reference = F)                                               \n    M                                        1.08       (0.83, 1.40)\n  Age                                                               \n    All                                      0.99       (0.98, 1.01)\n\n\n\n\nSee the Multivariable Cox Regression with Interaction Term tab.\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADTTE &lt;- data[[\"ADTTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_coxreg(\n      label = \"Cox Reg.\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\", \"ACTARMCD\"), \"ARM\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"), \"OS\"\n      ),\n      strata_var = choices_selected(\n        c(\"SEX\", \"STRATA1\", \"STRATA2\"), NULL\n      ),\n      cov_var = choices_selected(\n        c(\"AGE\", \"SEX\", \"RACE\"), c(\"AGE\", \"SEX\")\n      ),\n      multivariate = TRUE\n    )\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:38:30 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      broom_1.0.5                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                      scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              stringr_1.5.1           \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        utf8_1.2.4              \n[19] promises_1.2.1           rmarkdown_2.26           ps_1.7.6                \n[22] purrr_1.0.2              xfun_0.42                shinyvalidate_0.1.3     \n[25] cachem_1.0.8             teal.reporter_0.3.0.9005 jsonlite_1.8.8          \n[28] later_1.3.2              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            car_3.1-2                brio_1.1.4              \n[34] jquerylib_0.1.4          estimability_1.5         Rcpp_1.0.12             \n[37] knitr_1.45               zoo_1.8-12               teal.logger_0.1.3.9011  \n[40] httpuv_1.6.14            Matrix_1.6-5             splines_4.3.3           \n[43] tidyselect_1.2.0         abind_1.4-5              yaml_2.3.8              \n[46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          \n[49] teal.widgets_0.4.2.9006  lattice_0.22-5           tibble_3.2.1            \n[52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           \n[55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   \n[58] carData_3.0-5            checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[91] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Analysis based on multivariable Cox models is usually not performed for the Clinical Study Report (CSR) or regulatory documents, serving exploratory purposes only (e.g. for publication). In practice, the model usually includes only the main effects (without interaction terms). It produces the estimates for each of the covariates included in the model. The analysis follows the same principles (i.e. stratified vs. unstratified analysis and tie handling) as the general Cox model analysis also used in COXT01. Since there is usually no pre-specified hypothesis testing for such analysis, the p-values must be interpreted with caution.\n\n\nData Setup\nMultivariable Cox Regression\nMultivariable Cox Regression  with Interaction Term\nMultivariable Cox Regression  Specifying Covariates\nMultivariable Cox Regression  Specifying Covariates from ADSUB\nMultivariable Cox Regression  Setting Strata, Ties, Alpha Level, Statistics\nMultivariable Cox Regression  with Selection Process for Covariates\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(broom)\n\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadtte &lt;- df_explicit_na(adtte)\n\nanl &lt;- adtte %&gt;%\n  filter(\n    PARAMCD == \"OS\",\n    SEX %in% c(\"F\", \"M\"),\n    RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\")\n  ) %&gt;%\n  mutate(\n    ARM = droplevels(relevel(ARM, \"B: Placebo\")),\n    SEX = droplevels(SEX),\n    RACE = droplevels(RACE)\n  ) %&gt;%\n  mutate(EVENT = 1 - CNSR) %&gt;%\n  var_relabel(\n    ARM = \"Planned Arm\",\n    SEX = \"Sex\",\n    RACE = \"Race\",\n    AGE = \"Age\"\n  )\n\n\n\n\nThe summarize_coxreg function fits, tidies and arranges a Cox regression model in a table layout using the rtables framework. For a multivariable Cox regression model, argument multivar must be set to TRUE. Arguments variables and control can be specified to set up the model (see ?summarize_coxreg for more details and customization options). All variables specified within variables must be present in the data used when building the table.\nTo see the same model as a data.frame object, these two arguments (as well as the data) can be passed to the fit_coxreg_multivar function, and the resulting list tidied using broom::tidy().\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"SEX\", \"AGE\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_coxreg(variables = variables, multivar = TRUE) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\nEffect/Covariate Included in the Model   Hazard Ratio      95% CI      p-value\n——————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                    \n  Planned Arm (reference = B: Placebo)                                 0.2643 \n    A: Drug X                                0.96       (0.66, 1.42)   0.8536 \n    C: Combination                           1.27       (0.88, 1.83)   0.2010 \nCovariate:                                                                    \n  Sex (reference = F)                                                         \n    M                                        1.09       (0.80, 1.48)   0.5987 \n  Age                                                                         \n    All                                      0.99       (0.97, 1.01)   0.5104 \n\n\n\n\nThe estimation of interaction terms is not supported.\nInteraction terms are not included in the GDSR. For this reason and because we must take precautions when fitting such models, this functionality has not been translated in fit_coxreg_multivar. Please remove interaction terms or, if required by the study, refer to the survival::coxph function. Aside from this, using tern the developer must add the necessary variables to the analysis dataset during pre-processing based on ADVS or ADSUB. An example can be found in DMT01.\n\n\nThis option is not supported.\nSee the Multivariable Cox Regression with Interaction Term tab for more details.\n\n\nThis option is not supported.\nSee the Multivariable Cox Regression with Interaction Term tab for more details.\n\n\nAdditional controls can be customized using control_coxreg (see ?control_coxreg) such as the ties calculation method and the confidence level. Stratification variables can be added via the strata element of the variables list.\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARMCD\",\n  covariates = c(\"SEX\", \"AGE\"),\n  strata = \"RACE\"\n)\n\ncontrol &lt;- control_coxreg(\n  conf_level = 0.9,\n  ties = \"efron\"\n)\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control,\n    multivar = TRUE,\n    .stats = c(\"hr\", \"ci\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\nEffect/Covariate Included in the Model   Hazard Ratio      90% CI   \n————————————————————————————————————————————————————————————————————\nTreatment:                                                          \n  Planned Arm Code (reference = ARM A)                              \n    ARM B                                    1.03       (0.74, 1.42)\n    ARM C                                    1.30       (0.96, 1.77)\nCovariate:                                                          \n  Sex (reference = F)                                               \n    M                                        1.08       (0.83, 1.40)\n  Age                                                               \n    All                                      0.99       (0.98, 1.01)\n\n\n\n\nSee the Multivariable Cox Regression with Interaction Term tab.\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADTTE &lt;- data[[\"ADTTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_coxreg(\n      label = \"Cox Reg.\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\", \"ACTARMCD\"), \"ARM\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"), \"OS\"\n      ),\n      strata_var = choices_selected(\n        c(\"SEX\", \"STRATA1\", \"STRATA2\"), NULL\n      ),\n      cov_var = choices_selected(\n        c(\"AGE\", \"SEX\", \"RACE\"), c(\"AGE\", \"SEX\")\n      ),\n      multivariate = TRUE\n    )\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:37:23 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      broom_1.0.5                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                      scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              stringr_1.5.1           \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        utf8_1.2.4              \n[19] promises_1.2.1           rmarkdown_2.26           ps_1.7.6                \n[22] purrr_1.0.2              xfun_0.42                shinyvalidate_0.1.3     \n[25] cachem_1.0.8             teal.reporter_0.3.0.9005 jsonlite_1.8.8          \n[28] later_1.3.2              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            car_3.1-2                brio_1.1.4              \n[34] jquerylib_0.1.4          estimability_1.5         Rcpp_1.0.12             \n[37] knitr_1.45               zoo_1.8-12               teal.logger_0.1.3.9011  \n[40] httpuv_1.6.14            Matrix_1.6-5             splines_4.3.3           \n[43] tidyselect_1.2.0         abind_1.4-5              yaml_2.3.8              \n[46] codetools_0.2-19         websocket_1.4.1          processx_3.8.3          \n[49] teal.widgets_0.4.2.9008  lattice_0.22-5           tibble_3.2.1            \n[52] withr_3.0.0              coda_0.19-4.1            evaluate_0.23           \n[55] survival_3.5-8           pillar_1.9.0             shinycssloaders_1.0.0   \n[58] carData_3.0-5            checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[91] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -768,7 +768,7 @@
     "href": "tables/efficacy/aovt03.html",
     "title": "AOVT03",
     "section": "",
-    "text": "This example will focus on data from multiple visits and take the interaction between visits and arms into consideration.\n\n\nData Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\nadqs_in &lt;- adqs %&gt;%\n  filter(AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 5 DAY 36\")) %&gt;%\n  droplevels() %&gt;%\n  filter(PARAM %in% c(\"BFI All Questions\", \"Fatigue Interference\")) %&gt;%\n  mutate(CHG = ifelse(BMEASIFL == \"Y\", CHG, NA)) # only analyze evaluable population\n\n\n\n\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"AVISIT\", \"AVISIT*ARMCD\")),\n    conf_level = 0.95,\n    var_labels = \"WEEK 1 DAY 8\",\n    table_names = \"WEEK 1 DAY 8\",\n    interaction_y = \"WEEK 1 DAY 8\",\n    interaction_item = \"AVISIT\"\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"AVISIT\", \"AVISIT*ARMCD\")),\n    conf_level = 0.95,\n    var_labels = \"WEEK 2 DAY 15\",\n    table_names = \"WEEK 2 DAY 15\",\n    interaction_y = \"WEEK 2 DAY 15\",\n    interaction_item = \"AVISIT\"\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"AVISIT\", \"AVISIT*ARMCD\")),\n    conf_level = 0.95,\n    var_labels = \"WEEK 5 DAY 36\",\n    table_names = \"WEEK 5 DAY 36\",\n    interaction_y = \"WEEK 5 DAY 36\",\n    interaction_item = \"AVISIT\"\n  ) %&gt;%\n  build_table(adqs_in, alt_counts_df = adsl)\n\nresult\n\n                                    ARM A        ARM B           ARM C    \n                                   (N=134)      (N=134)         (N=132)   \n——————————————————————————————————————————————————————————————————————————\nBFI All Questions                                                         \n  WEEK 1 DAY 8                                                            \n    n                                68           73              62      \n    Adjusted Mean                   4.34         5.96            3.90     \n    Difference in Adjusted Means                 1.62            -0.44    \n      95% CI                                 (-1.75, 4.98)   (-3.94, 3.06)\n      p-value                                   0.3460          0.8059    \n  WEEK 2 DAY 15                                                           \n    n                                68           73              62      \n    Adjusted Mean                   12.99        11.23           9.86     \n    Difference in Adjusted Means                 -1.76           -3.13    \n      95% CI                                 (-5.12, 1.60)   (-6.64, 0.37)\n      p-value                                   0.3048          0.0795    \n  WEEK 5 DAY 36                                                           \n    n                                68           73              62      \n    Adjusted Mean                   23.88        23.08           28.21    \n    Difference in Adjusted Means                 -0.81           4.33     \n      95% CI                                 (-4.17, 2.56)   (0.83, 7.83) \n      p-value                                   0.6383          0.0155    \nFatigue Interference                                                      \n  WEEK 1 DAY 8                                                            \n    n                                68           73              62      \n    Adjusted Mean                   5.97         5.19            5.21     \n    Difference in Adjusted Means                 -0.78           -0.76    \n      95% CI                                 (-4.17, 2.61)   (-4.30, 2.78)\n      p-value                                   0.6522          0.6729    \n  WEEK 2 DAY 15                                                           \n    n                                68           73              62      \n    Adjusted Mean                   11.39        9.42            9.55     \n    Difference in Adjusted Means                 -1.96           -1.84    \n      95% CI                                 (-5.35, 1.43)   (-5.37, 1.70)\n      p-value                                   0.2560          0.3084    \n  WEEK 5 DAY 36                                                           \n    n                                68           73              62      \n    Adjusted Mean                   22.79        25.37           23.43    \n    Difference in Adjusted Means                 2.58            0.64     \n      95% CI                                 (-0.81, 5.97)   (-2.89, 4.18)\n      p-value                                   0.1353          0.7212    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:38:09 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] sandwich_3.1-0    utf8_1.2.4        generics_0.1.3    tidyr_1.3.1      \n [5] stringi_1.8.3     lattice_0.22-5    digest_0.6.34     evaluate_0.23    \n [9] grid_4.3.3        estimability_1.5  mvtnorm_1.2-4     fastmap_1.1.1    \n[13] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[17] survival_3.5-8    multcomp_1.4-25   purrr_1.0.2       fansi_1.0.6      \n[21] scales_1.3.0      TH.data_1.1-2     codetools_0.2-19  Rdpack_2.6       \n[25] cli_3.6.2         rlang_1.1.3       rbibutils_2.2.16  munsell_0.5.0    \n[29] splines_4.3.3     yaml_2.3.8        tools_4.3.3       checkmate_2.3.1  \n[33] coda_0.19-4.1     colorspace_2.1-0  ggplot2_3.5.0     broom_1.0.5      \n[37] vctrs_0.6.5       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.4  \n[41] emmeans_1.10.0    htmlwidgets_1.6.4 MASS_7.3-60.0.1   pkgconfig_2.0.3  \n[45] pillar_1.9.0      gtable_0.3.4      glue_1.7.0        xfun_0.42        \n[49] tibble_3.2.1      tidyselect_1.2.0  knitr_1.45        xtable_1.8-4     \n[53] htmltools_0.5.7   rmarkdown_2.26    testthat_3.2.1    compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "This example will focus on data from multiple visits and take the interaction between visits and arms into consideration.\n\n\nData Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\nadqs_in &lt;- adqs %&gt;%\n  filter(AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 5 DAY 36\")) %&gt;%\n  droplevels() %&gt;%\n  filter(PARAM %in% c(\"BFI All Questions\", \"Fatigue Interference\")) %&gt;%\n  mutate(CHG = ifelse(BMEASIFL == \"Y\", CHG, NA)) # only analyze evaluable population\n\n\n\n\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = drop_split_levels) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"AVISIT\", \"AVISIT*ARMCD\")),\n    conf_level = 0.95,\n    var_labels = \"WEEK 1 DAY 8\",\n    table_names = \"WEEK 1 DAY 8\",\n    interaction_y = \"WEEK 1 DAY 8\",\n    interaction_item = \"AVISIT\"\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"AVISIT\", \"AVISIT*ARMCD\")),\n    conf_level = 0.95,\n    var_labels = \"WEEK 2 DAY 15\",\n    table_names = \"WEEK 2 DAY 15\",\n    interaction_y = \"WEEK 2 DAY 15\",\n    interaction_item = \"AVISIT\"\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(arm = \"ARMCD\", covariates = c(\"BASE\", \"AVISIT\", \"AVISIT*ARMCD\")),\n    conf_level = 0.95,\n    var_labels = \"WEEK 5 DAY 36\",\n    table_names = \"WEEK 5 DAY 36\",\n    interaction_y = \"WEEK 5 DAY 36\",\n    interaction_item = \"AVISIT\"\n  ) %&gt;%\n  build_table(adqs_in, alt_counts_df = adsl)\n\nresult\n\n                                    ARM A        ARM B           ARM C    \n                                   (N=134)      (N=134)         (N=132)   \n——————————————————————————————————————————————————————————————————————————\nBFI All Questions                                                         \n  WEEK 1 DAY 8                                                            \n    n                                68           73              62      \n    Adjusted Mean                   4.34         5.96            3.90     \n    Difference in Adjusted Means                 1.62            -0.44    \n      95% CI                                 (-1.75, 4.98)   (-3.94, 3.06)\n      p-value                                   0.3460          0.8059    \n  WEEK 2 DAY 15                                                           \n    n                                68           73              62      \n    Adjusted Mean                   12.99        11.23           9.86     \n    Difference in Adjusted Means                 -1.76           -3.13    \n      95% CI                                 (-5.12, 1.60)   (-6.64, 0.37)\n      p-value                                   0.3048          0.0795    \n  WEEK 5 DAY 36                                                           \n    n                                68           73              62      \n    Adjusted Mean                   23.88        23.08           28.21    \n    Difference in Adjusted Means                 -0.81           4.33     \n      95% CI                                 (-4.17, 2.56)   (0.83, 7.83) \n      p-value                                   0.6383          0.0155    \nFatigue Interference                                                      \n  WEEK 1 DAY 8                                                            \n    n                                68           73              62      \n    Adjusted Mean                   5.97         5.19            5.21     \n    Difference in Adjusted Means                 -0.78           -0.76    \n      95% CI                                 (-4.17, 2.61)   (-4.30, 2.78)\n      p-value                                   0.6522          0.6729    \n  WEEK 2 DAY 15                                                           \n    n                                68           73              62      \n    Adjusted Mean                   11.39        9.42            9.55     \n    Difference in Adjusted Means                 -1.96           -1.84    \n      95% CI                                 (-5.35, 1.43)   (-5.37, 1.70)\n      p-value                                   0.2560          0.3084    \n  WEEK 5 DAY 36                                                           \n    n                                68           73              62      \n    Adjusted Mean                   22.79        25.37           23.43    \n    Difference in Adjusted Means                 2.58            0.64     \n      95% CI                                 (-0.81, 5.97)   (-2.89, 4.18)\n      p-value                                   0.1353          0.7212    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:37:02 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] sandwich_3.1-0    utf8_1.2.4        generics_0.1.3    tidyr_1.3.1      \n [5] stringi_1.8.3     lattice_0.22-5    digest_0.6.34     evaluate_0.23    \n [9] grid_4.3.3        estimability_1.5  mvtnorm_1.2-4     fastmap_1.1.1    \n[13] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[17] survival_3.5-8    multcomp_1.4-25   purrr_1.0.2       fansi_1.0.6      \n[21] scales_1.3.0      TH.data_1.1-2     codetools_0.2-19  Rdpack_2.6       \n[25] cli_3.6.2         rlang_1.1.3       rbibutils_2.2.16  munsell_0.5.0    \n[29] splines_4.3.3     yaml_2.3.8        tools_4.3.3       checkmate_2.3.1  \n[33] coda_0.19-4.1     colorspace_2.1-0  ggplot2_3.5.0     broom_1.0.5      \n[37] vctrs_0.6.5       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.4  \n[41] emmeans_1.10.0    htmlwidgets_1.6.4 MASS_7.3-60.0.1   pkgconfig_2.0.3  \n[45] pillar_1.9.0      gtable_0.3.4      glue_1.7.0        xfun_0.42        \n[49] tibble_3.2.1      tidyselect_1.2.0  knitr_1.45        xtable_1.8-4     \n[53] htmltools_0.5.7   rmarkdown_2.26    testthat_3.2.1    compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -780,7 +780,7 @@
     "href": "tables/efficacy/onct05.html",
     "title": "ONCT05",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Specifying  Class Variables\nTable Selecting Columns  and Changing the Alpha Level\nTable Setting Values  Indicating Response\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadrs &lt;- df_explicit_na(adrs)\n\nadsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ARM, SEX, RACE, STRATA1, STRATA2)\n\nadrs &lt;- adrs %&gt;%\n  filter(PARAMCD == \"INVET\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVALC)\n\nanl &lt;- inner_join(adsl, adrs, by = c(\"STUDYID\", \"USUBJID\"))\nanl_labels &lt;- var_labels(anl)\n\nanl &lt;- anl %&gt;%\n  filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %&gt;%\n  mutate(\n    # Reorder levels of factor to make the placebo group the reference arm.\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels()\n  ) %&gt;%\n  droplevels() %&gt;%\n  mutate(rsp = AVALC %in% c(\"CR\", \"PR\"))\n\nvar_labels(anl) &lt;- c(anl_labels, rsp = \"Is Response\")\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"STRATA2\")\n  ),\n  data = anl\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                       \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      95% CI   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       84          62.7%       134      100          74.6%          1.75      (1.04, 2.95)\nSex                                                                                                                              \n  F                         161     82        48          58.5%       79        61          77.2%          2.40      (1.21, 4.76)\n  M                         107     52        36          69.2%       55        39          70.9%          1.08      (0.47, 2.48)\nStratification Factor 2                                                                                                          \n  S1                        140     67        38          56.7%       73        56          76.7%          2.51      (1.22, 5.20)\n  S2                        128     67        46          68.7%       61        44          72.1%          1.18      (0.55, 2.53)\n\n\n\n\nHere, the levels of subgroup variables SEX and STRATA1 are reordered. STRATA1 is reordered by frequency.\n\n\nCodeanl_reorder &lt;- anl %&gt;%\n  mutate(\n    SEX = forcats::fct_relevel(SEX, \"M\", \"F\"),\n    STRATA1 = forcats::fct_infreq(STRATA1)\n  )\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA1\")),\n  data = anl_reorder\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                       \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      95% CI   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       84          62.7%       134      100          74.6%          1.75      (1.04, 2.95)\nSex                                                                                                                              \n  M                         107     52        36          69.2%       55        39          70.9%          1.08      (0.47, 2.48)\n  F                         161     82        48          58.5%       79        61          77.2%          2.40      (1.21, 4.76)\nStratification Factor 1                                                                                                          \n  C                         94      45        33          73.3%       49        37          75.5%          1.12      (0.44, 2.83)\n  B                         92      45        26          57.8%       47        32          68.1%          1.56      (0.66, 3.66)\n  A                         82      44        25          56.8%       38        31          81.6%          3.37      (1.22, 9.28)\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"STRATA2\")\n  ),\n  data = anl,\n  conf_level = 0.9,\n  method = \"chisq\"\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                                                    \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      90% CI      p-value (Chi-Squared Test)\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       84          62.7%       134      100          74.6%          1.75      (1.13, 2.72)             0.0351          \nSex                                                                                                                                                           \n  F                         161     82        48          58.5%       79        61          77.2%          2.40      (1.35, 4.27)             0.0113          \n  M                         107     52        36          69.2%       55        39          70.9%          1.08      (0.54, 2.17)             0.8497          \nStratification Factor 2                                                                                                                                       \n  S1                        140     67        38          56.7%       73        56          76.7%          2.51      (1.37, 4.63)             0.0119          \n  S2                        128     67        46          68.7%       61        44          72.1%          1.18      (0.62, 2.24)             0.6674          \n\n\n\n\nCreate a new variable new_rsp in anl data that uses new criteria for responder.\n\n\nCodeanl_new &lt;- anl %&gt;%\n  mutate(new_rsp = AVALC == \"CR\")\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"new_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"STRATA2\")\n  ),\n  data = anl_new\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                       \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      95% CI   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       47          35.1%       134       60          44.8%          1.50      (0.92, 2.45)\nSex                                                                                                                              \n  F                         161     82        25          30.5%       79        39          49.4%          2.22      (1.17, 4.24)\n  M                         107     52        22          42.3%       55        21          38.2%          0.84      (0.39, 1.83)\nStratification Factor 2                                                                                                          \n  S1                        140     67        21          31.3%       73        31          42.5%          1.62      (0.81, 3.24)\n  S2                        128     67        26          38.8%       61        29          47.5%          1.43      (0.71, 2.89)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# Use table, embedded in response forest plot module.\nlibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(forcats)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADRS &lt;- df_explicit_na(ADRS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_forest_rsp(\n      label = \"Forest Response\",\n      dataname = \"ADRS\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        \"ARMCD\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        \"BESRSPI\"\n      ),\n      subgroup_var = choices_selected(\n        variable_choices(ADSL, names(ADSL)),\n        c(\"SEX\")\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"STRATA1\", \"STRATA2\")),\n        \"STRATA2\"\n      ),\n      plot_height = c(600L, 200L, 2000L)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:37:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] forcats_1.0.0                    teal.modules.clinical_0.9.0.9009\n [3] teal.transform_0.5.0.9001        teal_0.15.2.9003                \n [5] teal.slice_0.5.0.9004            teal.data_0.5.0.9002            \n [7] teal.code_0.5.0.9003             shiny_1.8.0                     \n [9] scda_0.1.6.9017                  dplyr_1.1.4                     \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[73] cowplot_1.1.3            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                textshaping_0.3.7        fansi_1.0.6             \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] farver_2.1.1             memoise_2.0.1            htmltools_0.5.7         \n[91] lifecycle_1.0.4          shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Specifying  Class Variables\nTable Selecting Columns  and Changing the Alpha Level\nTable Setting Values  Indicating Response\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadrs &lt;- df_explicit_na(adrs)\n\nadsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ARM, SEX, RACE, STRATA1, STRATA2)\n\nadrs &lt;- adrs %&gt;%\n  filter(PARAMCD == \"INVET\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVALC)\n\nanl &lt;- inner_join(adsl, adrs, by = c(\"STUDYID\", \"USUBJID\"))\nanl_labels &lt;- var_labels(anl)\n\nanl &lt;- anl %&gt;%\n  filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %&gt;%\n  mutate(\n    # Reorder levels of factor to make the placebo group the reference arm.\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels()\n  ) %&gt;%\n  droplevels() %&gt;%\n  mutate(rsp = AVALC %in% c(\"CR\", \"PR\"))\n\nvar_labels(anl) &lt;- c(anl_labels, rsp = \"Is Response\")\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"STRATA2\")\n  ),\n  data = anl\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                       \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      95% CI   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       84          62.7%       134      100          74.6%          1.75      (1.04, 2.95)\nSex                                                                                                                              \n  F                         161     82        48          58.5%       79        61          77.2%          2.40      (1.21, 4.76)\n  M                         107     52        36          69.2%       55        39          70.9%          1.08      (0.47, 2.48)\nStratification Factor 2                                                                                                          \n  S1                        140     67        38          56.7%       73        56          76.7%          2.51      (1.22, 5.20)\n  S2                        128     67        46          68.7%       61        44          72.1%          1.18      (0.55, 2.53)\n\n\n\n\nHere, the levels of subgroup variables SEX and STRATA1 are reordered. STRATA1 is reordered by frequency.\n\n\nCodeanl_reorder &lt;- anl %&gt;%\n  mutate(\n    SEX = forcats::fct_relevel(SEX, \"M\", \"F\"),\n    STRATA1 = forcats::fct_infreq(STRATA1)\n  )\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA1\")),\n  data = anl_reorder\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                       \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      95% CI   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       84          62.7%       134      100          74.6%          1.75      (1.04, 2.95)\nSex                                                                                                                              \n  M                         107     52        36          69.2%       55        39          70.9%          1.08      (0.47, 2.48)\n  F                         161     82        48          58.5%       79        61          77.2%          2.40      (1.21, 4.76)\nStratification Factor 1                                                                                                          \n  C                         94      45        33          73.3%       49        37          75.5%          1.12      (0.44, 2.83)\n  B                         92      45        26          57.8%       47        32          68.1%          1.56      (0.66, 3.66)\n  A                         82      44        25          56.8%       38        31          81.6%          3.37      (1.22, 9.28)\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"STRATA2\")\n  ),\n  data = anl,\n  conf_level = 0.9,\n  method = \"chisq\"\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                                                    \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      90% CI      p-value (Chi-Squared Test)\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       84          62.7%       134      100          74.6%          1.75      (1.13, 2.72)             0.0351          \nSex                                                                                                                                                           \n  F                         161     82        48          58.5%       79        61          77.2%          2.40      (1.35, 4.27)             0.0113          \n  M                         107     52        36          69.2%       55        39          70.9%          1.08      (0.54, 2.17)             0.8497          \nStratification Factor 2                                                                                                                                       \n  S1                        140     67        38          56.7%       73        56          76.7%          2.51      (1.37, 4.63)             0.0119          \n  S2                        128     67        46          68.7%       61        44          72.1%          1.18      (0.62, 2.24)             0.6674          \n\n\n\n\nCreate a new variable new_rsp in anl data that uses new criteria for responder.\n\n\nCodeanl_new &lt;- anl %&gt;%\n  mutate(new_rsp = AVALC == \"CR\")\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"new_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"STRATA2\")\n  ),\n  data = anl_new\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nresult\n\n                                              B: Placebo                         A: Drug X                                       \nBaseline Risk Factors     Total n    n    Responders   Response (%)    n    Responders   Response (%)   Odds Ratio      95% CI   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAll Patients                268     134       47          35.1%       134       60          44.8%          1.50      (0.92, 2.45)\nSex                                                                                                                              \n  F                         161     82        25          30.5%       79        39          49.4%          2.22      (1.17, 4.24)\n  M                         107     52        22          42.3%       55        21          38.2%          0.84      (0.39, 1.83)\nStratification Factor 2                                                                                                          \n  S1                        140     67        21          31.3%       73        31          42.5%          1.62      (0.81, 3.24)\n  S2                        128     67        26          38.8%       61        29          47.5%          1.43      (0.71, 2.89)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# Use table, embedded in response forest plot module.\nlibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(forcats)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADRS &lt;- df_explicit_na(ADRS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_forest_rsp(\n      label = \"Forest Response\",\n      dataname = \"ADRS\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        \"ARMCD\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        \"BESRSPI\"\n      ),\n      subgroup_var = choices_selected(\n        variable_choices(ADSL, names(ADSL)),\n        c(\"SEX\")\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"STRATA1\", \"STRATA2\")),\n        \"STRATA2\"\n      ),\n      plot_height = c(600L, 200L, 2000L)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:36:30 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] forcats_1.0.0                    teal.modules.clinical_0.9.0.9010\n [3] teal.transform_0.5.0.9002        teal_0.15.2.9004                \n [5] teal.slice_0.5.0.9005            teal.data_0.5.0.9003            \n [7] teal.code_0.5.0.9003             shiny_1.8.0                     \n [9] scda_0.1.6.9017                  dplyr_1.1.4                     \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            mvtnorm_1.2-4           \n[73] cowplot_1.1.3            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                textshaping_0.3.7        fansi_1.0.6             \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] farver_2.1.1             memoise_2.0.1            htmltools_0.5.7         \n[91] lifecycle_1.0.4          shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -792,7 +792,7 @@
     "href": "tables/efficacy/lgrt02.html",
     "title": "LGRT02",
     "section": "",
-    "text": "Data Setup\nMulti-Variable Logistic Regression\nMulti-Variable Logistic Regression  with Interaction Term\nMulti-Variable Logistic Regression  Specifying Covariates\nMulti-Variable Logistic Regression Setting  an Event, Alpha Level, and Level for Interaction\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadrs &lt;- df_explicit_na(adrs)\n\nadsl &lt;- adsl %&gt;%\n  dplyr::filter(SEX %in% c(\"F\", \"M\"))\n\nadrs &lt;- adrs %&gt;%\n  dplyr::filter(PARAMCD == \"BESRSPI\") %&gt;%\n  dplyr::mutate(\n    Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),\n    SEX = factor(SEX, c(\"M\", \"F\")),\n    RACE = factor(\n      RACE,\n      levels = c(\n        \"AMERICAN INDIAN OR ALASKA NATIVE\", \"ASIAN\", \"BLACK OR AFRICAN AMERICAN\",\n        \"WHITE\", \"MULTIPLE\", \"NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\"\n      )\n    )\n  ) %&gt;%\n  var_relabel(Response = \"Response\", SEX = \"Sex\", RACE = \"Race\")\n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(response = \"Response\", arm = \"ARMCD\", covariates = c(\"SEX\", \"AGE\"))\n)\nconf_level &lt;- 0.95\ndf &lt;- broom::tidy(model, conf_level = conf_level)\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"Logistic regression\") %&gt;%\n  build_table(df = df)\nresult\n\nLogistic regression          Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio    Wald 95% CI    p-value\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nPlanned Arm Code                     2                                                                               0.0408 \n  Reference ARM A, n = 134                                                                                                  \n  ARM B, n = 134                     1                  -2.094             1.080           0.12      (0.01, 1.02)    0.0524 \n  ARM C, n = 132                     1                  -0.074             1.423           0.93      (0.06, 15.09)   0.9584 \nSex                                                                                                                         \n  Reference M, n = 169                                                                                                      \n  F, n = 231                         1                  0.331              0.695           1.39      (0.36, 5.44)    0.6339 \nAge                                                                                                                         \n  Age                                1                  0.070              0.054           1.07      (0.97, 1.19)    0.1945 \n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(\n    response = \"Response\",\n    arm = \"ARMCD\",\n    covariates = c(\"SEX\", \"AGE\"),\n    interaction = \"SEX\"\n  )\n)\nconf_level &lt;- 0.95\ndf &lt;- broom::tidy(model, conf_level = conf_level)\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"Logistic regression with interaction\") %&gt;%\n  build_table(df = df)\nresult\n\nLogistic regression with interaction    Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAge                                                                                                                                      \n  Age                                           1                  0.067              0.054           1.07       (0.96, 1.19)     0.2084 \nPlanned Arm Code                                2                                                                                 0.4882 \n  Reference ARM A, n = 134                                                                                                               \n  ARM B, n = 134                                1                 -17.850            2362.767                                     0.9940 \n    Sex                                                                                                                                  \n      F                                                                                               0.23       (0.02, 2.11)            \n      M                                                                                               0.00      (0.00, &gt;999.99)          \n  ARM C, n = 132                                1                 -16.442            2362.767                                     0.9944 \n    Sex                                                                                                                                  \n      F                                                                                             &gt;999.99     (0.00, &gt;999.99)          \n      M                                                                                               0.00      (0.00, &gt;999.99)          \nSex                                                                                                                                      \n  Reference M, n = 169                                                                                                                   \n  F, n = 231                                    1                 -16.044            2362.767                                     0.9946 \n    Planned Arm Code                                                                                                                     \n      ARM A                                                                                           0.00      (0.00, &gt;999.99)          \n      ARM B                                                                                           1.39       (0.29, 6.59)            \n      ARM C                                                                                         &gt;999.99     (0.00, &gt;999.99)          \nInteraction of Planned Arm Code * Sex           2                                                                                 0.9999 \n  Reference ARM A or M, n = 248                                                                                                          \n  ARM B * F, n = 82                             1                  16.373            2362.767                                     0.9945 \n  ARM C * F, n = 70                             1                  32.492            3156.732                                     0.9918 \n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(\n    response = \"Response\",\n    arm = \"ARMCD\",\n    covariates = c(\"SEX\", \"AGE\", \"RACE\")\n  )\n)\nconf_level &lt;- 0.95\ndf &lt;- broom::tidy(model, conf_level = conf_level)\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"y ~ ARM + SEX + AGE + RACE\") %&gt;%\n  build_table(df = df)\nresult\n\ny ~ ARM + SEX + AGE + RACE                             Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nPlanned Arm Code                                               2                                                                                 0.0346 \n  Reference ARM A, n = 134                                                                                                                              \n  ARM B, n = 134                                               1                  -2.162             1.084           0.12       (0.01, 0.96)     0.0461 \n  ARM C, n = 132                                               1                  -0.090             1.426           0.91       (0.06, 14.97)    0.9499 \nSex                                                                                                                                                     \n  Reference M, n = 169                                                                                                                                  \n  F, n = 231                                                   1                  0.364              0.701           1.44       (0.36, 5.69)     0.6032 \nAge                                                                                                                                                     \n  Age                                                          1                  0.071              0.053           1.07       (0.97, 1.19)     0.1866 \nRace                                                           5                                                                                 0.9685 \n  Reference AMERICAN INDIAN OR ALASKA NATIVE, n = 25                                                                                                    \n  ASIAN, n = 208                                               1                 -16.246            2017.122         0.00      (0.00, &gt;999.99)   0.9936 \n  BLACK OR AFRICAN AMERICAN, n = 91                            1                 -15.205            2017.122         0.00      (0.00, &gt;999.99)   0.9940 \n  WHITE, n = 74                                                1                 -15.955            2017.122         0.00      (0.00, &gt;999.99)   0.9937 \n  MULTIPLE, n = 1                                              1                  -0.363           10941.553         0.70      (0.00, &gt;999.99)   1.0000 \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER, n = 1             1                  1.036            10941.553         2.82      (0.00, &gt;999.99)   0.9999 \n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(\n    response = \"Response\",\n    arm = \"ARMCD\",\n    covariates = c(\"SEX\", \"AGE\"),\n    interaction = \"AGE\"\n  ),\n  response_definition = \"1 - response\"\n)\nconf_level &lt;- 0.9\ndf &lt;- broom::tidy(model, conf_level = conf_level, at = c(30, 50))\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"Estimations at age 30 and 50\") %&gt;%\n  build_table(df = df)\nresult\n\nEstimations at age 30 and 50            Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 90% CI      p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nSex                                                                                                                                       \n  Reference M, n = 169                                                                                                                    \n  F, n = 231                                    1                  -0.381             0.710           0.68        (0.21, 2.20)     0.5915 \nPlanned Arm Code                                2                                                                                  0.2768 \n  Reference ARM A, n = 134                                                                                                                \n  ARM B, n = 134                                1                 -20.020             13.714                                       0.1443 \n    Age                                                                                                                                   \n      30                                                                                             234.91     (0.30, &gt;999.99)           \n      50                                                                                            &gt;999.99     (0.04, &gt;999.99)           \n  ARM C, n = 132                                1                 -15.622             14.810                                       0.2915 \n    Age                                                                                                                                   \n      30                                                                                             31.95      (0.03, &gt;999.99)           \n      50                                                                                            &gt;999.99     (&lt;0.01, &gt;999.99)          \nAge                                                                                                                                       \n  Age                                           1                  -0.877             0.581                                        0.1309 \n    Planned Arm Code                                                                                                                      \n      ARM A                                                                                           0.42        (0.16, 1.08)            \n      ARM B                                                                                           0.97        (0.89, 1.06)            \n      ARM C                                                                                           0.79        (0.55, 1.11)            \nInteraction of Planned Arm Code * Age           2                                                                                  0.2213 \n  Reference ARM A, n = 134                                                                                                                \n  ARM B, n = 134                                1                  0.849              0.583                                        0.1449 \n  ARM C, n = 132                                1                  0.636              0.618                                        0.3034 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\") %&gt;%\n    filter(PARAMCD %in% c(\"BESRSPI\", \"INVET\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_logistic(\n      label = \"Logistic Regression\",\n      dataname = \"ADRS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        choices = value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BESRSPI\"\n      ),\n      cov_var = choices_selected(\n        choices = c(\"SEX\", \"AGE\", \"BMRKR1\", \"BMRKR2\"),\n        selected = \"SEX\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:36:57 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           shinyjs_2.1.0            promises_1.2.1          \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          abind_1.4-5             \n[25] multcomp_1.4-25          withr_3.0.0              purrr_1.0.2             \n[28] shinyWidgets_0.8.2       geepack_1.3.10           grid_4.3.3              \n[31] fansi_1.0.6              teal.logger_0.1.3.9011   xtable_1.8-4            \n[34] colorspace_2.1-0         ggplot2_3.5.0            emmeans_1.10.0          \n[37] scales_1.3.0             MASS_7.3-60.0.1          cli_3.6.2               \n[40] mvtnorm_1.2-4            rmarkdown_2.26           generics_0.1.3          \n[43] chromote_0.2.0           cachem_1.0.8             splines_4.3.3           \n[46] formatR_1.14             vctrs_0.6.5              webshot_0.5.5           \n[49] Matrix_1.6-5             sandwich_3.1-0           jsonlite_1.8.8          \n[52] carData_3.0-5            callr_3.7.5              car_3.1-2               \n[55] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[58] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[61] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[64] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[67] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[70] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[73] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[76] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[79] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[82] bslib_0.6.1              httpuv_1.6.14            Rcpp_1.0.12             \n[85] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[88] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[91] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nMulti-Variable Logistic Regression\nMulti-Variable Logistic Regression  with Interaction Term\nMulti-Variable Logistic Regression  Specifying Covariates\nMulti-Variable Logistic Regression Setting  an Event, Alpha Level, and Level for Interaction\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadrs &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadrs &lt;- df_explicit_na(adrs)\n\nadsl &lt;- adsl %&gt;%\n  dplyr::filter(SEX %in% c(\"F\", \"M\"))\n\nadrs &lt;- adrs %&gt;%\n  dplyr::filter(PARAMCD == \"BESRSPI\") %&gt;%\n  dplyr::mutate(\n    Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),\n    SEX = factor(SEX, c(\"M\", \"F\")),\n    RACE = factor(\n      RACE,\n      levels = c(\n        \"AMERICAN INDIAN OR ALASKA NATIVE\", \"ASIAN\", \"BLACK OR AFRICAN AMERICAN\",\n        \"WHITE\", \"MULTIPLE\", \"NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\"\n      )\n    )\n  ) %&gt;%\n  var_relabel(Response = \"Response\", SEX = \"Sex\", RACE = \"Race\")\n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(response = \"Response\", arm = \"ARMCD\", covariates = c(\"SEX\", \"AGE\"))\n)\nconf_level &lt;- 0.95\ndf &lt;- broom::tidy(model, conf_level = conf_level)\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"Logistic regression\") %&gt;%\n  build_table(df = df)\nresult\n\nLogistic regression          Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio    Wald 95% CI    p-value\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nPlanned Arm Code                     2                                                                               0.0408 \n  Reference ARM A, n = 134                                                                                                  \n  ARM B, n = 134                     1                  -2.094             1.080           0.12      (0.01, 1.02)    0.0524 \n  ARM C, n = 132                     1                  -0.074             1.423           0.93      (0.06, 15.09)   0.9584 \nSex                                                                                                                         \n  Reference M, n = 169                                                                                                      \n  F, n = 231                         1                  0.331              0.695           1.39      (0.36, 5.44)    0.6339 \nAge                                                                                                                         \n  Age                                1                  0.070              0.054           1.07      (0.97, 1.19)    0.1945 \n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(\n    response = \"Response\",\n    arm = \"ARMCD\",\n    covariates = c(\"SEX\", \"AGE\"),\n    interaction = \"SEX\"\n  )\n)\nconf_level &lt;- 0.95\ndf &lt;- broom::tidy(model, conf_level = conf_level)\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"Logistic regression with interaction\") %&gt;%\n  build_table(df = df)\nresult\n\nLogistic regression with interaction    Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAge                                                                                                                                      \n  Age                                           1                  0.067              0.054           1.07       (0.96, 1.19)     0.2084 \nPlanned Arm Code                                2                                                                                 0.4882 \n  Reference ARM A, n = 134                                                                                                               \n  ARM B, n = 134                                1                 -17.850            2362.767                                     0.9940 \n    Sex                                                                                                                                  \n      F                                                                                               0.23       (0.02, 2.11)            \n      M                                                                                               0.00      (0.00, &gt;999.99)          \n  ARM C, n = 132                                1                 -16.442            2362.767                                     0.9944 \n    Sex                                                                                                                                  \n      F                                                                                             &gt;999.99     (0.00, &gt;999.99)          \n      M                                                                                               0.00      (0.00, &gt;999.99)          \nSex                                                                                                                                      \n  Reference M, n = 169                                                                                                                   \n  F, n = 231                                    1                 -16.044            2362.767                                     0.9946 \n    Planned Arm Code                                                                                                                     \n      ARM A                                                                                           0.00      (0.00, &gt;999.99)          \n      ARM B                                                                                           1.39       (0.29, 6.59)            \n      ARM C                                                                                         &gt;999.99     (0.00, &gt;999.99)          \nInteraction of Planned Arm Code * Sex           2                                                                                 0.9999 \n  Reference ARM A or M, n = 248                                                                                                          \n  ARM B * F, n = 82                             1                  16.373            2362.767                                     0.9945 \n  ARM C * F, n = 70                             1                  32.492            3156.732                                     0.9918 \n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(\n    response = \"Response\",\n    arm = \"ARMCD\",\n    covariates = c(\"SEX\", \"AGE\", \"RACE\")\n  )\n)\nconf_level &lt;- 0.95\ndf &lt;- broom::tidy(model, conf_level = conf_level)\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"y ~ ARM + SEX + AGE + RACE\") %&gt;%\n  build_table(df = df)\nresult\n\ny ~ ARM + SEX + AGE + RACE                             Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nPlanned Arm Code                                               2                                                                                 0.0346 \n  Reference ARM A, n = 134                                                                                                                              \n  ARM B, n = 134                                               1                  -2.162             1.084           0.12       (0.01, 0.96)     0.0461 \n  ARM C, n = 132                                               1                  -0.090             1.426           0.91       (0.06, 14.97)    0.9499 \nSex                                                                                                                                                     \n  Reference M, n = 169                                                                                                                                  \n  F, n = 231                                                   1                  0.364              0.701           1.44       (0.36, 5.69)     0.6032 \nAge                                                                                                                                                     \n  Age                                                          1                  0.071              0.053           1.07       (0.97, 1.19)     0.1866 \nRace                                                           5                                                                                 0.9685 \n  Reference AMERICAN INDIAN OR ALASKA NATIVE, n = 25                                                                                                    \n  ASIAN, n = 208                                               1                 -16.246            2017.122         0.00      (0.00, &gt;999.99)   0.9936 \n  BLACK OR AFRICAN AMERICAN, n = 91                            1                 -15.205            2017.122         0.00      (0.00, &gt;999.99)   0.9940 \n  WHITE, n = 74                                                1                 -15.955            2017.122         0.00      (0.00, &gt;999.99)   0.9937 \n  MULTIPLE, n = 1                                              1                  -0.363           10941.553         0.70      (0.00, &gt;999.99)   1.0000 \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER, n = 1             1                  1.036            10941.553         2.82      (0.00, &gt;999.99)   0.9999 \n\n\n\n\n\nCodemodel &lt;- fit_logistic(\n  adrs,\n  variables = list(\n    response = \"Response\",\n    arm = \"ARMCD\",\n    covariates = c(\"SEX\", \"AGE\"),\n    interaction = \"AGE\"\n  ),\n  response_definition = \"1 - response\"\n)\nconf_level &lt;- 0.9\ndf &lt;- broom::tidy(model, conf_level = conf_level, at = c(30, 50))\n\n# empty string flag\ndf &lt;- df_explicit_na(df, na_level = \"_NA_\")\n\nresult &lt;- basic_table() %&gt;%\n  summarize_logistic(\n    conf_level = conf_level,\n    drop_and_remove_str = \"_NA_\"\n  ) %&gt;%\n  append_topleft(\"Estimations at age 30 and 50\") %&gt;%\n  build_table(df = df)\nresult\n\nEstimations at age 30 and 50            Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 90% CI      p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nSex                                                                                                                                       \n  Reference M, n = 169                                                                                                                    \n  F, n = 231                                    1                  -0.381             0.710           0.68        (0.21, 2.20)     0.5915 \nPlanned Arm Code                                2                                                                                  0.2768 \n  Reference ARM A, n = 134                                                                                                                \n  ARM B, n = 134                                1                 -20.020             13.714                                       0.1443 \n    Age                                                                                                                                   \n      30                                                                                             234.91     (0.30, &gt;999.99)           \n      50                                                                                            &gt;999.99     (0.04, &gt;999.99)           \n  ARM C, n = 132                                1                 -15.622             14.810                                       0.2915 \n    Age                                                                                                                                   \n      30                                                                                             31.95      (0.03, &gt;999.99)           \n      50                                                                                            &gt;999.99     (&lt;0.01, &gt;999.99)          \nAge                                                                                                                                       \n  Age                                           1                  -0.877             0.581                                        0.1309 \n    Planned Arm Code                                                                                                                      \n      ARM A                                                                                           0.42        (0.16, 1.08)            \n      ARM B                                                                                           0.97        (0.89, 1.06)            \n      ARM C                                                                                           0.79        (0.55, 1.11)            \nInteraction of Planned Arm Code * Age           2                                                                                  0.2213 \n  Reference ARM A, n = 134                                                                                                                \n  ARM B, n = 134                                1                  0.849              0.583                                        0.1449 \n  ARM C, n = 132                                1                  0.636              0.618                                        0.3034 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\") %&gt;%\n    filter(PARAMCD %in% c(\"BESRSPI\", \"INVET\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_logistic(\n      label = \"Logistic Regression\",\n      dataname = \"ADRS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        choices = value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BESRSPI\"\n      ),\n      cov_var = choices_selected(\n        choices = c(\"SEX\", \"AGE\", \"BMRKR1\", \"BMRKR2\"),\n        selected = \"SEX\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:35:52 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           shinyjs_2.1.0            promises_1.2.1          \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          abind_1.4-5             \n[25] multcomp_1.4-25          withr_3.0.0              purrr_1.0.2             \n[28] shinyWidgets_0.8.2       geepack_1.3.10           grid_4.3.3              \n[31] fansi_1.0.6              teal.logger_0.1.3.9011   xtable_1.8-4            \n[34] colorspace_2.1-0         ggplot2_3.5.0            emmeans_1.10.0          \n[37] scales_1.3.0             MASS_7.3-60.0.1          cli_3.6.2               \n[40] mvtnorm_1.2-4            rmarkdown_2.26           generics_0.1.3          \n[43] chromote_0.2.0           cachem_1.0.8             splines_4.3.3           \n[46] formatR_1.14             vctrs_0.6.5              webshot_0.5.5           \n[49] Matrix_1.6-5             sandwich_3.1-0           jsonlite_1.8.8          \n[52] carData_3.0-5            callr_3.7.5              car_3.1-2               \n[55] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[58] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[61] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[64] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[67] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[70] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[73] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[76] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[79] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[82] bslib_0.6.1              httpuv_1.6.14            Rcpp_1.0.12             \n[85] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[88] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[91] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -804,7 +804,7 @@
     "href": "tables/disclosures/eudrat02.html",
     "title": "EUDRAT02",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nFor illustrative purposes, the adae data is filtered by arm A: Drug X here.\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadae &lt;- df_explicit_na(adae)\n\nadae_serious &lt;- adae %&gt;% filter(AESER == \"Y\", SAFFL == \"Y\")\nadae_serious_arm &lt;- adae_serious %&gt;% filter(ARM == \"A: Drug X\")\n\nfilters_list &lt;- list(\n  related = with_label(c(AEREL = \"Y\"), \"Events (Related)\"),\n  fatal = with_label(c(AESDTH = \"Y\"), \"Events (Fatal)\"),\n  fatal_related = with_label(c(AEREL = \"Y\", AESDTH = \"Y\"), \"Events (Fatal & Related)\")\n)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    empty_stats = c(\"all\", \"related\", \"fatal\", \"fatal_related\"),\n    custom_label = \"Total number of patients with at least one serious adverse event\"\n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_serious_arm$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_serious_arm$AEDECOD)\n  ) %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_serious_arm)\nresult\n\nBody System or Organ Class                                                                                                                                     \n  Dictionary-Derived Term                                          Patients (All)   Events (All)   Events (Related)   Events (Fatal)   Events (Fatal & Related)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one serious adverse event        104                                                                                    \ncl A.1                                                                                                                                                         \n  dcd A.1.1.1.2                                                          48              68               0                 0                     0            \ncl B.1                                                                                                                                                         \n  dcd B.1.1.1.1                                                          47              56               56                56                    56           \ncl B.2                                                                                                                                                         \n  dcd B.2.2.3.1                                                          48              64               0                 0                     0            \ncl D.1                                                                                                                                                         \n  dcd D.1.1.1.1                                                          50              61               61                61                    61           \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:36:23 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nFor illustrative purposes, the adae data is filtered by arm A: Drug X here.\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadae &lt;- df_explicit_na(adae)\n\nadae_serious &lt;- adae %&gt;% filter(AESER == \"Y\", SAFFL == \"Y\")\nadae_serious_arm &lt;- adae_serious %&gt;% filter(ARM == \"A: Drug X\")\n\nfilters_list &lt;- list(\n  related = with_label(c(AEREL = \"Y\"), \"Events (Related)\"),\n  fatal = with_label(c(AESDTH = \"Y\"), \"Events (Fatal)\"),\n  fatal_related = with_label(c(AEREL = \"Y\", AESDTH = \"Y\"), \"Events (Fatal & Related)\")\n)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    empty_stats = c(\"all\", \"related\", \"fatal\", \"fatal_related\"),\n    custom_label = \"Total number of patients with at least one serious adverse event\"\n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_serious_arm$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_serious_arm$AEDECOD)\n  ) %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_serious_arm)\nresult\n\nBody System or Organ Class                                                                                                                                     \n  Dictionary-Derived Term                                          Patients (All)   Events (All)   Events (Related)   Events (Fatal)   Events (Fatal & Related)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one serious adverse event        104                                                                                    \ncl A.1                                                                                                                                                         \n  dcd A.1.1.1.2                                                          48              68               0                 0                     0            \ncl B.1                                                                                                                                                         \n  dcd B.1.1.1.1                                                          47              56               56                56                    56           \ncl B.2                                                                                                                                                         \n  dcd B.2.2.3.1                                                          48              64               0                 0                     0            \ncl D.1                                                                                                                                                         \n  dcd D.1.1.1.1                                                          50              61               61                61                    61           \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:35:20 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Disclosures",
@@ -816,7 +816,7 @@
     "href": "tables/disclosures/eudrat01.html",
     "title": "EUDRAT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nDefine a trimming function get_adae_trimmed to filter for AEs of greater than 5% incidence rate.\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_nonser &lt;- adae %&gt;% filter(AESER != \"Y\", SAFFL == \"Y\")\n\nget_adae_trimmed &lt;- function(adsl, adae, cutoff_rate) {\n  n_per_arm &lt;- adsl %&gt;%\n    dplyr::count(ARM)\n\n  anl_terms &lt;- adae %&gt;%\n    dplyr::group_by(ARM, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::count(\n      unique_terms = n_distinct(USUBJID)\n    ) %&gt;%\n    dplyr::select(-n) %&gt;%\n    dplyr::ungroup()\n\n  anl_terms &lt;- dplyr::left_join(\n    anl_terms,\n    n_per_arm,\n    by = \"ARM\"\n  ) %&gt;%\n    dplyr::mutate(\n      ae_rate = unique_terms / n\n    ) %&gt;%\n    dplyr::filter(ae_rate &gt;= cutoff_rate) %&gt;%\n    dplyr::select(AEDECOD) %&gt;%\n    unique()\n\n  anl &lt;- dplyr::left_join(\n    anl_terms,\n    adae,\n    by = \"AEDECOD\"\n  )\n  anl\n}\n\nadae_trim &lt;- get_adae_trimmed(adsl, adae_nonser, cutoff_rate = 0.05)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  summarize_patients_events_in_cols(\n    custom_label = \"Total number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5% and number of events\" \n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_trim$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adae_trim$AEDECOD)) %&gt;%\n  summarize_patients_events_in_cols(\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_trim)\nresult\n\nBody System or Organ Class                                                                                                                     A: Drug X                      B: Placebo                    C: Combination        \n  Dictionary-Derived Term                                                                                                            Patients (All)   Events (All)   Patients (All)   Events (All)   Patients (All)   Events (All)\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5% and number of events        106             360             112             367             112             421     \ncl A.1                                                                                                                                                                                                                            \n  dcd A.1.1.1.1                                                                                                                            50              64              45              62              63              88     \ncl B.2                                                                                                                                                                                                                            \n  dcd B.2.1.2.1                                                                                                                            49              65              44              62              52              66     \ncl C.1                                                                                                                                                                                                                            \n  dcd C.1.1.1.3                                                                                                                            43              55              46              63              43              64     \ncl C.2                                                                                                                                                                                                                            \n  dcd C.2.1.2.1                                                                                                                            35              48              48              53              55              65     \ncl D.1                                                                                                                                                                                                                            \n  dcd D.1.1.4.2                                                                                                                            48              66              42              55              50              64     \ncl D.2                                                                                                                                                                                                                            \n  dcd D.2.1.5.3                                                                                                                            47              62              58              72              57              74     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:36:10 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nDefine a trimming function get_adae_trimmed to filter for AEs of greater than 5% incidence rate.\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_nonser &lt;- adae %&gt;% filter(AESER != \"Y\", SAFFL == \"Y\")\n\nget_adae_trimmed &lt;- function(adsl, adae, cutoff_rate) {\n  n_per_arm &lt;- adsl %&gt;%\n    dplyr::count(ARM)\n\n  anl_terms &lt;- adae %&gt;%\n    dplyr::group_by(ARM, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::count(\n      unique_terms = n_distinct(USUBJID)\n    ) %&gt;%\n    dplyr::select(-n) %&gt;%\n    dplyr::ungroup()\n\n  anl_terms &lt;- dplyr::left_join(\n    anl_terms,\n    n_per_arm,\n    by = \"ARM\"\n  ) %&gt;%\n    dplyr::mutate(\n      ae_rate = unique_terms / n\n    ) %&gt;%\n    dplyr::filter(ae_rate &gt;= cutoff_rate) %&gt;%\n    dplyr::select(AEDECOD) %&gt;%\n    unique()\n\n  anl &lt;- dplyr::left_join(\n    anl_terms,\n    adae,\n    by = \"AEDECOD\"\n  )\n  anl\n}\n\nadae_trim &lt;- get_adae_trimmed(adsl, adae_nonser, cutoff_rate = 0.05)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  summarize_patients_events_in_cols(\n    custom_label = \"Total number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5% and number of events\" \n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_trim$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adae_trim$AEDECOD)) %&gt;%\n  summarize_patients_events_in_cols(\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_trim)\nresult\n\nBody System or Organ Class                                                                                                                     A: Drug X                      B: Placebo                    C: Combination        \n  Dictionary-Derived Term                                                                                                            Patients (All)   Events (All)   Patients (All)   Events (All)   Patients (All)   Events (All)\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5% and number of events        106             360             112             367             112             421     \ncl A.1                                                                                                                                                                                                                            \n  dcd A.1.1.1.1                                                                                                                            50              64              45              62              63              88     \ncl B.2                                                                                                                                                                                                                            \n  dcd B.2.1.2.1                                                                                                                            49              65              44              62              52              66     \ncl C.1                                                                                                                                                                                                                            \n  dcd C.1.1.1.3                                                                                                                            43              55              46              63              43              64     \ncl C.2                                                                                                                                                                                                                            \n  dcd C.2.1.2.1                                                                                                                            35              48              48              53              55              65     \ncl D.1                                                                                                                                                                                                                            \n  dcd D.1.1.4.2                                                                                                                            48              66              42              55              50              64     \ncl D.2                                                                                                                                                                                                                            \n  dcd D.2.1.5.3                                                                                                                            47              62              58              72              57              74     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:35:07 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Disclosures",
@@ -828,7 +828,7 @@
     "href": "tables/demography/dmt01.html",
     "title": "DMT01",
     "section": "",
-    "text": "Data Setup\nTable with an Additional  Study-Specific Continuous Variable\nTable with an Additional  Study-Specific Categorical Variable\nTable with Subgrouping  for Some Analyses\nTable with Additional Vital  Signs Baseline Values\nTable with Additional  Values from ADSUB\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(tidyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\nadsub &lt;- synthetic_cdisc_dataset(\"latest\", \"adsub\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadvs &lt;- df_explicit_na(advs)\nadsub &lt;- df_explicit_na(adsub)\n\n# Change description in variable SEX.\nadsl &lt;- adsl %&gt;%\n  mutate(\n    SEX = factor(case_when(\n      SEX == \"M\" ~ \"Male\",\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"U\" ~ \"Unknown\",\n      SEX == \"UNDIFFERENTIATED\" ~ \"Undifferentiated\"\n    )),\n    AGEGR1 = factor(\n      case_when(\n        between(AGE, 18, 40) ~ \"18-40\",\n        between(AGE, 41, 64) ~ \"41-64\",\n        AGE &gt; 64 ~ \"&gt;=65\"\n      ),\n      levels = c(\"18-40\", \"41-64\", \"&gt;=65\")\n    ),\n    BMRKR1_CAT = factor(\n      case_when(\n        BMRKR1 &lt; 3.5 ~ \"LOW\",\n        BMRKR1 &gt;= 3.5 & BMRKR1 &lt; 10 ~ \"MEDIUM\",\n        BMRKR1 &gt;= 10 ~ \"HIGH\"\n      ),\n      levels = c(\"LOW\", \"MEDIUM\", \"HIGH\")\n    )\n  ) %&gt;%\n  var_relabel(\n    BMRKR1_CAT = \"Biomarker 1 Categories\"\n  )\n# The developer needs to do pre-processing to add necessary variables based on ADVS to analysis dataset.\n# Obtain SBP, DBP and weight.\nget_param_advs &lt;- function(pname, plabel) {\n  ds &lt;- advs %&gt;%\n    filter(PARAM == plabel & AVISIT == \"BASELINE\") %&gt;%\n    select(USUBJID, AVAL)\n\n  colnames(ds) &lt;- c(\"USUBJID\", pname)\n\n  ds\n}\n# The developer needs to do pre-processing to add necessary variables based on ADSUB to analysis dataset.\n# Obtain baseline BMI (BBMISI).\nget_param_adsub &lt;- function(pname, plabel) {\n  ds &lt;- adsub %&gt;%\n    filter(PARAM == plabel) %&gt;%\n    select(USUBJID, AVAL)\n\n  colnames(ds) &lt;- c(\"USUBJID\", pname)\n\n  ds\n}\nadsl &lt;- adsl %&gt;%\n  left_join(get_param_advs(\"SBP\", \"Systolic Blood Pressure\"), by = \"USUBJID\") %&gt;%\n  left_join(get_param_advs(\"DBP\", \"Diastolic Blood Pressure\"), by = \"USUBJID\") %&gt;%\n  left_join(get_param_advs(\"WGT\", \"Weight\"), by = \"USUBJID\") %&gt;%\n  left_join(get_param_adsub(\"BBMISI\", \"Baseline BMI\"), by = \"USUBJID\")\n\n\n\n\n\nCodevars &lt;- c(\"AGE\", \"AGEGR1\", \"SEX\", \"ETHNIC\", \"RACE\", \"BMRKR1\")\nvar_labels &lt;- c(\n  \"Age (yr)\",\n  \"Age Group\",\n  \"Sex\",\n  \"Ethnicity\",\n  \"Race\",\n  \"Continous Level Biomarker 1\"\n)\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_vars(\n    vars = vars,\n    var_labels = var_labels\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination   All Patients\n                                                (N=134)       (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                               \n  n                                               134           134            132             400     \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4) \n  Median                                         33.0          35.0            35.0            34.0    \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0 \nAge Group                                                                                              \n  n                                               134           134            132             400     \n  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)     322 (80.5%) \n  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)      77 (19.2%) \n  &gt;=65                                             0             0           1 (0.8%)        1 (0.2%)  \nSex                                                                                                    \n  n                                               134           134            132             400     \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)      231 (57.8%) \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)      169 (42.2%) \nEthnicity                                                                                              \n  n                                               134           134            132             400     \n  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)       48 (12%)  \n  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)      308 (77%)  \n  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)       27 (6.8%)  \n  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)       17 (4.2%)  \nRace                                                                                                   \n  n                                               134           134            132             400     \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)      208 (52%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)      91 (22.8%) \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)      74 (18.5%) \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)       25 (6.2%)  \n  MULTIPLE                                         0         1 (0.7%)           0            1 (0.2%)  \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0            1 (0.2%)  \n  OTHER                                            0             0              0               0      \n  UNKNOWN                                          0             0              0               0      \nContinous Level Biomarker 1                                                                            \n  n                                               134           134            132             400     \n  Mean (SD)                                    6.0 (3.6)     5.7 (3.3)      5.6 (3.5)       5.8 (3.4)  \n  Median                                          5.4           4.8            4.6             4.8     \n  Min - Max                                   0.4 - 17.7    0.6 - 14.2      0.2 - 21.4      0.2 - 21.4 \n\n\n\n\n\nCodevars &lt;- c(\"AGE\", \"AGEGR1\", \"SEX\", \"ETHNIC\", \"RACE\", \"BMRKR1_CAT\")\nvar_labels &lt;- c(\n  \"Age (yr)\",\n  \"Age Group\",\n  \"Sex\",\n  \"Ethnicity\",\n  \"Race\",\n  \"Biomarker 1 Categories\"\n)\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = vars,\n    var_labels = var_labels\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination\n                                                (N=134)       (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                \n  n                                               134           134            132      \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  \n  Median                                         33.0          35.0            35.0     \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  \nAge Group                                                                               \n  n                                               134           134            132      \n  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)  \n  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)  \n  &gt;=65                                             0             0           1 (0.8%)   \nSex                                                                                     \n  n                                               134           134            132      \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)   \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)   \nEthnicity                                                                               \n  n                                               134           134            132      \n  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)  \n  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)  \n  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)   \n  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)   \nRace                                                                                    \n  n                                               134           134            132      \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)  \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)   \n  MULTIPLE                                         0         1 (0.7%)           0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0       \n  OTHER                                            0             0              0       \n  UNKNOWN                                          0             0              0       \nBiomarker 1 Categories                                                                  \n  n                                               134           134            132      \n  LOW                                         33 (24.6%)    41 (30.6%)      38 (28.8%)  \n  MEDIUM                                      84 (62.7%)    76 (56.7%)      80 (60.6%)  \n  HIGH                                        17 (12.7%)    17 (12.7%)      14 (10.6%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = c(\"AGE\", \"SEX\", \"RACE\"),\n    var_labels = c(\"Age\", \"Sex\", \"Race\")\n  ) %&gt;%\n  split_rows_by(\"STRATA1\",\n    split_fun = split_fun\n  ) %&gt;%\n  analyze_vars(\"BMRKR1\") %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination\n                                                (N=134)       (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————\nAge                                                                                     \n  n                                               134           134            132      \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  \n  Median                                         33.0          35.0            35.0     \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  \nSex                                                                                     \n  n                                               134           134            132      \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)   \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)   \nRace                                                                                    \n  n                                               134           134            132      \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)  \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)   \n  MULTIPLE                                         0         1 (0.7%)           0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0       \n  OTHER                                            0             0              0       \n  UNKNOWN                                          0             0              0       \nA                                                                                       \n  n                                               38            44              40      \n  Mean (SD)                                    5.8 (3.8)     5.4 (3.2)      5.1 (3.2)   \n  Median                                          5.1           4.5            3.8      \n  Min - Max                                   0.4 - 17.7    1.4 - 14.2      1.5 - 14.0  \nB                                                                                       \n  n                                               47            45              43      \n  Mean (SD)                                    6.1 (3.6)     5.8 (3.6)      5.7 (3.4)   \n  Median                                          5.2           4.8            5.1      \n  Min - Max                                   1.6 - 17.2    0.6 - 13.3      0.2 - 16.5  \nC                                                                                       \n  n                                               49            45              49      \n  Mean (SD)                                    6.0 (3.4)     5.9 (3.2)      6.0 (3.8)   \n  Median                                          5.8           5.6            4.5      \n  Min - Max                                   0.5 - 15.1    1.5 - 13.9      1.2 - 21.4  \n\n\n\n\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = c(\"AGE\", \"SEX\", \"RACE\", \"DBP\", \"SBP\"),\n    var_labels = c(\n      \"Age (yr)\",\n      \"Sex\",\n      \"Race\",\n      \"Diastolic Blood Pressure\",\n      \"Systolic Blood Pressure\"\n    )\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X      B: Placebo    C: Combination\n                                                (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                  \n  n                                               134            134             132      \n  Mean (SD)                                    33.8 (6.6)     35.4 (7.9)      35.4 (7.7)  \n  Median                                          33.0           35.0            35.0     \n  Min - Max                                   21.0 - 50.0    21.0 - 62.0     20.0 - 69.0  \nSex                                                                                       \n  n                                               134            134             132      \n  Female                                        79 (59%)      82 (61.2%)       70 (53%)   \n  Male                                          55 (41%)      52 (38.8%)       62 (47%)   \nRace                                                                                      \n  n                                               134            134             132      \n  ASIAN                                        68 (50.7%)      67 (50%)       73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                    31 (23.1%)     28 (20.9%)      32 (24.2%)  \n  WHITE                                        27 (20.1%)     26 (19.4%)      21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE               8 (6%)       11 (8.2%)        6 (4.5%)   \n  MULTIPLE                                         0           1 (0.7%)           0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0           1 (0.7%)           0       \n  OTHER                                            0              0               0       \n  UNKNOWN                                          0              0               0       \nDiastolic Blood Pressure                                                                  \n  n                                               134            134             132      \n  Mean (SD)                                   96.5 (19.9)    101.1 (19.9)    102.8 (19.5) \n  Median                                          96.0          100.4           102.0     \n  Min - Max                                   44.3 - 136.6   29.2 - 143.8    49.4 - 153.5 \nSystolic Blood Pressure                                                                   \n  n                                               134            134             132      \n  Mean (SD)                                   151.7 (31.5)   149.5 (26.5)    144.7 (30.1) \n  Median                                         150.1          153.0           146.5     \n  Min - Max                                   69.1 - 231.2   87.2 - 220.9    71.8 - 220.2 \n\n\n\n\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = c(\"AGE\", \"SEX\", \"RACE\", \"BBMISI\"),\n    var_labels = c(\n      \"Age (yr)\",\n      \"Sex\",\n      \"Race\",\n      \"Baseline BMI\"\n    )\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X     B: Placebo     C: Combination\n                                                (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                  \n  n                                               134            134             132      \n  Mean (SD)                                   33.8 (6.6)     35.4 (7.9)       35.4 (7.7)  \n  Median                                         33.0           35.0             35.0     \n  Min - Max                                   21.0 - 50.0    21.0 - 62.0     20.0 - 69.0  \nSex                                                                                       \n  n                                               134            134             132      \n  Female                                       79 (59%)      82 (61.2%)        70 (53%)   \n  Male                                         55 (41%)      52 (38.8%)        62 (47%)   \nRace                                                                                      \n  n                                               134            134             132      \n  ASIAN                                       68 (50.7%)      67 (50%)        73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)     28 (20.9%)       32 (24.2%)  \n  WHITE                                       27 (20.1%)     26 (19.4%)       21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)        11 (8.2%)        6 (4.5%)   \n  MULTIPLE                                         0          1 (0.7%)            0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0          1 (0.7%)            0       \n  OTHER                                            0              0               0       \n  UNKNOWN                                          0              0               0       \nBaseline BMI                                                                              \n  n                                               134            134             132      \n  Mean (SD)                                   30.0 (18.3)    32.4 (23.2)     30.1 (18.4)  \n  Median                                         27.1           31.1             30.0     \n  Min - Max                                   -6.9 - 75.9   -26.6 - 117.9    -44.2 - 87.5 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n  # Include `EOSDY` and `DCSREAS` variables below because they contain missing data.\n  stopifnot(\n    any(is.na(ADSL$EOSDY)),\n    any(is.na(ADSL$DCSREAS))\n  )\n})\ndatanames &lt;- \"ADSL\"\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary(\n      label = \"Demographic Table\",\n      dataname = \"ADSL\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      summarize_vars = choices_selected(\n        c(\"SEX\", \"RACE\", \"BMRKR2\", \"EOSDY\", \"DCSREAS\"),\n        c(\"SEX\", \"RACE\")\n      ),\n      useNA = \"ifany\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:56 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tidyr_1.3.1                     \n [9] dplyr_1.1.4                      scda_0.1.6.9017                 \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          glue_1.7.0               ps_1.7.6                \n[58] codetools_0.2-19         stringi_1.8.3            gtable_0.3.4            \n[61] later_1.3.2              shinycssloaders_1.0.0    munsell_0.5.0           \n[64] tibble_3.2.1             logger_0.3.0             pillar_1.9.0            \n[67] htmltools_0.5.7          brio_1.1.4               R6_2.5.1                \n[70] Rdpack_2.6               evaluate_0.23            lattice_0.22-5          \n[73] rbibutils_2.2.16         backports_1.4.1          memoise_2.0.1           \n[76] broom_1.0.5              teal.reporter_0.3.0.9005 httpuv_1.6.14           \n[79] bslib_0.6.1              Rcpp_1.0.12              shinyvalidate_0.1.3     \n[82] coda_0.19-4.1            nlme_3.1-164             checkmate_2.3.1         \n[85] xfun_0.42                zoo_1.8-12               forcats_1.0.0           \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nTable with an Additional  Study-Specific Continuous Variable\nTable with an Additional  Study-Specific Categorical Variable\nTable with Subgrouping  for Some Analyses\nTable with Additional Vital  Signs Baseline Values\nTable with Additional  Values from ADSUB\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(tidyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\nadsub &lt;- synthetic_cdisc_dataset(\"latest\", \"adsub\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadvs &lt;- df_explicit_na(advs)\nadsub &lt;- df_explicit_na(adsub)\n\n# Change description in variable SEX.\nadsl &lt;- adsl %&gt;%\n  mutate(\n    SEX = factor(case_when(\n      SEX == \"M\" ~ \"Male\",\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"U\" ~ \"Unknown\",\n      SEX == \"UNDIFFERENTIATED\" ~ \"Undifferentiated\"\n    )),\n    AGEGR1 = factor(\n      case_when(\n        between(AGE, 18, 40) ~ \"18-40\",\n        between(AGE, 41, 64) ~ \"41-64\",\n        AGE &gt; 64 ~ \"&gt;=65\"\n      ),\n      levels = c(\"18-40\", \"41-64\", \"&gt;=65\")\n    ),\n    BMRKR1_CAT = factor(\n      case_when(\n        BMRKR1 &lt; 3.5 ~ \"LOW\",\n        BMRKR1 &gt;= 3.5 & BMRKR1 &lt; 10 ~ \"MEDIUM\",\n        BMRKR1 &gt;= 10 ~ \"HIGH\"\n      ),\n      levels = c(\"LOW\", \"MEDIUM\", \"HIGH\")\n    )\n  ) %&gt;%\n  var_relabel(\n    BMRKR1_CAT = \"Biomarker 1 Categories\"\n  )\n# The developer needs to do pre-processing to add necessary variables based on ADVS to analysis dataset.\n# Obtain SBP, DBP and weight.\nget_param_advs &lt;- function(pname, plabel) {\n  ds &lt;- advs %&gt;%\n    filter(PARAM == plabel & AVISIT == \"BASELINE\") %&gt;%\n    select(USUBJID, AVAL)\n\n  colnames(ds) &lt;- c(\"USUBJID\", pname)\n\n  ds\n}\n# The developer needs to do pre-processing to add necessary variables based on ADSUB to analysis dataset.\n# Obtain baseline BMI (BBMISI).\nget_param_adsub &lt;- function(pname, plabel) {\n  ds &lt;- adsub %&gt;%\n    filter(PARAM == plabel) %&gt;%\n    select(USUBJID, AVAL)\n\n  colnames(ds) &lt;- c(\"USUBJID\", pname)\n\n  ds\n}\nadsl &lt;- adsl %&gt;%\n  left_join(get_param_advs(\"SBP\", \"Systolic Blood Pressure\"), by = \"USUBJID\") %&gt;%\n  left_join(get_param_advs(\"DBP\", \"Diastolic Blood Pressure\"), by = \"USUBJID\") %&gt;%\n  left_join(get_param_advs(\"WGT\", \"Weight\"), by = \"USUBJID\") %&gt;%\n  left_join(get_param_adsub(\"BBMISI\", \"Baseline BMI\"), by = \"USUBJID\")\n\n\n\n\n\nCodevars &lt;- c(\"AGE\", \"AGEGR1\", \"SEX\", \"ETHNIC\", \"RACE\", \"BMRKR1\")\nvar_labels &lt;- c(\n  \"Age (yr)\",\n  \"Age Group\",\n  \"Sex\",\n  \"Ethnicity\",\n  \"Race\",\n  \"Continous Level Biomarker 1\"\n)\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_vars(\n    vars = vars,\n    var_labels = var_labels\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination   All Patients\n                                                (N=134)       (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                               \n  n                                               134           134            132             400     \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4) \n  Median                                         33.0          35.0            35.0            34.0    \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0 \nAge Group                                                                                              \n  n                                               134           134            132             400     \n  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)     322 (80.5%) \n  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)      77 (19.2%) \n  &gt;=65                                             0             0           1 (0.8%)        1 (0.2%)  \nSex                                                                                                    \n  n                                               134           134            132             400     \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)      231 (57.8%) \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)      169 (42.2%) \nEthnicity                                                                                              \n  n                                               134           134            132             400     \n  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)       48 (12%)  \n  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)      308 (77%)  \n  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)       27 (6.8%)  \n  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)       17 (4.2%)  \nRace                                                                                                   \n  n                                               134           134            132             400     \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)      208 (52%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)      91 (22.8%) \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)      74 (18.5%) \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)       25 (6.2%)  \n  MULTIPLE                                         0         1 (0.7%)           0            1 (0.2%)  \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0            1 (0.2%)  \n  OTHER                                            0             0              0               0      \n  UNKNOWN                                          0             0              0               0      \nContinous Level Biomarker 1                                                                            \n  n                                               134           134            132             400     \n  Mean (SD)                                    6.0 (3.6)     5.7 (3.3)      5.6 (3.5)       5.8 (3.4)  \n  Median                                          5.4           4.8            4.6             4.8     \n  Min - Max                                   0.4 - 17.7    0.6 - 14.2      0.2 - 21.4      0.2 - 21.4 \n\n\n\n\n\nCodevars &lt;- c(\"AGE\", \"AGEGR1\", \"SEX\", \"ETHNIC\", \"RACE\", \"BMRKR1_CAT\")\nvar_labels &lt;- c(\n  \"Age (yr)\",\n  \"Age Group\",\n  \"Sex\",\n  \"Ethnicity\",\n  \"Race\",\n  \"Biomarker 1 Categories\"\n)\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = vars,\n    var_labels = var_labels\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination\n                                                (N=134)       (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                \n  n                                               134           134            132      \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  \n  Median                                         33.0          35.0            35.0     \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  \nAge Group                                                                               \n  n                                               134           134            132      \n  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)  \n  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)  \n  &gt;=65                                             0             0           1 (0.8%)   \nSex                                                                                     \n  n                                               134           134            132      \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)   \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)   \nEthnicity                                                                               \n  n                                               134           134            132      \n  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)  \n  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)  \n  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)   \n  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)   \nRace                                                                                    \n  n                                               134           134            132      \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)  \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)   \n  MULTIPLE                                         0         1 (0.7%)           0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0       \n  OTHER                                            0             0              0       \n  UNKNOWN                                          0             0              0       \nBiomarker 1 Categories                                                                  \n  n                                               134           134            132      \n  LOW                                         33 (24.6%)    41 (30.6%)      38 (28.8%)  \n  MEDIUM                                      84 (62.7%)    76 (56.7%)      80 (60.6%)  \n  HIGH                                        17 (12.7%)    17 (12.7%)      14 (10.6%)  \n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = c(\"AGE\", \"SEX\", \"RACE\"),\n    var_labels = c(\"Age\", \"Sex\", \"Race\")\n  ) %&gt;%\n  split_rows_by(\"STRATA1\",\n    split_fun = split_fun\n  ) %&gt;%\n  analyze_vars(\"BMRKR1\") %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination\n                                                (N=134)       (N=134)        (N=132)    \n————————————————————————————————————————————————————————————————————————————————————————\nAge                                                                                     \n  n                                               134           134            132      \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  \n  Median                                         33.0          35.0            35.0     \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  \nSex                                                                                     \n  n                                               134           134            132      \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)   \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)   \nRace                                                                                    \n  n                                               134           134            132      \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)  \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)   \n  MULTIPLE                                         0         1 (0.7%)           0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0       \n  OTHER                                            0             0              0       \n  UNKNOWN                                          0             0              0       \nA                                                                                       \n  n                                               38            44              40      \n  Mean (SD)                                    5.8 (3.8)     5.4 (3.2)      5.1 (3.2)   \n  Median                                          5.1           4.5            3.8      \n  Min - Max                                   0.4 - 17.7    1.4 - 14.2      1.5 - 14.0  \nB                                                                                       \n  n                                               47            45              43      \n  Mean (SD)                                    6.1 (3.6)     5.8 (3.6)      5.7 (3.4)   \n  Median                                          5.2           4.8            5.1      \n  Min - Max                                   1.6 - 17.2    0.6 - 13.3      0.2 - 16.5  \nC                                                                                       \n  n                                               49            45              49      \n  Mean (SD)                                    6.0 (3.4)     5.9 (3.2)      6.0 (3.8)   \n  Median                                          5.8           5.6            4.5      \n  Min - Max                                   0.5 - 15.1    1.5 - 13.9      1.2 - 21.4  \n\n\n\n\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = c(\"AGE\", \"SEX\", \"RACE\", \"DBP\", \"SBP\"),\n    var_labels = c(\n      \"Age (yr)\",\n      \"Sex\",\n      \"Race\",\n      \"Diastolic Blood Pressure\",\n      \"Systolic Blood Pressure\"\n    )\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X      B: Placebo    C: Combination\n                                                (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                  \n  n                                               134            134             132      \n  Mean (SD)                                    33.8 (6.6)     35.4 (7.9)      35.4 (7.7)  \n  Median                                          33.0           35.0            35.0     \n  Min - Max                                   21.0 - 50.0    21.0 - 62.0     20.0 - 69.0  \nSex                                                                                       \n  n                                               134            134             132      \n  Female                                        79 (59%)      82 (61.2%)       70 (53%)   \n  Male                                          55 (41%)      52 (38.8%)       62 (47%)   \nRace                                                                                      \n  n                                               134            134             132      \n  ASIAN                                        68 (50.7%)      67 (50%)       73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                    31 (23.1%)     28 (20.9%)      32 (24.2%)  \n  WHITE                                        27 (20.1%)     26 (19.4%)      21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE               8 (6%)       11 (8.2%)        6 (4.5%)   \n  MULTIPLE                                         0           1 (0.7%)           0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0           1 (0.7%)           0       \n  OTHER                                            0              0               0       \n  UNKNOWN                                          0              0               0       \nDiastolic Blood Pressure                                                                  \n  n                                               134            134             132      \n  Mean (SD)                                   96.5 (19.9)    101.1 (19.9)    102.8 (19.5) \n  Median                                          96.0          100.4           102.0     \n  Min - Max                                   44.3 - 136.6   29.2 - 143.8    49.4 - 153.5 \nSystolic Blood Pressure                                                                   \n  n                                               134            134             132      \n  Mean (SD)                                   151.7 (31.5)   149.5 (26.5)    144.7 (30.1) \n  Median                                         150.1          153.0           146.5     \n  Min - Max                                   69.1 - 231.2   87.2 - 220.9    71.8 - 220.2 \n\n\n\n\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  analyze_vars(\n    vars = c(\"AGE\", \"SEX\", \"RACE\", \"BBMISI\"),\n    var_labels = c(\n      \"Age (yr)\",\n      \"Sex\",\n      \"Race\",\n      \"Baseline BMI\"\n    )\n  ) %&gt;%\n  build_table(adsl)\n\nresult\n\n                                               A: Drug X     B: Placebo     C: Combination\n                                                (N=134)        (N=134)         (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                  \n  n                                               134            134             132      \n  Mean (SD)                                   33.8 (6.6)     35.4 (7.9)       35.4 (7.7)  \n  Median                                         33.0           35.0             35.0     \n  Min - Max                                   21.0 - 50.0    21.0 - 62.0     20.0 - 69.0  \nSex                                                                                       \n  n                                               134            134             132      \n  Female                                       79 (59%)      82 (61.2%)        70 (53%)   \n  Male                                         55 (41%)      52 (38.8%)        62 (47%)   \nRace                                                                                      \n  n                                               134            134             132      \n  ASIAN                                       68 (50.7%)      67 (50%)        73 (55.3%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)     28 (20.9%)       32 (24.2%)  \n  WHITE                                       27 (20.1%)     26 (19.4%)       21 (15.9%)  \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)        11 (8.2%)        6 (4.5%)   \n  MULTIPLE                                         0          1 (0.7%)            0       \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0          1 (0.7%)            0       \n  OTHER                                            0              0               0       \n  UNKNOWN                                          0              0               0       \nBaseline BMI                                                                              \n  n                                               134            134             132      \n  Mean (SD)                                   30.0 (18.3)    32.4 (23.2)     30.1 (18.4)  \n  Median                                         27.1           31.1             30.0     \n  Min - Max                                   -6.9 - 75.9   -26.6 - 117.9    -44.2 - 87.5 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n  # Include `EOSDY` and `DCSREAS` variables below because they contain missing data.\n  stopifnot(\n    any(is.na(ADSL$EOSDY)),\n    any(is.na(ADSL$DCSREAS))\n  )\n})\ndatanames &lt;- \"ADSL\"\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary(\n      label = \"Demographic Table\",\n      dataname = \"ADSL\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      summarize_vars = choices_selected(\n        c(\"SEX\", \"RACE\", \"BMRKR2\", \"EOSDY\", \"DCSREAS\"),\n        c(\"SEX\", \"RACE\")\n      ),\n      useNA = \"ifany\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:54 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tidyr_1.3.1                     \n [9] dplyr_1.1.4                      scda_0.1.6.9017                 \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          glue_1.7.0               ps_1.7.6                \n[58] codetools_0.2-19         stringi_1.8.3            gtable_0.3.4            \n[61] later_1.3.2              shinycssloaders_1.0.0    munsell_0.5.0           \n[64] tibble_3.2.1             logger_0.3.0             pillar_1.9.0            \n[67] htmltools_0.5.7          brio_1.1.4               R6_2.5.1                \n[70] Rdpack_2.6               evaluate_0.23            lattice_0.22-5          \n[73] rbibutils_2.2.16         backports_1.4.1          memoise_2.0.1           \n[76] broom_1.0.5              teal.reporter_0.3.0.9005 httpuv_1.6.14           \n[79] bslib_0.6.1              Rcpp_1.0.12              shinyvalidate_0.1.3     \n[82] coda_0.19-4.1            nlme_3.1-164             checkmate_2.3.1         \n[85] xfun_0.42                zoo_1.8-12               forcats_1.0.0           \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Demography",
@@ -840,7 +840,7 @@
     "href": "tables/pharmacokinetic/pkpt07.html",
     "title": "PKPT07",
     "section": "",
-    "text": "Data Setup\nStandard Table – Urine\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(stringr)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;%\n  filter(AVISIT == \"CYCLE 1 DAY 1\", str_detect(tolower(PARAM), regex(\"norm by dose\", ignore_case = TRUE)))\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\",\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"geom_mean\", \"geom_cv\", \"median\",\n      \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      geom_mean = \"Geometric Mean\",\n      geom_cv = \"CV % Geometric Mean\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                                \n  PK Parameter          n     Mean        SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A                                                                                                                        \n  RENALCLD (L/hr/mg)   134   0.00487   0.000965    19.8       0.00477              21.2           0.00497   0.00238   0.00726\nARM C                                                                                                                        \n  RENALCLD (L/hr/mg)   132   0.00511   0.000934    18.3       0.00502              19.7           0.00515   0.00236   0.00741\n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                               \n  PK Parameter          n     Mean       SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C                                                                                                                       \n  RENALCLD (L/hr/mg)   132   0.00509   0.00103    20.3       0.00499              21.4           0.00502   0.00236   0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] stringr_1.5.1         tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 dplyr_1.1.4          \n[7] scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table – Urine\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(stringr)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;%\n  filter(AVISIT == \"CYCLE 1 DAY 1\", str_detect(tolower(PARAM), regex(\"norm by dose\", ignore_case = TRUE)))\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\",\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"geom_mean\", \"geom_cv\", \"median\",\n      \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      geom_mean = \"Geometric Mean\",\n      geom_cv = \"CV % Geometric Mean\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                                \n  PK Parameter          n     Mean        SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A                                                                                                                        \n  RENALCLD (L/hr/mg)   134   0.00487   0.000965    19.8       0.00477              21.2           0.00497   0.00238   0.00726\nARM C                                                                                                                        \n  RENALCLD (L/hr/mg)   132   0.00511   0.000934    18.3       0.00502              19.7           0.00515   0.00236   0.00741\n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                               \n  PK Parameter          n     Mean       SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C                                                                                                                       \n  RENALCLD (L/hr/mg)   132   0.00509   0.00103    20.3       0.00499              21.4           0.00502   0.00236   0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:33 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] stringr_1.5.1         tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 dplyr_1.1.4          \n[7] scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -852,7 +852,7 @@
     "href": "tables/pharmacokinetic/pkpt11.html",
     "title": "PKPT11",
     "section": "",
-    "text": "Data Setup\nStandard Table – Plasma\nteal App\nReproducibility\n\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n# Comparator Dose - A: Drug X\narm_var &lt;- \"TRT01A\"\ncomp_dose &lt;- \"A: Drug X\"\nother_doses &lt;- as.character(unique(adpp[[arm_var]])[unique(adpp[[arm_var]]) != comp_dose])\n\nadpp &lt;- adpp %&gt;%\n  filter(AVISIT == \"CYCLE 1 DAY 1\") %&gt;%\n  mutate(\n    COMP = paste0(TRT01A, \"/\", comp_dose),\n    COMP_AVAL = ifelse(TRT01A == comp_dose, paste0(AVAL, comp_dose), AVAL)\n  )\n\nfor (dose in other_doses) {\n  temp_df &lt;- adpp[adpp[[arm_var]] == comp_dose, ]\n  temp_df$COMP &lt;- paste0(dose, \"/\", comp_dose)\n  adpp &lt;- rbind(adpp, temp_df)\n}\n\n# Plasma Drug X\nadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  var_relabel(COMP = \"Comparison\")\n\n# statistics function\ns_gmr &lt;- function(df,\n                  compare_dose = comp_dose, # comparator, defaults to comp_dose defined above (string)\n                  denom = TRUE, # whether to use comparator as denominator, defaults to TRUE (logical)\n                  arm_var = arm_var) { # arm variable, defaults to arm_var defined above (string)\n  which_num &lt;- !grepl(compare_dose, df[[arm_var]])\n\n  x_num &lt;- as.numeric(df[which_num, ][[\"AVAL\"]])\n  x_num &lt;- x_num[!is.na(x_num)]\n  x_num_no_negative_vals &lt;- x_num\n  x_num_no_negative_vals[x_num_no_negative_vals &lt;= 0] &lt;- NA\n\n  x_denom &lt;- as.numeric(gsub(compare_dose, \"\", df[!which_num, ][[\"AVAL\"]]))\n  x_denom &lt;- x_denom[!is.na(x_denom)]\n  x_denom_no_negative_vals &lt;- x_denom\n  x_denom_no_negative_vals[x_denom_no_negative_vals &lt;= 0] &lt;- NA\n\n  x_num_log &lt;- log(x_num_no_negative_vals)\n  x_denom_log &lt;- log(x_denom_no_negative_vals)\n\n  if (denom) {\n    geom_mean_ratio &lt;- exp(mean(x_num_log, na.rm = FALSE)) / exp(mean(x_denom_log, na.rm = FALSE))\n    geom_mean_ci &lt;- t.test(x_num_log, x_denom_log, conf.level = 0.90)$conf.int\n  } else {\n    geom_mean_ratio &lt;- exp(mean(x_denom_log, na.rm = FALSE)) / exp(mean(x_num_log, na.rm = FALSE))\n    geom_mean_ci &lt;- t.test(x_denom_log, x_num_log, conf.level = 0.90)$conf.int\n  }\n\n  list(\n    n = nrow(df),\n    geom_mean_ratio = geom_mean_ratio,\n    gmr_ci_lwr = exp(geom_mean_ci[1]),\n    gmr_ci_upr = exp(geom_mean_ci[2])\n  )\n}\n\nafun_pk_gmr &lt;- function(\n    .formats = list(\n      n = \"xx.\",\n      geom_mean_ratio = format_sigfig(3),\n      gmr_ci_lwr = format_sigfig(3),\n      gmr_ci_upr = format_sigfig(3)\n    ),\n    compare_dose = comp_dose,\n    denom = TRUE) {\n  checkmate::assert_list(.formats)\n  checkmate::assert_subset(names(.formats), c(\"n\", \"geom_mean_ratio\", \"gmr_ci_lwr\", \"gmr_ci_upr\"))\n\n  afun_lst &lt;- Map(\n    function(stat, fmt, compare_dose, denom, arm_var) {\n      function(df, .spl_context) {\n        x_stat &lt;- s_gmr(df, compare_dose = compare_dose, denom = denom, arm_var = arm_var)[[stat]]\n        rcell(x_stat, format = fmt, label = tail(.spl_context$value, 1))\n      }\n    },\n    stat = names(.formats),\n    fmt = .formats,\n    compare_dose = compare_dose,\n    denom = denom,\n    arm_var = arm_var\n  )\n\n  afun_lst\n}\n\n\n\n\n\nCode# create layout\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_fun = keep_split_levels(c(\"AUCinf obs (day*ug/mL)\", \"Cmax (ug/mL)\")),\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"COMP\",\n    split_fun = remove_split_levels(paste0(comp_dose, \"/\", comp_dose)),\n    indent_mod = 11L,\n    child_labels = \"hidden\"\n  ) %&gt;%\n  split_cols_by_multivar(\n    vars = rep(\"AVAL\", 4),\n    varlabels = c(\n      \"n\",\n      \"Geometric Mean Ratio\",\n      \"90% CI Lower Bound\",\n      \"90% CI Upper Bound\"\n    )\n  ) %&gt;%\n  analyze_colvars(\n    afun = afun_pk_gmr(),\n    extra_args = list(\n      compare_dose = comp_dose,\n      denom = TRUE,\n      arm_var = arm_var\n    )\n  ) %&gt;%\n  append_varlabels(adpp0, \"COMP\", 12L)\n\nresult &lt;- build_table(lyt, df = adpp0)\n\nmain_title(result) &lt;- paste0(\n  \"Estimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX Following \",\n  unique(adpp0$REGIMEN), \"\\nof \", comp_dose, \" in Comparison with \",\n  paste(other_doses, collapse = \" & \"), \", PK Population\"\n)\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT))\n\nresult\n\nEstimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX Following BID\nof A: Drug X in Comparison with C: Combination, PK Population\nAnalyte: Plasma Drug X\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nPK Parameter                                                                                                           \n                        Comparison                  n    Geometric Mean Ratio   90% CI Lower Bound   90% CI Upper Bound\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAUCinf obs (day*ug/mL)                                                                                                 \n                        C: Combination/A: Drug X   266          0.962                 0.925                 1.00       \nCmax (ug/mL)                                                                                                           \n                        C: Combination/A: Drug X   266          0.997                 0.958                 1.04       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:24 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table – Plasma\nteal App\nReproducibility\n\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n# Comparator Dose - A: Drug X\narm_var &lt;- \"TRT01A\"\ncomp_dose &lt;- \"A: Drug X\"\nother_doses &lt;- as.character(unique(adpp[[arm_var]])[unique(adpp[[arm_var]]) != comp_dose])\n\nadpp &lt;- adpp %&gt;%\n  filter(AVISIT == \"CYCLE 1 DAY 1\") %&gt;%\n  mutate(\n    COMP = paste0(TRT01A, \"/\", comp_dose),\n    COMP_AVAL = ifelse(TRT01A == comp_dose, paste0(AVAL, comp_dose), AVAL)\n  )\n\nfor (dose in other_doses) {\n  temp_df &lt;- adpp[adpp[[arm_var]] == comp_dose, ]\n  temp_df$COMP &lt;- paste0(dose, \"/\", comp_dose)\n  adpp &lt;- rbind(adpp, temp_df)\n}\n\n# Plasma Drug X\nadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  var_relabel(COMP = \"Comparison\")\n\n# statistics function\ns_gmr &lt;- function(df,\n                  compare_dose = comp_dose, # comparator, defaults to comp_dose defined above (string)\n                  denom = TRUE, # whether to use comparator as denominator, defaults to TRUE (logical)\n                  arm_var = arm_var) { # arm variable, defaults to arm_var defined above (string)\n  which_num &lt;- !grepl(compare_dose, df[[arm_var]])\n\n  x_num &lt;- as.numeric(df[which_num, ][[\"AVAL\"]])\n  x_num &lt;- x_num[!is.na(x_num)]\n  x_num_no_negative_vals &lt;- x_num\n  x_num_no_negative_vals[x_num_no_negative_vals &lt;= 0] &lt;- NA\n\n  x_denom &lt;- as.numeric(gsub(compare_dose, \"\", df[!which_num, ][[\"AVAL\"]]))\n  x_denom &lt;- x_denom[!is.na(x_denom)]\n  x_denom_no_negative_vals &lt;- x_denom\n  x_denom_no_negative_vals[x_denom_no_negative_vals &lt;= 0] &lt;- NA\n\n  x_num_log &lt;- log(x_num_no_negative_vals)\n  x_denom_log &lt;- log(x_denom_no_negative_vals)\n\n  if (denom) {\n    geom_mean_ratio &lt;- exp(mean(x_num_log, na.rm = FALSE)) / exp(mean(x_denom_log, na.rm = FALSE))\n    geom_mean_ci &lt;- t.test(x_num_log, x_denom_log, conf.level = 0.90)$conf.int\n  } else {\n    geom_mean_ratio &lt;- exp(mean(x_denom_log, na.rm = FALSE)) / exp(mean(x_num_log, na.rm = FALSE))\n    geom_mean_ci &lt;- t.test(x_denom_log, x_num_log, conf.level = 0.90)$conf.int\n  }\n\n  list(\n    n = nrow(df),\n    geom_mean_ratio = geom_mean_ratio,\n    gmr_ci_lwr = exp(geom_mean_ci[1]),\n    gmr_ci_upr = exp(geom_mean_ci[2])\n  )\n}\n\nafun_pk_gmr &lt;- function(\n    .formats = list(\n      n = \"xx.\",\n      geom_mean_ratio = format_sigfig(3),\n      gmr_ci_lwr = format_sigfig(3),\n      gmr_ci_upr = format_sigfig(3)\n    ),\n    compare_dose = comp_dose,\n    denom = TRUE) {\n  checkmate::assert_list(.formats)\n  checkmate::assert_subset(names(.formats), c(\"n\", \"geom_mean_ratio\", \"gmr_ci_lwr\", \"gmr_ci_upr\"))\n\n  afun_lst &lt;- Map(\n    function(stat, fmt, compare_dose, denom, arm_var) {\n      function(df, .spl_context) {\n        x_stat &lt;- s_gmr(df, compare_dose = compare_dose, denom = denom, arm_var = arm_var)[[stat]]\n        rcell(x_stat, format = fmt, label = tail(.spl_context$value, 1))\n      }\n    },\n    stat = names(.formats),\n    fmt = .formats,\n    compare_dose = compare_dose,\n    denom = denom,\n    arm_var = arm_var\n  )\n\n  afun_lst\n}\n\n\n\n\n\nCode# create layout\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_fun = keep_split_levels(c(\"AUCinf obs (day*ug/mL)\", \"Cmax (ug/mL)\")),\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"COMP\",\n    split_fun = remove_split_levels(paste0(comp_dose, \"/\", comp_dose)),\n    indent_mod = 11L,\n    child_labels = \"hidden\"\n  ) %&gt;%\n  split_cols_by_multivar(\n    vars = rep(\"AVAL\", 4),\n    varlabels = c(\n      \"n\",\n      \"Geometric Mean Ratio\",\n      \"90% CI Lower Bound\",\n      \"90% CI Upper Bound\"\n    )\n  ) %&gt;%\n  analyze_colvars(\n    afun = afun_pk_gmr(),\n    extra_args = list(\n      compare_dose = comp_dose,\n      denom = TRUE,\n      arm_var = arm_var\n    )\n  ) %&gt;%\n  append_varlabels(adpp0, \"COMP\", 12L)\n\nresult &lt;- build_table(lyt, df = adpp0)\n\nmain_title(result) &lt;- paste0(\n  \"Estimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX Following \",\n  unique(adpp0$REGIMEN), \"\\nof \", comp_dose, \" in Comparison with \",\n  paste(other_doses, collapse = \" & \"), \", PK Population\"\n)\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT))\n\nresult\n\nEstimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX Following BID\nof A: Drug X in Comparison with C: Combination, PK Population\nAnalyte: Plasma Drug X\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nPK Parameter                                                                                                           \n                        Comparison                  n    Geometric Mean Ratio   90% CI Lower Bound   90% CI Upper Bound\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nAUCinf obs (day*ug/mL)                                                                                                 \n                        C: Combination/A: Drug X   266          0.962                 0.925                 1.00       \nCmax (ug/mL)                                                                                                           \n                        C: Combination/A: Drug X   266          0.997                 0.958                 1.04       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:21 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -864,7 +864,7 @@
     "href": "tables/pharmacokinetic/pkct01.html",
     "title": "PKCT01",
     "section": "",
-    "text": "Data Setup\nStandard Table (Stats in Columns)\nTable Implementing 1/3 Imputation Rule\nTable Implementing 1/2 Imputation Rule\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n  filter(ACTARM == \"A: Drug X\")\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\") %&gt;%\n  filter(ACTARM == \"A: Drug X\", PARAM == \"Plasma Drug X\")\n\n# Setting up the data\nadpc_1 &lt;- adpc %&gt;%\n  mutate(\n    NFRLT = as.factor(NFRLT),\n    AVALCAT1 = as.factor(AVALCAT1),\n    NOMTPT = as.factor(paste(NFRLT, \"/\", PCTPT))\n  ) %&gt;%\n  select(NOMTPT, ACTARM, VISIT, AVAL, PARAM, AVALCAT1)\n\nadpc_1$NOMTPT &lt;- factor(\n  adpc_1$NOMTPT,\n  levels = levels(adpc_1$NOMTPT)[order(as.numeric(gsub(\".*?([0-9\\\\.]+).*\", \"\\\\1\", levels(adpc_1$NOMTPT))))]\n)\n\n# Row structure\nlyt_rows &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ACTARM\",\n    split_fun = drop_split_levels,\n    split_label = \"Treatment Group\",\n    label_pos = \"topleft\"\n  ) %&gt;%\n  add_rowcounts(alt_counts = TRUE) %&gt;%\n  split_rows_by(\n    var = \"VISIT\",\n    split_fun = drop_split_levels,\n    split_label = \"Visit\",\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"NOMTPT\",\n    split_fun = drop_split_levels,\n    split_label = \"Nominal Time (hr) / Timepoint\",\n    label_pos = \"topleft\",\n    child_labels = \"hidden\"\n  )\n\n\n\n\n\nCodelyt &lt;- lyt_rows %&gt;%\n  analyze_vars_in_cols(\n    vars = c(\"AVAL\", \"AVALCAT1\", rep(\"AVAL\", 8)),\n    .stats = c(\"n\", \"n_blq\", \"mean\", \"sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"min\", \"max\"),\n    .formats = c(\n      n = \"xx.\", n_blq = \"xx.\", mean = format_sigfig(3), sd = format_sigfig(3), cv = \"xx.x\", median = format_sigfig(3),\n      geom_mean = format_sigfig(3), geom_cv = \"xx.x\", min = format_sigfig(3), max = format_sigfig(3)\n    ),\n    .labels = c(\n      n = \"n\", n_blq = \"Number\\nof\\nLTRs/BLQs\", mean = \"Mean\", sd = \"SD\", cv = \"CV (%) Mean\",\n      geom_mean = \"Geometric Mean\", geom_cv = \"CV % Geometric Mean\", median = \"Median\", min = \"Minimum\", max = \"Maximum\"\n    ),\n    na_str = \"NE\",\n    .aligns = \"decimal\"\n  )\n\nresult &lt;- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %&gt;% prune_table()\n\n# Decorating\nmain_title(result) &lt;- \"Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\"\nsubtitles(result) &lt;- c(\n  \"Protocol: xxxxx\",\n  paste(\"Analyte: \", unique(adpc_1$PARAM)),\n  paste(\"Treatment:\", unique(adpc_1$ACTARM))\n)\nmain_footer(result) &lt;- \"NE: Not Estimable\"\n\nresult\n\nSummary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\nProtocol: xxxxx\nAnalyte:  Plasma Drug X\nTreatment: A: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group                            Number                                                                                                         \n  Visit                                      of                                                                                                           \n    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                                                                                                                         \n  Day 1                                                                                                                                                   \n    0 / Predose                     134      134       0        0            NE              NE                 NE             0         0          0     \n    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   \n    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   \n    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   \n    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   \n    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   \n    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  \n    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 \n    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830\n  Day 2                                                                                                                                                   \n    24 / 24H                        134      134       0        0            NE              NE                 NE             0         0          0     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNE: Not Estimable\n\n\n\n\n\nCodelyt &lt;- lyt_rows %&gt;%\n  analyze_vars_in_cols(\n    vars = c(\"AVAL\", \"AVALCAT1\", rep(\"AVAL\", 8)),\n    .stats = c(\"n\", \"n_blq\", \"mean\", \"sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"min\", \"max\"),\n    .formats = c(\n      n = \"xx.\", n_blq = \"xx.\", mean = format_sigfig(3), sd = format_sigfig(3), cv = \"xx.x\", median = format_sigfig(3),\n      geom_mean = format_sigfig(3), geom_cv = \"xx.x\", min = format_sigfig(3), max = format_sigfig(3)\n    ),\n    .labels = c(\n      n = \"n\", n_blq = \"Number\\nof\\nLTRs/BLQs\", mean = \"Mean\", sd = \"SD\", cv = \"CV (%) Mean\",\n      geom_mean = \"Geometric Mean\", geom_cv = \"CV % Geometric Mean\", median = \"Median\", min = \"Minimum\", max = \"Maximum\"\n    ),\n    imp_rule = \"1/3\",\n    .aligns = \"decimal\"\n  )\n\nresult &lt;- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %&gt;% prune_table()\n\n# Decorating\nmain_title(result) &lt;- \"Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\"\nsubtitles(result) &lt;- c(\n  \"Protocol: xxxxx\",\n  paste(\"Analyte: \", unique(adpc_1$PARAM)),\n  paste(\"Treatment:\", unique(adpc_1$ACTARM))\n)\nmain_footer(result) &lt;- c(\"NE: Not Estimable\", \"ND: Not Derived\")\n\nresult\n\nSummary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\nProtocol: xxxxx\nAnalyte:  Plasma Drug X\nTreatment: A: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group                            Number                                                                                                         \n  Visit                                      of                                                                                                           \n    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                                                                                                                         \n  Day 1                                                                                                                                                   \n    0 / Predose                     134      134        ND        ND         ND              NE                 ND             0           ND       0     \n    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   \n    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   \n    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   \n    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   \n    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   \n    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  \n    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 \n    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830\n  Day 2                                                                                                                                                   \n    24 / 24H                        134      134        ND        ND         ND              NE                 ND             0           ND       0     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNE: Not Estimable\nND: Not Derived\n\n\n\n\n\nCodelyt &lt;- lyt_rows %&gt;%\n  analyze_vars_in_cols(\n    vars = c(\"AVAL\", \"AVALCAT1\", rep(\"AVAL\", 8)),\n    .stats = c(\"n\", \"n_blq\", \"mean\", \"sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"min\", \"max\"),\n    .formats = c(\n      n = \"xx.\", n_blq = \"xx.\", mean = format_sigfig(3), sd = format_sigfig(3), cv = \"xx.x\", median = format_sigfig(3),\n      geom_mean = format_sigfig(3), geom_cv = \"xx.x\", min = format_sigfig(3), max = format_sigfig(3)\n    ),\n    .labels = c(\n      n = \"n\", n_blq = \"Number\\nof\\nLTRs/BLQs\", mean = \"Mean\", sd = \"SD\", cv = \"CV (%) Mean\",\n      geom_mean = \"Geometric Mean\", geom_cv = \"CV % Geometric Mean\", median = \"Median\", min = \"Minimum\", max = \"Maximum\"\n    ),\n    imp_rule = \"1/2\",\n    .aligns = \"decimal\"\n  )\n\nresult &lt;- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %&gt;% prune_table()\n\n# Decorate table\nmain_title(result) &lt;- \"Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\"\nsubtitles(result) &lt;- c(\n  \"Protocol: xxxxx\",\n  paste(\"Analyte: \", unique(adpc_1$PARAM)),\n  paste(\"Treatment:\", unique(adpc_1$ACTARM))\n)\nmain_footer(result) &lt;- \"ND: Not Derived\"\n\nresult\n\nSummary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\nProtocol: xxxxx\nAnalyte:  Plasma Drug X\nTreatment: A: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group                            Number                                                                                                         \n  Visit                                      of                                                                                                           \n    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                                                                                                                         \n  Day 1                                                                                                                                                   \n    0 / Predose                     134      134        ND        ND         ND              ND                 ND              ND         ND       0     \n    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   \n    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   \n    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   \n    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   \n    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   \n    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  \n    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 \n    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830\n  Day 2                                                                                                                                                   \n    24 / 24H                        134      134        ND        ND         ND              ND                 ND              ND         ND       0     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nND: Not Derived\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:12 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table (Stats in Columns)\nTable Implementing 1/3 Imputation Rule\nTable Implementing 1/2 Imputation Rule\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n  filter(ACTARM == \"A: Drug X\")\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\") %&gt;%\n  filter(ACTARM == \"A: Drug X\", PARAM == \"Plasma Drug X\")\n\n# Setting up the data\nadpc_1 &lt;- adpc %&gt;%\n  mutate(\n    NFRLT = as.factor(NFRLT),\n    AVALCAT1 = as.factor(AVALCAT1),\n    NOMTPT = as.factor(paste(NFRLT, \"/\", PCTPT))\n  ) %&gt;%\n  select(NOMTPT, ACTARM, VISIT, AVAL, PARAM, AVALCAT1)\n\nadpc_1$NOMTPT &lt;- factor(\n  adpc_1$NOMTPT,\n  levels = levels(adpc_1$NOMTPT)[order(as.numeric(gsub(\".*?([0-9\\\\.]+).*\", \"\\\\1\", levels(adpc_1$NOMTPT))))]\n)\n\n# Row structure\nlyt_rows &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ACTARM\",\n    split_fun = drop_split_levels,\n    split_label = \"Treatment Group\",\n    label_pos = \"topleft\"\n  ) %&gt;%\n  add_rowcounts(alt_counts = TRUE) %&gt;%\n  split_rows_by(\n    var = \"VISIT\",\n    split_fun = drop_split_levels,\n    split_label = \"Visit\",\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"NOMTPT\",\n    split_fun = drop_split_levels,\n    split_label = \"Nominal Time (hr) / Timepoint\",\n    label_pos = \"topleft\",\n    child_labels = \"hidden\"\n  )\n\n\n\n\n\nCodelyt &lt;- lyt_rows %&gt;%\n  analyze_vars_in_cols(\n    vars = c(\"AVAL\", \"AVALCAT1\", rep(\"AVAL\", 8)),\n    .stats = c(\"n\", \"n_blq\", \"mean\", \"sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"min\", \"max\"),\n    .formats = c(\n      n = \"xx.\", n_blq = \"xx.\", mean = format_sigfig(3), sd = format_sigfig(3), cv = \"xx.x\", median = format_sigfig(3),\n      geom_mean = format_sigfig(3), geom_cv = \"xx.x\", min = format_sigfig(3), max = format_sigfig(3)\n    ),\n    .labels = c(\n      n = \"n\", n_blq = \"Number\\nof\\nLTRs/BLQs\", mean = \"Mean\", sd = \"SD\", cv = \"CV (%) Mean\",\n      geom_mean = \"Geometric Mean\", geom_cv = \"CV % Geometric Mean\", median = \"Median\", min = \"Minimum\", max = \"Maximum\"\n    ),\n    na_str = \"NE\",\n    .aligns = \"decimal\"\n  )\n\nresult &lt;- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %&gt;% prune_table()\n\n# Decorating\nmain_title(result) &lt;- \"Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\"\nsubtitles(result) &lt;- c(\n  \"Protocol: xxxxx\",\n  paste(\"Analyte: \", unique(adpc_1$PARAM)),\n  paste(\"Treatment:\", unique(adpc_1$ACTARM))\n)\nmain_footer(result) &lt;- \"NE: Not Estimable\"\n\nresult\n\nSummary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\nProtocol: xxxxx\nAnalyte:  Plasma Drug X\nTreatment: A: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group                            Number                                                                                                         \n  Visit                                      of                                                                                                           \n    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                                                                                                                         \n  Day 1                                                                                                                                                   \n    0 / Predose                     134      134       0        0            NE              NE                 NE             0         0          0     \n    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   \n    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   \n    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   \n    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   \n    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   \n    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  \n    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 \n    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830\n  Day 2                                                                                                                                                   \n    24 / 24H                        134      134       0        0            NE              NE                 NE             0         0          0     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNE: Not Estimable\n\n\n\n\n\nCodelyt &lt;- lyt_rows %&gt;%\n  analyze_vars_in_cols(\n    vars = c(\"AVAL\", \"AVALCAT1\", rep(\"AVAL\", 8)),\n    .stats = c(\"n\", \"n_blq\", \"mean\", \"sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"min\", \"max\"),\n    .formats = c(\n      n = \"xx.\", n_blq = \"xx.\", mean = format_sigfig(3), sd = format_sigfig(3), cv = \"xx.x\", median = format_sigfig(3),\n      geom_mean = format_sigfig(3), geom_cv = \"xx.x\", min = format_sigfig(3), max = format_sigfig(3)\n    ),\n    .labels = c(\n      n = \"n\", n_blq = \"Number\\nof\\nLTRs/BLQs\", mean = \"Mean\", sd = \"SD\", cv = \"CV (%) Mean\",\n      geom_mean = \"Geometric Mean\", geom_cv = \"CV % Geometric Mean\", median = \"Median\", min = \"Minimum\", max = \"Maximum\"\n    ),\n    imp_rule = \"1/3\",\n    .aligns = \"decimal\"\n  )\n\nresult &lt;- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %&gt;% prune_table()\n\n# Decorating\nmain_title(result) &lt;- \"Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\"\nsubtitles(result) &lt;- c(\n  \"Protocol: xxxxx\",\n  paste(\"Analyte: \", unique(adpc_1$PARAM)),\n  paste(\"Treatment:\", unique(adpc_1$ACTARM))\n)\nmain_footer(result) &lt;- c(\"NE: Not Estimable\", \"ND: Not Derived\")\n\nresult\n\nSummary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\nProtocol: xxxxx\nAnalyte:  Plasma Drug X\nTreatment: A: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group                            Number                                                                                                         \n  Visit                                      of                                                                                                           \n    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                                                                                                                         \n  Day 1                                                                                                                                                   \n    0 / Predose                     134      134        ND        ND         ND              NE                 ND             0           ND       0     \n    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   \n    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   \n    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   \n    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   \n    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   \n    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  \n    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 \n    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830\n  Day 2                                                                                                                                                   \n    24 / 24H                        134      134        ND        ND         ND              NE                 ND             0           ND       0     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNE: Not Estimable\nND: Not Derived\n\n\n\n\n\nCodelyt &lt;- lyt_rows %&gt;%\n  analyze_vars_in_cols(\n    vars = c(\"AVAL\", \"AVALCAT1\", rep(\"AVAL\", 8)),\n    .stats = c(\"n\", \"n_blq\", \"mean\", \"sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"min\", \"max\"),\n    .formats = c(\n      n = \"xx.\", n_blq = \"xx.\", mean = format_sigfig(3), sd = format_sigfig(3), cv = \"xx.x\", median = format_sigfig(3),\n      geom_mean = format_sigfig(3), geom_cv = \"xx.x\", min = format_sigfig(3), max = format_sigfig(3)\n    ),\n    .labels = c(\n      n = \"n\", n_blq = \"Number\\nof\\nLTRs/BLQs\", mean = \"Mean\", sd = \"SD\", cv = \"CV (%) Mean\",\n      geom_mean = \"Geometric Mean\", geom_cv = \"CV % Geometric Mean\", median = \"Median\", min = \"Minimum\", max = \"Maximum\"\n    ),\n    imp_rule = \"1/2\",\n    .aligns = \"decimal\"\n  )\n\nresult &lt;- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %&gt;% prune_table()\n\n# Decorate table\nmain_title(result) &lt;- \"Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\"\nsubtitles(result) &lt;- c(\n  \"Protocol: xxxxx\",\n  paste(\"Analyte: \", unique(adpc_1$PARAM)),\n  paste(\"Treatment:\", unique(adpc_1$ACTARM))\n)\nmain_footer(result) &lt;- \"ND: Not Derived\"\n\nresult\n\nSummary of PK Concentrations by Nominal Time and Treatment: PK Evaluable\nProtocol: xxxxx\nAnalyte:  Plasma Drug X\nTreatment: A: Drug X\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group                            Number                                                                                                         \n  Visit                                      of                                                                                                           \n    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                                                                                                                         \n  Day 1                                                                                                                                                   \n    0 / Predose                     134      134        ND        ND         ND              ND                 ND              ND         ND       0     \n    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   \n    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   \n    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   \n    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   \n    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   \n    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  \n    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 \n    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830\n  Day 2                                                                                                                                                   \n    24 / 24H                        134      134        ND        ND         ND              ND                 ND              ND         ND       0     \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nND: Not Derived\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:10 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -876,7 +876,7 @@
     "href": "tables/pharmacokinetic/pkpt04.html",
     "title": "PKPT04",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;% filter(PPSPEC == \"Urine\", AVISIT == \"CYCLE 1 DAY 1\")\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  tern::analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\"n\", \"mean_sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"range\"),\n    .formats = c(\n      n = \"xx.\",\n      mean_sd = format_sigfig(3, \"xx (xx)\"),\n      cv = \"xx.x\",\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      median = format_sigfig(3),\n      range = format_sigfig(3, \"xx - xx\")\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n———————————————————————————————————————————————————————————————\nPK Parameter                  ARM A                ARM C       \n———————————————————————————————————————————————————————————————\nAe (mg)                                                        \n  n                            268                  264        \n  Mean (SD)                1.55 (0.338)         1.54 (0.298)   \n  CV (%)                       21.8                 19.4       \n  Geometric Mean               1.51                 1.51       \n  CV % Geometric Mean          23.0                 20.3       \n  Median                       1.55                 1.55       \n  Min - Max                0.702 - 2.46         0.850 - 2.21   \nFe (%)                                                         \n  n                            268                  264        \n  Mean (SD)                15.7 (3.35)          16.1 (3.10)    \n  CV (%)                       21.3                 19.3       \n  Geometric Mean               15.3                 15.8       \n  CV % Geometric Mean          22.2                 20.2       \n  Median                       15.8                 16.0       \n  Min - Max                8.15 - 24.5          8.50 - 24.4    \nCLR (L/hr)                                                     \n  n                            134                  132        \n  Mean (SD)              0.0492 (0.00961)     0.0502 (0.0105)  \n  CV (%)                       19.5                 20.9       \n  Geometric Mean              0.0482               0.0491      \n  CV % Geometric Mean          21.0                 21.9       \n  Median                      0.0491               0.0498      \n  Min - Max              0.0249 - 0.0751      0.0251 - 0.0856  \nRENALCLD (L/hr/mg)                                             \n  n                            134                  132        \n  Mean (SD)             0.00487 (0.000965)   0.00511 (0.000934)\n  CV (%)                       19.8                 18.3       \n  Geometric Mean             0.00477              0.00502      \n  CV % Geometric Mean          21.2                 19.7       \n  Median                     0.00497              0.00515      \n  Min - Max             0.00238 - 0.00726    0.00236 - 0.00741 \n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————\nPK Parameter                  ARM C      \n—————————————————————————————————————————\nAe (mg)                                  \n  n                            264       \n  Mean (SD)               1.60 (0.315)   \n  CV (%)                      19.7       \n  Geometric Mean              1.56       \n  CV % Geometric Mean         21.4       \n  Median                      1.60       \n  Min - Max               0.857 - 2.26   \nFe (%)                                   \n  n                            264       \n  Mean (SD)                15.8 (3.08)   \n  CV (%)                      19.4       \n  Geometric Mean              15.5       \n  CV % Geometric Mean         20.2       \n  Median                      15.7       \n  Min - Max                8.31 - 23.8   \nCLR (L/hr)                               \n  n                            132       \n  Mean (SD)              0.0497 (0.0101) \n  CV (%)                      20.3       \n  Geometric Mean             0.0486      \n  CV % Geometric Mean         22.0       \n  Median                     0.0491      \n  Min - Max              0.0184 - 0.0776 \nRENALCLD (L/hr/mg)                       \n  n                            132       \n  Mean (SD)             0.00509 (0.00103)\n  CV (%)                      20.3       \n  Geometric Mean             0.00499     \n  CV % Geometric Mean         21.4       \n  Median                     0.00502     \n  Min - Max             0.00236 - 0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:34:50 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;% filter(PPSPEC == \"Urine\", AVISIT == \"CYCLE 1 DAY 1\")\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  tern::analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\"n\", \"mean_sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"range\"),\n    .formats = c(\n      n = \"xx.\",\n      mean_sd = format_sigfig(3, \"xx (xx)\"),\n      cv = \"xx.x\",\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      median = format_sigfig(3),\n      range = format_sigfig(3, \"xx - xx\")\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n———————————————————————————————————————————————————————————————\nPK Parameter                  ARM A                ARM C       \n———————————————————————————————————————————————————————————————\nAe (mg)                                                        \n  n                            268                  264        \n  Mean (SD)                1.55 (0.338)         1.54 (0.298)   \n  CV (%)                       21.8                 19.4       \n  Geometric Mean               1.51                 1.51       \n  CV % Geometric Mean          23.0                 20.3       \n  Median                       1.55                 1.55       \n  Min - Max                0.702 - 2.46         0.850 - 2.21   \nFe (%)                                                         \n  n                            268                  264        \n  Mean (SD)                15.7 (3.35)          16.1 (3.10)    \n  CV (%)                       21.3                 19.3       \n  Geometric Mean               15.3                 15.8       \n  CV % Geometric Mean          22.2                 20.2       \n  Median                       15.8                 16.0       \n  Min - Max                8.15 - 24.5          8.50 - 24.4    \nCLR (L/hr)                                                     \n  n                            134                  132        \n  Mean (SD)              0.0492 (0.00961)     0.0502 (0.0105)  \n  CV (%)                       19.5                 20.9       \n  Geometric Mean              0.0482               0.0491      \n  CV % Geometric Mean          21.0                 21.9       \n  Median                      0.0491               0.0498      \n  Min - Max              0.0249 - 0.0751      0.0251 - 0.0856  \nRENALCLD (L/hr/mg)                                             \n  n                            134                  132        \n  Mean (SD)             0.00487 (0.000965)   0.00511 (0.000934)\n  CV (%)                       19.8                 18.3       \n  Geometric Mean             0.00477              0.00502      \n  CV % Geometric Mean          21.2                 19.7       \n  Median                     0.00497              0.00515      \n  Min - Max             0.00238 - 0.00726    0.00236 - 0.00741 \n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————\nPK Parameter                  ARM C      \n—————————————————————————————————————————\nAe (mg)                                  \n  n                            264       \n  Mean (SD)               1.60 (0.315)   \n  CV (%)                      19.7       \n  Geometric Mean              1.56       \n  CV % Geometric Mean         21.4       \n  Median                      1.60       \n  Min - Max               0.857 - 2.26   \nFe (%)                                   \n  n                            264       \n  Mean (SD)                15.8 (3.08)   \n  CV (%)                      19.4       \n  Geometric Mean              15.5       \n  CV % Geometric Mean         20.2       \n  Median                      15.7       \n  Min - Max                8.31 - 23.8   \nCLR (L/hr)                               \n  n                            132       \n  Mean (SD)              0.0497 (0.0101) \n  CV (%)                      20.3       \n  Geometric Mean             0.0486      \n  CV % Geometric Mean         22.0       \n  Median                     0.0491      \n  Min - Max              0.0184 - 0.0776 \nRENALCLD (L/hr/mg)                       \n  n                            132       \n  Mean (SD)             0.00509 (0.00103)\n  CV (%)                      20.3       \n  Geometric Mean             0.00499     \n  CV % Geometric Mean         21.4       \n  Median                     0.00502     \n  Min - Max             0.00236 - 0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:33:49 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -947,7 +947,7 @@
     "href": "tables/pharmacokinetic/pkpt05.html",
     "title": "PKPT05",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;% filter(PPSPEC == \"Urine\", AVISIT == \"CYCLE 1 DAY 1\")\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\",\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"geom_mean\", \"geom_cv\", \"median\",\n      \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      geom_mean = \"Geometric Mean\",\n      geom_cv = \"CV % Geometric Mean\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                                \n  PK Parameter          n     Mean        SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A                                                                                                                        \n  Ae (mg)              268    1.55      0.338      21.8         1.51               23.0            1.55      0.702     2.46  \n  Fe (%)               268    15.7       3.35      21.3         15.3               22.2            15.8      8.15      24.5  \n  CLR (L/hr)           134   0.0492    0.00961     19.5        0.0482              21.0           0.0491    0.0249    0.0751 \n  RENALCLD (L/hr/mg)   134   0.00487   0.000965    19.8       0.00477              21.2           0.00497   0.00238   0.00726\nARM C                                                                                                                        \n  Ae (mg)              264    1.54      0.298      19.4         1.51               20.3            1.55      0.850     2.21  \n  Fe (%)               264    16.1       3.10      19.3         15.8               20.2            16.0      8.50      24.4  \n  CLR (L/hr)           132   0.0502     0.0105     20.9        0.0491              21.9           0.0498    0.0251    0.0856 \n  RENALCLD (L/hr/mg)   132   0.00511   0.000934    18.3       0.00502              19.7           0.00515   0.00236   0.00741\n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                               \n  PK Parameter          n     Mean       SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C                                                                                                                       \n  Ae (mg)              264    1.60      0.315     19.7         1.56               21.4            1.60      0.857     2.26  \n  Fe (%)               264    15.8      3.08      19.4         15.5               20.2            15.7      8.31      23.8  \n  CLR (L/hr)           132   0.0497    0.0101     20.3        0.0486              22.0           0.0491    0.0184    0.0776 \n  RENALCLD (L/hr/mg)   132   0.00509   0.00103    20.3       0.00499              21.4           0.00502   0.00236   0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:34:44 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;% filter(PPSPEC == \"Urine\", AVISIT == \"CYCLE 1 DAY 1\")\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\",\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"geom_mean\", \"geom_cv\", \"median\",\n      \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      geom_mean = \"Geometric Mean\",\n      geom_cv = \"CV % Geometric Mean\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                                \n  PK Parameter          n     Mean        SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A                                                                                                                        \n  Ae (mg)              268    1.55      0.338      21.8         1.51               23.0            1.55      0.702     2.46  \n  Fe (%)               268    15.7       3.35      21.3         15.3               22.2            15.8      8.15      24.5  \n  CLR (L/hr)           134   0.0492    0.00961     19.5        0.0482              21.0           0.0491    0.0249    0.0751 \n  RENALCLD (L/hr/mg)   134   0.00487   0.000965    19.8       0.00477              21.2           0.00497   0.00238   0.00726\nARM C                                                                                                                        \n  Ae (mg)              264    1.54      0.298      19.4         1.51               20.3            1.55      0.850     2.21  \n  Fe (%)               264    16.1       3.10      19.3         15.8               20.2            16.0      8.50      24.4  \n  CLR (L/hr)           132   0.0502     0.0105     20.9        0.0491              21.9           0.0498    0.0251    0.0856 \n  RENALCLD (L/hr/mg)   132   0.00511   0.000934    18.3       0.00502              19.7           0.00515   0.00236   0.00741\n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Urine PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                               \n  PK Parameter          n     Mean       SD      CV (%)   Geometric Mean   CV % Geometric Mean   Median    Minimum   Maximum\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C                                                                                                                       \n  Ae (mg)              264    1.60      0.315     19.7         1.56               21.4            1.60      0.857     2.26  \n  Fe (%)               264    15.8      3.08      19.4         15.5               20.2            15.7      8.31      23.8  \n  CLR (L/hr)           132   0.0497    0.0101     20.3        0.0486              22.0           0.0491    0.0184    0.0776 \n  RENALCLD (L/hr/mg)   132   0.00509   0.00103    20.3       0.00499              21.4           0.00502   0.00236   0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:33:44 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -959,7 +959,7 @@
     "href": "tables/pharmacokinetic/pkpt03.html",
     "title": "PKPT03",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\n# Preprocess analysis data ----\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;%\n  filter(PPSPEC == \"Plasma\") %&gt;%\n  filter(AVISIT %in% c(\"CYCLE 1 DAY 1\", \"CYCLE 1 DAY 2\")) %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PARAM = reorder(PARAM, TLG_ORDER))\n\n# Preprocess subject-level data ----\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Workaround needed to include (N=xx) population counts\n# Repeat ADSL by the number of levels in AVISIT\n# Include AVISIT and dummy PARAM as it's needed for trim_levels_in_group\nadsl_tmp &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ARMCD) %&gt;%\n  unique() %&gt;%\n  mutate(PARAM = factor(NA_character_, levels = levels(adpp$PARAM)))\n\n# Data for Plasma Drug X example ----\nadpp_x &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  # Please do not replicate mutate statement below!\n  # It is used to make the random data in this example more realistic\n  # as not all parameters are always available across all visits.\n  mutate(\n    AVAL = if_else(\n      ARMCD == \"ARM A\" & AVISIT == \"CYCLE 1 DAY 1\" & PARAM == \"Cmax (ug/mL)\",\n      NA_real_, AVAL\n    )\n  )\n\nadpp_x_tmp &lt;- adpp_x %&gt;%\n  select(STUDYID, USUBJID, ARMCD, AVISIT) %&gt;%\n  unique()\n\nadsl_x_splitvars &lt;- adsl_tmp %&gt;%\n  left_join(adpp_x_tmp, by = c(\"STUDYID\", \"USUBJID\", \"ARMCD\")) %&gt;%\n  filter(!is.na(AVISIT))\n\n# Data for Plasma Drug Y example ----\nadpp_y &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\")\n\nadpp_y_tmp &lt;- adpp_y %&gt;%\n  select(STUDYID, USUBJID, ARMCD, AVISIT) %&gt;%\n  unique()\n\nadsl_y_splitvars &lt;- adsl_tmp %&gt;%\n  left_join(adpp_y_tmp, by = c(\"STUDYID\", \"USUBJID\", \"ARMCD\")) %&gt;%\n  filter(!is.na(AVISIT))\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"AVISIT\",\n    split_fun = drop_split_levels,\n    split_label = \"Visit\",\n    page_by = TRUE\n  ) %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"PARAM\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  add_rowcounts(alt_counts = TRUE) %&gt;%\n  split_rows_by(\n    var = \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\",\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"geom_mean\", \"geom_cv\", \"median\",\n      \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      geom_mean = \"Geometric Mean\",\n      geom_cv = \"CV % Geometric Mean\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    ),\n    na_str = \"NE\"\n  )\n\n\nPlasma Drug X\n\nCoderesult &lt;- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp_x$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp_x$PPCAT))\nresult &lt;- paginate_table(result, landscape = TRUE)\nresult\n\n$`CYCLE 1 DAY 1`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                             \n  Cmax (ug/mL)              0     NE     NE       NE           NE                 NE              NE       NE        NE   \n  AUCinf obs (day*ug/mL)   134   203    37.7     18.6         199                18.7            197       125       311  \n  CL obs (ml/day/kg)       134   5.04   1.04     20.6         4.93               22.4            5.08     2.25      7.39  \nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   30.0   5.46     18.2         29.5               18.9            29.8     15.9      47.6  \n  AUCinf obs (day*ug/mL)   132   195    37.8     19.4         192                20.1            196       103       315  \n  CL obs (ml/day/kg)       132   5.01   0.985    19.7         4.91               21.1            4.97     2.10      7.49  \n\n$`CYCLE 1 DAY 2`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 2\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                            \n  PK Parameter              n    Mean    SD    CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                            \n  Cmax (ug/mL)             134   29.4   6.22    21.2         28.6               24.4            29.8     10.1      43.5  \n  AUCinf obs (day*ug/mL)   134   202    41.2    20.4         197                21.4            200       105       294  \n  CL obs (ml/day/kg)       134   5.04   1.04    20.7         4.92               22.6            5.01     2.39      7.18  \nARM C (N=132)                                                                                                            \n  Cmax (ug/mL)             132   30.4   6.03    19.9         29.7               21.7            30.5     12.4      45.5  \n  AUCinf obs (day*ug/mL)   132   191    43.0    22.5         186                24.7            189      74.8       296  \n  CL obs (ml/day/kg)       132   5.07   1.07    21.1         4.96               22.4            5.01     2.48      7.50  \n\n\nPlasma Drug X: Remove Rows with 0s\n\nCoderesult &lt;- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars) %&gt;%\n  prune_table()\n\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp_x$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp_x$PPCAT))\nresult &lt;- paginate_table(result, landscape = TRUE)\nresult\n\n$`CYCLE 1 DAY 1`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                             \n  AUCinf obs (day*ug/mL)   134   203    37.7     18.6         199                18.7            197       125       311  \n  CL obs (ml/day/kg)       134   5.04   1.04     20.6         4.93               22.4            5.08     2.25      7.39  \nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   30.0   5.46     18.2         29.5               18.9            29.8     15.9      47.6  \n  AUCinf obs (day*ug/mL)   132   195    37.8     19.4         192                20.1            196       103       315  \n  CL obs (ml/day/kg)       132   5.01   0.985    19.7         4.91               21.1            4.97     2.10      7.49  \n\n$`CYCLE 1 DAY 2`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 2\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                            \n  PK Parameter              n    Mean    SD    CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                            \n  Cmax (ug/mL)             134   29.4   6.22    21.2         28.6               24.4            29.8     10.1      43.5  \n  AUCinf obs (day*ug/mL)   134   202    41.2    20.4         197                21.4            200       105       294  \n  CL obs (ml/day/kg)       134   5.04   1.04    20.7         4.92               22.6            5.01     2.39      7.18  \nARM C (N=132)                                                                                                            \n  Cmax (ug/mL)             132   30.4   6.03    19.9         29.7               21.7            30.5     12.4      45.5  \n  AUCinf obs (day*ug/mL)   132   191    43.0    22.5         186                24.7            189      74.8       296  \n  CL obs (ml/day/kg)       132   5.07   1.07    21.1         4.96               22.4            5.01     2.48      7.50  \n\n\nPlasma Drug Y\n\nCoderesult &lt;- build_table(lyt, df = adpp_y, alt_counts_df = adsl_y_splitvars)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp_y$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp_y$PPCAT))\nresult &lt;- paginate_table(result, landscape = TRUE)\nresult\n\n$`CYCLE 1 DAY 1`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y\nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   29.9   5.55     18.6         29.4               20.1            29.7     14.1      43.4  \n  AUCinf obs (day*ug/mL)   132   199    37.9     19.1         195                18.9            195       126       318  \n  CL obs (ml/day/kg)       132   4.96   0.895    18.1         4.87               18.7            4.94     2.99      7.21  \n\n$`CYCLE 1 DAY 2`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y\nVisit: CYCLE 1 DAY 2\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   30.7   6.12     19.9         30.1               21.9            31.4     12.9      48.3  \n  AUCinf obs (day*ug/mL)   132   199    40.0     20.1         194                22.2            197      79.2       295  \n  CL obs (ml/day/kg)       132   4.99   0.984    19.7         4.89               20.9            4.96     2.58      8.39  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:04 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\n# Preprocess analysis data ----\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;%\n  filter(PPSPEC == \"Plasma\") %&gt;%\n  filter(AVISIT %in% c(\"CYCLE 1 DAY 1\", \"CYCLE 1 DAY 2\")) %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PARAM = reorder(PARAM, TLG_ORDER))\n\n# Preprocess subject-level data ----\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Workaround needed to include (N=xx) population counts\n# Repeat ADSL by the number of levels in AVISIT\n# Include AVISIT and dummy PARAM as it's needed for trim_levels_in_group\nadsl_tmp &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ARMCD) %&gt;%\n  unique() %&gt;%\n  mutate(PARAM = factor(NA_character_, levels = levels(adpp$PARAM)))\n\n# Data for Plasma Drug X example ----\nadpp_x &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  # Please do not replicate mutate statement below!\n  # It is used to make the random data in this example more realistic\n  # as not all parameters are always available across all visits.\n  mutate(\n    AVAL = if_else(\n      ARMCD == \"ARM A\" & AVISIT == \"CYCLE 1 DAY 1\" & PARAM == \"Cmax (ug/mL)\",\n      NA_real_, AVAL\n    )\n  )\n\nadpp_x_tmp &lt;- adpp_x %&gt;%\n  select(STUDYID, USUBJID, ARMCD, AVISIT) %&gt;%\n  unique()\n\nadsl_x_splitvars &lt;- adsl_tmp %&gt;%\n  left_join(adpp_x_tmp, by = c(\"STUDYID\", \"USUBJID\", \"ARMCD\")) %&gt;%\n  filter(!is.na(AVISIT))\n\n# Data for Plasma Drug Y example ----\nadpp_y &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\")\n\nadpp_y_tmp &lt;- adpp_y %&gt;%\n  select(STUDYID, USUBJID, ARMCD, AVISIT) %&gt;%\n  unique()\n\nadsl_y_splitvars &lt;- adsl_tmp %&gt;%\n  left_join(adpp_y_tmp, by = c(\"STUDYID\", \"USUBJID\", \"ARMCD\")) %&gt;%\n  filter(!is.na(AVISIT))\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"AVISIT\",\n    split_fun = drop_split_levels,\n    split_label = \"Visit\",\n    page_by = TRUE\n  ) %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"PARAM\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  add_rowcounts(alt_counts = TRUE) %&gt;%\n  split_rows_by(\n    var = \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\",\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"geom_mean\", \"geom_cv\", \"median\",\n      \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      geom_mean = \"Geometric Mean\",\n      geom_cv = \"CV % Geometric Mean\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    ),\n    na_str = \"NE\"\n  )\n\n\nPlasma Drug X\n\nCoderesult &lt;- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp_x$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp_x$PPCAT))\nresult &lt;- paginate_table(result, landscape = TRUE)\nresult\n\n$`CYCLE 1 DAY 1`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                             \n  Cmax (ug/mL)              0     NE     NE       NE           NE                 NE              NE       NE        NE   \n  AUCinf obs (day*ug/mL)   134   203    37.7     18.6         199                18.7            197       125       311  \n  CL obs (ml/day/kg)       134   5.04   1.04     20.6         4.93               22.4            5.08     2.25      7.39  \nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   30.0   5.46     18.2         29.5               18.9            29.8     15.9      47.6  \n  AUCinf obs (day*ug/mL)   132   195    37.8     19.4         192                20.1            196       103       315  \n  CL obs (ml/day/kg)       132   5.01   0.985    19.7         4.91               21.1            4.97     2.10      7.49  \n\n$`CYCLE 1 DAY 2`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 2\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                            \n  PK Parameter              n    Mean    SD    CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                            \n  Cmax (ug/mL)             134   29.4   6.22    21.2         28.6               24.4            29.8     10.1      43.5  \n  AUCinf obs (day*ug/mL)   134   202    41.2    20.4         197                21.4            200       105       294  \n  CL obs (ml/day/kg)       134   5.04   1.04    20.7         4.92               22.6            5.01     2.39      7.18  \nARM C (N=132)                                                                                                            \n  Cmax (ug/mL)             132   30.4   6.03    19.9         29.7               21.7            30.5     12.4      45.5  \n  AUCinf obs (day*ug/mL)   132   191    43.0    22.5         186                24.7            189      74.8       296  \n  CL obs (ml/day/kg)       132   5.07   1.07    21.1         4.96               22.4            5.01     2.48      7.50  \n\n\nPlasma Drug X: Remove Rows with 0s\n\nCoderesult &lt;- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars) %&gt;%\n  prune_table()\n\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp_x$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp_x$PPCAT))\nresult &lt;- paginate_table(result, landscape = TRUE)\nresult\n\n$`CYCLE 1 DAY 1`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                             \n  AUCinf obs (day*ug/mL)   134   203    37.7     18.6         199                18.7            197       125       311  \n  CL obs (ml/day/kg)       134   5.04   1.04     20.6         4.93               22.4            5.08     2.25      7.39  \nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   30.0   5.46     18.2         29.5               18.9            29.8     15.9      47.6  \n  AUCinf obs (day*ug/mL)   132   195    37.8     19.4         192                20.1            196       103       315  \n  CL obs (ml/day/kg)       132   5.01   0.985    19.7         4.91               21.1            4.97     2.10      7.49  \n\n$`CYCLE 1 DAY 2`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X\nVisit: CYCLE 1 DAY 2\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                            \n  PK Parameter              n    Mean    SD    CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                                                            \n  Cmax (ug/mL)             134   29.4   6.22    21.2         28.6               24.4            29.8     10.1      43.5  \n  AUCinf obs (day*ug/mL)   134   202    41.2    20.4         197                21.4            200       105       294  \n  CL obs (ml/day/kg)       134   5.04   1.04    20.7         4.92               22.6            5.01     2.39      7.18  \nARM C (N=132)                                                                                                            \n  Cmax (ug/mL)             132   30.4   6.03    19.9         29.7               21.7            30.5     12.4      45.5  \n  AUCinf obs (day*ug/mL)   132   191    43.0    22.5         186                24.7            189      74.8       296  \n  CL obs (ml/day/kg)       132   5.07   1.07    21.1         4.96               22.4            5.01     2.48      7.50  \n\n\nPlasma Drug Y\n\nCoderesult &lt;- build_table(lyt, df = adpp_y, alt_counts_df = adsl_y_splitvars)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp_y$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp_y$PPCAT))\nresult &lt;- paginate_table(result, landscape = TRUE)\nresult\n\n$`CYCLE 1 DAY 1`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y\nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   29.9   5.55     18.6         29.4               20.1            29.7     14.1      43.4  \n  AUCinf obs (day*ug/mL)   132   199    37.9     19.1         195                18.9            195       126       318  \n  CL obs (ml/day/kg)       132   4.96   0.895    18.1         4.87               18.7            4.94     2.99      7.21  \n\n$`CYCLE 1 DAY 2`\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y\nVisit: CYCLE 1 DAY 2\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Arm                                                                                                             \n  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nARM C (N=132)                                                                                                             \n  Cmax (ug/mL)             132   30.7   6.12     19.9         30.1               21.9            31.4     12.9      48.3  \n  AUCinf obs (day*ug/mL)   132   199    40.0     20.1         194                22.2            197      79.2       295  \n  CL obs (ml/day/kg)       132   4.99   0.984    19.7         4.89               20.9            4.96     2.58      8.39  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:02 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -971,7 +971,7 @@
     "href": "tables/pharmacokinetic/pkpt08.html",
     "title": "PKPT08",
     "section": "",
-    "text": "Data Setup\nStandard Table – Plasma\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\nadpp &lt;- adpp %&gt;%\n  filter(PPSTINT != \"NA\" & PPENINT != \"NA\") %&gt;%\n  mutate(PPINT = factor(paste0(gsub(\"[PH]\", \"\", PPSTINT), \"-\", gsub(\"[PH]\", \"\", PPENINT))))\n\n\n\n\n\n\nCode# create layout\nlyt &lt;- basic_table() %&gt;%\n  add_overall_col(\"Accumulation Interval (hours)\") %&gt;%\n  split_cols_by(var = \"PPINT\") %&gt;%\n  split_cols_by(var = \"PKPARAM\") %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ACTARM\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"median\", \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    )\n  )\n\nadpp0 &lt;- adpp %&gt;%\n  filter(\n    AVISIT == \"CYCLE 1 DAY 1\",\n    PPCAT == \"Plasma Drug X\"\n  ) %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(\n    PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))\n  )\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\n  \"Summary of Cumulative Amount and Percentage of\", unique(adpp0$PPSPEC), \"Recovered from\\n\",\n  unique(adpp0$PPCAT), \"following\", unique(adpp0$REGIMEN), \"Administration for\",\n  unique(adpp0$PPINT)[length(unique(adpp0$PPINT))], \"hours, PK Population\"\n)\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\n\ncat(rtables::toString(result, indent_size = 10))\n\nSummary of Cumulative Amount and Percentage of Urine Recovered from\n Plasma Drug X following BID Administration for 0-24 hours, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————\n                        Accumulation Interval (hours)     \n                           0-12                0-24       \nTreatment Arm       Ae (mg)    Fe (%)    Ae (mg)    Fe (%)\n——————————————————————————————————————————————————————————\nARM A                                                     \n          n           134        134       134       134  \n          Mean        1.55      15.7       1.55      15.7 \n          SD         0.339      3.35      0.339      3.35 \n          CV (%)      21.9      21.4       21.9      21.4 \n          Median      1.55      15.8       1.55      15.8 \n          Minimum    0.702      8.15      0.702      8.15 \n          Maximum     2.46      24.5       2.46      24.5 \nARM C                                                     \n          n           132        132       132       132  \n          Mean        1.54      16.1       1.54      16.1 \n          SD         0.298      3.11      0.298      3.11 \n          CV (%)      19.4      19.3       19.4      19.3 \n          Median      1.55      16.0       1.55      16.0 \n          Minimum    0.850      8.50      0.850      8.50 \n          Maximum     2.21      24.4       2.21      24.4 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:18 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table – Plasma\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\nadpp &lt;- adpp %&gt;%\n  filter(PPSTINT != \"NA\" & PPENINT != \"NA\") %&gt;%\n  mutate(PPINT = factor(paste0(gsub(\"[PH]\", \"\", PPSTINT), \"-\", gsub(\"[PH]\", \"\", PPENINT))))\n\n\n\n\n\n\nCode# create layout\nlyt &lt;- basic_table() %&gt;%\n  add_overall_col(\"Accumulation Interval (hours)\") %&gt;%\n  split_cols_by(var = \"PPINT\") %&gt;%\n  split_cols_by(var = \"PKPARAM\") %&gt;%\n  split_rows_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ACTARM\"),\n    label_pos = \"topleft\",\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\n      \"n\", \"mean\", \"sd\", \"cv\",\n      \"median\", \"min\", \"max\"\n    ),\n    .labels = c(\n      n = \"n\",\n      mean = \"Mean\",\n      sd = \"SD\",\n      cv = \"CV (%)\",\n      median = \"Median\",\n      min = \"Minimum\",\n      max = \"Maximum\"\n    ),\n    .formats = c(\n      n = \"xx.\",\n      mean = format_sigfig(3),\n      sd = format_sigfig(3),\n      cv = \"xx.x\",\n      median = format_sigfig(3),\n      min = format_sigfig(3),\n      max = format_sigfig(3)\n    )\n  )\n\nadpp0 &lt;- adpp %&gt;%\n  filter(\n    AVISIT == \"CYCLE 1 DAY 1\",\n    PPCAT == \"Plasma Drug X\"\n  ) %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(\n    PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))\n  )\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\n  \"Summary of Cumulative Amount and Percentage of\", unique(adpp0$PPSPEC), \"Recovered from\\n\",\n  unique(adpp0$PPCAT), \"following\", unique(adpp0$REGIMEN), \"Administration for\",\n  unique(adpp0$PPINT)[length(unique(adpp0$PPINT))], \"hours, PK Population\"\n)\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\n\ncat(rtables::toString(result, indent_size = 10))\n\nSummary of Cumulative Amount and Percentage of Urine Recovered from\n Plasma Drug X following BID Administration for 0-24 hours, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n——————————————————————————————————————————————————————————\n                        Accumulation Interval (hours)     \n                           0-12                0-24       \nTreatment Arm       Ae (mg)    Fe (%)    Ae (mg)    Fe (%)\n——————————————————————————————————————————————————————————\nARM A                                                     \n          n           134        134       134       134  \n          Mean        1.55      15.7       1.55      15.7 \n          SD         0.339      3.35      0.339      3.35 \n          CV (%)      21.9      21.4       21.9      21.4 \n          Median      1.55      15.8       1.55      15.8 \n          Minimum    0.702      8.15      0.702      8.15 \n          Maximum     2.46      24.5       2.46      24.5 \nARM C                                                     \n          n           132        132       132       132  \n          Mean        1.54      16.1       1.54      16.1 \n          SD         0.298      3.11      0.298      3.11 \n          CV (%)      19.4      19.3       19.4      19.3 \n          Median      1.55      16.0       1.55      16.0 \n          Minimum    0.850      8.50      0.850      8.50 \n          Maximum     2.21      24.4       2.21      24.4 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:16 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -983,7 +983,7 @@
     "href": "tables/pharmacokinetic/pkpt02.html",
     "title": "PKPT02",
     "section": "",
-    "text": "Data Setup\nStandard Table – Plasma\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;% filter(PPSPEC == \"Plasma\", AVISIT == \"CYCLE 1 DAY 1\")\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  tern::analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\"n\", \"mean_sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"range\"),\n    .formats = c(\n      n = \"xx.\",\n      mean_sd = format_sigfig(3, \"xx (xx)\"),\n      cv = \"xx.x\",\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      median = format_sigfig(3),\n      range = format_sigfig(3, \"xx - xx\")\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n———————————————————————————————————————————————————\nPK Parameter                ARM A         ARM C    \n———————————————————————————————————————————————————\nCmax (ug/mL)                                       \n  n                          134           132     \n  Mean (SD)              30.2 (6.24)   30.0 (5.46) \n  CV (%)                    20.6           18.2    \n  Geometric Mean            29.6           29.5    \n  CV % Geometric Mean       21.0           18.9    \n  Median                    29.9           29.8    \n  Min - Max              17.5 - 48.7   15.9 - 47.6 \nAUCinf obs (day*ug/mL)                             \n  n                          134           132     \n  Mean (SD)              203 (37.7)     195 (37.8) \n  CV (%)                    18.6           19.4    \n  Geometric Mean             199           192     \n  CV % Geometric Mean       18.7           20.1    \n  Median                     197           196     \n  Min - Max               125 - 311     103 - 315  \nCL obs (ml/day/kg)                                 \n  n                          134           132     \n  Mean (SD)              5.04 (1.04)   5.01 (0.985)\n  CV (%)                    20.6           19.7    \n  Geometric Mean            4.93           4.91    \n  CV % Geometric Mean       22.4           21.1    \n  Median                    5.08           4.97    \n  Min - Max              2.25 - 7.39   2.10 - 7.49 \n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————\nPK Parameter                ARM C    \n—————————————————————————————————————\nCmax (ug/mL)                         \n  n                          132     \n  Mean (SD)              29.9 (5.55) \n  CV (%)                     18.6    \n  Geometric Mean             29.4    \n  CV % Geometric Mean        20.1    \n  Median                     29.7    \n  Min - Max              14.1 - 43.4 \nAUCinf obs (day*ug/mL)               \n  n                          132     \n  Mean (SD)               199 (37.9) \n  CV (%)                     19.1    \n  Geometric Mean             195     \n  CV % Geometric Mean        18.9    \n  Median                     195     \n  Min - Max               126 - 318  \nCL obs (ml/day/kg)                   \n  n                          132     \n  Mean (SD)              4.96 (0.895)\n  CV (%)                     18.1    \n  Geometric Mean             4.87    \n  CV % Geometric Mean        18.7    \n  Median                     4.94    \n  Min - Max              2.99 - 7.21 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:29 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table – Plasma\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;% filter(PPSPEC == \"Plasma\", AVISIT == \"CYCLE 1 DAY 1\")\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  tern::analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\"n\", \"mean_sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"range\"),\n    .formats = c(\n      n = \"xx.\",\n      mean_sd = format_sigfig(3, \"xx (xx)\"),\n      cv = \"xx.x\",\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      median = format_sigfig(3),\n      range = format_sigfig(3, \"xx - xx\")\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp0$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n———————————————————————————————————————————————————\nPK Parameter                ARM A         ARM C    \n———————————————————————————————————————————————————\nCmax (ug/mL)                                       \n  n                          134           132     \n  Mean (SD)              30.2 (6.24)   30.0 (5.46) \n  CV (%)                    20.6           18.2    \n  Geometric Mean            29.6           29.5    \n  CV % Geometric Mean       21.0           18.9    \n  Median                    29.9           29.8    \n  Min - Max              17.5 - 48.7   15.9 - 47.6 \nAUCinf obs (day*ug/mL)                             \n  n                          134           132     \n  Mean (SD)              203 (37.7)     195 (37.8) \n  CV (%)                    18.6           19.4    \n  Geometric Mean             199           192     \n  CV % Geometric Mean       18.7           20.1    \n  Median                     197           196     \n  Min - Max               125 - 311     103 - 315  \nCL obs (ml/day/kg)                                 \n  n                          134           132     \n  Mean (SD)              5.04 (1.04)   5.01 (0.985)\n  CV (%)                    20.6           19.7    \n  Geometric Mean            4.93           4.91    \n  CV % Geometric Mean       22.4           21.1    \n  Median                    5.08           4.97    \n  Min - Max              2.25 - 7.39   2.10 - 7.49 \n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of\", unique(adpp1$PPSPEC), \"PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Plasma PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————\nPK Parameter                ARM C    \n—————————————————————————————————————\nCmax (ug/mL)                         \n  n                          132     \n  Mean (SD)              29.9 (5.55) \n  CV (%)                     18.6    \n  Geometric Mean             29.4    \n  CV % Geometric Mean        20.1    \n  Median                     29.7    \n  Min - Max              14.1 - 43.4 \nAUCinf obs (day*ug/mL)               \n  n                          132     \n  Mean (SD)               199 (37.9) \n  CV (%)                     19.1    \n  Geometric Mean             195     \n  CV % Geometric Mean        18.9    \n  Median                     195     \n  Min - Max               126 - 318  \nCL obs (ml/day/kg)                   \n  n                          132     \n  Mean (SD)              4.96 (0.895)\n  CV (%)                     18.1    \n  Geometric Mean             4.87    \n  CV % Geometric Mean        18.7    \n  Median                     4.94    \n  Min - Max              2.99 - 7.21 \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:27 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -995,7 +995,7 @@
     "href": "tables/pharmacokinetic/pkpt06.html",
     "title": "PKPT06",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(stringr)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;%\n  filter(AVISIT == \"CYCLE 1 DAY 1\", str_detect(tolower(PARAM), regex(\"norm by dose\", ignore_case = TRUE)))\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  tern::analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\"n\", \"mean_sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"range\"),\n    .formats = c(\n      n = \"xx.\",\n      mean_sd = format_sigfig(3, \"xx (xx)\"),\n      cv = \"xx.x\",\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      median = format_sigfig(3),\n      range = format_sigfig(3, \"xx - xx\")\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n———————————————————————————————————————————————————————————————\nPK Parameter                  ARM A                ARM C       \n———————————————————————————————————————————————————————————————\nRENALCLD (L/hr/mg)                                             \n  n                            134                  132        \n  Mean (SD)             0.00487 (0.000965)   0.00511 (0.000934)\n  CV (%)                       19.8                 18.3       \n  Geometric Mean             0.00477              0.00502      \n  CV % Geometric Mean          21.2                 19.7       \n  Median                     0.00497              0.00515      \n  Min - Max             0.00238 - 0.00726    0.00236 - 0.00741 \n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————\nPK Parameter                  ARM C      \n—————————————————————————————————————————\nRENALCLD (L/hr/mg)                       \n  n                            132       \n  Mean (SD)             0.00509 (0.00103)\n  CV (%)                      20.3       \n  Geometric Mean             0.00499     \n  CV % Geometric Mean         21.4       \n  Median                     0.00502     \n  Min - Max             0.00236 - 0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:35:41 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] stringr_1.5.1         tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 dplyr_1.1.4          \n[7] scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(stringr)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp &lt;- adpp %&gt;%\n  filter(AVISIT == \"CYCLE 1 DAY 1\", str_detect(tolower(PARAM), regex(\"norm by dose\", ignore_case = TRUE)))\n\n\n\n\n\nCode# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\n    var = \"ARMCD\",\n    split_fun = trim_levels_in_group(\"ARMCD\"),\n    split_label = \"Treatment Arm\"\n  ) %&gt;%\n  split_rows_by(\n    var = \"PKPARAM\",\n    label_pos = \"topleft\",\n    split_label = \"PK Parameter\"\n  ) %&gt;%\n  tern::analyze_vars(\n    vars = \"AVAL\",\n    .stats = c(\"n\", \"mean_sd\", \"cv\", \"geom_mean\", \"geom_cv\", \"median\", \"range\"),\n    .formats = c(\n      n = \"xx.\",\n      mean_sd = format_sigfig(3, \"xx (xx)\"),\n      cv = \"xx.x\",\n      geom_mean = format_sigfig(3),\n      geom_cv = \"xx.x\",\n      median = format_sigfig(3),\n      range = format_sigfig(3, \"xx - xx\")\n    )\n  )\n\n\nPlasma Drug X\n\n\nCodeadpp0 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug X\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp0)\nmain_title(result) &lt;- paste(\"Summary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp0$PPCAT), \"\\nVisit:\", unique(adpp0$AVISIT))\nresult\n\nSummary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug X \nVisit: CYCLE 1 DAY 1\n\n———————————————————————————————————————————————————————————————\nPK Parameter                  ARM A                ARM C       \n———————————————————————————————————————————————————————————————\nRENALCLD (L/hr/mg)                                             \n  n                            134                  132        \n  Mean (SD)             0.00487 (0.000965)   0.00511 (0.000934)\n  CV (%)                       19.8                 18.3       \n  Geometric Mean             0.00477              0.00502      \n  CV % Geometric Mean          21.2                 19.7       \n  Median                     0.00497              0.00515      \n  Min - Max             0.00238 - 0.00726    0.00236 - 0.00741 \n\n\nPlasma Drug Y\n\n\nCodeadpp1 &lt;- adpp %&gt;%\n  filter(PPCAT == \"Plasma Drug Y\") %&gt;%\n  h_pkparam_sort() %&gt;%\n  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, \" (\", AVALU, \")\"))) %&gt;%\n  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))\n\nresult &lt;- build_table(lyt, df = adpp1)\nmain_title(result) &lt;- paste(\"Summary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\")\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adpp1$PPCAT), \"\\nVisit:\", unique(adpp1$AVISIT))\nresult\n\nSummary of Dose-Normalized PK Parameter by Treatment Arm, PK Population\nAnalyte: Plasma Drug Y \nVisit: CYCLE 1 DAY 1\n\n—————————————————————————————————————————\nPK Parameter                  ARM C      \n—————————————————————————————————————————\nRENALCLD (L/hr/mg)                       \n  n                            132       \n  Mean (SD)             0.00509 (0.00103)\n  CV (%)                      20.3       \n  Geometric Mean             0.00499     \n  CV % Geometric Mean         21.4       \n  Median                     0.00502     \n  Min - Max             0.00236 - 0.00794\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:34:38 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] stringr_1.5.1         tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 dplyr_1.1.4          \n[7] scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26    tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Pharmacokinetic",
@@ -1007,7 +1007,7 @@
     "href": "tables/deaths/dtht01.html",
     "title": "DTHT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable for Studies Collecting Death  Information from Public Records\nTable Adding Details for “All other causes”  Category for Studies Collecting Death  Information from Public Records\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl) %&gt;% filter(SAFFL == \"Y\")\n\n# Reorder the levels in \"DTHCAT\" to put Other category at the end.\nadsl$DTHCAT &lt;- factor(adsl$DTHCAT, levels = c(\"ADVERSE EVENT\", \"PROGRESSIVE DISEASE\", \"OTHER\", \"&lt;Missing&gt;\"))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(vars = c(\"DTHCAT\"), var_labels = c(\"Primary Cause of Death\"))\n\nresult &lt;- build_table(lyt, df = adsl)\nresult\n\n                         A: Drug X    B: Placebo   C: Combination   All Patients\n                          (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths   25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                          \n  n                          25           23             22              70     \n  ADVERSE EVENT           9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE     8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                   8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(vars = c(\"DTHCAT\"), var_labels = c(\"Primary Cause of Death\")) %&gt;%\n  split_rows_by(\"DTHCAT\", split_fun = keep_split_levels(\"OTHER\"), child_labels = \"hidden\") %&gt;%\n  analyze_vars(\n    \"DTHCAUS\",\n    .stats = \"count_fraction\",\n    .indent_mods = c(\"count_fraction\" = 2L),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"LDDTHGR1\",\n    nested = FALSE,\n    var_labels = \"Days from last drug administration\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  split_rows_by(\n    \"LDDTHGR1\",\n    split_fun = remove_split_levels(\"&lt;Missing&gt;\"),\n    split_label = \"Primary cause by days from last study drug administration\",\n    label_pos = \"visible\"\n  ) %&gt;%\n  analyze_vars(\"DTHCAT\")\n\nresult &lt;- build_table(lyt, df = adsl) %&gt;% prune_table()\n\nresult\n\n                                                            A: Drug X    B: Placebo   C: Combination   All Patients\n                                                             (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths                                      25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                                                             \n  n                                                             25           23             22              70     \n  ADVERSE EVENT                                              9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE                                        8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                                                      8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n    LOST TO FOLLOW UP                                        2 (25%)      2 (20%)       2 (33.3%)        6 (25%)   \n    MISSING                                                  2 (25%)      3 (30%)       2 (33.3%)       7 (29.2%)  \n    Post-study reporting of death                           1 (12.5%)     2 (20%)       1 (16.7%)       4 (16.7%)  \n    SUICIDE                                                  2 (25%)      2 (20%)       1 (16.7%)       5 (20.8%)  \n    UNKNOWN                                                 1 (12.5%)     1 (10%)           0            2 (8.3%)  \nDays from last drug administration                                                                                 \n  n                                                             25           23             22              70     \n  &lt;=30                                                       14 (56%)    11 (47.8%)     14 (63.6%)      39 (55.7%) \n  &gt;30                                                        11 (44%)    12 (52.2%)     8 (36.4%)       31 (44.3%) \nPrimary cause by days from last study drug administration                                                          \n  &lt;=30                                                                                                             \n    n                                                           14           11             14              39     \n    ADVERSE EVENT                                           4 (28.6%)    2 (18.2%)      6 (42.9%)       12 (30.8%) \n    PROGRESSIVE DISEASE                                     6 (42.9%)    3 (27.3%)      4 (28.6%)       13 (33.3%) \n    OTHER                                                   4 (28.6%)    6 (54.5%)      4 (28.6%)       14 (35.9%) \n  &gt;30                                                                                                              \n    n                                                           11           12             8               31     \n    ADVERSE EVENT                                           5 (45.5%)    5 (41.7%)       4 (50%)        14 (45.2%) \n    PROGRESSIVE DISEASE                                     2 (18.2%)     3 (25%)        2 (25%)        7 (22.6%)  \n    OTHER                                                   4 (36.4%)    4 (33.3%)       2 (25%)        10 (32.3%) \n\n\n\n\n\nCodedthcaus_levels &lt;- levels(adsl[adsl$DTHCAT == \"OTHER\", ]$DTHCAUS)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(\n    vars = c(\"DTHCAT\"),\n    var_labels = c(\"Primary Cause of Death\"),\n    table_names = \"primary_cause\"\n  ) %&gt;%\n  split_rows_by(\n    \"DTHCAT\",\n    split_fun = keep_split_levels(\"OTHER\"),\n    child_labels = \"hidden\"\n  ) %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[5],\n    .labels = c(count_fraction = \"Post-study reportings of death\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"post_study_deaths\"\n  ) %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[-5],\n    .labels = c(count_fraction = \"All other causes\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"all_other_causes\"\n  )\n\nresult &lt;- build_table(lyt, df = adsl)\n\nresult\n\n                                     A: Drug X    B: Placebo   C: Combination   All Patients\n                                      (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths               25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                                      \n  n                                      25           23             22              70     \n  ADVERSE EVENT                       9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE                 8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                               8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n    Post-study reportings of death   1 (12.5%)    2 (20.0%)      1 (16.7%)       4 (16.7%)  \n    All other causes                 7 (87.5%)    8 (80.0%)      5 (83.3%)       20 (83.3%) \n\n\n\n\n\nCodedthcaus_levels &lt;- levels(adsl[adsl$DTHCAT == \"OTHER\", ]$DTHCAUS)\n\n# create a helper variable DTHCAUS_other\nadsl &lt;- adsl %&gt;%\n  mutate(\n    DTHCAUS_other = factor(ifelse(\n      DTHCAT == \"OTHER\" & DTHCAUS != \"Post-study reporting of death\", as.character(DTHCAUS), NA\n    ), levels = c(\"LOST TO FOLLOW UP\", \"SUICIDE\", \"UNKNOWN\", \"MISSING\")) %&gt;% explicit_na()\n  )\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(\n    vars = c(\"DTHCAT\"),\n    var_labels = c(\"Primary Cause of Death\"),\n    table_names = \"primary_cause\"\n  ) %&gt;%\n  split_rows_by(\"DTHCAT\", split_fun = keep_split_levels(\"OTHER\"), child_labels = \"hidden\") %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[5],\n    .labels = c(count_fraction = \"Post-study reportings of death\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"post_study_deaths\"\n  ) %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[-5],\n    .labels = c(count_fraction = \"All other causes\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"all_other_causes\"\n  ) %&gt;%\n  analyze_vars(\n    \"DTHCAUS_other\",\n    .stats = \"count_fraction\",\n    .indent_mods = c(\"count_fraction\" = 3L),\n    show_labels = \"hidden\"\n  )\n\nresult &lt;- build_table(lyt, df = adsl)\n\nresult\n\n                                     A: Drug X    B: Placebo   C: Combination   All Patients\n                                      (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths               25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                                      \n  n                                      25           23             22              70     \n  ADVERSE EVENT                       9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE                 8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                               8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n    Post-study reportings of death   1 (12.5%)    2 (20.0%)      1 (16.7%)       4 (16.7%)  \n    All other causes                 7 (87.5%)    8 (80.0%)      5 (83.3%)       20 (83.3%) \n      LOST TO FOLLOW UP              2 (28.6%)     2 (25%)        2 (40%)         6 (30%)   \n      SUICIDE                        2 (28.6%)     2 (25%)        1 (20%)         5 (25%)   \n      UNKNOWN                        1 (14.3%)    1 (12.5%)          0            2 (10%)   \n      MISSING                        2 (28.6%)    3 (37.5%)       2 (40%)         7 (35%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:36:04 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable for Studies Collecting Death  Information from Public Records\nTable Adding Details for “All other causes”  Category for Studies Collecting Death  Information from Public Records\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl) %&gt;% filter(SAFFL == \"Y\")\n\n# Reorder the levels in \"DTHCAT\" to put Other category at the end.\nadsl$DTHCAT &lt;- factor(adsl$DTHCAT, levels = c(\"ADVERSE EVENT\", \"PROGRESSIVE DISEASE\", \"OTHER\", \"&lt;Missing&gt;\"))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(vars = c(\"DTHCAT\"), var_labels = c(\"Primary Cause of Death\"))\n\nresult &lt;- build_table(lyt, df = adsl)\nresult\n\n                         A: Drug X    B: Placebo   C: Combination   All Patients\n                          (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths   25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                          \n  n                          25           23             22              70     \n  ADVERSE EVENT           9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE     8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                   8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(vars = c(\"DTHCAT\"), var_labels = c(\"Primary Cause of Death\")) %&gt;%\n  split_rows_by(\"DTHCAT\", split_fun = keep_split_levels(\"OTHER\"), child_labels = \"hidden\") %&gt;%\n  analyze_vars(\n    \"DTHCAUS\",\n    .stats = \"count_fraction\",\n    .indent_mods = c(\"count_fraction\" = 2L),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"LDDTHGR1\",\n    nested = FALSE,\n    var_labels = \"Days from last drug administration\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  split_rows_by(\n    \"LDDTHGR1\",\n    split_fun = remove_split_levels(\"&lt;Missing&gt;\"),\n    split_label = \"Primary cause by days from last study drug administration\",\n    label_pos = \"visible\"\n  ) %&gt;%\n  analyze_vars(\"DTHCAT\")\n\nresult &lt;- build_table(lyt, df = adsl) %&gt;% prune_table()\n\nresult\n\n                                                            A: Drug X    B: Placebo   C: Combination   All Patients\n                                                             (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths                                      25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                                                             \n  n                                                             25           23             22              70     \n  ADVERSE EVENT                                              9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE                                        8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                                                      8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n    LOST TO FOLLOW UP                                        2 (25%)      2 (20%)       2 (33.3%)        6 (25%)   \n    MISSING                                                  2 (25%)      3 (30%)       2 (33.3%)       7 (29.2%)  \n    Post-study reporting of death                           1 (12.5%)     2 (20%)       1 (16.7%)       4 (16.7%)  \n    SUICIDE                                                  2 (25%)      2 (20%)       1 (16.7%)       5 (20.8%)  \n    UNKNOWN                                                 1 (12.5%)     1 (10%)           0            2 (8.3%)  \nDays from last drug administration                                                                                 \n  n                                                             25           23             22              70     \n  &lt;=30                                                       14 (56%)    11 (47.8%)     14 (63.6%)      39 (55.7%) \n  &gt;30                                                        11 (44%)    12 (52.2%)     8 (36.4%)       31 (44.3%) \nPrimary cause by days from last study drug administration                                                          \n  &lt;=30                                                                                                             \n    n                                                           14           11             14              39     \n    ADVERSE EVENT                                           4 (28.6%)    2 (18.2%)      6 (42.9%)       12 (30.8%) \n    PROGRESSIVE DISEASE                                     6 (42.9%)    3 (27.3%)      4 (28.6%)       13 (33.3%) \n    OTHER                                                   4 (28.6%)    6 (54.5%)      4 (28.6%)       14 (35.9%) \n  &gt;30                                                                                                              \n    n                                                           11           12             8               31     \n    ADVERSE EVENT                                           5 (45.5%)    5 (41.7%)       4 (50%)        14 (45.2%) \n    PROGRESSIVE DISEASE                                     2 (18.2%)     3 (25%)        2 (25%)        7 (22.6%)  \n    OTHER                                                   4 (36.4%)    4 (33.3%)       2 (25%)        10 (32.3%) \n\n\n\n\n\nCodedthcaus_levels &lt;- levels(adsl[adsl$DTHCAT == \"OTHER\", ]$DTHCAUS)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(\n    vars = c(\"DTHCAT\"),\n    var_labels = c(\"Primary Cause of Death\"),\n    table_names = \"primary_cause\"\n  ) %&gt;%\n  split_rows_by(\n    \"DTHCAT\",\n    split_fun = keep_split_levels(\"OTHER\"),\n    child_labels = \"hidden\"\n  ) %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[5],\n    .labels = c(count_fraction = \"Post-study reportings of death\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"post_study_deaths\"\n  ) %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[-5],\n    .labels = c(count_fraction = \"All other causes\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"all_other_causes\"\n  )\n\nresult &lt;- build_table(lyt, df = adsl)\n\nresult\n\n                                     A: Drug X    B: Placebo   C: Combination   All Patients\n                                      (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths               25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                                      \n  n                                      25           23             22              70     \n  ADVERSE EVENT                       9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE                 8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                               8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n    Post-study reportings of death   1 (12.5%)    2 (20.0%)      1 (16.7%)       4 (16.7%)  \n    All other causes                 7 (87.5%)    8 (80.0%)      5 (83.3%)       20 (83.3%) \n\n\n\n\n\nCodedthcaus_levels &lt;- levels(adsl[adsl$DTHCAT == \"OTHER\", ]$DTHCAUS)\n\n# create a helper variable DTHCAUS_other\nadsl &lt;- adsl %&gt;%\n  mutate(\n    DTHCAUS_other = factor(ifelse(\n      DTHCAT == \"OTHER\" & DTHCAUS != \"Post-study reporting of death\", as.character(DTHCAUS), NA\n    ), levels = c(\"LOST TO FOLLOW UP\", \"SUICIDE\", \"UNKNOWN\", \"MISSING\")) %&gt;% explicit_na()\n  )\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE)) %&gt;%\n  count_values(\n    \"DTHFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Total number of deaths\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\")\n  ) %&gt;%\n  analyze_vars(\n    vars = c(\"DTHCAT\"),\n    var_labels = c(\"Primary Cause of Death\"),\n    table_names = \"primary_cause\"\n  ) %&gt;%\n  split_rows_by(\"DTHCAT\", split_fun = keep_split_levels(\"OTHER\"), child_labels = \"hidden\") %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[5],\n    .labels = c(count_fraction = \"Post-study reportings of death\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"post_study_deaths\"\n  ) %&gt;%\n  count_values(\n    \"DTHCAUS\",\n    values = dthcaus_levels[-5],\n    .labels = c(count_fraction = \"All other causes\"),\n    .formats = c(count_fraction = \"xx (xx.x%)\"),\n    .indent_mods = c(count_fraction = 2L),\n    table_names = \"all_other_causes\"\n  ) %&gt;%\n  analyze_vars(\n    \"DTHCAUS_other\",\n    .stats = \"count_fraction\",\n    .indent_mods = c(\"count_fraction\" = 3L),\n    show_labels = \"hidden\"\n  )\n\nresult &lt;- build_table(lyt, df = adsl)\n\nresult\n\n                                     A: Drug X    B: Placebo   C: Combination   All Patients\n                                      (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of deaths               25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \nPrimary Cause of Death                                                                      \n  n                                      25           23             22              70     \n  ADVERSE EVENT                       9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) \n  PROGRESSIVE DISEASE                 8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) \n  OTHER                               8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) \n    Post-study reportings of death   1 (12.5%)    2 (20.0%)      1 (16.7%)       4 (16.7%)  \n    All other causes                 7 (87.5%)    8 (80.0%)      5 (83.3%)       20 (83.3%) \n      LOST TO FOLLOW UP              2 (28.6%)     2 (25%)        2 (40%)         6 (30%)   \n      SUICIDE                        2 (28.6%)     2 (25%)        1 (20%)         5 (25%)   \n      UNKNOWN                        1 (14.3%)    1 (12.5%)          0            2 (10%)   \n      MISSING                        2 (28.6%)    3 (37.5%)       2 (40%)         7 (35%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:35:01 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Deaths",
@@ -1019,7 +1019,7 @@
     "href": "tables/disclosures/disclosurest01.html",
     "title": "DISCLOSUREST01",
     "section": "",
-    "text": "Patient Disposition Table  (CTgov & EudraCT)\nDemographic Table  (CTgov & EudraCT)\nEnrollment by Country  Table (EudraCT only)\nNon-Serious Adverse Events  Reported in \\(\\geq\\) 5% of Patients in Any  Treatment Group (CTgov & EudraCT)\nSerious Adverse Events, Fatal SAEs  and SAEs Related to Study Medication,  by Treatment Group (CTgov & EudraCT)\nDeath Table  (EudraCT only)\nReproducibility\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\nset.seed(1, kind = \"Mersenne-Twister\")\n\n# Add additional disposition variables to adsl.\nadsl0 &lt;- adsl %&gt;%\n  mutate(\n    STSTFL = case_when(\n      is.na(RANDDT) ~ \"N\",\n      TRUE ~ \"Y\"\n    ) %&gt;% as.factor(),\n    COMPSTUD = sample(\n      c(\"Y\", \"N\"),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    STUDONS = sample(\n      c(\"Alive: On Treatment\", \"Alive: In Follow-up\", NA),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    STDDRS = sample(\n      c(\n        \"Death\", \"Lost To Follow-Up\",\n        \"Protocol Violation\", \"Withdrawal By Subject\",\n        \"Other\"\n      ),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    GOTTRT = ifelse(!is.na(ACTARMCD), \"Y\", \"N\") %&gt;%\n      as.factor(),\n    DISTRTFL = sample(\n      c(\"Y\", \"N\"),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    TRTDRS = sample(\n      c(\n        \"ADVERSE EVENT\", \"PROGRESSIVE DISEASE\",\n        \"PHYSICIAN DECISION\", \"LACK OF EFFICACY\",\n        \"OTHER\"\n      ),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    STUDONS = case_when(COMPSTUD == \"N\" ~ STUDONS),\n    STDDRS = case_when(COMPSTUD == \"N\" & is.na(STUDONS) ~ STDDRS),\n    DISSTDFL = case_when(!is.na(STDDRS) ~ \"Y\"),\n    DISTRTFL = case_when(GOTTRT == \"Y\" ~ DISTRTFL),\n    TRTDRS = case_when(DISTRTFL == \"Y\" ~ TRTDRS),\n    DRSCAT = case_when(\n      TRTDRS %in% c(\"ADVERSE EVENT\", \"PHYSICIAN DECISION\") ~ \"Safety\",\n      !is.na(TRTDRS) ~ \"Other\"\n    )\n  ) %&gt;%\n  var_relabel(\n    STSTFL = \"Started Study\",\n    COMPSTUD = \"Complete Study\",\n    STUDONS = \"On-study Status\",\n    DISSTDFL = \"Discontinued Study\",\n    STDDRS = \"Reason for Study Discontinuation\",\n    GOTTRT = \"Received Treatment\",\n    DISTRTFL = \"Discontinued Treatment\",\n    TRTDRS = \"Reason for Treatment Discontinuation\",\n    DRSCAT = \"Subcategory for Treatment Discontinuation\"\n  )\n\n\nPatient Disposition Table\n\nCode# Define the split function\nsplit_fun &lt;- keep_split_levels(\"Y\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_values(\"STSTFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Started Study\")\n  ) %&gt;%\n  count_values(\"COMPSTUD\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Completed Study\")\n  ) %&gt;%\n  split_rows_by(\n    \"DISSTDFL\",\n    split_fun = split_fun\n  ) %&gt;%\n  summarize_row_groups(label_fstr = \"Discontinued Study\") %&gt;%\n  analyze_vars(\n    \"STDDRS\",\n    .stats = \"count_fraction\"\n  ) %&gt;%\n  append_topleft(\"Status\")\n\nresult &lt;- build_table(lyt = lyt, df = adsl0)\nresult\n\n                            A: Drug X      B: Placebo     C: Combination   All Patients \nStatus                       (N=134)         (N=134)         (N=132)          (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————\nStarted Study             134 (100.00%)   134 (100.00%)   132 (100.00%)    400 (100.00%)\nCompleted Study            65 (48.51%)     67 (50.00%)     69 (52.27%)     201 (50.25%) \nDiscontinued Study         15 (11.2%)      28 (20.9%)       24 (18.2%)      67 (16.8%)  \n  Death                      6 (40%)        5 (17.9%)       4 (16.7%)       15 (22.4%)  \n  Lost To Follow-Up         1 (6.7%)        3 (10.7%)       8 (33.3%)       12 (17.9%)  \n  Other                     2 (13.3%)       6 (21.4%)       5 (20.8%)       13 (19.4%)  \n  Protocol Violation        4 (26.7%)       4 (14.3%)       4 (16.7%)       12 (17.9%)  \n  Withdrawal By Subject     2 (13.3%)      10 (35.7%)       3 (12.5%)       15 (22.4%)  \n\n\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\n# Change description in variable SEX.\nadsl &lt;- adsl %&gt;%\n  mutate(\n    SEX = factor(case_when(\n      SEX == \"M\" ~ \"Male\",\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"U\" ~ \"Unknown\",\n      SEX == \"UNDIFFERENTIATED\" ~ \"Undifferentiated\"\n    )),\n    AGEGRP = factor(\n      case_when(\n        between(AGE, 18, 40) ~ \"18-40\",\n        between(AGE, 41, 64) ~ \"41-64\",\n        AGE &gt; 64 ~ \"&gt;=65\"\n      ),\n      levels = c(\"18-40\", \"41-64\", \"&gt;=65\")\n    )\n  )\n\n\nDemographic Table\n\nCodevars &lt;- c(\"AGE\", \"AGEGRP\", \"SEX\", \"RACE\", \"ETHNIC\")\nvar_labels &lt;- c(\"Age (yr)\", \"Age group\", \"Sex\", \"Race\", \"Ethnicity\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_vars(\n    vars = vars,\n    var_labels = var_labels\n  )\n\nresult &lt;- build_table(lyt = lyt, df = adsl)\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination   All Patients\n                                                (N=134)       (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                               \n  n                                               134           134            132             400     \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4) \n  Median                                         33.0          35.0            35.0            34.0    \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0 \nAge group                                                                                              \n  n                                               134           134            132             400     \n  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)     322 (80.5%) \n  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)      77 (19.2%) \n  &gt;=65                                             0             0           1 (0.8%)        1 (0.2%)  \nSex                                                                                                    \n  n                                               134           134            132             400     \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)      231 (57.8%) \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)      169 (42.2%) \nRace                                                                                                   \n  n                                               134           134            132             400     \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)      208 (52%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)      91 (22.8%) \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)      74 (18.5%) \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)       25 (6.2%)  \n  MULTIPLE                                         0         1 (0.7%)           0            1 (0.2%)  \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0            1 (0.2%)  \n  OTHER                                            0             0              0               0      \n  UNKNOWN                                          0             0              0               0      \nEthnicity                                                                                              \n  n                                               134           134            132             400     \n  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)       48 (12%)  \n  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)      308 (77%)  \n  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)       27 (6.8%)  \n  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)       17 (4.2%)  \n\n\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadsl_labels &lt;- var_labels(adsl)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\nadsl &lt;- adsl %&gt;%\n  mutate(COUNTRY = droplevels(COUNTRY)) %&gt;%\n  arrange(REGION1, COUNTRY)\n\nvar_labels(adsl) &lt;- c(adsl_labels)\n\n\nEnrollment by Country Table\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_vars(\"COUNTRY\") %&gt;%\n  append_varlabels(adsl, \"COUNTRY\")\n\nresult &lt;- build_table(lyt, adsl)\nresult\n\n          A: Drug X    B: Placebo   C: Combination   All Patients\nCountry    (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————\nn            134          134            132             400     \nCHN       74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \nUSA       10 (7.5%)    13 (9.7%)      17 (12.9%)       40 (10%)  \nBRA       13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \nPAK        12 (9%)      9 (6.7%)      10 (7.6%)       31 (7.8%)  \nNGA         8 (6%)      7 (5.2%)      11 (8.3%)       26 (6.5%)  \nRUS        5 (3.7%)      8 (6%)        6 (4.5%)       19 (4.8%)  \nJPN        5 (3.7%)      4 (3%)        9 (6.8%)       18 (4.5%)  \nGBR         4 (3%)      3 (2.2%)       2 (1.5%)        9 (2.2%)  \nCAN        3 (2.2%)     2 (1.5%)       3 (2.3%)         8 (2%)   \n\n\n\n\n\nData Setup\nTrimming function get_adae_trimmed is defined to filter AEs with greater than 5% incidence rate.\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_nonser &lt;- adae %&gt;% filter(AESER != \"Y\", SAFFL == \"Y\")\n\nget_adae_trimmed &lt;- function(adsl, adae, cutoff_rate) {\n  n_per_arm &lt;- adsl %&gt;%\n    dplyr::count(ARM)\n\n  anl_terms &lt;- adae %&gt;%\n    dplyr::group_by(ARM, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::count(\n      unique_terms = n_distinct(USUBJID)\n    ) %&gt;%\n    dplyr::select(-n) %&gt;%\n    dplyr::ungroup()\n\n  anl_terms &lt;- dplyr::left_join(\n    anl_terms,\n    n_per_arm,\n    by = \"ARM\"\n  ) %&gt;%\n    dplyr::mutate(\n      ae_rate = unique_terms / n\n    ) %&gt;%\n    dplyr::filter(ae_rate &gt;= cutoff_rate) %&gt;%\n    dplyr::select(AEDECOD) %&gt;%\n    unique()\n\n  anl &lt;- dplyr::left_join(\n    anl_terms,\n    adae,\n    by = \"AEDECOD\"\n  )\n  anl\n}\n\nadae_trim &lt;- get_adae_trimmed(adsl, adae_nonser, cutoff_rate = 0.05)\n\n\nNon-Serious Adverse Events Report in \\(\\geq\\) 5% of Patients in Any Treatment Group\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  summarize_patients_events_in_cols(\n    custom_label = \"Total number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5%\" \n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_trim$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adae_trim$AEDECOD)) %&gt;%\n  summarize_patients_events_in_cols(\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_trim)\nresult\n\nBody System or Organ Class                                                                                                A: Drug X                      B: Placebo                    C: Combination        \n  Dictionary-Derived Term                                                                                       Patients (All)   Events (All)   Patients (All)   Events (All)   Patients (All)   Events (All)\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5%        106             360             112             367             112             421     \ncl A.1                                                                                                                                                                                                       \n  dcd A.1.1.1.1                                                                                                       50              64              45              62              63              88     \ncl B.2                                                                                                                                                                                                       \n  dcd B.2.1.2.1                                                                                                       49              65              44              62              52              66     \ncl C.1                                                                                                                                                                                                       \n  dcd C.1.1.1.3                                                                                                       43              55              46              63              43              64     \ncl C.2                                                                                                                                                                                                       \n  dcd C.2.1.2.1                                                                                                       35              48              48              53              55              65     \ncl D.1                                                                                                                                                                                                       \n  dcd D.1.1.4.2                                                                                                       48              66              42              55              50              64     \ncl D.2                                                                                                                                                                                                       \n  dcd D.2.1.5.3                                                                                                       47              62              58              72              57              74     \n\n\n\n\n\nFor illustrative purposes, the adae data is filtered by arm “A: Drug X” here.\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadae &lt;- df_explicit_na(adae)\n\nadae_serious &lt;- adae %&gt;% filter(AESER == \"Y\", SAFFL == \"Y\")\nadae_serious_arm &lt;- adae_serious %&gt;% filter(ARM == \"A: Drug X\")\n\nfilters_list &lt;- list(\n  related = with_label(c(AEREL = \"Y\"), \"Events (Related)\"),\n  fatal = with_label(c(AESDTH = \"Y\"), \"Events (Fatal)\"),\n  fatal_related = with_label(c(AEREL = \"Y\", AESDTH = \"Y\"), \"Events (Fatal & Related)\")\n)\n\n\nSerious Adverse Events, Fatal SAEs and SAEs Related to Study Medication, by Treatment Group\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    empty_stats = c(\"all\", \"related\", \"fatal\", \"fatal_related\"),\n    custom_label = \"Total number of patients with at least one serious adverse event\"\n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_trim$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_serious_arm$AEDECOD)\n  ) %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_serious_arm)\nresult\n\nBody System or Organ Class                                                                                                                                     \n  Dictionary-Derived Term                                          Patients (All)   Events (All)   Events (Related)   Events (Fatal)   Events (Fatal & Related)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one serious adverse event        104                                                                                    \ncl A.1                                                                                                                                                         \n  dcd A.1.1.1.2                                                          48              68               0                 0                     0            \ncl B.1                                                                                                                                                         \n  dcd B.1.1.1.1                                                          47              56               56                56                    56           \ncl B.2                                                                                                                                                         \n  dcd B.2.2.3.1                                                          48              64               0                 0                     0            \ncl D.1                                                                                                                                                         \n  dcd D.1.1.1.1                                                          50              61               61                61                    61           \n\n\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n\nDeath Table\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"AESDTH\" = \"Y\"),\n    .labels = c(count_fraction = \"Total Number of Deaths\"),\n    .formats = c(count_fraction = \"xx (xx.xx%)\")\n  )\nresult &lt;- build_table(lyt, adae, alt_counts_df = adsl)\nresult\n\n                          A: Drug X    B: Placebo    C: Combination   All Patients\n                           (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————\nTotal Number of Deaths   76 (62.30%)   70 (56.91%)    75 (62.50%)     221 (60.55%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:36:17 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Patient Disposition Table  (CTgov & EudraCT)\nDemographic Table  (CTgov & EudraCT)\nEnrollment by Country  Table (EudraCT only)\nNon-Serious Adverse Events  Reported in \\(\\geq\\) 5% of Patients in Any  Treatment Group (CTgov & EudraCT)\nSerious Adverse Events, Fatal SAEs  and SAEs Related to Study Medication,  by Treatment Group (CTgov & EudraCT)\nDeath Table  (EudraCT only)\nReproducibility\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\nset.seed(1, kind = \"Mersenne-Twister\")\n\n# Add additional disposition variables to adsl.\nadsl0 &lt;- adsl %&gt;%\n  mutate(\n    STSTFL = case_when(\n      is.na(RANDDT) ~ \"N\",\n      TRUE ~ \"Y\"\n    ) %&gt;% as.factor(),\n    COMPSTUD = sample(\n      c(\"Y\", \"N\"),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    STUDONS = sample(\n      c(\"Alive: On Treatment\", \"Alive: In Follow-up\", NA),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    STDDRS = sample(\n      c(\n        \"Death\", \"Lost To Follow-Up\",\n        \"Protocol Violation\", \"Withdrawal By Subject\",\n        \"Other\"\n      ),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    GOTTRT = ifelse(!is.na(ACTARMCD), \"Y\", \"N\") %&gt;%\n      as.factor(),\n    DISTRTFL = sample(\n      c(\"Y\", \"N\"),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    TRTDRS = sample(\n      c(\n        \"ADVERSE EVENT\", \"PROGRESSIVE DISEASE\",\n        \"PHYSICIAN DECISION\", \"LACK OF EFFICACY\",\n        \"OTHER\"\n      ),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% as.factor(),\n    STUDONS = case_when(COMPSTUD == \"N\" ~ STUDONS),\n    STDDRS = case_when(COMPSTUD == \"N\" & is.na(STUDONS) ~ STDDRS),\n    DISSTDFL = case_when(!is.na(STDDRS) ~ \"Y\"),\n    DISTRTFL = case_when(GOTTRT == \"Y\" ~ DISTRTFL),\n    TRTDRS = case_when(DISTRTFL == \"Y\" ~ TRTDRS),\n    DRSCAT = case_when(\n      TRTDRS %in% c(\"ADVERSE EVENT\", \"PHYSICIAN DECISION\") ~ \"Safety\",\n      !is.na(TRTDRS) ~ \"Other\"\n    )\n  ) %&gt;%\n  var_relabel(\n    STSTFL = \"Started Study\",\n    COMPSTUD = \"Complete Study\",\n    STUDONS = \"On-study Status\",\n    DISSTDFL = \"Discontinued Study\",\n    STDDRS = \"Reason for Study Discontinuation\",\n    GOTTRT = \"Received Treatment\",\n    DISTRTFL = \"Discontinued Treatment\",\n    TRTDRS = \"Reason for Treatment Discontinuation\",\n    DRSCAT = \"Subcategory for Treatment Discontinuation\"\n  )\n\n\nPatient Disposition Table\n\nCode# Define the split function\nsplit_fun &lt;- keep_split_levels(\"Y\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_values(\"STSTFL\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Started Study\")\n  ) %&gt;%\n  count_values(\"COMPSTUD\",\n    values = \"Y\",\n    .labels = c(count_fraction = \"Completed Study\")\n  ) %&gt;%\n  split_rows_by(\n    \"DISSTDFL\",\n    split_fun = split_fun\n  ) %&gt;%\n  summarize_row_groups(label_fstr = \"Discontinued Study\") %&gt;%\n  analyze_vars(\n    \"STDDRS\",\n    .stats = \"count_fraction\"\n  ) %&gt;%\n  append_topleft(\"Status\")\n\nresult &lt;- build_table(lyt = lyt, df = adsl0)\nresult\n\n                            A: Drug X      B: Placebo     C: Combination   All Patients \nStatus                       (N=134)         (N=134)         (N=132)          (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————\nStarted Study             134 (100.00%)   134 (100.00%)   132 (100.00%)    400 (100.00%)\nCompleted Study            65 (48.51%)     67 (50.00%)     69 (52.27%)     201 (50.25%) \nDiscontinued Study         15 (11.2%)      28 (20.9%)       24 (18.2%)      67 (16.8%)  \n  Death                      6 (40%)        5 (17.9%)       4 (16.7%)       15 (22.4%)  \n  Lost To Follow-Up         1 (6.7%)        3 (10.7%)       8 (33.3%)       12 (17.9%)  \n  Other                     2 (13.3%)       6 (21.4%)       5 (20.8%)       13 (19.4%)  \n  Protocol Violation        4 (26.7%)       4 (14.3%)       4 (16.7%)       12 (17.9%)  \n  Withdrawal By Subject     2 (13.3%)      10 (35.7%)       3 (12.5%)       15 (22.4%)  \n\n\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\n# Change description in variable SEX.\nadsl &lt;- adsl %&gt;%\n  mutate(\n    SEX = factor(case_when(\n      SEX == \"M\" ~ \"Male\",\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"U\" ~ \"Unknown\",\n      SEX == \"UNDIFFERENTIATED\" ~ \"Undifferentiated\"\n    )),\n    AGEGRP = factor(\n      case_when(\n        between(AGE, 18, 40) ~ \"18-40\",\n        between(AGE, 41, 64) ~ \"41-64\",\n        AGE &gt; 64 ~ \"&gt;=65\"\n      ),\n      levels = c(\"18-40\", \"41-64\", \"&gt;=65\")\n    )\n  )\n\n\nDemographic Table\n\nCodevars &lt;- c(\"AGE\", \"AGEGRP\", \"SEX\", \"RACE\", \"ETHNIC\")\nvar_labels &lt;- c(\"Age (yr)\", \"Age group\", \"Sex\", \"Race\", \"Ethnicity\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_vars(\n    vars = vars,\n    var_labels = var_labels\n  )\n\nresult &lt;- build_table(lyt = lyt, df = adsl)\nresult\n\n                                               A: Drug X    B: Placebo    C: Combination   All Patients\n                                                (N=134)       (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————————————————\nAge (yr)                                                                                               \n  n                                               134           134            132             400     \n  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4) \n  Median                                         33.0          35.0            35.0            34.0    \n  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0 \nAge group                                                                                              \n  n                                               134           134            132             400     \n  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)     322 (80.5%) \n  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)      77 (19.2%) \n  &gt;=65                                             0             0           1 (0.8%)        1 (0.2%)  \nSex                                                                                                    \n  n                                               134           134            132             400     \n  Female                                       79 (59%)     82 (61.2%)       70 (53%)      231 (57.8%) \n  Male                                         55 (41%)     52 (38.8%)       62 (47%)      169 (42.2%) \nRace                                                                                                   \n  n                                               134           134            132             400     \n  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)      208 (52%)  \n  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)      91 (22.8%) \n  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)      74 (18.5%) \n  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)       25 (6.2%)  \n  MULTIPLE                                         0         1 (0.7%)           0            1 (0.2%)  \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0            1 (0.2%)  \n  OTHER                                            0             0              0               0      \n  UNKNOWN                                          0             0              0               0      \nEthnicity                                                                                              \n  n                                               134           134            132             400     \n  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)       48 (12%)  \n  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)      308 (77%)  \n  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)       27 (6.8%)  \n  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)       17 (4.2%)  \n\n\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadsl_labels &lt;- var_labels(adsl)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\nadsl &lt;- adsl %&gt;%\n  mutate(COUNTRY = droplevels(COUNTRY)) %&gt;%\n  arrange(REGION1, COUNTRY)\n\nvar_labels(adsl) &lt;- c(adsl_labels)\n\n\nEnrollment by Country Table\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  analyze_vars(\"COUNTRY\") %&gt;%\n  append_varlabels(adsl, \"COUNTRY\")\n\nresult &lt;- build_table(lyt, adsl)\nresult\n\n          A: Drug X    B: Placebo   C: Combination   All Patients\nCountry    (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————\nn            134          134            132             400     \nCHN       74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \nUSA       10 (7.5%)    13 (9.7%)      17 (12.9%)       40 (10%)  \nBRA       13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \nPAK        12 (9%)      9 (6.7%)      10 (7.6%)       31 (7.8%)  \nNGA         8 (6%)      7 (5.2%)      11 (8.3%)       26 (6.5%)  \nRUS        5 (3.7%)      8 (6%)        6 (4.5%)       19 (4.8%)  \nJPN        5 (3.7%)      4 (3%)        9 (6.8%)       18 (4.5%)  \nGBR         4 (3%)      3 (2.2%)       2 (1.5%)        9 (2.2%)  \nCAN        3 (2.2%)     2 (1.5%)       3 (2.3%)         8 (2%)   \n\n\n\n\n\nData Setup\nTrimming function get_adae_trimmed is defined to filter AEs with greater than 5% incidence rate.\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_nonser &lt;- adae %&gt;% filter(AESER != \"Y\", SAFFL == \"Y\")\n\nget_adae_trimmed &lt;- function(adsl, adae, cutoff_rate) {\n  n_per_arm &lt;- adsl %&gt;%\n    dplyr::count(ARM)\n\n  anl_terms &lt;- adae %&gt;%\n    dplyr::group_by(ARM, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::count(\n      unique_terms = n_distinct(USUBJID)\n    ) %&gt;%\n    dplyr::select(-n) %&gt;%\n    dplyr::ungroup()\n\n  anl_terms &lt;- dplyr::left_join(\n    anl_terms,\n    n_per_arm,\n    by = \"ARM\"\n  ) %&gt;%\n    dplyr::mutate(\n      ae_rate = unique_terms / n\n    ) %&gt;%\n    dplyr::filter(ae_rate &gt;= cutoff_rate) %&gt;%\n    dplyr::select(AEDECOD) %&gt;%\n    unique()\n\n  anl &lt;- dplyr::left_join(\n    anl_terms,\n    adae,\n    by = \"AEDECOD\"\n  )\n  anl\n}\n\nadae_trim &lt;- get_adae_trimmed(adsl, adae_nonser, cutoff_rate = 0.05)\n\n\nNon-Serious Adverse Events Report in \\(\\geq\\) 5% of Patients in Any Treatment Group\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  summarize_patients_events_in_cols(\n    custom_label = \"Total number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5%\" \n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_trim$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adae_trim$AEDECOD)) %&gt;%\n  summarize_patients_events_in_cols(\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_trim)\nresult\n\nBody System or Organ Class                                                                                                A: Drug X                      B: Placebo                    C: Combination        \n  Dictionary-Derived Term                                                                                       Patients (All)   Events (All)   Patients (All)   Events (All)   Patients (All)   Events (All)\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one non-serious adverse event occuring at a relative frequency of &gt;=5%        106             360             112             367             112             421     \ncl A.1                                                                                                                                                                                                       \n  dcd A.1.1.1.1                                                                                                       50              64              45              62              63              88     \ncl B.2                                                                                                                                                                                                       \n  dcd B.2.1.2.1                                                                                                       49              65              44              62              52              66     \ncl C.1                                                                                                                                                                                                       \n  dcd C.1.1.1.3                                                                                                       43              55              46              63              43              64     \ncl C.2                                                                                                                                                                                                       \n  dcd C.2.1.2.1                                                                                                       35              48              48              53              55              65     \ncl D.1                                                                                                                                                                                                       \n  dcd D.1.1.4.2                                                                                                       48              66              42              55              50              64     \ncl D.2                                                                                                                                                                                                       \n  dcd D.2.1.5.3                                                                                                       47              62              58              72              57              74     \n\n\n\n\n\nFor illustrative purposes, the adae data is filtered by arm “A: Drug X” here.\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadae &lt;- df_explicit_na(adae)\n\nadae_serious &lt;- adae %&gt;% filter(AESER == \"Y\", SAFFL == \"Y\")\nadae_serious_arm &lt;- adae_serious %&gt;% filter(ARM == \"A: Drug X\")\n\nfilters_list &lt;- list(\n  related = with_label(c(AEREL = \"Y\"), \"Events (Related)\"),\n  fatal = with_label(c(AESDTH = \"Y\"), \"Events (Fatal)\"),\n  fatal_related = with_label(c(AEREL = \"Y\", AESDTH = \"Y\"), \"Events (Fatal & Related)\")\n)\n\n\nSerious Adverse Events, Fatal SAEs and SAEs Related to Study Medication, by Treatment Group\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    empty_stats = c(\"all\", \"related\", \"fatal\", \"fatal_related\"),\n    custom_label = \"Total number of patients with at least one serious adverse event\"\n  ) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    nested = FALSE,\n    split_fun = split_fun,\n    indent_mod = -1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_trim$AEBODSYS)\n  ) %&gt;%\n  split_rows_by(\"AEDECOD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adae_serious_arm$AEDECOD)\n  ) %&gt;%\n  summarize_patients_events_in_cols(\n    filters_list = filters_list,\n    col_split = FALSE\n  )\n\nresult &lt;- build_table(lyt, adae_serious_arm)\nresult\n\nBody System or Organ Class                                                                                                                                     \n  Dictionary-Derived Term                                          Patients (All)   Events (All)   Events (Related)   Events (Fatal)   Events (Fatal & Related)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one serious adverse event        104                                                                                    \ncl A.1                                                                                                                                                         \n  dcd A.1.1.1.2                                                          48              68               0                 0                     0            \ncl B.1                                                                                                                                                         \n  dcd B.1.1.1.1                                                          47              56               56                56                    56           \ncl B.2                                                                                                                                                         \n  dcd B.2.2.3.1                                                          48              64               0                 0                     0            \ncl D.1                                                                                                                                                         \n  dcd D.1.1.1.1                                                          50              61               61                61                    61           \n\n\n\n\n\nData Setup\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n\nDeath Table\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"AESDTH\" = \"Y\"),\n    .labels = c(count_fraction = \"Total Number of Deaths\"),\n    .formats = c(count_fraction = \"xx (xx.xx%)\")\n  )\nresult &lt;- build_table(lyt, adae, alt_counts_df = adsl)\nresult\n\n                          A: Drug X    B: Placebo    C: Combination   All Patients\n                           (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————\nTotal Number of Deaths   76 (62.30%)   70 (56.91%)    75 (62.50%)     221 (60.55%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:35:14 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Disclosures",
@@ -1031,7 +1031,7 @@
     "href": "tables/exposure/ext01.html",
     "title": "EXT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Optional Analyses\nTable with User-Specified  Categories for Missed Doses\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nlibrary(tidyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\nadex &lt;- adex %&gt;%\n  filter(PARCAT1 == \"OVERALL\") %&gt;%\n  select(STUDYID, USUBJID, ACTARM, PARAMCD, PARAM, AVAL, PARCAT2) %&gt;%\n  mutate(\n    PARAMCD = as.character(PARAMCD),\n    AVALC = \"\"\n  ) %&gt;%\n  droplevels()\n\n# Add new param tdurd for treatment duration.\nset.seed(99)\ntdurd_adsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ACTARM) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(1:150, size = nrow(adsl), replace = TRUE),\n    AVALC = case_when(\n      0 &lt;= AVAL & AVAL &lt;= 30 ~ \"0 - 30\",\n      31 &lt;= AVAL & AVAL &lt;= 60 ~ \"31 - 60\",\n      61 &lt;= AVAL & AVAL &lt;= 90 ~ \"61 - 90\",\n      TRUE ~ \"&gt;= 91\"\n    )\n  )\ntdurd &lt;- adex %&gt;%\n  filter(PARAMCD == \"TNDOSE\") %&gt;%\n  select(STUDYID, USUBJID, PARCAT2) %&gt;%\n  left_join(tdurd_adsl, by = c(\"STUDYID\", \"USUBJID\"))\n\n# Add new param tndosmis for missed doses.\ntndosmis_adsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ACTARM) %&gt;%\n  mutate(\n    PARAMCD = \"TNDOSMIS\",\n    PARAM = \"Total number of missed doses during study\",\n    AVAL = sample(0:20, size = nrow(adsl), replace = TRUE),\n    AVALC = \"\"\n  )\ntndosmis &lt;- adex %&gt;%\n  filter(PARAMCD == \"TNDOSE\") %&gt;%\n  select(STUDYID, USUBJID, PARCAT2) %&gt;%\n  left_join(tndosmis_adsl, by = c(\"STUDYID\", \"USUBJID\"))\n\nadex &lt;- dplyr::bind_rows(adex, tdurd, tndosmis) %&gt;%\n  mutate(PARAM = factor(\n    PARAM,\n    levels = c(\n      \"Overall duration (days)\", \"Total dose administered\", \"Total number of doses administered\",\n      \"Total number of missed doses during study\"\n    )\n  ))\n\n\n\n\n\nCode# When summary table contains only categorical or only numeric parameters\n\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARCAT2\", split_label = \"\\nParameter Category (Drug A/Drug B)\", label_pos = \"topleft\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun) %&gt;%\n  analyze_vars(vars = \"AVAL\")\n\nresult &lt;- build_table(lyt = lyt, df = adex, alt_counts_df = adsl)\n\nresult\n\n                                                 A: Drug X        B: Placebo      C: Combination \nParameter Category (Drug A/Drug B)                (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————\nDrug A                                                                                           \n  Overall duration (days)                                                                        \n    n                                               75                67                75       \n    Mean (SD)                                   74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  \n    Median                                         77.0              80.0              78.0      \n    Min - Max                                   5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  \n  Total dose administered                                                                        \n    n                                               75                67                75       \n    Mean (SD)                                 6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)\n    Median                                        6720.0            6480.0            7200.0     \n    Min - Max                                 4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0\n  Total number of doses administered                                                             \n    n                                               75                67                75       \n    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                          7.0               7.0               7.0      \n    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Total number of missed doses during study                                                      \n    n                                               75                67                75       \n    Mean (SD)                                   10.5 (5.9)        10.0 (6.1)         9.5 (5.5)   \n    Median                                         10.0              11.0               9.0      \n    Min - Max                                   0.0 - 20.0        0.0 - 19.0        0.0 - 20.0   \nDrug B                                                                                           \n  Overall duration (days)                                                                        \n    n                                               59                67                57       \n    Mean (SD)                                   77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  \n    Median                                         79.0              76.0              65.0      \n    Min - Max                                   2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  \n  Total dose administered                                                                        \n    n                                               59                67                57       \n    Mean (SD)                                 6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)\n    Median                                        6720.0            6240.0            6480.0     \n    Min - Max                                 4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0\n  Total number of doses administered                                                             \n    n                                               59                67                57       \n    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                          7.0               7.0               7.0      \n    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Total number of missed doses during study                                                      \n    n                                               59                67                57       \n    Mean (SD)                                    9.1 (6.5)        10.0 (6.3)        10.7 (5.8)   \n    Median                                          9.0              10.0              11.0      \n    Min - Max                                   0.0 - 20.0        0.0 - 20.0        1.0 - 20.0   \n\n\n\n\n\nCode# When summary table contains both categorical and numeric parameters,\n# developer needs to do pre-processing to transform dataset to wide format.\nadex_avalc_wide &lt;- adex %&gt;%\n  filter(PARAMCD == \"TDURD\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVALC\n  ) %&gt;%\n  mutate(\n    TDURDC = factor(TDURD, levels = c(\"0 - 30\", \"31 - 60\", \"61 - 90\", \"&gt;= 91\"))\n  ) %&gt;%\n  select(-TDURD)\n\nanl &lt;- adex %&gt;%\n  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVAL\n  ) %&gt;%\n  left_join(adex_avalc_wide, by = c(\"STUDYID\", \"USUBJID\", \"PARCAT2\")) %&gt;%\n  var_relabel(\n    TDOSE = \"Total dose administered\",\n    TNDOSE = \"Total number of doses administered\",\n    TDURD = \"Overall duration (days)\",\n    TNDOSMIS = \"Total number of missed doses during study\",\n    TDURDC = \"Overall duration (days)\"\n  )\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARCAT2\", split_label = \"\\nParameter Category (Drug A/Drug B)\", label_pos = \"topleft\") %&gt;%\n  analyze_vars(\n    vars = c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\"),\n    var_labels = var_labels(anl)[c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\")]\n  )\n\nresult &lt;- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)\n\nresult\n\n                                          A: Drug X        B: Placebo      C: Combination \nParameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nDrug A                                                                                    \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  \n    Median                                  77.0              80.0              78.0      \n    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    \n    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   \n    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    \n    &gt;= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   \n  Total dose administered                                                                 \n    n                                        75                67                75       \n    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)\n    Median                                 6720.0            6480.0            7200.0     \n    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0\n  Total number of doses administered                                                      \n    n                                        75                67                75       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \nDrug B                                                                                    \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  \n    Median                                  79.0              76.0              65.0      \n    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   \n    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   \n    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   \n    &gt;= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   \n  Total dose administered                                                                 \n    n                                        59                67                57       \n    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)\n    Median                                 6720.0            6240.0            6480.0     \n    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0\n  Total number of doses administered                                                      \n    n                                        59                67                57       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n\n\n\n\n\nCode# When summary table contains both categorical and numeric parameters,\n# developer needs to do pre-processing to transform dataset to wide format.\nadex_avalc_wide &lt;- adex %&gt;%\n  filter(PARAMCD == \"TDURD\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVALC\n  ) %&gt;%\n  mutate(\n    TDURDC = factor(TDURD, levels = c(\"0 - 30\", \"31 - 60\", \"61 - 90\", \"&gt;= 91\"))\n  ) %&gt;%\n  select(-TDURD)\n\nanl &lt;- adex %&gt;%\n  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVAL\n  ) %&gt;%\n  left_join(adex_avalc_wide, by = c(\"STUDYID\", \"USUBJID\", \"PARCAT2\")) %&gt;%\n  var_relabel(\n    TDOSE = \"Total dose administered\",\n    TNDOSE = \"Total number of doses administered\",\n    TDURD = \"Overall duration (days)\",\n    TNDOSMIS = \"Total number of missed doses during study\",\n    TDURDC = \"Overall duration (days)\"\n  )\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARCAT2\", split_label = \"\\nParameter Category (Drug A/Drug B)\", label_pos = \"topleft\") %&gt;%\n  analyze_vars(\n    vars = c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\"),\n    var_labels = var_labels(anl)[c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\")]\n  ) %&gt;%\n  count_missed_doses(\n    \"TNDOSMIS\",\n    thresholds = c(1, 5, 10, 15),\n    var_labels = \"Missed Doses\"\n  )\n\nresult &lt;- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)\n\nresult\n\n                                          A: Drug X        B: Placebo      C: Combination \nParameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nDrug A                                                                                    \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  \n    Median                                  77.0              80.0              78.0      \n    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    \n    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   \n    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    \n    &gt;= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   \n  Total dose administered                                                                 \n    n                                        75                67                75       \n    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)\n    Median                                 6720.0            6480.0            7200.0     \n    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0\n  Total number of doses administered                                                      \n    n                                        75                67                75       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Missed Doses                                                                            \n    n                                        75                67                75       \n    At least 1 missed dose               74 (55.2%)         63 (47%)         73 (55.3%)   \n    At least 5 missed doses               59 (44%)         49 (36.6%)        59 (44.7%)   \n    At least 10 missed doses             41 (30.6%)        38 (28.4%)         37 (28%)    \n    At least 15 missed doses             26 (19.4%)        21 (15.7%)        16 (12.1%)   \nDrug B                                                                                    \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  \n    Median                                  79.0              76.0              65.0      \n    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   \n    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   \n    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   \n    &gt;= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   \n  Total dose administered                                                                 \n    n                                        59                67                57       \n    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)\n    Median                                 6720.0            6240.0            6480.0     \n    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0\n  Total number of doses administered                                                      \n    n                                        59                67                57       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Missed Doses                                                                            \n    n                                        59                67                57       \n    At least 1 missed dose               56 (41.8%)         63 (47%)         57 (43.2%)   \n    At least 5 missed doses              41 (30.6%)        50 (37.3%)        47 (35.6%)   \n    At least 10 missed doses             27 (20.1%)        36 (26.9%)        31 (23.5%)   \n    At least 15 missed doses             17 (12.7%)        18 (13.4%)        20 (15.2%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n  adex_labels &lt;- unname(col_labels(ADEX))\n  ADEX &lt;- ADEX %&gt;%\n    filter(PARCAT1 == \"OVERALL\") %&gt;%\n    mutate(\n      AVALCAT1 = case_when(\n        PARAMCD == \"TDOSE\" & AVAL &lt; 5000 ~ \"LOW\",\n        PARAMCD == \"TDOSE\" & AVAL &gt;= 5000 ~ \"HIGH\",\n        PARAMCD == \"TNDOSE\" & AVAL &lt; 10 ~ \"&lt; 10\",\n        PARAMCD == \"TNDOSE\" & AVAL &gt;= 10 ~ \"&gt;= 10\"\n      )\n    )\n  col_labels(ADEX) &lt;- c(adex_labels, \"\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Exposure Table\",\n      dataname = \"ADEX\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADEX, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEX, c(\"PARCAT2\", \"PARAM\")),\n        selected = c(\"PARCAT2\", \"PARAM\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADEX, c(\"AVAL\", \"AVALCAT1\")),\n        selected = c(\"AVAL\", \"AVALCAT1\")\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = c(\"TDOSE\", \"TNDOSE\")\n      ),\n      denominator = choices_selected(\n        choices = c(\"n\", \"N\", \"omit\"),\n        selected = \"N\"\n      )\n    )\n  ), # Set initial filter state as single study drug to produce smaller table\n  filter = teal_slices(\n    teal_slice(\"ADEX\", \"PARCAT2\", selected = \"Drug A\"),\n    teal_slice(\"ADEX\", \"AVAL\", selected = NULL)\n  )\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADEX-filter-ADEX_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:36:39 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tidyr_1.3.1                     \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013            scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               rbibutils_2.2.16        \n[76] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[79] fansi_1.0.6              viridisLite_0.4.2        geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          httr_1.4.7              \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Optional Analyses\nTable with User-Specified  Categories for Missed Doses\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nlibrary(tidyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\nadex &lt;- adex %&gt;%\n  filter(PARCAT1 == \"OVERALL\") %&gt;%\n  select(STUDYID, USUBJID, ACTARM, PARAMCD, PARAM, AVAL, PARCAT2) %&gt;%\n  mutate(\n    PARAMCD = as.character(PARAMCD),\n    AVALC = \"\"\n  ) %&gt;%\n  droplevels()\n\n# Add new param tdurd for treatment duration.\nset.seed(99)\ntdurd_adsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ACTARM) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(1:150, size = nrow(adsl), replace = TRUE),\n    AVALC = case_when(\n      0 &lt;= AVAL & AVAL &lt;= 30 ~ \"0 - 30\",\n      31 &lt;= AVAL & AVAL &lt;= 60 ~ \"31 - 60\",\n      61 &lt;= AVAL & AVAL &lt;= 90 ~ \"61 - 90\",\n      TRUE ~ \"&gt;= 91\"\n    )\n  )\ntdurd &lt;- adex %&gt;%\n  filter(PARAMCD == \"TNDOSE\") %&gt;%\n  select(STUDYID, USUBJID, PARCAT2) %&gt;%\n  left_join(tdurd_adsl, by = c(\"STUDYID\", \"USUBJID\"))\n\n# Add new param tndosmis for missed doses.\ntndosmis_adsl &lt;- adsl %&gt;%\n  select(STUDYID, USUBJID, ACTARM) %&gt;%\n  mutate(\n    PARAMCD = \"TNDOSMIS\",\n    PARAM = \"Total number of missed doses during study\",\n    AVAL = sample(0:20, size = nrow(adsl), replace = TRUE),\n    AVALC = \"\"\n  )\ntndosmis &lt;- adex %&gt;%\n  filter(PARAMCD == \"TNDOSE\") %&gt;%\n  select(STUDYID, USUBJID, PARCAT2) %&gt;%\n  left_join(tndosmis_adsl, by = c(\"STUDYID\", \"USUBJID\"))\n\nadex &lt;- dplyr::bind_rows(adex, tdurd, tndosmis) %&gt;%\n  mutate(PARAM = factor(\n    PARAM,\n    levels = c(\n      \"Overall duration (days)\", \"Total dose administered\", \"Total number of doses administered\",\n      \"Total number of missed doses during study\"\n    )\n  ))\n\n\n\n\n\nCode# When summary table contains only categorical or only numeric parameters\n\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARCAT2\", split_label = \"\\nParameter Category (Drug A/Drug B)\", label_pos = \"topleft\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun) %&gt;%\n  analyze_vars(vars = \"AVAL\")\n\nresult &lt;- build_table(lyt = lyt, df = adex, alt_counts_df = adsl)\n\nresult\n\n                                                 A: Drug X        B: Placebo      C: Combination \nParameter Category (Drug A/Drug B)                (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————\nDrug A                                                                                           \n  Overall duration (days)                                                                        \n    n                                               75                67                75       \n    Mean (SD)                                   74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  \n    Median                                         77.0              80.0              78.0      \n    Min - Max                                   5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  \n  Total dose administered                                                                        \n    n                                               75                67                75       \n    Mean (SD)                                 6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)\n    Median                                        6720.0            6480.0            7200.0     \n    Min - Max                                 4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0\n  Total number of doses administered                                                             \n    n                                               75                67                75       \n    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                          7.0               7.0               7.0      \n    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Total number of missed doses during study                                                      \n    n                                               75                67                75       \n    Mean (SD)                                   10.5 (5.9)        10.0 (6.1)         9.5 (5.5)   \n    Median                                         10.0              11.0               9.0      \n    Min - Max                                   0.0 - 20.0        0.0 - 19.0        0.0 - 20.0   \nDrug B                                                                                           \n  Overall duration (days)                                                                        \n    n                                               59                67                57       \n    Mean (SD)                                   77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  \n    Median                                         79.0              76.0              65.0      \n    Min - Max                                   2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  \n  Total dose administered                                                                        \n    n                                               59                67                57       \n    Mean (SD)                                 6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)\n    Median                                        6720.0            6240.0            6480.0     \n    Min - Max                                 4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0\n  Total number of doses administered                                                             \n    n                                               59                67                57       \n    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                          7.0               7.0               7.0      \n    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Total number of missed doses during study                                                      \n    n                                               59                67                57       \n    Mean (SD)                                    9.1 (6.5)        10.0 (6.3)        10.7 (5.8)   \n    Median                                          9.0              10.0              11.0      \n    Min - Max                                   0.0 - 20.0        0.0 - 20.0        1.0 - 20.0   \n\n\n\n\n\nCode# When summary table contains both categorical and numeric parameters,\n# developer needs to do pre-processing to transform dataset to wide format.\nadex_avalc_wide &lt;- adex %&gt;%\n  filter(PARAMCD == \"TDURD\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVALC\n  ) %&gt;%\n  mutate(\n    TDURDC = factor(TDURD, levels = c(\"0 - 30\", \"31 - 60\", \"61 - 90\", \"&gt;= 91\"))\n  ) %&gt;%\n  select(-TDURD)\n\nanl &lt;- adex %&gt;%\n  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVAL\n  ) %&gt;%\n  left_join(adex_avalc_wide, by = c(\"STUDYID\", \"USUBJID\", \"PARCAT2\")) %&gt;%\n  var_relabel(\n    TDOSE = \"Total dose administered\",\n    TNDOSE = \"Total number of doses administered\",\n    TDURD = \"Overall duration (days)\",\n    TNDOSMIS = \"Total number of missed doses during study\",\n    TDURDC = \"Overall duration (days)\"\n  )\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARCAT2\", split_label = \"\\nParameter Category (Drug A/Drug B)\", label_pos = \"topleft\") %&gt;%\n  analyze_vars(\n    vars = c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\"),\n    var_labels = var_labels(anl)[c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\")]\n  )\n\nresult &lt;- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)\n\nresult\n\n                                          A: Drug X        B: Placebo      C: Combination \nParameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nDrug A                                                                                    \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  \n    Median                                  77.0              80.0              78.0      \n    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    \n    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   \n    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    \n    &gt;= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   \n  Total dose administered                                                                 \n    n                                        75                67                75       \n    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)\n    Median                                 6720.0            6480.0            7200.0     \n    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0\n  Total number of doses administered                                                      \n    n                                        75                67                75       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \nDrug B                                                                                    \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  \n    Median                                  79.0              76.0              65.0      \n    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   \n    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   \n    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   \n    &gt;= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   \n  Total dose administered                                                                 \n    n                                        59                67                57       \n    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)\n    Median                                 6720.0            6240.0            6480.0     \n    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0\n  Total number of doses administered                                                      \n    n                                        59                67                57       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n\n\n\n\n\nCode# When summary table contains both categorical and numeric parameters,\n# developer needs to do pre-processing to transform dataset to wide format.\nadex_avalc_wide &lt;- adex %&gt;%\n  filter(PARAMCD == \"TDURD\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVALC\n  ) %&gt;%\n  mutate(\n    TDURDC = factor(TDURD, levels = c(\"0 - 30\", \"31 - 60\", \"61 - 90\", \"&gt;= 91\"))\n  ) %&gt;%\n  select(-TDURD)\n\nanl &lt;- adex %&gt;%\n  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),\n    names_from = PARAMCD,\n    values_from = AVAL\n  ) %&gt;%\n  left_join(adex_avalc_wide, by = c(\"STUDYID\", \"USUBJID\", \"PARCAT2\")) %&gt;%\n  var_relabel(\n    TDOSE = \"Total dose administered\",\n    TNDOSE = \"Total number of doses administered\",\n    TDURD = \"Overall duration (days)\",\n    TNDOSMIS = \"Total number of missed doses during study\",\n    TDURDC = \"Overall duration (days)\"\n  )\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"PARCAT2\", split_label = \"\\nParameter Category (Drug A/Drug B)\", label_pos = \"topleft\") %&gt;%\n  analyze_vars(\n    vars = c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\"),\n    var_labels = var_labels(anl)[c(\"TDURD\", \"TDURDC\", \"TDOSE\", \"TNDOSE\")]\n  ) %&gt;%\n  count_missed_doses(\n    \"TNDOSMIS\",\n    thresholds = c(1, 5, 10, 15),\n    var_labels = \"Missed Doses\"\n  )\n\nresult &lt;- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)\n\nresult\n\n                                          A: Drug X        B: Placebo      C: Combination \nParameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————\nDrug A                                                                                    \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  \n    Median                                  77.0              80.0              78.0      \n    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  \n  Overall duration (days)                                                                 \n    n                                        75                67                75       \n    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    \n    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   \n    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    \n    &gt;= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   \n  Total dose administered                                                                 \n    n                                        75                67                75       \n    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)\n    Median                                 6720.0            6480.0            7200.0     \n    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0\n  Total number of doses administered                                                      \n    n                                        75                67                75       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Missed Doses                                                                            \n    n                                        75                67                75       \n    At least 1 missed dose               74 (55.2%)         63 (47%)         73 (55.3%)   \n    At least 5 missed doses               59 (44%)         49 (36.6%)        59 (44.7%)   \n    At least 10 missed doses             41 (30.6%)        38 (28.4%)         37 (28%)    \n    At least 15 missed doses             26 (19.4%)        21 (15.7%)        16 (12.1%)   \nDrug B                                                                                    \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  \n    Median                                  79.0              76.0              65.0      \n    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  \n  Overall duration (days)                                                                 \n    n                                        59                67                57       \n    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   \n    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   \n    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   \n    &gt;= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   \n  Total dose administered                                                                 \n    n                                        59                67                57       \n    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)\n    Median                                 6720.0            6240.0            6480.0     \n    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0\n  Total number of doses administered                                                      \n    n                                        59                67                57       \n    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   \n    Median                                   7.0               7.0               7.0      \n    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   \n  Missed Doses                                                                            \n    n                                        59                67                57       \n    At least 1 missed dose               56 (41.8%)         63 (47%)         57 (43.2%)   \n    At least 5 missed doses              41 (30.6%)        50 (37.3%)        47 (35.6%)   \n    At least 10 missed doses             27 (20.1%)        36 (26.9%)        31 (23.5%)   \n    At least 15 missed doses             17 (12.7%)        18 (13.4%)        20 (15.2%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n  adex_labels &lt;- unname(col_labels(ADEX))\n  ADEX &lt;- ADEX %&gt;%\n    filter(PARCAT1 == \"OVERALL\") %&gt;%\n    mutate(\n      AVALCAT1 = case_when(\n        PARAMCD == \"TDOSE\" & AVAL &lt; 5000 ~ \"LOW\",\n        PARAMCD == \"TDOSE\" & AVAL &gt;= 5000 ~ \"HIGH\",\n        PARAMCD == \"TNDOSE\" & AVAL &lt; 10 ~ \"&lt; 10\",\n        PARAMCD == \"TNDOSE\" & AVAL &gt;= 10 ~ \"&gt;= 10\"\n      )\n    )\n  col_labels(ADEX) &lt;- c(adex_labels, \"\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Exposure Table\",\n      dataname = \"ADEX\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADEX, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEX, c(\"PARCAT2\", \"PARAM\")),\n        selected = c(\"PARCAT2\", \"PARAM\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADEX, c(\"AVAL\", \"AVALCAT1\")),\n        selected = c(\"AVAL\", \"AVALCAT1\")\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = c(\"TDOSE\", \"TNDOSE\")\n      ),\n      denominator = choices_selected(\n        choices = c(\"n\", \"N\", \"omit\"),\n        selected = \"N\"\n      )\n    )\n  ), # Set initial filter state as single study drug to produce smaller table\n  filter = teal_slices(\n    teal_slice(\"ADEX\", \"PARCAT2\", selected = \"Drug A\"),\n    teal_slice(\"ADEX\", \"AVAL\", selected = NULL)\n  )\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADEX-filter-ADEX_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:35:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tidyr_1.3.1                     \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015            scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               rbibutils_2.2.16        \n[76] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[79] fansi_1.0.6              viridisLite_0.4.2        geepack_1.3.10          \n[82] gtable_0.3.4             sass_0.4.8               digest_0.6.34           \n[85] TH.data_1.1-2            htmlwidgets_1.6.4        memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          httr_1.4.7              \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Exposure",
@@ -1043,7 +1043,7 @@
     "href": "tables/efficacy/cmht01.html",
     "title": "CMHT01",
     "section": "",
-    "text": "Data Setup\nTable of Single Parameter\nTable of Multiple Parameters\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\n\n\n\n\n\nCodeanl_01 &lt;- adqs %&gt;%\n  filter(PARAMCD == \"FKSI-FWB\" & AVISIT == \"WEEK 1 DAY 8\") %&gt;%\n  mutate(is_rsp = PCHG &gt; 15) %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\") %&gt;%\n  mutate(strata = interaction(STRATA1, STRATA2, drop = TRUE))\n\nlyt_01 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(vars = \"is_rsp\", table_names = \"est_prop\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Unstratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(vars = \"is_rsp\", table_names = \"test_prop\") %&gt;%\n  estimate_odds_ratio(vars = \"is_rsp\", table_names = \"est_or\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Stratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"est_prop_diff_strat\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"test_prop_strat\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    variables = list(strata = \"strata\", arm = \"ARM\"),\n    table_names = \"est_or_strat\"\n  )\n\nresult &lt;- build_table(\n  lyt = lyt_01,\n  df = anl_01,\n  alt_counts_df = adsl\n)\nresult\n\n                                            A: Drug X         B: Placebo         C: Combination  \n                                             (N=134)           (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————————\nResponders                                  49 (36.6%)        51 (38.1%)           47 (35.6%)    \n95% CI (Wald, with correction)             (28.0, 45.1)      (29.5, 46.7)         (27.1, 44.2)   \nUnstratified Analysis                                                                            \n  Difference in Response rate (%)                                1.5                  -1.0       \n    95% CI (Wald, with correction)                          (-10.8, 13.8)        (-13.3, 11.3)   \n  p-value (Chi-Squared Test)                                    0.8006               0.8704      \nOdds Ratio (95% CI)                                       1.07 (0.65 - 1.75)   0.96 (0.58 - 1.58)\nStratified Analysis                                                                              \n  Difference in Response rate (%)                                2.8                  -1.5       \n    95% CI (CMH, without correction)                         (-8.2, 13.8)         (-13.0, 9.9)   \n  p-value (Cochran-Mantel-Haenszel Test)                        0.6397               0.7952      \nOdds Ratio (95% CI)                                       1.12 (0.68 - 1.83)   0.99 (0.60 - 1.64)\n\n\n\n\n\n\nCodeanl_02 &lt;- adqs %&gt;%\n  filter(AVISIT == \"WEEK 1 DAY 8\") %&gt;%\n  mutate(is_rsp = PCHG &gt; 15) %&gt;%\n  mutate(PARAM = droplevels(PARAM)) %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\") %&gt;%\n  mutate(strata = interaction(STRATA1, STRATA2, drop = TRUE))\n\nvar_labels(anl_02)[\"PARAM\"] &lt;- \"Parameter\"\n\nsplit_fun &lt;- drop_split_levels\n\nlyt_02 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  split_rows_by(\n    var = \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(anl_02$PARAM)\n  ) %&gt;%\n  estimate_proportion(vars = \"is_rsp\", table_names = \"est_prop\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Unstratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(vars = \"is_rsp\", table_names = \"test_prop\") %&gt;%\n  estimate_odds_ratio(vars = \"is_rsp\", table_names = \"est_or\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Stratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"est_prop_diff_strat\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"test_prop_strat\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    variables = list(strata = \"strata\", arm = \"ARM\"),\n    table_names = \"est_or_strat\"\n  )\n\nresult &lt;- build_table(\n  lyt = lyt_02,\n  df = anl_02,\n  alt_counts_df = adsl\n)\nresult\n\n                                              A: Drug X         B: Placebo         C: Combination  \nParameter                                      (N=134)           (N=134)              (N=132)      \n———————————————————————————————————————————————————————————————————————————————————————————————————\nBFI All Questions                                                                                  \n  Responders                                  57 (42.5%)        65 (48.5%)           50 (37.9%)    \n  95% CI (Wald, with correction)             (33.8, 51.3)      (39.7, 57.3)         (29.2, 46.5)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                6.0                  -4.7       \n      95% CI (Wald, with correction)                           (-6.7, 18.6)         (-17.2, 7.9)   \n    p-value (Chi-Squared Test)                                    0.3264               0.4385      \n  Odds Ratio (95% CI)                                       1.27 (0.79 - 2.06)   0.82 (0.50 - 1.35)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                6.7                  -5.1       \n      95% CI (CMH, without correction)                         (-5.0, 18.4)         (-16.8, 6.6)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.2781               0.4056      \n  Odds Ratio (95% CI)                                       1.30 (0.80 - 2.11)   0.80 (0.49 - 1.32)\nFatigue Interference                                                                               \n  Responders                                  66 (49.3%)        50 (37.3%)           48 (36.4%)    \n  95% CI (Wald, with correction)             (40.4, 58.1)      (28.8, 45.9)         (27.8, 44.9)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                               -11.9                -12.9       \n      95% CI (Wald, with correction)                           (-24.5, 0.6)        (-25.4, -0.3)   \n    p-value (Chi-Squared Test)                                    0.0485               0.0337      \n  Odds Ratio (95% CI)                                       0.61 (0.38 - 1.00)   0.59 (0.36 - 0.96)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                               -12.0                -13.3       \n      95% CI (CMH, without correction)                        (-23.7, -0.2)        (-24.9, -1.6)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.0518               0.0303      \n  Odds Ratio (95% CI)                                       0.61 (0.37 - 0.99)   0.58 (0.36 - 0.96)\nFKSI-19 All Questions                                                                              \n  Responders                                  60 (44.8%)        57 (42.5%)           68 (51.5%)    \n  95% CI (Wald, with correction)             (36.0, 53.6)      (33.8, 51.3)         (42.6, 60.4)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                -2.2                 6.7        \n      95% CI (Wald, with correction)                          (-14.9, 10.4)         (-6.0, 19.5)   \n    p-value (Chi-Squared Test)                                    0.7118               0.2714      \n  Odds Ratio (95% CI)                                       0.91 (0.56 - 1.48)   1.31 (0.81 - 2.12)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                -2.8                 5.6        \n      95% CI (CMH, without correction)                         (-14.3, 8.8)         (-6.2, 17.4)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.6442               0.3683      \n  Odds Ratio (95% CI)                                       0.90 (0.56 - 1.47)   1.26 (0.77 - 2.04)\nFunction/Well-Being (GF1,GF3,GF7)                                                                  \n  Responders                                  49 (36.6%)        51 (38.1%)           47 (35.6%)    \n  95% CI (Wald, with correction)             (28.0, 45.1)      (29.5, 46.7)         (27.1, 44.2)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                1.5                  -1.0       \n      95% CI (Wald, with correction)                          (-10.8, 13.8)        (-13.3, 11.3)   \n    p-value (Chi-Squared Test)                                    0.8006               0.8704      \n  Odds Ratio (95% CI)                                       1.07 (0.65 - 1.75)   0.96 (0.58 - 1.58)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                2.8                  -1.5       \n      95% CI (CMH, without correction)                         (-8.2, 13.8)         (-13.0, 9.9)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.6397               0.7952      \n  Odds Ratio (95% CI)                                       1.12 (0.68 - 1.83)   0.99 (0.60 - 1.64)\nTreatment Side Effects (GP2,C5,GP5)                                                                \n  Responders                                  62 (46.3%)        49 (36.6%)           51 (38.6%)    \n  95% CI (Wald, with correction)             (37.5, 55.1)      (28.0, 45.1)         (30.0, 47.3)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                -9.7                 -7.6       \n      95% CI (Wald, with correction)                           (-22.2, 2.8)         (-20.2, 5.0)   \n    p-value (Chi-Squared Test)                                    0.1069               0.2080      \n  Odds Ratio (95% CI)                                       0.67 (0.41 - 1.09)   0.73 (0.45 - 1.19)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                -9.6                 -7.1       \n      95% CI (CMH, without correction)                         (-21.0, 1.8)         (-18.8, 4.6)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.1168               0.2426      \n  Odds Ratio (95% CI)                                       0.67 (0.41 - 1.10)   0.73 (0.45 - 1.19)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ARMCD = list(ref = \"ARM B\", comp = c(\"ARM A\", \"ARM C\")),\n  ARM = list(ref = \"B: Placebo\", comp = c(\"A: Drug X\", \"C: Combination\"))\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_binary_outcome(\n      label = \"Responders\",\n      dataname = \"ADRS\",\n      paramcd = choices_selected(\n        choices = value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BESRSPI\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      strata_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"SEX\", \"BMRKR2\")),\n        select = NULL\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:37:18 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nTable of Single Parameter\nTable of Multiple Parameters\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\n\n\n\n\n\nCodeanl_01 &lt;- adqs %&gt;%\n  filter(PARAMCD == \"FKSI-FWB\" & AVISIT == \"WEEK 1 DAY 8\") %&gt;%\n  mutate(is_rsp = PCHG &gt; 15) %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\") %&gt;%\n  mutate(strata = interaction(STRATA1, STRATA2, drop = TRUE))\n\nlyt_01 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  estimate_proportion(vars = \"is_rsp\", table_names = \"est_prop\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Unstratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(vars = \"is_rsp\", table_names = \"test_prop\") %&gt;%\n  estimate_odds_ratio(vars = \"is_rsp\", table_names = \"est_or\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Stratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"est_prop_diff_strat\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"test_prop_strat\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    variables = list(strata = \"strata\", arm = \"ARM\"),\n    table_names = \"est_or_strat\"\n  )\n\nresult &lt;- build_table(\n  lyt = lyt_01,\n  df = anl_01,\n  alt_counts_df = adsl\n)\nresult\n\n                                            A: Drug X         B: Placebo         C: Combination  \n                                             (N=134)           (N=134)              (N=132)      \n—————————————————————————————————————————————————————————————————————————————————————————————————\nResponders                                  49 (36.6%)        51 (38.1%)           47 (35.6%)    \n95% CI (Wald, with correction)             (28.0, 45.1)      (29.5, 46.7)         (27.1, 44.2)   \nUnstratified Analysis                                                                            \n  Difference in Response rate (%)                                1.5                  -1.0       \n    95% CI (Wald, with correction)                          (-10.8, 13.8)        (-13.3, 11.3)   \n  p-value (Chi-Squared Test)                                    0.8006               0.8704      \nOdds Ratio (95% CI)                                       1.07 (0.65 - 1.75)   0.96 (0.58 - 1.58)\nStratified Analysis                                                                              \n  Difference in Response rate (%)                                2.8                  -1.5       \n    95% CI (CMH, without correction)                         (-8.2, 13.8)         (-13.0, 9.9)   \n  p-value (Cochran-Mantel-Haenszel Test)                        0.6397               0.7952      \nOdds Ratio (95% CI)                                       1.12 (0.68 - 1.83)   0.99 (0.60 - 1.64)\n\n\n\n\n\n\nCodeanl_02 &lt;- adqs %&gt;%\n  filter(AVISIT == \"WEEK 1 DAY 8\") %&gt;%\n  mutate(is_rsp = PCHG &gt; 15) %&gt;%\n  mutate(PARAM = droplevels(PARAM)) %&gt;%\n  mutate(ARM = relevel(ARM, ref = \"A: Drug X\")) %&gt;%\n  var_relabel(ARM = \"Description of Planned Arm\") %&gt;%\n  mutate(strata = interaction(STRATA1, STRATA2, drop = TRUE))\n\nvar_labels(anl_02)[\"PARAM\"] &lt;- \"Parameter\"\n\nsplit_fun &lt;- drop_split_levels\n\nlyt_02 &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  split_rows_by(\n    var = \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(anl_02$PARAM)\n  ) %&gt;%\n  estimate_proportion(vars = \"is_rsp\", table_names = \"est_prop\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Unstratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    table_names = \"est_prop_diff\"\n  ) %&gt;%\n  test_proportion_diff(vars = \"is_rsp\", table_names = \"test_prop\") %&gt;%\n  estimate_odds_ratio(vars = \"is_rsp\", table_names = \"est_or\") %&gt;%\n  estimate_proportion_diff(\n    var_labels = \"Stratified Analysis\",\n    vars = \"is_rsp\",\n    show_labels = \"visible\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"est_prop_diff_strat\"\n  ) %&gt;%\n  test_proportion_diff(\n    vars = \"is_rsp\",\n    method = \"cmh\",\n    variables = list(strata = \"strata\"),\n    table_names = \"test_prop_strat\"\n  ) %&gt;%\n  estimate_odds_ratio(\n    vars = \"is_rsp\",\n    variables = list(strata = \"strata\", arm = \"ARM\"),\n    table_names = \"est_or_strat\"\n  )\n\nresult &lt;- build_table(\n  lyt = lyt_02,\n  df = anl_02,\n  alt_counts_df = adsl\n)\nresult\n\n                                              A: Drug X         B: Placebo         C: Combination  \nParameter                                      (N=134)           (N=134)              (N=132)      \n———————————————————————————————————————————————————————————————————————————————————————————————————\nBFI All Questions                                                                                  \n  Responders                                  57 (42.5%)        65 (48.5%)           50 (37.9%)    \n  95% CI (Wald, with correction)             (33.8, 51.3)      (39.7, 57.3)         (29.2, 46.5)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                6.0                  -4.7       \n      95% CI (Wald, with correction)                           (-6.7, 18.6)         (-17.2, 7.9)   \n    p-value (Chi-Squared Test)                                    0.3264               0.4385      \n  Odds Ratio (95% CI)                                       1.27 (0.79 - 2.06)   0.82 (0.50 - 1.35)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                6.7                  -5.1       \n      95% CI (CMH, without correction)                         (-5.0, 18.4)         (-16.8, 6.6)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.2781               0.4056      \n  Odds Ratio (95% CI)                                       1.30 (0.80 - 2.11)   0.80 (0.49 - 1.32)\nFatigue Interference                                                                               \n  Responders                                  66 (49.3%)        50 (37.3%)           48 (36.4%)    \n  95% CI (Wald, with correction)             (40.4, 58.1)      (28.8, 45.9)         (27.8, 44.9)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                               -11.9                -12.9       \n      95% CI (Wald, with correction)                           (-24.5, 0.6)        (-25.4, -0.3)   \n    p-value (Chi-Squared Test)                                    0.0485               0.0337      \n  Odds Ratio (95% CI)                                       0.61 (0.38 - 1.00)   0.59 (0.36 - 0.96)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                               -12.0                -13.3       \n      95% CI (CMH, without correction)                        (-23.7, -0.2)        (-24.9, -1.6)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.0518               0.0303      \n  Odds Ratio (95% CI)                                       0.61 (0.37 - 0.99)   0.58 (0.36 - 0.96)\nFKSI-19 All Questions                                                                              \n  Responders                                  60 (44.8%)        57 (42.5%)           68 (51.5%)    \n  95% CI (Wald, with correction)             (36.0, 53.6)      (33.8, 51.3)         (42.6, 60.4)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                -2.2                 6.7        \n      95% CI (Wald, with correction)                          (-14.9, 10.4)         (-6.0, 19.5)   \n    p-value (Chi-Squared Test)                                    0.7118               0.2714      \n  Odds Ratio (95% CI)                                       0.91 (0.56 - 1.48)   1.31 (0.81 - 2.12)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                -2.8                 5.6        \n      95% CI (CMH, without correction)                         (-14.3, 8.8)         (-6.2, 17.4)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.6442               0.3683      \n  Odds Ratio (95% CI)                                       0.90 (0.56 - 1.47)   1.26 (0.77 - 2.04)\nFunction/Well-Being (GF1,GF3,GF7)                                                                  \n  Responders                                  49 (36.6%)        51 (38.1%)           47 (35.6%)    \n  95% CI (Wald, with correction)             (28.0, 45.1)      (29.5, 46.7)         (27.1, 44.2)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                1.5                  -1.0       \n      95% CI (Wald, with correction)                          (-10.8, 13.8)        (-13.3, 11.3)   \n    p-value (Chi-Squared Test)                                    0.8006               0.8704      \n  Odds Ratio (95% CI)                                       1.07 (0.65 - 1.75)   0.96 (0.58 - 1.58)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                2.8                  -1.5       \n      95% CI (CMH, without correction)                         (-8.2, 13.8)         (-13.0, 9.9)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.6397               0.7952      \n  Odds Ratio (95% CI)                                       1.12 (0.68 - 1.83)   0.99 (0.60 - 1.64)\nTreatment Side Effects (GP2,C5,GP5)                                                                \n  Responders                                  62 (46.3%)        49 (36.6%)           51 (38.6%)    \n  95% CI (Wald, with correction)             (37.5, 55.1)      (28.0, 45.1)         (30.0, 47.3)   \n  Unstratified Analysis                                                                            \n    Difference in Response rate (%)                                -9.7                 -7.6       \n      95% CI (Wald, with correction)                           (-22.2, 2.8)         (-20.2, 5.0)   \n    p-value (Chi-Squared Test)                                    0.1069               0.2080      \n  Odds Ratio (95% CI)                                       0.67 (0.41 - 1.09)   0.73 (0.45 - 1.19)\n  Stratified Analysis                                                                              \n    Difference in Response rate (%)                                -9.6                 -7.1       \n      95% CI (CMH, without correction)                         (-21.0, 1.8)         (-18.8, 4.6)   \n    p-value (Cochran-Mantel-Haenszel Test)                        0.1168               0.2426      \n  Odds Ratio (95% CI)                                       0.67 (0.41 - 1.10)   0.73 (0.45 - 1.19)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADRS &lt;- data[[\"ADRS\"]]\narm_ref_comp &lt;- list(\n  ARMCD = list(ref = \"ARM B\", comp = c(\"ARM A\", \"ARM C\")),\n  ARM = list(ref = \"B: Placebo\", comp = c(\"A: Drug X\", \"C: Combination\"))\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_binary_outcome(\n      label = \"Responders\",\n      dataname = \"ADRS\",\n      paramcd = choices_selected(\n        choices = value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BESRSPI\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      strata_var = choices_selected(\n        choices = variable_choices(ADRS, c(\"SEX\", \"BMRKR2\")),\n        select = NULL\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:36:13 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -1055,7 +1055,7 @@
     "href": "tables/efficacy/mmrmt01.html",
     "title": "MMRMT01",
     "section": "",
-    "text": "Data Setup\nLeast Squares Means\nFixed Effects\nCovariance Matrix\nModel Diagnostics\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern.mmrm)\nlibrary(scda)\nlibrary(broom)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\nadqs_f &lt;- adqs %&gt;%\n  dplyr::filter(PARAMCD == \"FKSI-FWB\" & !AVISIT %in% c(\"BASELINE\", \"SCREENING\")) %&gt;%\n  droplevels() %&gt;%\n  dplyr::mutate(ARMCD = factor(ARMCD, levels = c(\"ARM B\", \"ARM A\", \"ARM C\"))) %&gt;%\n  dplyr::mutate(\n    AVISITN = rank(AVISITN) %&gt;%\n      as.factor() %&gt;%\n      as.numeric() %&gt;%\n      as.factor()\n  )\nadsl_sub &lt;- adqs_f %&gt;%\n  dplyr::filter(!is.na(CHG)) %&gt;%\n  distinct(USUBJID) %&gt;%\n  left_join(adsl, by = \"USUBJID\")\nvar_labels(adqs_f) &lt;- var_labels(adqs)\n\n\n\n\nConsidering the treatment variable in the model\n\nCodemmrm_results &lt;- fit_mmrm(\n  vars = list(\n    response = \"CHG\",\n    covariates = c(\"BASE\", \"STRATA1\", \"BMRKR2\"),\n    id = \"USUBJID\",\n    arm = \"ARMCD\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f,\n  weights_emmeans = \"equal\"\n)\n\ndf &lt;- tidy(mmrm_results)\nattr(df$AVISIT, \"label\") &lt;- \"Visit\"\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = mmrm_results$ref_level) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df$AVISIT)) %&gt;%\n  summarize_lsmeans(show_relative = \"increase\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df, alt_counts_df = adsl_sub)\n\nresult\n\nVisit                                      ARM B              ARM A              ARM C      \n  Statistics                              (N=134)            (N=134)            (N=132)     \n————————————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8                                                                                \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   3.488 (0.687)      4.246 (0.687)      3.163 (0.692)  \n    95% CI                             (2.136, 4.839)     (2.895, 5.598)     (1.803, 4.523) \n  Difference in Adjusted Means (SE)                       0.759 (0.973)      -0.325 (0.976) \n    95% CI                                               (-1.155, 2.672)    (-2.243, 1.593) \n    Relative Increase (%)                                     21.8%              -9.3%      \n    p-value (MMRM)                                            0.4362             0.7393     \nWEEK 2 DAY 15                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   9.135 (0.768)      9.018 (0.767)      8.509 (0.773)  \n    95% CI                            (7.626, 10.644)    (7.510, 10.527)    (6.991, 10.028) \n  Difference in Adjusted Means (SE)                       -0.117 (1.087)     -0.626 (1.090) \n    95% CI                                               (-2.253, 2.020)    (-2.768, 1.517) \n    Relative Increase (%)                                     -1.3%              -6.8%      \n    p-value (MMRM)                                            0.9147             0.5662     \nWEEK 3 DAY 22                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.547 (0.871)     16.014 (0.871)     15.789 (0.877) \n    95% CI                            (11.835, 15.259)   (14.303, 17.726)   (14.066, 17.513)\n  Difference in Adjusted Means (SE)                       2.467 (1.232)      2.242 (1.236)  \n    95% CI                                                (0.044, 4.890)    (-0.187, 4.672) \n    Relative Increase (%)                                     18.2%              16.6%      \n    p-value (MMRM)                                            0.0460             0.0704     \nWEEK 4 DAY 29                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.102 (0.995)     19.479 (0.995)     19.511 (1.002) \n    95% CI                            (16.146, 20.059)   (17.523, 21.435)   (17.541, 21.481)\n  Difference in Adjusted Means (SE)                       1.377 (1.408)      1.409 (1.413)  \n    95% CI                                               (-1.392, 4.145)    (-1.368, 4.186) \n    Relative Increase (%)                                      7.6%               7.8%      \n    p-value (MMRM)                                            0.3288             0.3192     \nWEEK 5 DAY 36                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   23.503 (1.058)     24.931 (1.058)     23.704 (1.065) \n    95% CI                            (21.423, 25.583)   (22.851, 27.011)   (21.610, 25.798)\n  Difference in Adjusted Means (SE)                       1.428 (1.497)      0.201 (1.502)  \n    95% CI                                               (-1.515, 4.371)    (-2.752, 3.153) \n    Relative Increase (%)                                      6.1%               0.9%      \n    p-value (MMRM)                                            0.3408             0.8937     \n\n\nNot considering the treatment variable in the model\n\nCodemmrm_results_no_arm &lt;- fit_mmrm(\n  vars = list(\n    response = \"CHG\",\n    covariates = c(\"BMRKR2\", \"STRATA1\"),\n    id = \"USUBJID\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f,\n  weights_emmeans = \"equal\",\n  parallel = TRUE\n)\n\ndf_no_arm &lt;- tidy(mmrm_results_no_arm)\nattr(df_no_arm$AVISIT, \"label\") &lt;- \"Visit\"\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df_no_arm$AVISIT)) %&gt;%\n  summarize_lsmeans(arms = FALSE) %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df_no_arm, alt_counts_df = adsl_sub)\n\nresult\n\nVisit                    All Patients  \n  Statistics               (N=400)     \n———————————————————————————————————————\nWEEK 1 DAY 8                           \n  n                          400       \n  Adjusted Mean (SE)    3.615 (0.620)  \n    95% CI              (2.396, 4.834) \nWEEK 2 DAY 15                          \n  n                          400       \n  Adjusted Mean (SE)    8.870 (0.602)  \n    95% CI             (7.687, 10.053) \nWEEK 3 DAY 22                          \n  n                          400       \n  Adjusted Mean (SE)    15.094 (0.683) \n    95% CI             (13.751, 16.437)\nWEEK 4 DAY 29                          \n  n                          400       \n  Adjusted Mean (SE)    19.009 (0.717) \n    95% CI             (17.599, 20.418)\nWEEK 5 DAY 36                          \n  n                          400       \n  Adjusted Mean (SE)    24.028 (0.747) \n    95% CI             (22.560, 25.496)\n\n\nAdding baseline rows\nIt may be of interest to summarize some different statistics at the baseline visit or summarize a different variable in the data set not used in the MMRM. For example, the model may use the variable CHG but the baseline visit row may summarize the AVAL variable, thus we would need to create two tables and then combine them to accomplish this.\n\nCode# First have the least-square means table.\na &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = mmrm_results$ref_level) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df$AVISIT)) %&gt;%\n  summarize_lsmeans(show_relative = \"increase\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df, alt_counts_df = adsl_sub)\n\n# Second prepare the baseline values summary table.\nbaseline_dat &lt;- adqs %&gt;%\n  filter(AVISIT == \"BASELINE\") %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  droplevels()\n\nb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\") %&gt;%\n  split_rows_by(\"AVISIT\") %&gt;%\n  analyze_vars(\"AVAL\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(baseline_dat, alt_counts_df = adsl_sub)\n\n# Now we can combine them as follows.\ncol_info(b) &lt;- EmptyColInfo\nrbind(b, a)\n\nVisit                                      ARM B              ARM A              ARM C      \n  Statistics                              (N=134)            (N=134)            (N=132)     \n————————————————————————————————————————————————————————————————————————————————————————————\nBASELINE                                                                                    \n  n                                         134                134                132       \n  Mean (SD)                              49.9 (7.4)         49.7 (8.3)         50.3 (9.1)   \n  Median                                    48.7               49.3               49.7      \n  Min - Max                             33.7 - 65.9        25.8 - 71.5        26.0 - 70.0   \nWEEK 1 DAY 8                                                                                \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   3.488 (0.687)      4.246 (0.687)      3.163 (0.692)  \n    95% CI                             (2.136, 4.839)     (2.895, 5.598)     (1.803, 4.523) \n  Difference in Adjusted Means (SE)                       0.759 (0.973)      -0.325 (0.976) \n    95% CI                                               (-1.155, 2.672)    (-2.243, 1.593) \n    Relative Increase (%)                                     21.8%              -9.3%      \n    p-value (MMRM)                                            0.4362             0.7393     \nWEEK 2 DAY 15                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   9.135 (0.768)      9.018 (0.767)      8.509 (0.773)  \n    95% CI                            (7.626, 10.644)    (7.510, 10.527)    (6.991, 10.028) \n  Difference in Adjusted Means (SE)                       -0.117 (1.087)     -0.626 (1.090) \n    95% CI                                               (-2.253, 2.020)    (-2.768, 1.517) \n    Relative Increase (%)                                     -1.3%              -6.8%      \n    p-value (MMRM)                                            0.9147             0.5662     \nWEEK 3 DAY 22                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.547 (0.871)     16.014 (0.871)     15.789 (0.877) \n    95% CI                            (11.835, 15.259)   (14.303, 17.726)   (14.066, 17.513)\n  Difference in Adjusted Means (SE)                       2.467 (1.232)      2.242 (1.236)  \n    95% CI                                                (0.044, 4.890)    (-0.187, 4.672) \n    Relative Increase (%)                                     18.2%              16.6%      \n    p-value (MMRM)                                            0.0460             0.0704     \nWEEK 4 DAY 29                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.102 (0.995)     19.479 (0.995)     19.511 (1.002) \n    95% CI                            (16.146, 20.059)   (17.523, 21.435)   (17.541, 21.481)\n  Difference in Adjusted Means (SE)                       1.377 (1.408)      1.409 (1.413)  \n    95% CI                                               (-1.392, 4.145)    (-1.368, 4.186) \n    Relative Increase (%)                                      7.6%               7.8%      \n    p-value (MMRM)                                            0.3288             0.3192     \nWEEK 5 DAY 36                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   23.503 (1.058)     24.931 (1.058)     23.704 (1.065) \n    95% CI                            (21.423, 25.583)   (22.851, 27.011)   (21.610, 25.798)\n  Difference in Adjusted Means (SE)                       1.428 (1.497)      0.201 (1.502)  \n    95% CI                                               (-1.515, 4.371)    (-2.752, 3.153) \n    Relative Increase (%)                                      6.1%               0.9%      \n    p-value (MMRM)                                            0.3408             0.8937     \n\n\nConsidering visit averages\nIt may also be of interest to summarize several different statistics for an averaged combination of various visits in the MMRM. For example, you may want to see the statistics for the average of the first 2 visits, or the average statistics of all visits combined. This can be accomplished by specifying the averages_emmeans argument when fitting the MMRM model.\n\nCodemmrm_results_avg_visits &lt;- fit_mmrm(\n  vars = list(\n    response = \"CHG\",\n    covariates = c(\"BASE\", \"STRATA1\", \"BMRKR2\"),\n    id = \"USUBJID\",\n    arm = \"ARMCD\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f,\n  weights_emmeans = \"equal\",\n  averages_emmeans = list(\n    \"WEEKS 1-2\" = c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\"),\n    \"WEEKS 3-5\" = c(\"WEEK 3 DAY 22\", \"WEEK 4 DAY 29\", \"WEEK 5 DAY 36\"),\n    \"ALL VISITS\" = c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\", \"WEEK 4 DAY 29\", \"WEEK 5 DAY 36\")\n  )\n)\n\ndf_avgs &lt;- tidy(mmrm_results_avg_visits)\nattr(df_avgs$AVISIT, \"label\") &lt;- \"Visit\"\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = mmrm_results_avg_visits$ref_level) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df_avgs$AVISIT)) %&gt;%\n  summarize_lsmeans(show_relative = \"increase\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df_avgs, alt_counts_df = adsl_sub)\n\nresult\n\nVisit                                      ARM B              ARM A              ARM C      \n  Statistics                              (N=134)            (N=134)            (N=132)     \n————————————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8                                                                                \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   3.488 (0.687)      4.246 (0.687)      3.163 (0.692)  \n    95% CI                             (2.136, 4.839)     (2.895, 5.598)     (1.803, 4.523) \n  Difference in Adjusted Means (SE)                       0.759 (0.973)      -0.325 (0.976) \n    95% CI                                               (-1.155, 2.672)    (-2.243, 1.593) \n    Relative Increase (%)                                     21.8%              -9.3%      \n    p-value (MMRM)                                            0.4362             0.7393     \nWEEK 2 DAY 15                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   9.135 (0.768)      9.018 (0.767)      8.509 (0.773)  \n    95% CI                            (7.626, 10.644)    (7.510, 10.527)    (6.991, 10.028) \n  Difference in Adjusted Means (SE)                       -0.117 (1.087)     -0.626 (1.090) \n    95% CI                                               (-2.253, 2.020)    (-2.768, 1.517) \n    Relative Increase (%)                                     -1.3%              -6.8%      \n    p-value (MMRM)                                            0.9147             0.5662     \nWEEK 3 DAY 22                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.547 (0.871)     16.014 (0.871)     15.789 (0.877) \n    95% CI                            (11.835, 15.259)   (14.303, 17.726)   (14.066, 17.513)\n  Difference in Adjusted Means (SE)                       2.467 (1.232)      2.242 (1.236)  \n    95% CI                                                (0.044, 4.890)    (-0.187, 4.672) \n    Relative Increase (%)                                     18.2%              16.6%      \n    p-value (MMRM)                                            0.0460             0.0704     \nWEEK 4 DAY 29                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.102 (0.995)     19.479 (0.995)     19.511 (1.002) \n    95% CI                            (16.146, 20.059)   (17.523, 21.435)   (17.541, 21.481)\n  Difference in Adjusted Means (SE)                       1.377 (1.408)      1.409 (1.413)  \n    95% CI                                               (-1.392, 4.145)    (-1.368, 4.186) \n    Relative Increase (%)                                      7.6%               7.8%      \n    p-value (MMRM)                                            0.3288             0.3192     \nWEEK 5 DAY 36                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   23.503 (1.058)     24.931 (1.058)     23.704 (1.065) \n    95% CI                            (21.423, 25.583)   (22.851, 27.011)   (21.610, 25.798)\n  Difference in Adjusted Means (SE)                       1.428 (1.497)      0.201 (1.502)  \n    95% CI                                               (-1.515, 4.371)    (-2.752, 3.153) \n    Relative Increase (%)                                      6.1%               0.9%      \n    p-value (MMRM)                                            0.3408             0.8937     \nWEEKS 1-2                                                                                   \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   6.311 (0.514)      6.632 (0.514)      5.836 (0.516)  \n    95% CI                             (5.301, 7.322)     (5.623, 7.642)     (4.821, 6.852) \n  Difference in Adjusted Means (SE)                       0.321 (0.728)      -0.475 (0.729) \n    95% CI                                               (-1.111, 1.753)    (-1.909, 0.959) \n    Relative Increase (%)                                      5.1%              -7.5%      \n    p-value (MMRM)                                            0.6596             0.5151     \nWEEKS 3-5                                                                                   \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.384 (0.560)     20.141 (0.560)     19.668 (0.563) \n    95% CI                            (17.284, 19.485)   (19.041, 21.241)   (18.562, 20.775)\n  Difference in Adjusted Means (SE)                       1.757 (0.793)      1.284 (0.794)  \n    95% CI                                                (0.198, 3.316)    (-0.278, 2.846) \n    Relative Increase (%)                                      9.6%               7.0%      \n    p-value (MMRM)                                            0.0273             0.1068     \nALL VISITS                                                                                  \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.555 (0.388)     14.738 (0.388)     14.135 (0.389) \n    95% CI                            (12.792, 14.318)   (13.976, 15.500)   (13.370, 14.900)\n  Difference in Adjusted Means (SE)                       1.183 (0.551)      0.580 (0.551)  \n    95% CI                                                (0.100, 2.266)    (-0.502, 1.663) \n    Relative Increase (%)                                      8.7%               4.3%      \n    p-value (MMRM)                                            0.0324             0.2924     \n\n\n\n\n\nConsidering the treatment variable in the model\n\n\nCodeas.rtable(mmrm_results, type = \"fixed\")\n\n                                 Estimate   Std. Error   t value    df    Pr(&gt;|t|)\n——————————————————————————————————————————————————————————————————————————————————\n(Intercept)                      55.7483      1.5244     36.5713    510   &lt;0.0001 \nBASE                             -1.0196      0.0246     -41.4362   392   &lt;0.0001 \nSTRATA1B                         -0.0369      0.5345     -0.0691    392    0.9450 \nSTRATA1C                          0.0205      0.5238      0.0392    392    0.9688 \nBMRKR2MEDIUM                     -0.3840      0.5198     -0.7386    392    0.4606 \nBMRKR2HIGH                       -1.0056      0.5259     -1.9123    392    0.0566 \nARMCDARM A                        0.7587      0.9733      0.7795    396    0.4362 \nARMCDARM C                       -0.3248      0.9756     -0.3329    394    0.7393 \nAVISITWEEK 2 DAY 15               5.6472      1.0331      5.4665    397   &lt;0.0001 \nAVISITWEEK 3 DAY 22              10.0591      1.0897      9.2311    397   &lt;0.0001 \nAVISITWEEK 4 DAY 29              14.6145      1.2176     12.0027    397   &lt;0.0001 \nAVISITWEEK 5 DAY 36              20.0154      1.2584     15.9057    397   &lt;0.0001 \nARMCDARM A:AVISITWEEK 2 DAY 15   -0.8752      1.4610     -0.5990    397    0.5495 \nARMCDARM C:AVISITWEEK 2 DAY 15   -0.3007      1.4665     -0.2050    397    0.8376 \nARMCDARM A:AVISITWEEK 3 DAY 22    1.7085      1.5411      1.1087    397    0.2682 \nARMCDARM C:AVISITWEEK 3 DAY 22    2.5673      1.5469      1.6597    397    0.0978 \nARMCDARM A:AVISITWEEK 4 DAY 29    0.6181      1.7220      0.3589    397    0.7198 \nARMCDARM C:AVISITWEEK 4 DAY 29    1.7337      1.7285      1.0030    397    0.3165 \nARMCDARM A:AVISITWEEK 5 DAY 36    0.6690      1.7796      0.3759    397    0.7072 \nARMCDARM C:AVISITWEEK 5 DAY 36    0.5257      1.7863      0.2943    397    0.7687 \n\n\nNot considering the treatment variable in the model\n\n\nCodeas.rtable(mmrm_results_no_arm, type = \"fixed\")\n\n                      Estimate   Std. Error   t value   df    Pr(&gt;|t|)\n——————————————————————————————————————————————————————————————————————\n(Intercept)            1.5624      1.1751     1.3296    466    0.1843 \nBMRKR2MEDIUM           1.0924      1.1878     0.9196    395    0.3583 \nBMRKR2HIGH             1.4619      1.1971     1.2212    395    0.2227 \nSTRATA1B               2.6321      1.2180     2.1610    395    0.0313 \nSTRATA1C               0.9717      1.2008     0.8092    395    0.4189 \nAVISITWEEK 2 DAY 15    5.2548      0.5967     8.8061    399   &lt;0.0001 \nAVISITWEEK 3 DAY 22   11.4787      0.6314     18.1802   399   &lt;0.0001 \nAVISITWEEK 4 DAY 29   15.3937      0.7039     21.8696   399   &lt;0.0001 \nAVISITWEEK 5 DAY 36   20.4130      0.7266     28.0921   399   &lt;0.0001 \n\n\n\n\n\n\n\nCodeas.rtable(mmrm_results, type = \"cov\")\n\n                WEEK 1 DAY 8   WEEK 2 DAY 15   WEEK 3 DAY 22   WEEK 4 DAY 29   WEEK 5 DAY 36\n————————————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8      63.0140         -0.6664         2.5965          -1.6231         0.2589    \nWEEK 2 DAY 15     -0.6664         78.6592         1.7004         -11.8677         -0.4261   \nWEEK 3 DAY 22      2.5965         1.7004         101.2949         -5.8424         7.1145    \nWEEK 4 DAY 29     -1.6231        -11.8677         -5.8424        132.4034         -5.3739   \nWEEK 5 DAY 36      0.2589         -0.4261         7.1145          -5.3739        149.6937   \n\n\n\n\nModel diagnostics are currently available to evaluate choice of covariates for the fixed and random effects. Statistics to evaluate choice of covariance structure are being investigated and will be included in a future release.\n\nCodeas.rtable(mmrm_results, type = \"diagnostic\")\n\n                 Diagnostic statistic value\n———————————————————————————————————————————\nREML criterion           14835.9968        \nAIC                      14865.9968        \nAICc                     14866.2412        \nBIC                      14925.8688        \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\") %&gt;%\n    filter(ABLFL != \"Y\" & ABLFL2 != \"Y\") %&gt;%\n    filter(AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\")) %&gt;%\n    mutate(\n      AVISIT = as.factor(AVISIT),\n      AVISITN = rank(AVISITN) %&gt;%\n        as.factor() %&gt;%\n        as.numeric() %&gt;%\n        as.factor() # making consecutive numeric factor\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADQS &lt;- data[[\"ADQS\"]]\narm_ref_comp &lt;- list(\n  ARMCD = list(\n    ref = \"ARM A\",\n    comp = c(\"ARM B\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_a_mmrm(\n      label = \"MMRM\",\n      dataname = \"ADQS\",\n      aval_var = choices_selected(c(\"AVAL\", \"CHG\"), \"CHG\"),\n      id_var = choices_selected(c(\"USUBJID\", \"SUBJID\"), \"USUBJID\"),\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARMCD\"),\n      visit_var = choices_selected(c(\"AVISIT\", \"AVISITN\"), \"AVISIT\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"FKSI-FWB\"\n      ),\n      cov_var = choices_selected(c(\"BASE\", \"AGE\", \"SEX\", \"BASE:AVISIT\"), NULL),\n      conf_level = choices_selected(c(0.95, 0.9, 0.8), 0.95)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:37:59 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      broom_1.0.5                     \n [9] scda_0.1.6.9017                  tern.mmrm_0.3.0.9005            \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9013           \n[15] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               mmrm_0.3.11              formatR_1.14            \n [4] tern.gee_0.1.3.9004      logger_0.3.0             testthat_3.2.1          \n [7] sandwich_3.1-0           rlang_1.1.3              multcomp_1.4-25         \n[10] compiler_4.3.3           callr_3.7.5              vctrs_0.6.5             \n[13] stringr_1.5.1            pkgconfig_2.0.3          fastmap_1.1.1           \n[16] backports_1.4.1          ellipsis_0.3.2           fontawesome_0.5.2       \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 purrr_1.0.2              xfun_0.42               \n[25] shinyvalidate_0.1.3      cachem_1.0.8             teal.reporter_0.3.0.9005\n[28] jsonlite_1.8.8           later_1.3.2              parallel_4.3.3          \n[31] R6_2.5.1                 bslib_0.6.1              stringi_1.8.3           \n[34] parallelly_1.37.1        brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               TMB_1.9.10               codetools_0.2-19        \n[49] websocket_1.4.1          processx_3.8.3           teal.widgets_0.4.2.9006 \n[52] lattice_0.22-5           tibble_3.2.1             withr_3.0.0             \n[55] coda_0.19-4.1            evaluate_0.23            survival_3.5-8          \n[58] shinycssloaders_1.0.0    pillar_1.9.0             checkmate_2.3.1         \n[61] shinyjs_2.1.0            generics_0.1.3           chromote_0.2.0          \n[64] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[67] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[70] tools_4.3.3              webshot2_0.1.1           webshot_0.5.5           \n[73] forcats_1.0.0            mvtnorm_1.2-4            cowplot_1.1.3           \n[76] grid_4.3.3               tidyr_1.3.1              rbibutils_2.2.16        \n[79] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[82] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[85] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[88] htmlwidgets_1.6.4        memoise_2.0.1            htmltools_0.5.7         \n[91] lifecycle_1.0.4          shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nLeast Squares Means\nFixed Effects\nCovariance Matrix\nModel Diagnostics\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern.mmrm)\nlibrary(scda)\nlibrary(broom)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\nadqs_f &lt;- adqs %&gt;%\n  dplyr::filter(PARAMCD == \"FKSI-FWB\" & !AVISIT %in% c(\"BASELINE\", \"SCREENING\")) %&gt;%\n  droplevels() %&gt;%\n  dplyr::mutate(ARMCD = factor(ARMCD, levels = c(\"ARM B\", \"ARM A\", \"ARM C\"))) %&gt;%\n  dplyr::mutate(\n    AVISITN = rank(AVISITN) %&gt;%\n      as.factor() %&gt;%\n      as.numeric() %&gt;%\n      as.factor()\n  )\nadsl_sub &lt;- adqs_f %&gt;%\n  dplyr::filter(!is.na(CHG)) %&gt;%\n  distinct(USUBJID) %&gt;%\n  left_join(adsl, by = \"USUBJID\")\nvar_labels(adqs_f) &lt;- var_labels(adqs)\n\n\n\n\nConsidering the treatment variable in the model\n\nCodemmrm_results &lt;- fit_mmrm(\n  vars = list(\n    response = \"CHG\",\n    covariates = c(\"BASE\", \"STRATA1\", \"BMRKR2\"),\n    id = \"USUBJID\",\n    arm = \"ARMCD\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f,\n  weights_emmeans = \"equal\"\n)\n\ndf &lt;- tidy(mmrm_results)\nattr(df$AVISIT, \"label\") &lt;- \"Visit\"\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = mmrm_results$ref_level) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df$AVISIT)) %&gt;%\n  summarize_lsmeans(show_relative = \"increase\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df, alt_counts_df = adsl_sub)\n\nresult\n\nVisit                                      ARM B              ARM A              ARM C      \n  Statistics                              (N=134)            (N=134)            (N=132)     \n————————————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8                                                                                \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   3.488 (0.687)      4.246 (0.687)      3.163 (0.692)  \n    95% CI                             (2.136, 4.839)     (2.895, 5.598)     (1.803, 4.523) \n  Difference in Adjusted Means (SE)                       0.759 (0.973)      -0.325 (0.976) \n    95% CI                                               (-1.155, 2.672)    (-2.243, 1.593) \n    Relative Increase (%)                                     21.8%              -9.3%      \n    p-value (MMRM)                                            0.4362             0.7393     \nWEEK 2 DAY 15                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   9.135 (0.768)      9.018 (0.767)      8.509 (0.773)  \n    95% CI                            (7.626, 10.644)    (7.510, 10.527)    (6.991, 10.028) \n  Difference in Adjusted Means (SE)                       -0.117 (1.087)     -0.626 (1.090) \n    95% CI                                               (-2.253, 2.020)    (-2.768, 1.517) \n    Relative Increase (%)                                     -1.3%              -6.8%      \n    p-value (MMRM)                                            0.9147             0.5662     \nWEEK 3 DAY 22                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.547 (0.871)     16.014 (0.871)     15.789 (0.877) \n    95% CI                            (11.835, 15.259)   (14.303, 17.726)   (14.066, 17.513)\n  Difference in Adjusted Means (SE)                       2.467 (1.232)      2.242 (1.236)  \n    95% CI                                                (0.044, 4.890)    (-0.187, 4.672) \n    Relative Increase (%)                                     18.2%              16.6%      \n    p-value (MMRM)                                            0.0460             0.0704     \nWEEK 4 DAY 29                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.102 (0.995)     19.479 (0.995)     19.511 (1.002) \n    95% CI                            (16.146, 20.059)   (17.523, 21.435)   (17.541, 21.481)\n  Difference in Adjusted Means (SE)                       1.377 (1.408)      1.409 (1.413)  \n    95% CI                                               (-1.392, 4.145)    (-1.368, 4.186) \n    Relative Increase (%)                                      7.6%               7.8%      \n    p-value (MMRM)                                            0.3288             0.3192     \nWEEK 5 DAY 36                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   23.503 (1.058)     24.931 (1.058)     23.704 (1.065) \n    95% CI                            (21.423, 25.583)   (22.851, 27.011)   (21.610, 25.798)\n  Difference in Adjusted Means (SE)                       1.428 (1.497)      0.201 (1.502)  \n    95% CI                                               (-1.515, 4.371)    (-2.752, 3.153) \n    Relative Increase (%)                                      6.1%               0.9%      \n    p-value (MMRM)                                            0.3408             0.8937     \n\n\nNot considering the treatment variable in the model\n\nCodemmrm_results_no_arm &lt;- fit_mmrm(\n  vars = list(\n    response = \"CHG\",\n    covariates = c(\"BMRKR2\", \"STRATA1\"),\n    id = \"USUBJID\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f,\n  weights_emmeans = \"equal\",\n  parallel = TRUE\n)\n\ndf_no_arm &lt;- tidy(mmrm_results_no_arm)\nattr(df_no_arm$AVISIT, \"label\") &lt;- \"Visit\"\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df_no_arm$AVISIT)) %&gt;%\n  summarize_lsmeans(arms = FALSE) %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df_no_arm, alt_counts_df = adsl_sub)\n\nresult\n\nVisit                    All Patients  \n  Statistics               (N=400)     \n———————————————————————————————————————\nWEEK 1 DAY 8                           \n  n                          400       \n  Adjusted Mean (SE)    3.615 (0.620)  \n    95% CI              (2.396, 4.834) \nWEEK 2 DAY 15                          \n  n                          400       \n  Adjusted Mean (SE)    8.870 (0.602)  \n    95% CI             (7.687, 10.053) \nWEEK 3 DAY 22                          \n  n                          400       \n  Adjusted Mean (SE)    15.094 (0.683) \n    95% CI             (13.751, 16.437)\nWEEK 4 DAY 29                          \n  n                          400       \n  Adjusted Mean (SE)    19.009 (0.717) \n    95% CI             (17.599, 20.418)\nWEEK 5 DAY 36                          \n  n                          400       \n  Adjusted Mean (SE)    24.028 (0.747) \n    95% CI             (22.560, 25.496)\n\n\nAdding baseline rows\nIt may be of interest to summarize some different statistics at the baseline visit or summarize a different variable in the data set not used in the MMRM. For example, the model may use the variable CHG but the baseline visit row may summarize the AVAL variable, thus we would need to create two tables and then combine them to accomplish this.\n\nCode# First have the least-square means table.\na &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = mmrm_results$ref_level) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df$AVISIT)) %&gt;%\n  summarize_lsmeans(show_relative = \"increase\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df, alt_counts_df = adsl_sub)\n\n# Second prepare the baseline values summary table.\nbaseline_dat &lt;- adqs %&gt;%\n  filter(AVISIT == \"BASELINE\") %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  droplevels()\n\nb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\") %&gt;%\n  split_rows_by(\"AVISIT\") %&gt;%\n  analyze_vars(\"AVAL\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(baseline_dat, alt_counts_df = adsl_sub)\n\n# Now we can combine them as follows.\ncol_info(b) &lt;- EmptyColInfo\nrbind(b, a)\n\nVisit                                      ARM B              ARM A              ARM C      \n  Statistics                              (N=134)            (N=134)            (N=132)     \n————————————————————————————————————————————————————————————————————————————————————————————\nBASELINE                                                                                    \n  n                                         134                134                132       \n  Mean (SD)                              49.9 (7.4)         49.7 (8.3)         50.3 (9.1)   \n  Median                                    48.7               49.3               49.7      \n  Min - Max                             33.7 - 65.9        25.8 - 71.5        26.0 - 70.0   \nWEEK 1 DAY 8                                                                                \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   3.488 (0.687)      4.246 (0.687)      3.163 (0.692)  \n    95% CI                             (2.136, 4.839)     (2.895, 5.598)     (1.803, 4.523) \n  Difference in Adjusted Means (SE)                       0.759 (0.973)      -0.325 (0.976) \n    95% CI                                               (-1.155, 2.672)    (-2.243, 1.593) \n    Relative Increase (%)                                     21.8%              -9.3%      \n    p-value (MMRM)                                            0.4362             0.7393     \nWEEK 2 DAY 15                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   9.135 (0.768)      9.018 (0.767)      8.509 (0.773)  \n    95% CI                            (7.626, 10.644)    (7.510, 10.527)    (6.991, 10.028) \n  Difference in Adjusted Means (SE)                       -0.117 (1.087)     -0.626 (1.090) \n    95% CI                                               (-2.253, 2.020)    (-2.768, 1.517) \n    Relative Increase (%)                                     -1.3%              -6.8%      \n    p-value (MMRM)                                            0.9147             0.5662     \nWEEK 3 DAY 22                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.547 (0.871)     16.014 (0.871)     15.789 (0.877) \n    95% CI                            (11.835, 15.259)   (14.303, 17.726)   (14.066, 17.513)\n  Difference in Adjusted Means (SE)                       2.467 (1.232)      2.242 (1.236)  \n    95% CI                                                (0.044, 4.890)    (-0.187, 4.672) \n    Relative Increase (%)                                     18.2%              16.6%      \n    p-value (MMRM)                                            0.0460             0.0704     \nWEEK 4 DAY 29                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.102 (0.995)     19.479 (0.995)     19.511 (1.002) \n    95% CI                            (16.146, 20.059)   (17.523, 21.435)   (17.541, 21.481)\n  Difference in Adjusted Means (SE)                       1.377 (1.408)      1.409 (1.413)  \n    95% CI                                               (-1.392, 4.145)    (-1.368, 4.186) \n    Relative Increase (%)                                      7.6%               7.8%      \n    p-value (MMRM)                                            0.3288             0.3192     \nWEEK 5 DAY 36                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   23.503 (1.058)     24.931 (1.058)     23.704 (1.065) \n    95% CI                            (21.423, 25.583)   (22.851, 27.011)   (21.610, 25.798)\n  Difference in Adjusted Means (SE)                       1.428 (1.497)      0.201 (1.502)  \n    95% CI                                               (-1.515, 4.371)    (-2.752, 3.153) \n    Relative Increase (%)                                      6.1%               0.9%      \n    p-value (MMRM)                                            0.3408             0.8937     \n\n\nConsidering visit averages\nIt may also be of interest to summarize several different statistics for an averaged combination of various visits in the MMRM. For example, you may want to see the statistics for the average of the first 2 visits, or the average statistics of all visits combined. This can be accomplished by specifying the averages_emmeans argument when fitting the MMRM model.\n\nCodemmrm_results_avg_visits &lt;- fit_mmrm(\n  vars = list(\n    response = \"CHG\",\n    covariates = c(\"BASE\", \"STRATA1\", \"BMRKR2\"),\n    id = \"USUBJID\",\n    arm = \"ARMCD\",\n    visit = \"AVISIT\"\n  ),\n  data = adqs_f,\n  weights_emmeans = \"equal\",\n  averages_emmeans = list(\n    \"WEEKS 1-2\" = c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\"),\n    \"WEEKS 3-5\" = c(\"WEEK 3 DAY 22\", \"WEEK 4 DAY 29\", \"WEEK 5 DAY 36\"),\n    \"ALL VISITS\" = c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\", \"WEEK 4 DAY 29\", \"WEEK 5 DAY 36\")\n  )\n)\n\ndf_avgs &lt;- tidy(mmrm_results_avg_visits)\nattr(df_avgs$AVISIT, \"label\") &lt;- \"Visit\"\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = mmrm_results_avg_visits$ref_level) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(df_avgs$AVISIT)) %&gt;%\n  summarize_lsmeans(show_relative = \"increase\") %&gt;%\n  append_topleft(\"  Statistics\") %&gt;%\n  build_table(df_avgs, alt_counts_df = adsl_sub)\n\nresult\n\nVisit                                      ARM B              ARM A              ARM C      \n  Statistics                              (N=134)            (N=134)            (N=132)     \n————————————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8                                                                                \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   3.488 (0.687)      4.246 (0.687)      3.163 (0.692)  \n    95% CI                             (2.136, 4.839)     (2.895, 5.598)     (1.803, 4.523) \n  Difference in Adjusted Means (SE)                       0.759 (0.973)      -0.325 (0.976) \n    95% CI                                               (-1.155, 2.672)    (-2.243, 1.593) \n    Relative Increase (%)                                     21.8%              -9.3%      \n    p-value (MMRM)                                            0.4362             0.7393     \nWEEK 2 DAY 15                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   9.135 (0.768)      9.018 (0.767)      8.509 (0.773)  \n    95% CI                            (7.626, 10.644)    (7.510, 10.527)    (6.991, 10.028) \n  Difference in Adjusted Means (SE)                       -0.117 (1.087)     -0.626 (1.090) \n    95% CI                                               (-2.253, 2.020)    (-2.768, 1.517) \n    Relative Increase (%)                                     -1.3%              -6.8%      \n    p-value (MMRM)                                            0.9147             0.5662     \nWEEK 3 DAY 22                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.547 (0.871)     16.014 (0.871)     15.789 (0.877) \n    95% CI                            (11.835, 15.259)   (14.303, 17.726)   (14.066, 17.513)\n  Difference in Adjusted Means (SE)                       2.467 (1.232)      2.242 (1.236)  \n    95% CI                                                (0.044, 4.890)    (-0.187, 4.672) \n    Relative Increase (%)                                     18.2%              16.6%      \n    p-value (MMRM)                                            0.0460             0.0704     \nWEEK 4 DAY 29                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.102 (0.995)     19.479 (0.995)     19.511 (1.002) \n    95% CI                            (16.146, 20.059)   (17.523, 21.435)   (17.541, 21.481)\n  Difference in Adjusted Means (SE)                       1.377 (1.408)      1.409 (1.413)  \n    95% CI                                               (-1.392, 4.145)    (-1.368, 4.186) \n    Relative Increase (%)                                      7.6%               7.8%      \n    p-value (MMRM)                                            0.3288             0.3192     \nWEEK 5 DAY 36                                                                               \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   23.503 (1.058)     24.931 (1.058)     23.704 (1.065) \n    95% CI                            (21.423, 25.583)   (22.851, 27.011)   (21.610, 25.798)\n  Difference in Adjusted Means (SE)                       1.428 (1.497)      0.201 (1.502)  \n    95% CI                                               (-1.515, 4.371)    (-2.752, 3.153) \n    Relative Increase (%)                                      6.1%               0.9%      \n    p-value (MMRM)                                            0.3408             0.8937     \nWEEKS 1-2                                                                                   \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   6.311 (0.514)      6.632 (0.514)      5.836 (0.516)  \n    95% CI                             (5.301, 7.322)     (5.623, 7.642)     (4.821, 6.852) \n  Difference in Adjusted Means (SE)                       0.321 (0.728)      -0.475 (0.729) \n    95% CI                                               (-1.111, 1.753)    (-1.909, 0.959) \n    Relative Increase (%)                                      5.1%              -7.5%      \n    p-value (MMRM)                                            0.6596             0.5151     \nWEEKS 3-5                                                                                   \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   18.384 (0.560)     20.141 (0.560)     19.668 (0.563) \n    95% CI                            (17.284, 19.485)   (19.041, 21.241)   (18.562, 20.775)\n  Difference in Adjusted Means (SE)                       1.757 (0.793)      1.284 (0.794)  \n    95% CI                                                (0.198, 3.316)    (-0.278, 2.846) \n    Relative Increase (%)                                      9.6%               7.0%      \n    p-value (MMRM)                                            0.0273             0.1068     \nALL VISITS                                                                                  \n  n                                         134                134                132       \n  Adjusted Mean (SE)                   13.555 (0.388)     14.738 (0.388)     14.135 (0.389) \n    95% CI                            (12.792, 14.318)   (13.976, 15.500)   (13.370, 14.900)\n  Difference in Adjusted Means (SE)                       1.183 (0.551)      0.580 (0.551)  \n    95% CI                                                (0.100, 2.266)    (-0.502, 1.663) \n    Relative Increase (%)                                      8.7%               4.3%      \n    p-value (MMRM)                                            0.0324             0.2924     \n\n\n\n\n\nConsidering the treatment variable in the model\n\n\nCodeas.rtable(mmrm_results, type = \"fixed\")\n\n                                 Estimate   Std. Error   t value    df    Pr(&gt;|t|)\n——————————————————————————————————————————————————————————————————————————————————\n(Intercept)                      55.7483      1.5244     36.5713    510   &lt;0.0001 \nBASE                             -1.0196      0.0246     -41.4362   392   &lt;0.0001 \nSTRATA1B                         -0.0369      0.5345     -0.0691    392    0.9450 \nSTRATA1C                          0.0205      0.5238      0.0392    392    0.9688 \nBMRKR2MEDIUM                     -0.3840      0.5198     -0.7386    392    0.4606 \nBMRKR2HIGH                       -1.0056      0.5259     -1.9123    392    0.0566 \nARMCDARM A                        0.7587      0.9733      0.7795    396    0.4362 \nARMCDARM C                       -0.3248      0.9756     -0.3329    394    0.7393 \nAVISITWEEK 2 DAY 15               5.6472      1.0331      5.4665    397   &lt;0.0001 \nAVISITWEEK 3 DAY 22              10.0591      1.0897      9.2311    397   &lt;0.0001 \nAVISITWEEK 4 DAY 29              14.6145      1.2176     12.0027    397   &lt;0.0001 \nAVISITWEEK 5 DAY 36              20.0154      1.2584     15.9057    397   &lt;0.0001 \nARMCDARM A:AVISITWEEK 2 DAY 15   -0.8752      1.4610     -0.5990    397    0.5495 \nARMCDARM C:AVISITWEEK 2 DAY 15   -0.3007      1.4665     -0.2050    397    0.8376 \nARMCDARM A:AVISITWEEK 3 DAY 22    1.7085      1.5411      1.1087    397    0.2682 \nARMCDARM C:AVISITWEEK 3 DAY 22    2.5673      1.5469      1.6597    397    0.0978 \nARMCDARM A:AVISITWEEK 4 DAY 29    0.6181      1.7220      0.3589    397    0.7198 \nARMCDARM C:AVISITWEEK 4 DAY 29    1.7337      1.7285      1.0030    397    0.3165 \nARMCDARM A:AVISITWEEK 5 DAY 36    0.6690      1.7796      0.3759    397    0.7072 \nARMCDARM C:AVISITWEEK 5 DAY 36    0.5257      1.7863      0.2943    397    0.7687 \n\n\nNot considering the treatment variable in the model\n\n\nCodeas.rtable(mmrm_results_no_arm, type = \"fixed\")\n\n                      Estimate   Std. Error   t value   df    Pr(&gt;|t|)\n——————————————————————————————————————————————————————————————————————\n(Intercept)            1.5624      1.1751     1.3296    466    0.1843 \nBMRKR2MEDIUM           1.0924      1.1878     0.9196    395    0.3583 \nBMRKR2HIGH             1.4619      1.1971     1.2212    395    0.2227 \nSTRATA1B               2.6321      1.2180     2.1610    395    0.0313 \nSTRATA1C               0.9717      1.2008     0.8092    395    0.4189 \nAVISITWEEK 2 DAY 15    5.2548      0.5967     8.8061    399   &lt;0.0001 \nAVISITWEEK 3 DAY 22   11.4787      0.6314     18.1802   399   &lt;0.0001 \nAVISITWEEK 4 DAY 29   15.3937      0.7039     21.8696   399   &lt;0.0001 \nAVISITWEEK 5 DAY 36   20.4130      0.7266     28.0921   399   &lt;0.0001 \n\n\n\n\n\n\n\nCodeas.rtable(mmrm_results, type = \"cov\")\n\n                WEEK 1 DAY 8   WEEK 2 DAY 15   WEEK 3 DAY 22   WEEK 4 DAY 29   WEEK 5 DAY 36\n————————————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8      63.0140         -0.6664         2.5965          -1.6231         0.2589    \nWEEK 2 DAY 15     -0.6664         78.6592         1.7004         -11.8677         -0.4261   \nWEEK 3 DAY 22      2.5965         1.7004         101.2949         -5.8424         7.1145    \nWEEK 4 DAY 29     -1.6231        -11.8677         -5.8424        132.4034         -5.3739   \nWEEK 5 DAY 36      0.2589         -0.4261         7.1145          -5.3739        149.6937   \n\n\n\n\nModel diagnostics are currently available to evaluate choice of covariates for the fixed and random effects. Statistics to evaluate choice of covariance structure are being investigated and will be included in a future release.\n\nCodeas.rtable(mmrm_results, type = \"diagnostic\")\n\n                 Diagnostic statistic value\n———————————————————————————————————————————\nREML criterion           14835.9968        \nAIC                      14865.9968        \nAICc                     14866.2412        \nBIC                      14925.8688        \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\") %&gt;%\n    filter(ABLFL != \"Y\" & ABLFL2 != \"Y\") %&gt;%\n    filter(AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\")) %&gt;%\n    mutate(\n      AVISIT = as.factor(AVISIT),\n      AVISITN = rank(AVISITN) %&gt;%\n        as.factor() %&gt;%\n        as.numeric() %&gt;%\n        as.factor() # making consecutive numeric factor\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADQS &lt;- data[[\"ADQS\"]]\narm_ref_comp &lt;- list(\n  ARMCD = list(\n    ref = \"ARM A\",\n    comp = c(\"ARM B\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_a_mmrm(\n      label = \"MMRM\",\n      dataname = \"ADQS\",\n      aval_var = choices_selected(c(\"AVAL\", \"CHG\"), \"CHG\"),\n      id_var = choices_selected(c(\"USUBJID\", \"SUBJID\"), \"USUBJID\"),\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARMCD\"),\n      visit_var = choices_selected(c(\"AVISIT\", \"AVISITN\"), \"AVISIT\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"FKSI-FWB\"\n      ),\n      cov_var = choices_selected(c(\"BASE\", \"AGE\", \"SEX\", \"BASE:AVISIT\"), NULL),\n      conf_level = choices_selected(c(0.95, 0.9, 0.8), 0.95)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:36:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      broom_1.0.5                     \n [9] scda_0.1.6.9017                  tern.mmrm_0.3.0.9005            \n[11] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[13] magrittr_2.0.3                   formatters_0.5.5.9015           \n[15] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               mmrm_0.3.11              formatR_1.14            \n [4] tern.gee_0.1.3.9004      logger_0.3.0             testthat_3.2.1          \n [7] sandwich_3.1-0           rlang_1.1.3              multcomp_1.4-25         \n[10] compiler_4.3.3           callr_3.7.5              vctrs_0.6.5             \n[13] stringr_1.5.1            pkgconfig_2.0.3          fastmap_1.1.1           \n[16] backports_1.4.1          ellipsis_0.3.2           fontawesome_0.5.2       \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 purrr_1.0.2              xfun_0.42               \n[25] shinyvalidate_0.1.3      cachem_1.0.8             teal.reporter_0.3.0.9005\n[28] jsonlite_1.8.8           later_1.3.2              parallel_4.3.3          \n[31] R6_2.5.1                 bslib_0.6.1              stringi_1.8.3           \n[34] parallelly_1.37.1        brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               TMB_1.9.10               codetools_0.2-19        \n[49] websocket_1.4.1          processx_3.8.3           teal.widgets_0.4.2.9008 \n[52] lattice_0.22-5           tibble_3.2.1             withr_3.0.0             \n[55] coda_0.19-4.1            evaluate_0.23            survival_3.5-8          \n[58] shinycssloaders_1.0.0    pillar_1.9.0             checkmate_2.3.1         \n[61] shinyjs_2.1.0            generics_0.1.3           chromote_0.2.0          \n[64] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[67] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[70] tools_4.3.3              webshot2_0.1.1           webshot_0.5.5           \n[73] forcats_1.0.0            mvtnorm_1.2-4            cowplot_1.1.3           \n[76] grid_4.3.3               tidyr_1.3.1              rbibutils_2.2.16        \n[79] colorspace_2.1-0         nlme_3.1-164             cli_3.6.2               \n[82] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[85] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[88] htmlwidgets_1.6.4        memoise_2.0.1            htmltools_0.5.7         \n[91] lifecycle_1.0.4          shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -1067,7 +1067,7 @@
     "href": "tables/efficacy/ratet01.html",
     "title": "RATET01",
     "section": "",
-    "text": "This example will focus on data from a single visit “WEEK 1 DAY 8” and a single endpoint “FKSI-FWB”. Only evaluable populations will be analyzed.\n\n\nData Setup\nCustomized Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nanl &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\") %&gt;%\n  filter(PARAMCD == \"TNE\")\n\n# Ensure number of exacerbation is a factor and NAs are explicit missing levels.\nanl$AVAL_f &lt;- as.factor(anl$AVAL)\nanl &lt;- df_explicit_na(anl)\n\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"B: Placebo\", split_fun = ref_group_position(\"first\")) %&gt;%\n  analyze_vars(\n    \"AVAL_f\",\n    var_labels = \"Number of exacerbations per patient\",\n    .stats = c(\"count_fraction\"),\n    .formats = c(\"count_fraction\" = \"xx (xx.xx%)\"),\n    .labels = c(\"Number of exacerbations per patient\")\n  ) %&gt;%\n  summarize_glm_count(\n    vars = \"AVAL\",\n    variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = NULL),\n    conf_level = 0.95,\n    distribution = \"poisson\",\n    rate_mean_method = \"emmeans\",\n    var_labels = \"Unadjusted exacerbation rate (per year)\",\n    table_names = \"unadj\",\n    .stats = c(\"rate\"),\n    .labels = c(rate = \"Rate\")\n  ) %&gt;%\n  summarize_glm_count(\n    vars = \"AVAL\",\n    variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = c(\"REGION1\")),\n    conf_level = 0.95,\n    distribution = \"quasipoisson\",\n    rate_mean_method = \"ppmeans\",\n    var_labels = \"Adjusted (QP) exacerbation rate (per year)\",\n    table_names = \"adj-qp\",\n    .stats = c(\"rate\", \"rate_ci\", \"rate_ratio\", \"rate_ratio_ci\", \"pval\"),\n    .labels = c(\n      rate = \"Rate\", rate_ci = \"Rate CI\", rate_ratio = \"Rate Ratio\",\n      rate_ratio_ci = \"Rate Ratio CI\", pval = \"p value\"\n    )\n  ) %&gt;%\n  summarize_glm_count(\n    vars = \"AVAL\",\n    variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = c(\"REGION1\")),\n    conf_level = 0.95,\n    distribution = \"negbin\",\n    rate_mean_method = \"emmeans\",\n    var_labels = \"Adjusted (NB) exacerbation rate (per year)\",\n    table_names = \"adj-nb\",\n    .stats = c(\"rate\", \"rate_ci\", \"rate_ratio\", \"rate_ratio_ci\", \"pval\"),\n    .labels = c(\n      rate = \"Rate\", rate_ci = \"Rate CI\", rate_ratio = \"Rate Ratio\",\n      rate_ratio_ci = \"Rate Ratio CI\", pval = \"p value\"\n    )\n  )\nresult &lt;- build_table(\n  lyt = lyt,\n  df = anl\n)\nresult\n\n                                                B: Placebo         A: Drug X        C: Combination \n                                                 (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of exacerbations per patient                                                                \n  0                                             10 (7.46%)         6 (4.48%)          11 (8.33%)   \n  1                                            25 (18.66%)        21 (15.67%)        14 (10.61%)   \n  2                                            38 (28.36%)        41 (30.60%)        33 (25.00%)   \n  3                                            22 (16.42%)        26 (19.40%)        21 (15.91%)   \n  4                                             13 (9.70%)        18 (13.43%)        30 (22.73%)   \n  5                                             11 (8.21%)         10 (7.46%)         12 (9.09%)   \n  6                                             10 (7.46%)         7 (5.22%)          7 (5.30%)    \n  7                                             3 (2.24%)          4 (2.99%)          3 (2.27%)    \n  8                                             1 (0.75%)          1 (0.75%)          1 (0.76%)    \n  9                                             1 (0.75%)          0 (0.00%)          0 (0.00%)    \nUnadjusted exacerbation rate (per year)                                                            \n  Rate                                            7.2364             8.2148             9.8131     \nAdjusted (QP) exacerbation rate (per year)                                                         \n  Rate                                            2.4550             2.8514             3.4304     \n  Rate CI                                    (1.6194, 3.7219)   (1.8974, 4.2850)   (2.2946, 5.1284)\n  Rate Ratio                                                         1.1615             1.3973     \n  Rate Ratio CI                                                 (0.6462, 2.0877)   (0.7789, 2.5067)\n  p value                                                            0.6169             0.2619     \nAdjusted (NB) exacerbation rate (per year)                                                         \n  Rate                                            2.7374             2.8150             2.9789     \n  Rate CI                                    (2.3932, 3.1311)   (2.4681, 3.2106)   (2.6152, 3.3932)\n  Rate Ratio                                                         1.0283             1.0882     \n  Rate Ratio CI                                                 (0.8856, 1.1941)   (0.9381, 1.2624)\n  p value                                                            0.7140             0.2643     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In Progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:38:16 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] sandwich_3.1-0    utf8_1.2.4        generics_0.1.3    tidyr_1.3.1      \n [5] stringi_1.8.3     lattice_0.22-5    digest_0.6.34     evaluate_0.23    \n [9] grid_4.3.3        estimability_1.5  mvtnorm_1.2-4     fastmap_1.1.1    \n[13] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[17] survival_3.5-8    multcomp_1.4-25   purrr_1.0.2       fansi_1.0.6      \n[21] scales_1.3.0      TH.data_1.1-2     codetools_0.2-19  Rdpack_2.6       \n[25] cli_3.6.2         rlang_1.1.3       rbibutils_2.2.16  munsell_0.5.0    \n[29] splines_4.3.3     yaml_2.3.8        tools_4.3.3       checkmate_2.3.1  \n[33] coda_0.19-4.1     colorspace_2.1-0  ggplot2_3.5.0     forcats_1.0.0    \n[37] broom_1.0.5       vctrs_0.6.5       R6_2.5.1          zoo_1.8-12       \n[41] lifecycle_1.0.4   emmeans_1.10.0    htmlwidgets_1.6.4 MASS_7.3-60.0.1  \n[45] pkgconfig_2.0.3   pillar_1.9.0      gtable_0.3.4      glue_1.7.0       \n[49] xfun_0.42         tibble_3.2.1      tidyselect_1.2.0  knitr_1.45       \n[53] xtable_1.8-4      htmltools_0.5.7   rmarkdown_2.26    testthat_3.2.1   \n[57] compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "This example will focus on data from a single visit “WEEK 1 DAY 8” and a single endpoint “FKSI-FWB”. Only evaluable populations will be analyzed.\n\n\nData Setup\nCustomized Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nanl &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\") %&gt;%\n  filter(PARAMCD == \"TNE\")\n\n# Ensure number of exacerbation is a factor and NAs are explicit missing levels.\nanl$AVAL_f &lt;- as.factor(anl$AVAL)\nanl &lt;- df_explicit_na(anl)\n\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"B: Placebo\", split_fun = ref_group_position(\"first\")) %&gt;%\n  analyze_vars(\n    \"AVAL_f\",\n    var_labels = \"Number of exacerbations per patient\",\n    .stats = c(\"count_fraction\"),\n    .formats = c(\"count_fraction\" = \"xx (xx.xx%)\"),\n    .labels = c(\"Number of exacerbations per patient\")\n  ) %&gt;%\n  summarize_glm_count(\n    vars = \"AVAL\",\n    variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = NULL),\n    conf_level = 0.95,\n    distribution = \"poisson\",\n    rate_mean_method = \"emmeans\",\n    var_labels = \"Unadjusted exacerbation rate (per year)\",\n    table_names = \"unadj\",\n    .stats = c(\"rate\"),\n    .labels = c(rate = \"Rate\")\n  ) %&gt;%\n  summarize_glm_count(\n    vars = \"AVAL\",\n    variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = c(\"REGION1\")),\n    conf_level = 0.95,\n    distribution = \"quasipoisson\",\n    rate_mean_method = \"ppmeans\",\n    var_labels = \"Adjusted (QP) exacerbation rate (per year)\",\n    table_names = \"adj-qp\",\n    .stats = c(\"rate\", \"rate_ci\", \"rate_ratio\", \"rate_ratio_ci\", \"pval\"),\n    .labels = c(\n      rate = \"Rate\", rate_ci = \"Rate CI\", rate_ratio = \"Rate Ratio\",\n      rate_ratio_ci = \"Rate Ratio CI\", pval = \"p value\"\n    )\n  ) %&gt;%\n  summarize_glm_count(\n    vars = \"AVAL\",\n    variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = c(\"REGION1\")),\n    conf_level = 0.95,\n    distribution = \"negbin\",\n    rate_mean_method = \"emmeans\",\n    var_labels = \"Adjusted (NB) exacerbation rate (per year)\",\n    table_names = \"adj-nb\",\n    .stats = c(\"rate\", \"rate_ci\", \"rate_ratio\", \"rate_ratio_ci\", \"pval\"),\n    .labels = c(\n      rate = \"Rate\", rate_ci = \"Rate CI\", rate_ratio = \"Rate Ratio\",\n      rate_ratio_ci = \"Rate Ratio CI\", pval = \"p value\"\n    )\n  )\nresult &lt;- build_table(\n  lyt = lyt,\n  df = anl\n)\nresult\n\n                                                B: Placebo         A: Drug X        C: Combination \n                                                 (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————————————————\nNumber of exacerbations per patient                                                                \n  0                                             10 (7.46%)         6 (4.48%)          11 (8.33%)   \n  1                                            25 (18.66%)        21 (15.67%)        14 (10.61%)   \n  2                                            38 (28.36%)        41 (30.60%)        33 (25.00%)   \n  3                                            22 (16.42%)        26 (19.40%)        21 (15.91%)   \n  4                                             13 (9.70%)        18 (13.43%)        30 (22.73%)   \n  5                                             11 (8.21%)         10 (7.46%)         12 (9.09%)   \n  6                                             10 (7.46%)         7 (5.22%)          7 (5.30%)    \n  7                                             3 (2.24%)          4 (2.99%)          3 (2.27%)    \n  8                                             1 (0.75%)          1 (0.75%)          1 (0.76%)    \n  9                                             1 (0.75%)          0 (0.00%)          0 (0.00%)    \nUnadjusted exacerbation rate (per year)                                                            \n  Rate                                            7.2364             8.2148             9.8131     \nAdjusted (QP) exacerbation rate (per year)                                                         \n  Rate                                            2.4550             2.8514             3.4304     \n  Rate CI                                    (1.6194, 3.7219)   (1.8974, 4.2850)   (2.2946, 5.1284)\n  Rate Ratio                                                         1.1615             1.3973     \n  Rate Ratio CI                                                 (0.6462, 2.0877)   (0.7789, 2.5067)\n  p value                                                            0.6169             0.2619     \nAdjusted (NB) exacerbation rate (per year)                                                         \n  Rate                                            2.7374             2.8150             2.9789     \n  Rate CI                                    (2.3932, 3.1311)   (2.4681, 3.2106)   (2.6152, 3.3932)\n  Rate Ratio                                                         1.0283             1.0882     \n  Rate Ratio CI                                                 (0.8856, 1.1941)   (0.9381, 1.2624)\n  p value                                                            0.7140             0.2643     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In Progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:37:09 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] sandwich_3.1-0    utf8_1.2.4        generics_0.1.3    tidyr_1.3.1      \n [5] stringi_1.8.3     lattice_0.22-5    digest_0.6.34     evaluate_0.23    \n [9] grid_4.3.3        estimability_1.5  mvtnorm_1.2-4     fastmap_1.1.1    \n[13] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[17] survival_3.5-8    multcomp_1.4-25   purrr_1.0.2       fansi_1.0.6      \n[21] scales_1.3.0      TH.data_1.1-2     codetools_0.2-19  Rdpack_2.6       \n[25] cli_3.6.2         rlang_1.1.3       rbibutils_2.2.16  munsell_0.5.0    \n[29] splines_4.3.3     yaml_2.3.8        tools_4.3.3       checkmate_2.3.1  \n[33] coda_0.19-4.1     colorspace_2.1-0  ggplot2_3.5.0     forcats_1.0.0    \n[37] broom_1.0.5       vctrs_0.6.5       R6_2.5.1          zoo_1.8-12       \n[41] lifecycle_1.0.4   emmeans_1.10.0    htmlwidgets_1.6.4 MASS_7.3-60.0.1  \n[45] pkgconfig_2.0.3   pillar_1.9.0      gtable_0.3.4      glue_1.7.0       \n[49] xfun_0.42         tibble_3.2.1      tidyselect_1.2.0  knitr_1.45       \n[53] xtable_1.8-4      htmltools_0.5.7   rmarkdown_2.26    testthat_3.2.1   \n[57] compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -1079,7 +1079,7 @@
     "href": "tables/efficacy/aovt01.html",
     "title": "AOVT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\nadqs_multi &lt;- filter(adqs, AVISIT == \"WEEK 1 DAY 8\")\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nl &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  split_rows_by(\"PARAMCD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adqs_multi$PARAMCD)\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(\n      arm = \"ARMCD\",\n      covariates = c(\"BASE\", \"STRATA1\")\n    ),\n    conf_level = 0.95,\n    var_labels = \"Adjusted mean\"\n  )\n\nresult &lt;- build_table(\n  lyt = l,\n  df = adqs_multi,\n  alt_counts_df = adsl\n)\n\nresult\n\nParameter Code                     ARM A       ARM B           ARM C    \n————————————————————————————————————————————————————————————————————————\nBFIALL                                                                  \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  4.47        6.33            4.02     \n    Difference in Adjusted Means               1.85            -0.46    \n      95% CI                               (-0.14, 3.85)   (-2.45, 1.54)\n      p-value                                 0.0679          0.6539    \nFATIGI                                                                  \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  5.42        4.83            4.56     \n    Difference in Adjusted Means               -0.59           -0.86    \n      95% CI                               (-2.58, 1.41)   (-2.87, 1.15)\n      p-value                                 0.5644          0.4026    \nFKSI-FWB                                                                \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  4.29        3.51            3.06     \n    Difference in Adjusted Means               -0.79           -1.24    \n      95% CI                               (-2.71, 1.14)   (-3.17, 0.69)\n      p-value                                 0.4221          0.2088    \nFKSI-TSE                                                                \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  4.70        3.84            4.45     \n    Difference in Adjusted Means               -0.86           -0.25    \n      95% CI                               (-2.80, 1.09)   (-2.20, 1.70)\n      p-value                                 0.3858          0.8007    \nFKSIALL                                                                 \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  5.03        5.82            6.44     \n    Difference in Adjusted Means               0.79            1.42     \n      95% CI                               (-1.17, 2.76)   (-0.56, 3.39)\n      p-value                                 0.4288          0.1591    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADQS &lt;- df_explicit_na(ADQS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADQS &lt;- data[[\"ADQS\"]]\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_ancova(\n      label = \"ANCOVA table\",\n      dataname = \"ADQS\",\n      avisit = choices_selected(\n        choices = value_choices(ADQS, \"AVISIT\"),\n        selected = \"WEEK 1 DAY 8\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ACTARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      aval_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"CHG\", \"AVAL\")),\n        selected = \"CHG\"\n      ),\n      cov_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"BASE\", \"STRATA1\", \"SEX\")),\n        selected = \"STRATA1\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = c(\"FKSI-FWB\", \"BFIALL\")\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:38:50 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\nadqs_multi &lt;- filter(adqs, AVISIT == \"WEEK 1 DAY 8\")\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nl &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  split_rows_by(\"PARAMCD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adqs_multi$PARAMCD)\n  ) %&gt;%\n  summarize_ancova(\n    vars = \"CHG\",\n    variables = list(\n      arm = \"ARMCD\",\n      covariates = c(\"BASE\", \"STRATA1\")\n    ),\n    conf_level = 0.95,\n    var_labels = \"Adjusted mean\"\n  )\n\nresult &lt;- build_table(\n  lyt = l,\n  df = adqs_multi,\n  alt_counts_df = adsl\n)\n\nresult\n\nParameter Code                     ARM A       ARM B           ARM C    \n————————————————————————————————————————————————————————————————————————\nBFIALL                                                                  \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  4.47        6.33            4.02     \n    Difference in Adjusted Means               1.85            -0.46    \n      95% CI                               (-0.14, 3.85)   (-2.45, 1.54)\n      p-value                                 0.0679          0.6539    \nFATIGI                                                                  \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  5.42        4.83            4.56     \n    Difference in Adjusted Means               -0.59           -0.86    \n      95% CI                               (-2.58, 1.41)   (-2.87, 1.15)\n      p-value                                 0.5644          0.4026    \nFKSI-FWB                                                                \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  4.29        3.51            3.06     \n    Difference in Adjusted Means               -0.79           -1.24    \n      95% CI                               (-2.71, 1.14)   (-3.17, 0.69)\n      p-value                                 0.4221          0.2088    \nFKSI-TSE                                                                \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  4.70        3.84            4.45     \n    Difference in Adjusted Means               -0.86           -0.25    \n      95% CI                               (-2.80, 1.09)   (-2.20, 1.70)\n      p-value                                 0.3858          0.8007    \nFKSIALL                                                                 \n  Adjusted mean                                                         \n    n                               134         134             132     \n    Adjusted Mean                  5.03        5.82            6.44     \n    Difference in Adjusted Means               0.79            1.42     \n      95% CI                               (-1.17, 2.76)   (-0.56, 3.39)\n      p-value                                 0.4288          0.1591    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADQS &lt;- df_explicit_na(ADQS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADQS &lt;- data[[\"ADQS\"]]\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_ancova(\n      label = \"ANCOVA table\",\n      dataname = \"ADQS\",\n      avisit = choices_selected(\n        choices = value_choices(ADQS, \"AVISIT\"),\n        selected = \"WEEK 1 DAY 8\"\n      ),\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ACTARMCD\")),\n        selected = \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      aval_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"CHG\", \"AVAL\")),\n        selected = \"CHG\"\n      ),\n      cov_var = choices_selected(\n        choices = variable_choices(ADQS, c(\"BASE\", \"STRATA1\", \"SEX\")),\n        selected = \"STRATA1\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = c(\"FKSI-FWB\", \"BFIALL\")\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:37:42 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -1091,7 +1091,7 @@
     "href": "tables/efficacy/cfbt01.html",
     "title": "CFBT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\n# This example focuses on \"BFI All Questions\" starting from baseline.\nadqs &lt;- adqs %&gt;%\n  dplyr::filter(\n    PARAM == \"BFI All Questions\",\n    AVISIT != \"SCREENING\"\n  )\n\n# Define the split function for AVISIT\nsplit_fun &lt;- drop_split_levels\n\n\n\n\n\nCodeafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adqs$PARAM)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adqs$AVISIT)\n  ) %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt, adqs)\nresult\n\n                               A: Drug X                        B: Placebo                      C: Combination         \nParameter                             Change from                       Change from                       Change from  \n  Analysis Visit    Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBFI All Questions                                                                                                      \n  BASELINE                                                                                                             \n    n                    134                               134                               132                       \n    Mean (SD)        49.93 (7.44)                      49.74 (8.29)                      50.30 (9.06)                  \n    Median              48.69                             49.28                             49.74                      \n    Min - Max       33.72 - 65.91                     25.82 - 71.47                     26.04 - 69.99                  \n  WEEK 1 DAY 8                                                                                                         \n    n                    134              134              134              134              132              132      \n    Mean (SD)        54.51 (8.61)     4.58 (11.23)     56.33 (7.86)     6.59 (11.81)     54.03 (8.31)     3.72 (12.66) \n    Median              55.15             5.47            56.39             6.96            53.76             3.71     \n    Min - Max       34.26 - 75.42    -25.52 - 29.68   36.82 - 74.45    -25.10 - 31.05   26.89 - 75.95    -26.81 - 33.75\n  WEEK 2 DAY 15                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)       60.98 (10.31)    11.05 (12.46)     59.68 (9.59)     9.94 (12.84)     60.11 (8.76)     9.80 (12.59) \n    Median              60.60            10.45            58.18             8.95            61.08            10.04     \n    Min - Max       35.94 - 96.53    -14.02 - 45.01   40.44 - 84.70    -17.59 - 40.52   32.76 - 78.25    -26.57 - 39.56\n  WEEK 3 DAY 22                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)        64.64 (9.88)    14.72 (11.99)    65.78 (10.17)    16.04 (13.80)    63.65 (10.50)    13.35 (13.39) \n    Median              65.21            15.10            66.28            15.49            63.79            12.24     \n    Min - Max       40.49 - 95.35    -16.76 - 43.40   42.16 - 92.32    -17.70 - 46.85   29.29 - 88.41    -22.40 - 39.94\n  WEEK 4 DAY 29                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)       69.43 (11.12)    19.51 (13.83)    69.79 (11.46)    20.05 (14.38)    70.68 (10.23)    20.37 (12.99) \n    Median              69.22            19.90            70.37            20.99            70.85            20.94     \n    Min - Max       38.38 - 95.48    -17.34 - 49.18   45.79 - 93.33    -19.66 - 58.05   30.53 - 90.61    -14.01 - 60.23\n  WEEK 5 DAY 36                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)       74.31 (12.44)    24.38 (14.75)    74.73 (12.96)    24.99 (14.67)    75.89 (13.54)    25.59 (17.18) \n    Median              76.12            25.20            74.13            22.81            76.70            25.32     \n    Min - Max       39.45 - 103.92   -7.53 - 56.23    38.19 - 109.61   -18.42 - 64.15   43.79 - 102.40   -16.11 - 67.78\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADQS &lt;- df_explicit_na(ADQS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADQS &lt;- data[[\"ADQS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Efficacy Data and Change from Baseline by Visit\",\n      dataname = \"ADQS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADQS, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADQS, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\", \"CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BFIALL\"\n      ),\n      add_total = FALSE,\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADQS\", \"AVISIT\", selected = NULL),\n    teal_slice(\"ADQS\", \"AVAL\", selected = NULL)\n  )\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADQS-filter-ADQS_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:39:22 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadqs &lt;- df_explicit_na(adqs)\n\n# This example focuses on \"BFI All Questions\" starting from baseline.\nadqs &lt;- adqs %&gt;%\n  dplyr::filter(\n    PARAM == \"BFI All Questions\",\n    AVISIT != \"SCREENING\"\n  )\n\n# Define the split function for AVISIT\nsplit_fun &lt;- drop_split_levels\n\n\n\n\n\nCodeafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adqs$PARAM)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adqs$AVISIT)\n  ) %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt, adqs)\nresult\n\n                               A: Drug X                        B: Placebo                      C: Combination         \nParameter                             Change from                       Change from                       Change from  \n  Analysis Visit    Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBFI All Questions                                                                                                      \n  BASELINE                                                                                                             \n    n                    134                               134                               132                       \n    Mean (SD)        49.93 (7.44)                      49.74 (8.29)                      50.30 (9.06)                  \n    Median              48.69                             49.28                             49.74                      \n    Min - Max       33.72 - 65.91                     25.82 - 71.47                     26.04 - 69.99                  \n  WEEK 1 DAY 8                                                                                                         \n    n                    134              134              134              134              132              132      \n    Mean (SD)        54.51 (8.61)     4.58 (11.23)     56.33 (7.86)     6.59 (11.81)     54.03 (8.31)     3.72 (12.66) \n    Median              55.15             5.47            56.39             6.96            53.76             3.71     \n    Min - Max       34.26 - 75.42    -25.52 - 29.68   36.82 - 74.45    -25.10 - 31.05   26.89 - 75.95    -26.81 - 33.75\n  WEEK 2 DAY 15                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)       60.98 (10.31)    11.05 (12.46)     59.68 (9.59)     9.94 (12.84)     60.11 (8.76)     9.80 (12.59) \n    Median              60.60            10.45            58.18             8.95            61.08            10.04     \n    Min - Max       35.94 - 96.53    -14.02 - 45.01   40.44 - 84.70    -17.59 - 40.52   32.76 - 78.25    -26.57 - 39.56\n  WEEK 3 DAY 22                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)        64.64 (9.88)    14.72 (11.99)    65.78 (10.17)    16.04 (13.80)    63.65 (10.50)    13.35 (13.39) \n    Median              65.21            15.10            66.28            15.49            63.79            12.24     \n    Min - Max       40.49 - 95.35    -16.76 - 43.40   42.16 - 92.32    -17.70 - 46.85   29.29 - 88.41    -22.40 - 39.94\n  WEEK 4 DAY 29                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)       69.43 (11.12)    19.51 (13.83)    69.79 (11.46)    20.05 (14.38)    70.68 (10.23)    20.37 (12.99) \n    Median              69.22            19.90            70.37            20.99            70.85            20.94     \n    Min - Max       38.38 - 95.48    -17.34 - 49.18   45.79 - 93.33    -19.66 - 58.05   30.53 - 90.61    -14.01 - 60.23\n  WEEK 5 DAY 36                                                                                                        \n    n                    134              134              134              134              132              132      \n    Mean (SD)       74.31 (12.44)    24.38 (14.75)    74.73 (12.96)    24.99 (14.67)    75.89 (13.54)    25.59 (17.18) \n    Median              76.12            25.20            74.13            22.81            76.70            25.32     \n    Min - Max       39.45 - 103.92   -7.53 - 56.23    38.19 - 109.61   -18.42 - 64.15   43.79 - 102.40   -16.11 - 67.78\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADQS &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADQS &lt;- df_explicit_na(ADQS)\n})\ndatanames &lt;- c(\"ADSL\", \"ADQS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADQS &lt;- data[[\"ADQS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Efficacy Data and Change from Baseline by Visit\",\n      dataname = \"ADQS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADQS, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADQS, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\", \"CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADQS, \"PARAMCD\", \"PARAM\"),\n        selected = \"BFIALL\"\n      ),\n      add_total = FALSE,\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADQS\", \"AVISIT\", selected = NULL),\n    teal_slice(\"ADQS\", \"AVAL\", selected = NULL)\n  )\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADQS-filter-ADQS_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:38:13 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -1103,7 +1103,7 @@
     "href": "tables/efficacy/coxt01.html",
     "title": "COXT01",
     "section": "",
-    "text": "Cox models are the most commonly used methods to estimate the magnitude of the effect in survival analyses. It assumes proportional hazards; that is, it assumes that the ratio of the hazards of the two groups (e.g. two arms) is constant over time. This ratio is referred to as the “hazard ratio” and is one of the most commonly reported metrics to describe the effect size in survival analysis.\n\n\nData Setup\nCox Regression\nCox Regression  with Interaction Term\nCox Regression  Specifying Covariates\nCox Regression Setting  Strata, Ties, Alpha Level\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadtte &lt;- df_explicit_na(adtte)\n\nadsl_filtered &lt;- adsl %&gt;% dplyr::filter(\n  RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\")\n)\nadtte_filtered &lt;- dplyr::inner_join(\n  x = adsl_filtered[, c(\"STUDYID\", \"USUBJID\")],\n  y = adtte,\n  by = c(\"STUDYID\", \"USUBJID\")\n)\n\nanl &lt;- adtte_filtered %&gt;%\n  filter(PARAMCD == \"OS\") %&gt;%\n  mutate(EVENT = 1 - CNSR) %&gt;%\n  filter(ARM %in% c(\"B: Placebo\", \"A: Drug X\")) %&gt;%\n  mutate(ARM = droplevels(relevel(ARM, \"B: Placebo\"))) %&gt;%\n  mutate(RACE = droplevels(RACE) %&gt;% formatters::with_label(\"Race\"))\n\n# Add variable for column split label\nanl &lt;- anl %&gt;% mutate(col_label = \"Treatment Effect Adjusted for Covariate\")\n\n\n\n\nThe summarize_coxreg function fits, tidies and arranges a Cox regression model in a table layout using the rtables framework. For a Cox regression model, arguments variables, control, and at can be specified (see ?summarize_coxreg for more details and customization options). All variables specified within variables must be present in the data used when building the table.\nTo see the same model as a data.frame object, these three arguments (as well as the data) can be passed to the fit_coxreg_univar function, and the resulting list tidied using broom::tidy().\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"SEX\", \"RACE\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(variables = variables) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                             Treatment Effect Adjusted for Covariate     \nEffect/Covariate Included in the Model     n     Hazard Ratio       95% CI       p-value \n—————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                               \n  A: Drug X vs control (B: Placebo)       247        0.97        (0.66, 1.43)     0.8934 \nCovariate:                                                                               \n  Age                                     247        0.95        (0.65, 1.40)     0.7948 \n  Sex                                     247        0.98        (0.67, 1.43)     0.8970 \n  Race                                    247        0.98        (0.67, 1.44)     0.9239 \n\n\n\n\nThe argument control can be used to modify standard outputs; control_coxreg() helps in adopting the right settings (see ?control_coxreg). For instance, control is used to include the interaction terms.\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"RACE\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control_coxreg(interaction = TRUE),\n    .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                                      Treatment Effect Adjusted for Covariate             \nEffect/Covariate Included in the Model    n    Hazard Ratio      95% CI      p-value   Interaction p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                                                \n  A: Drug X vs control (B: Placebo)      247       0.97       (0.66, 1.43)   0.8934                       \nCovariate:                                                                                                \n  Age                                    247                                                 0.7878       \n    34                                             0.95       (0.65, 1.40)                                \n  Race                                   247                                                 0.6850       \n    ASIAN                                          1.05       (0.63, 1.75)                                \n    BLACK OR AFRICAN AMERICAN                      1.08       (0.51, 2.29)                                \n    WHITE                                          0.67       (0.27, 1.71)                                \n\n\n\n\nThe optional argument at allows the user to provide the expected level of estimation for the interaction when the predictor is a quantitative variable. For instance, it might be relevant to choose the age at which the hazard ratio should be estimated. If no input is provided to at, the median value is used in the row name (as in the previous tab).\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"SEX\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control_coxreg(interaction = TRUE),\n    at = list(AGE = c(30, 40, 50)),\n    .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                                      Treatment Effect Adjusted for Covariate             \nEffect/Covariate Included in the Model    n    Hazard Ratio      95% CI      p-value   Interaction p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                                                \n  A: Drug X vs control (B: Placebo)      247       0.97       (0.66, 1.43)   0.8934                       \nCovariate:                                                                                                \n  Age                                    247                                                 0.7878       \n    30                                             0.98       (0.63, 1.51)                                \n    40                                             0.91       (0.54, 1.51)                                \n    50                                             0.84       (0.32, 2.20)                                \n  Sex                                    247                                                 0.1455       \n    F                                              0.77       (0.47, 1.27)                                \n    M                                              1.38       (0.75, 2.52)                                \n\n\n\n\nAdditional controls can be customized using control_coxreg (see ?control_coxreg) such as the ties calculation method and the confidence level. Stratification variables can be added via the strata element of the variables list.\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"RACE\"),\n  strata = \"SEX\"\n)\n\ncontrol &lt;- control_coxreg(\n  ties = \"breslow\",\n  interaction = TRUE,\n  conf_level = 0.90\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control,\n    at = list(AGE = c(30, 40, 50)),\n    .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                                      Treatment Effect Adjusted for Covariate             \nEffect/Covariate Included in the Model    n    Hazard Ratio      90% CI      p-value   Interaction p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                                                \n  A: Drug X vs control (B: Placebo)      247       0.98       (0.71, 1.35)   0.9063                       \nCovariate:                                                                                                \n  Age                                    247                                                 0.7733       \n    30                                             0.98       (0.68, 1.42)                                \n    40                                             0.91       (0.59, 1.39)                                \n    50                                             0.84       (0.38, 1.87)                                \n  Race                                   247                                                 0.6501       \n    ASIAN                                          1.07       (0.64, 1.77)                                \n    BLACK OR AFRICAN AMERICAN                      1.08       (0.51, 2.29)                                \n    WHITE                                          0.66       (0.26, 1.67)                                \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADTTE &lt;- df_explicit_na(ADTTE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADTTE &lt;- data[[\"ADTTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_coxreg(\n      label = \"Cox Reg.\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(c(\"ARM\", \"ACTARMCD\"), \"ARM\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"), \"OS\"\n      ),\n      strata_var = choices_selected(\n        c(\"SEX\", \"STRATA1\", \"STRATA2\"), NULL\n      ),\n      cov_var = choices_selected(\n        c(\"AGE\", \"SEX\", \"RACE\"), \"AGE\"\n      ),\n      multivariate = FALSE\n    )\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:39:52 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              stringr_1.5.1           \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        utf8_1.2.4              \n[19] promises_1.2.1           rmarkdown_2.26           ps_1.7.6                \n[22] purrr_1.0.2              xfun_0.42                shinyvalidate_0.1.3     \n[25] cachem_1.0.8             teal.reporter_0.3.0.9005 jsonlite_1.8.8          \n[28] later_1.3.2              broom_1.0.5              R6_2.5.1                \n[31] bslib_0.6.1              stringi_1.8.3            car_3.1-2               \n[34] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[37] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[43] splines_4.3.3            tidyselect_1.2.0         abind_1.4-5             \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    carData_3.0-5            checkmate_2.3.1         \n[61] shinyjs_2.1.0            generics_0.1.3           chromote_0.2.0          \n[64] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[67] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[70] tools_4.3.3              webshot2_0.1.1           webshot_0.5.5           \n[73] forcats_1.0.0            mvtnorm_1.2-4            grid_4.3.3              \n[76] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[79] nlme_3.1-164             cli_3.6.2                fansi_1.0.6             \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Cox models are the most commonly used methods to estimate the magnitude of the effect in survival analyses. It assumes proportional hazards; that is, it assumes that the ratio of the hazards of the two groups (e.g. two arms) is constant over time. This ratio is referred to as the “hazard ratio” and is one of the most commonly reported metrics to describe the effect size in survival analysis.\n\n\nData Setup\nCox Regression\nCox Regression  with Interaction Term\nCox Regression  Specifying Covariates\nCox Regression Setting  Strata, Ties, Alpha Level\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadtte &lt;- df_explicit_na(adtte)\n\nadsl_filtered &lt;- adsl %&gt;% dplyr::filter(\n  RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\")\n)\nadtte_filtered &lt;- dplyr::inner_join(\n  x = adsl_filtered[, c(\"STUDYID\", \"USUBJID\")],\n  y = adtte,\n  by = c(\"STUDYID\", \"USUBJID\")\n)\n\nanl &lt;- adtte_filtered %&gt;%\n  filter(PARAMCD == \"OS\") %&gt;%\n  mutate(EVENT = 1 - CNSR) %&gt;%\n  filter(ARM %in% c(\"B: Placebo\", \"A: Drug X\")) %&gt;%\n  mutate(ARM = droplevels(relevel(ARM, \"B: Placebo\"))) %&gt;%\n  mutate(RACE = droplevels(RACE) %&gt;% formatters::with_label(\"Race\"))\n\n# Add variable for column split label\nanl &lt;- anl %&gt;% mutate(col_label = \"Treatment Effect Adjusted for Covariate\")\n\n\n\n\nThe summarize_coxreg function fits, tidies and arranges a Cox regression model in a table layout using the rtables framework. For a Cox regression model, arguments variables, control, and at can be specified (see ?summarize_coxreg for more details and customization options). All variables specified within variables must be present in the data used when building the table.\nTo see the same model as a data.frame object, these three arguments (as well as the data) can be passed to the fit_coxreg_univar function, and the resulting list tidied using broom::tidy().\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"SEX\", \"RACE\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(variables = variables) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                             Treatment Effect Adjusted for Covariate     \nEffect/Covariate Included in the Model     n     Hazard Ratio       95% CI       p-value \n—————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                               \n  A: Drug X vs control (B: Placebo)       247        0.97        (0.66, 1.43)     0.8934 \nCovariate:                                                                               \n  Age                                     247        0.95        (0.65, 1.40)     0.7948 \n  Sex                                     247        0.98        (0.67, 1.43)     0.8970 \n  Race                                    247        0.98        (0.67, 1.44)     0.9239 \n\n\n\n\nThe argument control can be used to modify standard outputs; control_coxreg() helps in adopting the right settings (see ?control_coxreg). For instance, control is used to include the interaction terms.\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"RACE\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control_coxreg(interaction = TRUE),\n    .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                                      Treatment Effect Adjusted for Covariate             \nEffect/Covariate Included in the Model    n    Hazard Ratio      95% CI      p-value   Interaction p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                                                \n  A: Drug X vs control (B: Placebo)      247       0.97       (0.66, 1.43)   0.8934                       \nCovariate:                                                                                                \n  Age                                    247                                                 0.7878       \n    34                                             0.95       (0.65, 1.40)                                \n  Race                                   247                                                 0.6850       \n    ASIAN                                          1.05       (0.63, 1.75)                                \n    BLACK OR AFRICAN AMERICAN                      1.08       (0.51, 2.29)                                \n    WHITE                                          0.67       (0.27, 1.71)                                \n\n\n\n\nThe optional argument at allows the user to provide the expected level of estimation for the interaction when the predictor is a quantitative variable. For instance, it might be relevant to choose the age at which the hazard ratio should be estimated. If no input is provided to at, the median value is used in the row name (as in the previous tab).\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"SEX\")\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control_coxreg(interaction = TRUE),\n    at = list(AGE = c(30, 40, 50)),\n    .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                                      Treatment Effect Adjusted for Covariate             \nEffect/Covariate Included in the Model    n    Hazard Ratio      95% CI      p-value   Interaction p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                                                \n  A: Drug X vs control (B: Placebo)      247       0.97       (0.66, 1.43)   0.8934                       \nCovariate:                                                                                                \n  Age                                    247                                                 0.7878       \n    30                                             0.98       (0.63, 1.51)                                \n    40                                             0.91       (0.54, 1.51)                                \n    50                                             0.84       (0.32, 2.20)                                \n  Sex                                    247                                                 0.1455       \n    F                                              0.77       (0.47, 1.27)                                \n    M                                              1.38       (0.75, 2.52)                                \n\n\n\n\nAdditional controls can be customized using control_coxreg (see ?control_coxreg) such as the ties calculation method and the confidence level. Stratification variables can be added via the strata element of the variables list.\n\nCodevariables &lt;- list(\n  time = \"AVAL\",\n  event = \"EVENT\",\n  arm = \"ARM\",\n  covariates = c(\"AGE\", \"RACE\"),\n  strata = \"SEX\"\n)\n\ncontrol &lt;- control_coxreg(\n  ties = \"breslow\",\n  interaction = TRUE,\n  conf_level = 0.90\n)\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"col_label\") %&gt;%\n  summarize_coxreg(\n    variables = variables,\n    control = control,\n    at = list(AGE = c(30, 40, 50)),\n    .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\")\n  ) %&gt;%\n  append_topleft(\"Effect/Covariate Included in the Model\")\n\nresult &lt;- build_table(lyt = lyt, df = anl)\nresult\n\n                                                      Treatment Effect Adjusted for Covariate             \nEffect/Covariate Included in the Model    n    Hazard Ratio      90% CI      p-value   Interaction p-value\n——————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment:                                                                                                \n  A: Drug X vs control (B: Placebo)      247       0.98       (0.71, 1.35)   0.9063                       \nCovariate:                                                                                                \n  Age                                    247                                                 0.7733       \n    30                                             0.98       (0.68, 1.42)                                \n    40                                             0.91       (0.59, 1.39)                                \n    50                                             0.84       (0.38, 1.87)                                \n  Race                                   247                                                 0.6501       \n    ASIAN                                          1.07       (0.64, 1.77)                                \n    BLACK OR AFRICAN AMERICAN                      1.08       (0.51, 2.29)                                \n    WHITE                                          0.66       (0.26, 1.67)                                \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADTTE &lt;- df_explicit_na(ADTTE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADTTE &lt;- data[[\"ADTTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_coxreg(\n      label = \"Cox Reg.\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(c(\"ARM\", \"ACTARMCD\"), \"ARM\"),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"), \"OS\"\n      ),\n      strata_var = choices_selected(\n        c(\"SEX\", \"STRATA1\", \"STRATA2\"), NULL\n      ),\n      cov_var = choices_selected(\n        c(\"AGE\", \"SEX\", \"RACE\"), \"AGE\"\n      ),\n      multivariate = FALSE\n    )\n  )\n)\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:38:41 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              stringr_1.5.1           \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        utf8_1.2.4              \n[19] promises_1.2.1           rmarkdown_2.26           ps_1.7.6                \n[22] purrr_1.0.2              xfun_0.42                shinyvalidate_0.1.3     \n[25] cachem_1.0.8             teal.reporter_0.3.0.9005 jsonlite_1.8.8          \n[28] later_1.3.2              broom_1.0.5              R6_2.5.1                \n[31] bslib_0.6.1              stringi_1.8.3            car_3.1-2               \n[34] brio_1.1.4               jquerylib_0.1.4          estimability_1.5        \n[37] Rcpp_1.0.12              knitr_1.45               zoo_1.8-12              \n[40] teal.logger_0.1.3.9011   httpuv_1.6.14            Matrix_1.6-5            \n[43] splines_4.3.3            tidyselect_1.2.0         abind_1.4-5             \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    carData_3.0-5            checkmate_2.3.1         \n[61] shinyjs_2.1.0            generics_0.1.3           chromote_0.2.0          \n[64] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[67] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[70] tools_4.3.3              webshot2_0.1.1           webshot_0.5.5           \n[73] forcats_1.0.0            mvtnorm_1.2-4            grid_4.3.3              \n[76] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[79] nlme_3.1-164             cli_3.6.2                fansi_1.0.6             \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] shinyWidgets_0.8.2       mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -1115,7 +1115,7 @@
     "href": "tables/efficacy/ttet01.html",
     "title": "TTET01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable Modifying Analysis Details  like Conf. Type, Ties, Alpha Level\nTable with  Stratified Analysis\nTable Modifying Time Point for  the “XX Months” Analysis\nTable Requesting  &gt; 1 p-value\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure that character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadtte &lt;- df_explicit_na(adtte)\n\nadtte_f &lt;- adtte %&gt;%\n  dplyr::filter(PARAMCD == \"OS\") %&gt;%\n  dplyr::mutate(\n    AVAL = day2month(AVAL),\n    is_event = CNSR == 0,\n    is_not_event = CNSR == 1,\n    EVNT1 = factor(\n      case_when(\n        is_event == TRUE ~ \"Patients with event (%)\",\n        is_event == FALSE ~ \"Patients without event (%)\"\n      ),\n      levels = c(\"Patients with event (%)\", \"Patients without event (%)\")\n    ),\n    EVNTDESC = factor(EVNTDESC)\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    var = \"ARM\", ref_group = \"A: Drug X\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    time_point = c(6, 12),\n    is_event = \"is_event\",\n    method = \"both\",\n    control = control_surv_timepoint()\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  Hazard Ratio                                           1.00              1.29      \n  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  \n6 Months                                                                             \n  Patients remaining at risk            97                97                90       \n  Event Free Rate (%)                  72.39             72.39             68.18     \n  95% CI                          (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) \n  Difference in Event Free Rate                          0.00              -4.21     \n    95% CI                                          (-10.71, 10.71)   (-15.18, 6.77) \n    p-value (Z-test)                                    1.0000            0.4525     \n12 Months                                                                            \n  Patients remaining at risk            49                48                37       \n  Event Free Rate (%)                  56.72             56.72             47.73     \n  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n  Difference in Event Free Rate                          0.00              -8.99     \n    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) \n    p-value (Z-test)                                    1.0000            0.1406     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  analyze_vars(\n    \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    method = \"surv\",\n    time_point = c(6, 12)\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl)\nresult\n\n                                  A: Drug X        B: Placebo      C: Combination \n                                   (N=134)           (N=134)           (N=132)    \n——————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)          58 (43.3%)        58 (43.3%)        69 (52.3%)   \nPatients without event (%)       76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                            \n  Median                             NA                NA                9.4      \n    95% CI                        (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                  5.6, NA           5.6, NA           5.0, NA    \n  Range                        0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                             \n  p-value (log-rank)                                 0.9998            0.1541     \n  Hazard Ratio                                        1.00              1.29      \n  95% CI                                          (0.69, 1.44)      (0.91, 1.83)  \n6 Months                                                                          \n  Patients remaining at risk         97                97                90       \n  Event Free Rate (%)               72.39             72.39             68.18     \n  95% CI                       (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) \n12 Months                                                                         \n  Patients remaining at risk         49                48                37       \n  Event Free Rate (%)               56.72             56.72             47.73     \n  95% CI                       (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n——————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n——————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    control = control_surv_time(conf_level = 0.9, conf_type = \"log-log\"),\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(\n      pval_method = \"log-rank\",\n      conf_level = 0.95,\n      ties = \"efron\"\n    ),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    time_point = 12,\n    control = control_surv_timepoint(conf_level = 0.9, conf_type = \"log-log\"),\n    table_names_suffix = \"_log_log\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    show_labels = \"hidden\",\n    is_event = \"is_event\",\n    time_point = 12,\n    method = \"surv_diff\",\n    control = control_surv_timepoint(conf_level = 0.975),\n    table_names_suffix = \"_975_pct\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                   A: Drug X        B: Placebo      C: Combination \n                                    (N=134)           (N=134)           (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)           58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                      \n    Death                             58                58                69       \nPatients without event (%)        76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                             \n  Median                              NA                NA                9.4      \n    90% CI                         (9.6, NA)         (9.6, NA)         (7.7, NA)   \n  25% and 75%-ile                   5.6, NA           5.6, NA           5.0, NA    \n  Range                         0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                              \n  p-value (log-rank)                                  0.9998            0.1541     \n  Hazard Ratio                                         1.00              1.29      \n  95% CI                                           (0.69, 1.44)      (0.91, 1.83)  \n12 Months                                                                          \n  Patients remaining at risk          49                48                37       \n  Event Free Rate (%)                56.72             56.72             47.73     \n  90% CI                        (49.37, 63.41)    (49.37, 63.41)    (40.42, 54.66) \nDifference in Event Free Rate                          0.00              -8.99     \n  97.5% CI                                        (-13.57, 13.57)   (-22.66, 4.69) \n  p-value (Z-test)                                    1.0000            0.1406     \n———————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n———————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Stratified Analysis\",\n    strata = \"SEX\",\n    table_names = \"coxph_stratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    method = \"both\",\n    time_point = 12\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  Hazard Ratio                                           1.00              1.29      \n  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  \nStratified Analysis                                                                  \n  p-value (log-rank)                                    0.9978            0.1733     \n  Hazard Ratio                                           1.00              1.27      \n  95% CI                                             (0.69, 1.44)      (0.90, 1.81)  \n12 Months                                                                            \n  Patients remaining at risk            49                48                37       \n  Event Free Rate (%)                  56.72             56.72             47.73     \n  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n  Difference in Event Free Rate                          0.00              -8.99     \n    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) \n    p-value (Z-test)                                    1.0000            0.1406     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    time_point = 6,\n    method = \"both\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  Hazard Ratio                                           1.00              1.29      \n  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  \n6 Months                                                                             \n  Patients remaining at risk            97                97                90       \n  Event Free Rate (%)                  72.39             72.39             68.18     \n  95% CI                          (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) \n  Difference in Event Free Rate                          0.00              -4.21     \n    95% CI                                          (-10.71, 10.71)   (-15.18, 6.77) \n    p-value (Z-test)                                    1.0000            0.4525     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    .stats = \"pvalue\",\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    show_labels = \"hidden\",\n    control = control_coxph(pval_method = \"wald\"),\n    .stats = \"pvalue\",\n    .indent_mods = c(pvalue = 1L),\n    table_names = \"coxph_wald_pvalue\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    show_labels = \"hidden\",\n    control = control_coxph(pval_method = \"likelihood\"),\n    .indent_mods = c(pvalue = 1L, hr = 2L, hr_ci = 3L),\n    table_names = \"coxph_likelihood_pvalue\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    time_point = 12,\n    method = \"both\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  p-value (wald)                                        0.9998            0.1552     \n  p-value (likelihood)                                  0.9998            0.1543     \n    Hazard Ratio                                         1.00              1.29      \n      95% CI                                         (0.69, 1.44)      (0.91, 1.83)  \n12 Months                                                                            \n  Patients remaining at risk            49                48                37       \n  Event Free Rate (%)                  56.72             56.72             47.73     \n  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n  Difference in Event Free Rate                          0.00              -8.99     \n    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) \n    p-value (Z-test)                                    1.0000            0.1406     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADTTE &lt;- df_explicit_na(ADTTE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To Event Table\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"),\n        \"OS\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        \"SEX\"\n      ),\n      time_points = choices_selected(c(6, 8), 6),\n      event_desc_var = choices_selected(\n        variable_choices(ADTTE, \"EVNTDESC\"),\n        \"EVNTDESC\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:40:33 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Selecting  Sections to Display\nTable Modifying Analysis Details  like Conf. Type, Ties, Alpha Level\nTable with  Stratified Analysis\nTable Modifying Time Point for  the “XX Months” Analysis\nTable Requesting  &gt; 1 p-value\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadtte &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n# Ensure that character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadtte &lt;- df_explicit_na(adtte)\n\nadtte_f &lt;- adtte %&gt;%\n  dplyr::filter(PARAMCD == \"OS\") %&gt;%\n  dplyr::mutate(\n    AVAL = day2month(AVAL),\n    is_event = CNSR == 0,\n    is_not_event = CNSR == 1,\n    EVNT1 = factor(\n      case_when(\n        is_event == TRUE ~ \"Patients with event (%)\",\n        is_event == FALSE ~ \"Patients without event (%)\"\n      ),\n      levels = c(\"Patients with event (%)\", \"Patients without event (%)\")\n    ),\n    EVNTDESC = factor(EVNTDESC)\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    var = \"ARM\", ref_group = \"A: Drug X\"\n  ) %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    time_point = c(6, 12),\n    is_event = \"is_event\",\n    method = \"both\",\n    control = control_surv_timepoint()\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  Hazard Ratio                                           1.00              1.29      \n  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  \n6 Months                                                                             \n  Patients remaining at risk            97                97                90       \n  Event Free Rate (%)                  72.39             72.39             68.18     \n  95% CI                          (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) \n  Difference in Event Free Rate                          0.00              -4.21     \n    95% CI                                          (-10.71, 10.71)   (-15.18, 6.77) \n    p-value (Z-test)                                    1.0000            0.4525     \n12 Months                                                                            \n  Patients remaining at risk            49                48                37       \n  Event Free Rate (%)                  56.72             56.72             47.73     \n  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n  Difference in Event Free Rate                          0.00              -8.99     \n    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) \n    p-value (Z-test)                                    1.0000            0.1406     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  analyze_vars(\n    \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    method = \"surv\",\n    time_point = c(6, 12)\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl)\nresult\n\n                                  A: Drug X        B: Placebo      C: Combination \n                                   (N=134)           (N=134)           (N=132)    \n——————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)          58 (43.3%)        58 (43.3%)        69 (52.3%)   \nPatients without event (%)       76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                            \n  Median                             NA                NA                9.4      \n    95% CI                        (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                  5.6, NA           5.6, NA           5.0, NA    \n  Range                        0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                             \n  p-value (log-rank)                                 0.9998            0.1541     \n  Hazard Ratio                                        1.00              1.29      \n  95% CI                                          (0.69, 1.44)      (0.91, 1.83)  \n6 Months                                                                          \n  Patients remaining at risk         97                97                90       \n  Event Free Rate (%)               72.39             72.39             68.18     \n  95% CI                       (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) \n12 Months                                                                         \n  Patients remaining at risk         49                48                37       \n  Event Free Rate (%)               56.72             56.72             47.73     \n  95% CI                       (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n——————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n——————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    control = control_surv_time(conf_level = 0.9, conf_type = \"log-log\"),\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(\n      pval_method = \"log-rank\",\n      conf_level = 0.95,\n      ties = \"efron\"\n    ),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    time_point = 12,\n    control = control_surv_timepoint(conf_level = 0.9, conf_type = \"log-log\"),\n    table_names_suffix = \"_log_log\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    show_labels = \"hidden\",\n    is_event = \"is_event\",\n    time_point = 12,\n    method = \"surv_diff\",\n    control = control_surv_timepoint(conf_level = 0.975),\n    table_names_suffix = \"_975_pct\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                   A: Drug X        B: Placebo      C: Combination \n                                    (N=134)           (N=134)           (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)           58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                      \n    Death                             58                58                69       \nPatients without event (%)        76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                             \n  Median                              NA                NA                9.4      \n    90% CI                         (9.6, NA)         (9.6, NA)         (7.7, NA)   \n  25% and 75%-ile                   5.6, NA           5.6, NA           5.0, NA    \n  Range                         0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                              \n  p-value (log-rank)                                  0.9998            0.1541     \n  Hazard Ratio                                         1.00              1.29      \n  95% CI                                           (0.69, 1.44)      (0.91, 1.83)  \n12 Months                                                                          \n  Patients remaining at risk          49                48                37       \n  Event Free Rate (%)                56.72             56.72             47.73     \n  90% CI                        (49.37, 63.41)    (49.37, 63.41)    (40.42, 54.66) \nDifference in Event Free Rate                          0.00              -8.99     \n  97.5% CI                                        (-13.57, 13.57)   (-22.66, 4.69) \n  p-value (Z-test)                                    1.0000            0.1406     \n———————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n———————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Stratified Analysis\",\n    strata = \"SEX\",\n    table_names = \"coxph_stratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    method = \"both\",\n    time_point = 12\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  Hazard Ratio                                           1.00              1.29      \n  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  \nStratified Analysis                                                                  \n  p-value (log-rank)                                    0.9978            0.1733     \n  Hazard Ratio                                           1.00              1.27      \n  95% CI                                             (0.69, 1.44)      (0.90, 1.81)  \n12 Months                                                                            \n  Patients remaining at risk            49                48                37       \n  Event Free Rate (%)                  56.72             56.72             47.73     \n  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n  Difference in Event Free Rate                          0.00              -8.99     \n    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) \n    p-value (Z-test)                                    1.0000            0.1406     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    time_point = 6,\n    method = \"both\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  Hazard Ratio                                           1.00              1.29      \n  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  \n6 Months                                                                             \n  Patients remaining at risk            97                97                90       \n  Event Free Rate (%)                  72.39             72.39             68.18     \n  95% CI                          (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) \n  Difference in Event Free Rate                          0.00              -4.21     \n    95% CI                                          (-10.71, 10.71)   (-15.18, 6.77) \n    p-value (Z-test)                                    1.0000            0.4525     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %&gt;%\n  analyze_vars(\n    vars = \"is_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients with event (%)\")\n  ) %&gt;%\n  split_rows_by(\n    \"EVNT1\",\n    split_label = \"Earliest contributing event\",\n    split_fun = keep_split_levels(\"Patients with event (%)\"),\n    label_pos = \"visible\",\n    child_labels = \"hidden\",\n    indent_mod = 1L,\n  ) %&gt;%\n  analyze(\"EVNTDESC\") %&gt;%\n  analyze_vars(\n    vars = \"is_not_event\",\n    .stats = \"count_fraction\",\n    .labels = c(count_fraction = \"Patients without event (%)\"),\n    nested = FALSE,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = \"Time to Event (Months)\",\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = c(\"Unstratified Analysis\"),\n    control = control_coxph(pval_method = \"log-rank\"),\n    .stats = \"pvalue\",\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    show_labels = \"hidden\",\n    control = control_coxph(pval_method = \"wald\"),\n    .stats = \"pvalue\",\n    .indent_mods = c(pvalue = 1L),\n    table_names = \"coxph_wald_pvalue\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    show_labels = \"hidden\",\n    control = control_coxph(pval_method = \"likelihood\"),\n    .indent_mods = c(pvalue = 1L, hr = 2L, hr_ci = 3L),\n    table_names = \"coxph_likelihood_pvalue\"\n  ) %&gt;%\n  surv_timepoint(\n    vars = \"AVAL\",\n    var_labels = \"Months\",\n    is_event = \"is_event\",\n    time_point = 12,\n    method = \"both\"\n  )\n\nresult &lt;- build_table(lyt, df = adtte_f, alt_counts_df = adsl) %&gt;%\n  prune_table()\n\nresult\n\n                                     A: Drug X        B: Placebo      C: Combination \n                                      (N=134)           (N=134)           (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\nPatients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   \n  Earliest contributing event                                                        \n    Death                               58                58                69       \nPatients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   \nTime to Event (Months)                                                               \n  Median                                NA                NA                9.4      \n    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   \n  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    \n  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}\nUnstratified Analysis                                                                \n  p-value (log-rank)                                    0.9998            0.1541     \n  p-value (wald)                                        0.9998            0.1552     \n  p-value (likelihood)                                  0.9998            0.1543     \n    Hazard Ratio                                         1.00              1.29      \n      95% CI                                         (0.69, 1.44)      (0.91, 1.83)  \n12 Months                                                                            \n  Patients remaining at risk            49                48                37       \n  Event Free Rate (%)                  56.72             56.72             47.73     \n  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) \n  Difference in Event Free Rate                          0.00              -8.99     \n    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) \n    p-value (Z-test)                                    1.0000            0.1406     \n—————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observation: range maximum\n—————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADTTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adtte\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADTTE &lt;- df_explicit_na(ADTTE)\n})\ndatanames &lt;- c(\"ADSL\", \"ADTTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADTTE &lt;- data[[\"ADTTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To Event Table\",\n      dataname = \"ADTTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADTTE, \"PARAMCD\", \"PARAM\"),\n        \"OS\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"SEX\", \"BMRKR2\")),\n        \"SEX\"\n      ),\n      time_points = choices_selected(c(6, 8), 6),\n      event_desc_var = choices_selected(\n        variable_choices(ADTTE, \"EVNTDESC\"),\n        \"EVNTDESC\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:39:21 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Efficacy",
@@ -1127,7 +1127,7 @@
     "href": "tables/concomitant-medications/cmt01.html",
     "title": "CMT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Showing Medications Prior  to the Start of the Study\nTable Sorted by Total Column Showing  Additional “All Patients” Column\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\n# The following tables require ADCM data structure that follows the legacy concomitant medication coding.\n# WHO coding is not used.\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\n# Below step is to set up example data based on legacy standard.\nadcm &lt;- adcm %&gt;%\n  select(-starts_with(\"ATC\")) %&gt;%\n  unique()\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadcm &lt;- df_explicit_na(adcm)\n\nadcm_con &lt;- adcm %&gt;%\n  filter(ATIREL == \"CONCOMITANT\")\n\nadcm_prior &lt;- adcm %&gt;%\n  filter(ATIREL == \"PRIOR\")\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\"CMCLAS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = 1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_con, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_con,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedication Class                                            A: Drug X    B: Placebo    C: Combination\n  Standardized Medication Name                               (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment       117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                     415           414            460      \n  medcl A                                                                                            \n    Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n    Total number of treatments                                 134           137            143      \n    medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n    medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  medcl B                                                                                            \n    Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n    Total number of treatments                                 141           137            162      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \n  medcl C                                                                                            \n    Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n    Total number of treatments                                 140           140            155      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\"CMCLAS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_prior, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_prior,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedication Class                                         A: Drug X    B: Placebo   C: Combination\n  Standardized Medication Name                            (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     89 (66.4%)   95 (70.9%)    106 (80.3%)  \nTotal number of treatments                                  194          208            243      \nmedcl A                                                                                          \n  Total number of patients with at least one treatment   54 (40.3%)   49 (36.6%)     69 (52.3%)  \n  Total number of treatments                                 71           70             99      \n  medname A_1/3                                          54 (40.3%)   49 (36.6%)     69 (52.3%)  \nmedcl B                                                                                          \n  Total number of patients with at least one treatment   76 (56.7%)   80 (59.7%)     81 (61.4%)  \n  Total number of treatments                                123          138            144      \n  medname B_2/4                                          52 (38.8%)   55 (41.0%)     56 (42.4%)  \n  medname B_3/4                                          47 (35.1%)   47 (35.1%)     52 (39.4%)  \n\n\nTable with Subtotal Per  Medication Class Suppressed\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\"CMCLAS\",\n    split_fun = split_fun, child_labels = \"visible\",\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .labels = c(unique = \"Total number of patients with at least one treatment\"),\n    .stats = \"unique\"\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_con, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_con,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedication Class                                          A: Drug X    B: Placebo    C: Combination\n  Standardized Medication Name                             (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nmedcl A                                                                                            \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nmedcl B                                                                                            \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nmedcl C                                                                                            \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      \"Total number of patients with at least one treatment (%)\",\n      \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"CMCLAS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      \"Total number of patients with at least one treatment (%)\",\n      \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_con, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_con,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  )\n\nresult\n\nMedication Class                                              A: Drug X    B: Placebo    C: Combination   All Patients\n  Standardized Medication Name                                 (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment (%)     117 (87.3%)   116 (86.6%)    116 (87.9%)     349 (87.2%) \nTotal number of treatments                                       415           414            460             1289    \nmedcl C                                                                                                               \n  Total number of patients with at least one treatment (%)   82 (61.2%)    84 (62.7%)      89 (67.4%)     255 (63.7%) \n  Total number of treatments                                     140           140            155             435     \n  medname C_2/2                                              52 (38.8%)    58 (43.3%)      60 (45.5%)     170 (42.5%) \n  medname C_1/2                                              51 (38.1%)    50 (37.3%)      56 (42.4%)     157 (39.2%) \nmedcl B                                                                                                               \n  Total number of patients with at least one treatment (%)   83 (61.9%)    74 (55.2%)      88 (66.7%)     245 (61.3%) \n  Total number of treatments                                     141           137            162             440     \n  medname B_1/4                                              52 (38.8%)    57 (42.5%)      59 (44.7%)     168 (42.0%) \n  medname B_4/4                                              50 (37.3%)    45 (33.6%)      55 (41.7%)     150 (37.5%) \nmedcl A                                                                                                               \n  Total number of patients with at least one treatment (%)   75 (56.0%)    79 (59.0%)      81 (61.4%)     235 (58.8%) \n  Total number of treatments                                     134           137            143             414     \n  medname A_2/3                                              53 (39.6%)    50 (37.3%)      56 (42.4%)     159 (39.8%) \n  medname A_3/3                                              45 (33.6%)    54 (40.3%)      48 (36.4%)     147 (36.8%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nHere, adcm is processed to keep only one path per treatment.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n\n  ADCM &lt;- ADCM %&gt;%\n    group_by(STUDYID, USUBJID, CMDECOD) %&gt;%\n    slice(1) %&gt;%\n    ungroup()\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Concomitant Medication Table\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, \"CMDECOD\"),\n        selected = \"CMDECOD\"\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, \"CMCLAS\"),\n        selected = c(\"CMCLAS\")\n      ),\n      add_total = TRUE,\n      event_type = \"treatment\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:41:10 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Showing Medications Prior  to the Start of the Study\nTable Sorted by Total Column Showing  Additional “All Patients” Column\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\n# The following tables require ADCM data structure that follows the legacy concomitant medication coding.\n# WHO coding is not used.\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\n# Below step is to set up example data based on legacy standard.\nadcm &lt;- adcm %&gt;%\n  select(-starts_with(\"ATC\")) %&gt;%\n  unique()\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadcm &lt;- df_explicit_na(adcm)\n\nadcm_con &lt;- adcm %&gt;%\n  filter(ATIREL == \"CONCOMITANT\")\n\nadcm_prior &lt;- adcm %&gt;%\n  filter(ATIREL == \"PRIOR\")\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\"CMCLAS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    nested = FALSE,\n    indent_mod = 1L,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_con, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_con,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedication Class                                            A: Drug X    B: Placebo    C: Combination\n  Standardized Medication Name                               (N=134)       (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment       117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                     415           414            460      \n  medcl A                                                                                            \n    Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n    Total number of treatments                                 134           137            143      \n    medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n    medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \n  medcl B                                                                                            \n    Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n    Total number of treatments                                 141           137            162      \n    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n    medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \n  medcl C                                                                                            \n    Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n    Total number of treatments                                 140           140            155      \n    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n    medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\"CMCLAS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_prior, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_prior,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedication Class                                         A: Drug X    B: Placebo   C: Combination\n  Standardized Medication Name                            (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     89 (66.4%)   95 (70.9%)    106 (80.3%)  \nTotal number of treatments                                  194          208            243      \nmedcl A                                                                                          \n  Total number of patients with at least one treatment   54 (40.3%)   49 (36.6%)     69 (52.3%)  \n  Total number of treatments                                 71           70             99      \n  medname A_1/3                                          54 (40.3%)   49 (36.6%)     69 (52.3%)  \nmedcl B                                                                                          \n  Total number of patients with at least one treatment   76 (56.7%)   80 (59.7%)     81 (61.4%)  \n  Total number of treatments                                123          138            144      \n  medname B_2/4                                          52 (38.8%)   55 (41.0%)     56 (42.4%)  \n  medname B_3/4                                          47 (35.1%)   47 (35.1%)     52 (39.4%)  \n\n\nTable with Subtotal Per  Medication Class Suppressed\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\"CMCLAS\",\n    split_fun = split_fun, child_labels = \"visible\",\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .labels = c(unique = \"Total number of patients with at least one treatment\"),\n    .stats = \"unique\"\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_con, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_con,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\nMedication Class                                          A: Drug X    B: Placebo    C: Combination\n  Standardized Medication Name                             (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  \nTotal number of treatments                                   415           414            460      \nmedcl A                                                                                            \n  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  \n  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  \n  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  \nmedcl B                                                                                            \n  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  \n  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  \n  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  \nmedcl C                                                                                            \n  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  \n  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  \n  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      \"Total number of patients with at least one treatment (%)\",\n      \"Total number of treatments\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"CMCLAS\",\n    split_fun = split_fun,\n    child_labels = \"visible\",\n    nested = FALSE,\n    label_pos = \"topleft\",\n    split_label = obj_label(adcm_con$CMCLAS)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      \"Total number of patients with at least one treatment (%)\",\n      \"Total number of treatments\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\", .indent_mods = -1L) %&gt;%\n  append_varlabels(adcm_con, \"CMDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adcm_con,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\", \"*\", \"CMDECOD\"),\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"CMCLAS\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  )\n\nresult\n\nMedication Class                                              A: Drug X    B: Placebo    C: Combination   All Patients\n  Standardized Medication Name                                 (N=134)       (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment (%)     117 (87.3%)   116 (86.6%)    116 (87.9%)     349 (87.2%) \nTotal number of treatments                                       415           414            460             1289    \nmedcl C                                                                                                               \n  Total number of patients with at least one treatment (%)   82 (61.2%)    84 (62.7%)      89 (67.4%)     255 (63.7%) \n  Total number of treatments                                     140           140            155             435     \n  medname C_2/2                                              52 (38.8%)    58 (43.3%)      60 (45.5%)     170 (42.5%) \n  medname C_1/2                                              51 (38.1%)    50 (37.3%)      56 (42.4%)     157 (39.2%) \nmedcl B                                                                                                               \n  Total number of patients with at least one treatment (%)   83 (61.9%)    74 (55.2%)      88 (66.7%)     245 (61.3%) \n  Total number of treatments                                     141           137            162             440     \n  medname B_1/4                                              52 (38.8%)    57 (42.5%)      59 (44.7%)     168 (42.0%) \n  medname B_4/4                                              50 (37.3%)    45 (33.6%)      55 (41.7%)     150 (37.5%) \nmedcl A                                                                                                               \n  Total number of patients with at least one treatment (%)   75 (56.0%)    79 (59.0%)      81 (61.4%)     235 (58.8%) \n  Total number of treatments                                     134           137            143             414     \n  medname A_2/3                                              53 (39.6%)    50 (37.3%)      56 (42.4%)     159 (39.8%) \n  medname A_3/3                                              45 (33.6%)    54 (40.3%)      48 (36.4%)     147 (36.8%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nHere, adcm is processed to keep only one path per treatment.\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n\n  ADCM &lt;- ADCM %&gt;%\n    group_by(STUDYID, USUBJID, CMDECOD) %&gt;%\n    slice(1) %&gt;%\n    ungroup()\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Concomitant Medication Table\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, \"CMDECOD\"),\n        selected = \"CMDECOD\"\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, \"CMCLAS\"),\n        selected = c(\"CMCLAS\")\n      ),\n      add_total = TRUE,\n      event_type = \"treatment\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:39:56 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Concomitant Medications",
@@ -1139,7 +1139,7 @@
     "href": "tables/concomitant-medications/cmt02_pt.html",
     "title": "CMT02_PT",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nadcm$CMSEQ &lt;- as.factor(adcm$CMSEQ)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n# If you want your missing levels to show as \"No Coding Available\", please use\nadsl &lt;- df_explicit_na(adsl, na_level = \"No Coding Available\")\nadcm &lt;- df_explicit_na(adcm, na_level = \"No Coding Available\")\n\n# Keep only safety-evaluable patients and concomitant medications\nadsl &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\")\n\n# Keep only concomitant medications\nadcm &lt;- adcm %&gt;% filter(ATIREL == \"CONCOMITANT\")\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\") %&gt;%\n  append_topleft(paste(\"\\nOther Treatment\"))\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  sort_at_path(\n    path = c(\"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                        A: Drug X    B: Placebo    C: Combination   All Patients\nOther Treatment                                          (N=134)       (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment   117 (87.3%)   116 (86.6%)    116 (87.9%)     349 (87.2%) \nTotal number of treatments                                 415           414            460             1289    \nmedname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)     170 (42.5%) \nmedname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)     168 (42.0%) \nmedname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)     159 (39.8%) \nmedname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)     157 (39.2%) \nmedname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)     150 (37.5%) \nmedname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)     147 (36.8%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\nadcm_keys &lt;- c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"CMSEQ\", \"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n  ADCM$CMSEQ &lt;- as.integer(ADCM$CMSEQ)\n\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\njoin_keys(data)[\"ADCM\", \"ADCM\"] &lt;- adcm_keys\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_mult_events(\n      label = \"Concomitant Medications by Medication Class and Preferred Name\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      seq_var = choices_selected(\"CMSEQ\", selected = \"CMSEQ\", fixed = TRUE),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")),\n        selected = c(\"ATC1\")\n      ),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, c(\"CMDECOD\")),\n        selected = c(\"CMDECOD\")\n      ),\n      add_total = TRUE,\n      event_type = \"treatment\"\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n    teal_slice(\"ADCM\", \"ATIREL\", selected = \"CONCOMITANT\")\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:41:44 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nadcm$CMSEQ &lt;- as.factor(adcm$CMSEQ)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n# If you want your missing levels to show as \"No Coding Available\", please use\nadsl &lt;- df_explicit_na(adsl, na_level = \"No Coding Available\")\nadcm &lt;- df_explicit_na(adcm, na_level = \"No Coding Available\")\n\n# Keep only safety-evaluable patients and concomitant medications\nadsl &lt;- adsl %&gt;%\n  filter(SAFFL == \"Y\")\n\n# Keep only concomitant medications\nadcm &lt;- adcm %&gt;% filter(ATIREL == \"CONCOMITANT\")\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  add_overall_col(\"All Patients\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    count_by = \"CMSEQ\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one treatment\",\n      nonunique = \"Total number of treatments\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"CMDECOD\") %&gt;%\n  append_topleft(paste(\"\\nOther Treatment\"))\n\nresult &lt;- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %&gt;%\n  sort_at_path(\n    path = c(\"CMDECOD\"),\n    scorefun = score_occurrences\n  )\n\nresult\n\n                                                        A: Drug X    B: Placebo    C: Combination   All Patients\nOther Treatment                                          (N=134)       (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one treatment   117 (87.3%)   116 (86.6%)    116 (87.9%)     349 (87.2%) \nTotal number of treatments                                 415           414            460             1289    \nmedname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)     170 (42.5%) \nmedname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)     168 (42.0%) \nmedname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)     159 (39.8%) \nmedname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)     157 (39.2%) \nmedname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)     150 (37.5%) \nmedname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)     147 (36.8%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\nadcm_keys &lt;- c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"CMSEQ\", \"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADCM &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n  ADCM$CMSEQ &lt;- as.integer(ADCM$CMSEQ)\n\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADCM &lt;- df_explicit_na(ADCM)\n})\ndatanames &lt;- c(\"ADSL\", \"ADCM\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\njoin_keys(data)[\"ADCM\", \"ADCM\"] &lt;- adcm_keys\n\n## Reusable Configuration For Modules\nADCM &lt;- data[[\"ADCM\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_mult_events(\n      label = \"Concomitant Medications by Medication Class and Preferred Name\",\n      dataname = \"ADCM\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      seq_var = choices_selected(\"CMSEQ\", selected = \"CMSEQ\", fixed = TRUE),\n      hlt = choices_selected(\n        choices = variable_choices(ADCM, c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\")),\n        selected = c(\"ATC1\")\n      ),\n      llt = choices_selected(\n        choices = variable_choices(ADCM, c(\"CMDECOD\")),\n        selected = c(\"CMDECOD\")\n      ),\n      add_total = TRUE,\n      event_type = \"treatment\"\n    )\n  ),\n  filter = teal_slices(\n    teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"),\n    teal_slice(\"ADCM\", \"ATIREL\", selected = \"CONCOMITANT\")\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:40:29 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Concomitant Medications",
@@ -1151,7 +1151,7 @@
     "href": "tables/ECG/egt04.html",
     "title": "EGT04",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nFor the EGT04 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purposes, missing data are added to the example dataset.\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nset.seed(123)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg, omit_columns = c(\"AVALC\", \"BASEC\"))\n\nadeg_labels &lt;- var_labels(adeg)\n\nadeg_f &lt;- subset(\n  adeg,\n  PARAMCD == \"ECGINTP\" & # Analysis in terms of \"NORMAL\"/\"ABNORMAL\" (AVALC)\n    SAFFL == \"Y\" & # \"Safety Population Flag\"\n    ONTRTFL == \"Y\" & # \"On Treatment Record Flag\"\n    WORS02FL == \"Y\" # \"Worst Post-Baseline Observation\"\n)\n\nadeg_f$AVALC[sample(seq_len(nrow(adeg_f)), size = 5)] &lt;- \"Missing\"\nadeg_f$BASEC[sample(seq_len(nrow(adeg_f)), size = 5)] &lt;- \"Missing\"\nadeg_f$AVALC &lt;- factor(\n  adeg_f$AVALC,\n  levels = c(\"NORMAL\", \"ABNORMAL\", \"Missing\"),\n  labels = c(\"Normal\", \"Abnormal\", \"Missing\")\n)\nadeg_f$BASEC &lt;- factor(\n  adeg_f$BASEC,\n  levels = c(\"NORMAL\", \"ABNORMAL\", \"Missing\"),\n  labels = c(\"Normal\", \"Abnormal\", \"Missing\")\n)\n\nvar_labels(adeg_f) &lt;- adeg_labels\nadeg_f &lt;- adeg_f %&gt;%\n  var_relabel(BASEC = \"Baseline\")\n\n# Temprary solution for over arching column\nadeg_f &lt;- adeg_f %&gt;% mutate(postbaseline_label = \"Post-Baseline\")\n\n\n\n\nThe EGT04 template consists of a stacked list of contingency tables, one per group. For each group, the n’s across all cells must add up to the group N in the analysis, and percentages are calculated using N as the denominator.\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"postbaseline_label\") %&gt;%\n  split_cols_by(\"AVALC\") %&gt;%\n  split_rows_by(\"ARM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f$ARM)) %&gt;%\n  add_rowcounts() %&gt;%\n  analyze_vars(\n    \"BASEC\",\n    denom = \"N_row\",\n    .stats = \"count_fraction\",\n    na.rm = FALSE\n  ) %&gt;%\n  append_varlabels(adeg_f, c(\"BASEC\"), indent = 1L)\n\nresult &lt;- build_table(lyt, adeg_f)\nresult\n\nDescription of Planned Arm             Post-Baseline           \n  Baseline                     Normal      Abnormal    Missing \n———————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                              \n  Normal                     20 (14.9%)   81 (60.4%)   1 (0.7%)\n  Abnormal                   11 (8.2%)    19 (14.2%)      0    \n  Missing                        0         1 (0.7%)    1 (0.7%)\nB: Placebo (N=134)                                             \n  Normal                     18 (13.4%)   81 (60.4%)   2 (1.5%)\n  Abnormal                    3 (2.2%)    28 (20.9%)      0    \n  Missing                        0         2 (1.5%)       0    \nC: Combination (N=132)                                         \n  Normal                     26 (19.7%)   69 (52.3%)   1 (0.8%)\n  Abnormal                    6 (4.5%)     29 (22%)       0    \n  Missing                     1 (0.8%)        0           0    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:42:09 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\nFor the EGT04 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purposes, missing data are added to the example dataset.\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\nset.seed(123)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg, omit_columns = c(\"AVALC\", \"BASEC\"))\n\nadeg_labels &lt;- var_labels(adeg)\n\nadeg_f &lt;- subset(\n  adeg,\n  PARAMCD == \"ECGINTP\" & # Analysis in terms of \"NORMAL\"/\"ABNORMAL\" (AVALC)\n    SAFFL == \"Y\" & # \"Safety Population Flag\"\n    ONTRTFL == \"Y\" & # \"On Treatment Record Flag\"\n    WORS02FL == \"Y\" # \"Worst Post-Baseline Observation\"\n)\n\nadeg_f$AVALC[sample(seq_len(nrow(adeg_f)), size = 5)] &lt;- \"Missing\"\nadeg_f$BASEC[sample(seq_len(nrow(adeg_f)), size = 5)] &lt;- \"Missing\"\nadeg_f$AVALC &lt;- factor(\n  adeg_f$AVALC,\n  levels = c(\"NORMAL\", \"ABNORMAL\", \"Missing\"),\n  labels = c(\"Normal\", \"Abnormal\", \"Missing\")\n)\nadeg_f$BASEC &lt;- factor(\n  adeg_f$BASEC,\n  levels = c(\"NORMAL\", \"ABNORMAL\", \"Missing\"),\n  labels = c(\"Normal\", \"Abnormal\", \"Missing\")\n)\n\nvar_labels(adeg_f) &lt;- adeg_labels\nadeg_f &lt;- adeg_f %&gt;%\n  var_relabel(BASEC = \"Baseline\")\n\n# Temprary solution for over arching column\nadeg_f &lt;- adeg_f %&gt;% mutate(postbaseline_label = \"Post-Baseline\")\n\n\n\n\nThe EGT04 template consists of a stacked list of contingency tables, one per group. For each group, the n’s across all cells must add up to the group N in the analysis, and percentages are calculated using N as the denominator.\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"postbaseline_label\") %&gt;%\n  split_cols_by(\"AVALC\") %&gt;%\n  split_rows_by(\"ARM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f$ARM)) %&gt;%\n  add_rowcounts() %&gt;%\n  analyze_vars(\n    \"BASEC\",\n    denom = \"N_row\",\n    .stats = \"count_fraction\",\n    na.rm = FALSE\n  ) %&gt;%\n  append_varlabels(adeg_f, c(\"BASEC\"), indent = 1L)\n\nresult &lt;- build_table(lyt, adeg_f)\nresult\n\nDescription of Planned Arm             Post-Baseline           \n  Baseline                     Normal      Abnormal    Missing \n———————————————————————————————————————————————————————————————\nA: Drug X (N=134)                                              \n  Normal                     20 (14.9%)   81 (60.4%)   1 (0.7%)\n  Abnormal                   11 (8.2%)    19 (14.2%)      0    \n  Missing                        0         1 (0.7%)    1 (0.7%)\nB: Placebo (N=134)                                             \n  Normal                     18 (13.4%)   81 (60.4%)   2 (1.5%)\n  Abnormal                    3 (2.2%)    28 (20.9%)      0    \n  Missing                        0         2 (1.5%)       0    \nC: Combination (N=132)                                         \n  Normal                     26 (19.7%)   69 (52.3%)   1 (0.8%)\n  Abnormal                    6 (4.5%)     29 (22%)       0    \n  Missing                     1 (0.8%)        0           0    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:40:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ECG",
@@ -1163,7 +1163,7 @@
     "href": "tables/ECG/egt02.html",
     "title": "EGT02",
     "section": "",
-    "text": "Data Setup\nECG Abnormalities Regardless  of Abnormality at Baseline\nECG Abnormalities Among Subjects  Without Abnormality at Baseline\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg)\n\n# Note:  We keep only post-baseline for analysis.\nadeg_f &lt;- adeg %&gt;%\n  filter(ONTRTFL == \"Y\") %&gt;%\n  filter(PARAM %in% c(\"Heart Rate\", \"QT Duration\", \"RR Duration\")) %&gt;%\n  filter(ANRIND != \"&lt;Missing&gt;\") %&gt;%\n  var_relabel(\n    PARAM = \"Assessment\",\n    ANRIND = \"Abnormality\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adeg_f$PARAM)\n  ) %&gt;%\n  count_abnormal(\"ANRIND\", abnormal = list(Low = \"LOW\", High = \"HIGH\"), exclude_base_abn = FALSE) %&gt;%\n  append_varlabels(adeg_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)\nresult\n\nAssessment        A: Drug X        B: Placebo     C: Combination\n  Abnormality      (N=134)          (N=134)          (N=132)    \n————————————————————————————————————————————————————————————————\nHeart Rate                                                      \n  Low           40/134 (29.9%)   43/134 (32.1%)    37/132 (28%) \n  High          39/134 (29.1%)   45/134 (33.6%)   36/132 (27.3%)\nQT Duration                                                     \n  Low           33/134 (24.6%)   44/134 (32.8%)   47/132 (35.6%)\n  High          30/134 (22.4%)   42/134 (31.3%)   34/132 (25.8%)\nRR Duration                                                     \n  Low           45/134 (33.6%)   26/134 (19.4%)   38/132 (28.8%)\n  High          29/134 (21.6%)   49/134 (36.6%)   27/132 (20.5%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adeg_f$PARAM)\n  ) %&gt;%\n  count_abnormal(\"ANRIND\", abnormal = list(Low = \"LOW\", High = \"HIGH\"), exclude_base_abn = TRUE) %&gt;%\n  append_varlabels(adeg_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)\nresult\n\nAssessment        A: Drug X        B: Placebo     C: Combination\n  Abnormality      (N=134)          (N=134)          (N=132)    \n————————————————————————————————————————————————————————————————\nHeart Rate                                                      \n  Low           39/128 (30.5%)   42/124 (33.9%)   33/117 (28.2%)\n  High          37/127 (29.1%)   43/129 (33.3%)   31/122 (25.4%)\nQT Duration                                                     \n  Low           29/113 (25.7%)   42/126 (33.3%)   43/124 (34.7%)\n  High           30/125 (24%)    40/130 (30.8%)   30/119 (25.2%)\nRR Duration                                                     \n  Low           45/129 (34.9%)   26/123 (21.1%)   37/126 (29.4%)\n  High          27/127 (21.3%)   43/122 (35.2%)    25/125 (20%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEG, subset = c(\"EGCAT\", \"PARAM\", \"AVISIT\")),\n        selected = c(\"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADEG, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(Low = \"LOW\", High = \"HIGH\"),\n      exclude_base_abn = FALSE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:42:40 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            scda_0.1.6.9017                 \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nECG Abnormalities Regardless  of Abnormality at Baseline\nECG Abnormalities Among Subjects  Without Abnormality at Baseline\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadeg &lt;- df_explicit_na(adeg)\n\n# Note:  We keep only post-baseline for analysis.\nadeg_f &lt;- adeg %&gt;%\n  filter(ONTRTFL == \"Y\") %&gt;%\n  filter(PARAM %in% c(\"Heart Rate\", \"QT Duration\", \"RR Duration\")) %&gt;%\n  filter(ANRIND != \"&lt;Missing&gt;\") %&gt;%\n  var_relabel(\n    PARAM = \"Assessment\",\n    ANRIND = \"Abnormality\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adeg_f$PARAM)\n  ) %&gt;%\n  count_abnormal(\"ANRIND\", abnormal = list(Low = \"LOW\", High = \"HIGH\"), exclude_base_abn = FALSE) %&gt;%\n  append_varlabels(adeg_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)\nresult\n\nAssessment        A: Drug X        B: Placebo     C: Combination\n  Abnormality      (N=134)          (N=134)          (N=132)    \n————————————————————————————————————————————————————————————————\nHeart Rate                                                      \n  Low           40/134 (29.9%)   43/134 (32.1%)    37/132 (28%) \n  High          39/134 (29.1%)   45/134 (33.6%)   36/132 (27.3%)\nQT Duration                                                     \n  Low           33/134 (24.6%)   44/134 (32.8%)   47/132 (35.6%)\n  High          30/134 (22.4%)   42/134 (31.3%)   34/132 (25.8%)\nRR Duration                                                     \n  Low           45/134 (33.6%)   26/134 (19.4%)   38/132 (28.8%)\n  High          29/134 (21.6%)   49/134 (36.6%)   27/132 (20.5%)\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(adeg_f$PARAM)\n  ) %&gt;%\n  count_abnormal(\"ANRIND\", abnormal = list(Low = \"LOW\", High = \"HIGH\"), exclude_base_abn = TRUE) %&gt;%\n  append_varlabels(adeg_f, \"ANRIND\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)\nresult\n\nAssessment        A: Drug X        B: Placebo     C: Combination\n  Abnormality      (N=134)          (N=134)          (N=132)    \n————————————————————————————————————————————————————————————————\nHeart Rate                                                      \n  Low           39/128 (30.5%)   42/124 (33.9%)   33/117 (28.2%)\n  High          37/127 (29.1%)   43/129 (33.3%)   31/122 (25.4%)\nQT Duration                                                     \n  Low           29/113 (25.7%)   42/126 (33.3%)   43/124 (34.7%)\n  High           30/125 (24%)    40/130 (30.8%)   30/119 (25.2%)\nRR Duration                                                     \n  Low           45/129 (34.9%)   26/123 (21.1%)   37/126 (29.4%)\n  High          27/127 (21.3%)   43/122 (35.2%)    25/125 (20%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_abnormality(\n      label = \"Abnormality Table\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADEG, subset = c(\"EGCAT\", \"PARAM\", \"AVISIT\")),\n        selected = c(\"PARAM\"),\n        keep_order = TRUE\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADEG, subset = \"ANRIND\"),\n        selected = \"ANRIND\",\n        fixed = TRUE\n      ),\n      abnormal = list(Low = \"LOW\", High = \"HIGH\"),\n      exclude_base_abn = FALSE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:41:23 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            scda_0.1.6.9017                 \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ECG",
@@ -1175,7 +1175,7 @@
     "href": "tables/ECG/egt03.html",
     "title": "EGT03",
     "section": "",
-    "text": "Data Setup\nTable of Baseline Versus  Minimum Post-Baseline\nTable of Baseline Versus  Maximum Post-Baseline\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadeg &lt;- df_explicit_na(adeg)\n\nadeg_labels &lt;- var_labels(adeg)\n\nadeg_f_pbmin &lt;- subset(\n  adeg,\n  PARAMCD == \"HR\" & # Heart Rate\n    SAFFL == \"Y\" & # \"Safety Population Flag\"\n    ONTRTFL == \"Y\" & # \"On Treatment Record Flag\"\n    AVISIT == \"POST-BASELINE MINIMUM\" # \"Analysis Visit\"\n)\n\nadeg_f_pbmax &lt;- subset(\n  adeg,\n  PARAMCD == \"HR\" & # Heart Rate\n    SAFFL == \"Y\" & # \"Safety Population Flag\"\n    ONTRTFL == \"Y\" & # \"On Treatment Record Flag\"\n    AVISIT == \"POST-BASELINE MAXIMUM\" # \"Analysis Visit\"\n)\n\nvar_labels(adeg_f_pbmin) &lt;- adeg_labels\nvar_labels(adeg_f_pbmax) &lt;- adeg_labels\n\n\n\n\nFor the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purposes, missing data are added to the example dataset.\n\nCodeset.seed(123, kind = \"Mersenne-Twister\")\n\nadeg_f_pbmin$BNRIND &lt;- factor(\n  adeg_f_pbmin$BNRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\nadeg_f_pbmin$ANRIND &lt;- factor(\n  adeg_f_pbmin$ANRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\n\nadeg_f_pbmin$BNRIND[sample(seq_len(nrow(adeg_f_pbmin)), size = 5)] &lt;- \"Missing\"\nadeg_f_pbmin$ANRIND[sample(seq_len(nrow(adeg_f_pbmin)), size = 5)] &lt;- \"Missing\"\n\nattr(adeg_f_pbmin$ANRIND, \"label\") &lt;- \"Analysis Reference Range Indicator\"\nattr(adeg_f_pbmin$BNRIND, \"label\") &lt;- \"Baseline Reference Range Indicator\"\n\n# Temporary solution for overarching column\nadeg_f_pbmin &lt;- adeg_f_pbmin %&gt;% mutate(min_label = \"Minimum Post-Baseline Assessment\")\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"min_label\") %&gt;%\n  split_cols_by(\"ANRIND\") %&gt;%\n  split_rows_by(\"ARMCD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f_pbmin$ARMCD)) %&gt;%\n  add_rowcounts() %&gt;%\n  analyze_vars(\"BNRIND\", denom = \"N_row\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adeg_f_pbmin, c(\"BNRIND\"), indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adeg_f_pbmin\n)\n\nresult\n\nPlanned Arm Code                           Minimum Post-Baseline Assessment     \n  Baseline Reference Range Indicator      LOW         NORMAL     HIGH   Missing \n————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                   \n  LOW                                   1 (0.7%)     5 (3.7%)     0        0    \n  NORMAL                               36 (26.9%)   83 (61.9%)    0     1 (0.7%)\n  HIGH                                  2 (1.5%)      4 (3%)      0        0    \n  Missing                               1 (0.7%)        0         0     1 (0.7%)\nARM B (N=134)                                                                   \n  LOW                                   1 (0.7%)     9 (6.7%)     0        0    \n  NORMAL                               40 (29.9%)    75 (56%)     0     2 (1.5%)\n  HIGH                                  1 (0.7%)      4 (3%)      0        0    \n  Missing                               1 (0.7%)     1 (0.7%)     0        0    \nARM C (N=132)                                                                   \n  LOW                                    4 (3%)     11 (8.3%)     0        0    \n  NORMAL                               31 (23.5%)   75 (56.8%)    0        0    \n  HIGH                                  1 (0.8%)     8 (6.1%)     0     1 (0.8%)\n  Missing                               1 (0.8%)        0         0        0    \n\n\n\n\nFor the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purpose, missing data are added to the example dataset.\n\nCodeset.seed(123, kind = \"Mersenne-Twister\")\n\nadeg_f_pbmax$BNRIND &lt;- factor(\n  adeg_f_pbmax$BNRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\nadeg_f_pbmax$ANRIND &lt;- factor(\n  adeg_f_pbmax$ANRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\n\nadeg_f_pbmax$BNRIND[sample(seq_len(nrow(adeg_f_pbmax)), size = 5)] &lt;- \"Missing\"\nadeg_f_pbmax$ANRIND[sample(seq_len(nrow(adeg_f_pbmax)), size = 5)] &lt;- \"Missing\"\n\nattr(adeg_f_pbmax$ANRIND, \"label\") &lt;- \"Analysis Reference Range Indicator\"\nattr(adeg_f_pbmax$BNRIND, \"label\") &lt;- \"Baseline Reference Range Indicator\"\n\n# Temporary solution for overarching column\nadeg_f_pbmax &lt;- adeg_f_pbmax %&gt;% mutate(max_label = \"Maximum Post-Baseline Assessment\")\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"max_label\") %&gt;%\n  split_cols_by(\"ANRIND\") %&gt;%\n  split_rows_by(\"ARMCD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f_pbmax$ARMCD)) %&gt;%\n  add_rowcounts() %&gt;%\n  analyze_vars(\"BNRIND\", denom = \"N_row\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adeg_f_pbmax, c(\"BNRIND\"), indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adeg_f_pbmax\n)\n\nresult\n\nPlanned Arm Code                           Maximum Post-Baseline Assessment     \n  Baseline Reference Range Indicator   LOW      NORMAL        HIGH      Missing \n————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                   \n  LOW                                   0      2 (1.5%)      4 (3%)        0    \n  NORMAL                                0     88 (65.7%)   31 (23.1%)   1 (0.7%)\n  HIGH                                  0      5 (3.7%)     1 (0.7%)       0    \n  Missing                               0         0         1 (0.7%)    1 (0.7%)\nARM B (N=134)                                                                   \n  LOW                                   0       8 (6%)      2 (1.5%)       0    \n  NORMAL                                0     76 (56.7%)   39 (29.1%)   2 (1.5%)\n  HIGH                                  0      3 (2.2%)     2 (1.5%)       0    \n  Missing                               0      1 (0.7%)     1 (0.7%)       0    \nARM C (N=132)                                                                   \n  LOW                                   0     12 (9.1%)     3 (2.3%)       0    \n  NORMAL                                0     79 (59.8%)   27 (20.5%)      0    \n  HIGH                                  0      5 (3.8%)      4 (3%)     1 (0.8%)\n  Missing                               0         0         1 (0.8%)       0    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_shift_by_arm(\n      label = \"Shift by Arm Table\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        value_choices(ADEG, \"PARAMCD\"),\n        selected = \"HR\"\n      ),\n      visit_var = choices_selected(\n        value_choices(ADEG, \"AVISIT\"),\n        selected = \"POST-BASELINE MINIMUM\"\n      ),\n      aval_var = choices_selected(\n        variable_choices(ADEG, subset = \"ANRIND\"),\n        selected = \"ANRIND\", fixed = TRUE\n      ),\n      baseline_var = choices_selected(\n        variable_choices(ADEG, subset = \"BNRIND\"),\n        selected = \"BNRIND\", fixed = TRUE\n      ),\n      treatment_flag_var = choices_selected(\n        variable_choices(ADEG, subset = \"ONTRTFL\"),\n        selected = \"ONTRTFL\", fixed = TRUE\n      ),\n      treatment_flag = choices_selected(\n        value_choices(ADEG, \"ONTRTFL\"),\n        selected = \"Y\", fixed = TRUE\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:43:18 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nTable of Baseline Versus  Minimum Post-Baseline\nTable of Baseline Versus  Maximum Post-Baseline\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadeg &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadeg &lt;- df_explicit_na(adeg)\n\nadeg_labels &lt;- var_labels(adeg)\n\nadeg_f_pbmin &lt;- subset(\n  adeg,\n  PARAMCD == \"HR\" & # Heart Rate\n    SAFFL == \"Y\" & # \"Safety Population Flag\"\n    ONTRTFL == \"Y\" & # \"On Treatment Record Flag\"\n    AVISIT == \"POST-BASELINE MINIMUM\" # \"Analysis Visit\"\n)\n\nadeg_f_pbmax &lt;- subset(\n  adeg,\n  PARAMCD == \"HR\" & # Heart Rate\n    SAFFL == \"Y\" & # \"Safety Population Flag\"\n    ONTRTFL == \"Y\" & # \"On Treatment Record Flag\"\n    AVISIT == \"POST-BASELINE MAXIMUM\" # \"Analysis Visit\"\n)\n\nvar_labels(adeg_f_pbmin) &lt;- adeg_labels\nvar_labels(adeg_f_pbmax) &lt;- adeg_labels\n\n\n\n\nFor the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purposes, missing data are added to the example dataset.\n\nCodeset.seed(123, kind = \"Mersenne-Twister\")\n\nadeg_f_pbmin$BNRIND &lt;- factor(\n  adeg_f_pbmin$BNRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\nadeg_f_pbmin$ANRIND &lt;- factor(\n  adeg_f_pbmin$ANRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\n\nadeg_f_pbmin$BNRIND[sample(seq_len(nrow(adeg_f_pbmin)), size = 5)] &lt;- \"Missing\"\nadeg_f_pbmin$ANRIND[sample(seq_len(nrow(adeg_f_pbmin)), size = 5)] &lt;- \"Missing\"\n\nattr(adeg_f_pbmin$ANRIND, \"label\") &lt;- \"Analysis Reference Range Indicator\"\nattr(adeg_f_pbmin$BNRIND, \"label\") &lt;- \"Baseline Reference Range Indicator\"\n\n# Temporary solution for overarching column\nadeg_f_pbmin &lt;- adeg_f_pbmin %&gt;% mutate(min_label = \"Minimum Post-Baseline Assessment\")\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"min_label\") %&gt;%\n  split_cols_by(\"ANRIND\") %&gt;%\n  split_rows_by(\"ARMCD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f_pbmin$ARMCD)) %&gt;%\n  add_rowcounts() %&gt;%\n  analyze_vars(\"BNRIND\", denom = \"N_row\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adeg_f_pbmin, c(\"BNRIND\"), indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adeg_f_pbmin\n)\n\nresult\n\nPlanned Arm Code                           Minimum Post-Baseline Assessment     \n  Baseline Reference Range Indicator      LOW         NORMAL     HIGH   Missing \n————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                   \n  LOW                                   1 (0.7%)     5 (3.7%)     0        0    \n  NORMAL                               36 (26.9%)   83 (61.9%)    0     1 (0.7%)\n  HIGH                                  2 (1.5%)      4 (3%)      0        0    \n  Missing                               1 (0.7%)        0         0     1 (0.7%)\nARM B (N=134)                                                                   \n  LOW                                   1 (0.7%)     9 (6.7%)     0        0    \n  NORMAL                               40 (29.9%)    75 (56%)     0     2 (1.5%)\n  HIGH                                  1 (0.7%)      4 (3%)      0        0    \n  Missing                               1 (0.7%)     1 (0.7%)     0        0    \nARM C (N=132)                                                                   \n  LOW                                    4 (3%)     11 (8.3%)     0        0    \n  NORMAL                               31 (23.5%)   75 (56.8%)    0        0    \n  HIGH                                  1 (0.8%)     8 (6.1%)     0     1 (0.8%)\n  Missing                               1 (0.8%)        0         0        0    \n\n\n\n\nFor the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purpose, missing data are added to the example dataset.\n\nCodeset.seed(123, kind = \"Mersenne-Twister\")\n\nadeg_f_pbmax$BNRIND &lt;- factor(\n  adeg_f_pbmax$BNRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\nadeg_f_pbmax$ANRIND &lt;- factor(\n  adeg_f_pbmax$ANRIND,\n  levels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\"),\n  labels = c(\"LOW\", \"NORMAL\", \"HIGH\", \"Missing\")\n)\n\nadeg_f_pbmax$BNRIND[sample(seq_len(nrow(adeg_f_pbmax)), size = 5)] &lt;- \"Missing\"\nadeg_f_pbmax$ANRIND[sample(seq_len(nrow(adeg_f_pbmax)), size = 5)] &lt;- \"Missing\"\n\nattr(adeg_f_pbmax$ANRIND, \"label\") &lt;- \"Analysis Reference Range Indicator\"\nattr(adeg_f_pbmax$BNRIND, \"label\") &lt;- \"Baseline Reference Range Indicator\"\n\n# Temporary solution for overarching column\nadeg_f_pbmax &lt;- adeg_f_pbmax %&gt;% mutate(max_label = \"Maximum Post-Baseline Assessment\")\n\n# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table() %&gt;%\n  split_cols_by(\"max_label\") %&gt;%\n  split_cols_by(\"ANRIND\") %&gt;%\n  split_rows_by(\"ARMCD\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adeg_f_pbmax$ARMCD)) %&gt;%\n  add_rowcounts() %&gt;%\n  analyze_vars(\"BNRIND\", denom = \"N_row\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adeg_f_pbmax, c(\"BNRIND\"), indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adeg_f_pbmax\n)\n\nresult\n\nPlanned Arm Code                           Maximum Post-Baseline Assessment     \n  Baseline Reference Range Indicator   LOW      NORMAL        HIGH      Missing \n————————————————————————————————————————————————————————————————————————————————\nARM A (N=134)                                                                   \n  LOW                                   0      2 (1.5%)      4 (3%)        0    \n  NORMAL                                0     88 (65.7%)   31 (23.1%)   1 (0.7%)\n  HIGH                                  0      5 (3.7%)     1 (0.7%)       0    \n  Missing                               0         0         1 (0.7%)    1 (0.7%)\nARM B (N=134)                                                                   \n  LOW                                   0       8 (6%)      2 (1.5%)       0    \n  NORMAL                                0     76 (56.7%)   39 (29.1%)   2 (1.5%)\n  HIGH                                  0      3 (2.2%)     2 (1.5%)       0    \n  Missing                               0      1 (0.7%)     1 (0.7%)       0    \nARM C (N=132)                                                                   \n  LOW                                   0     12 (9.1%)     3 (2.3%)       0    \n  NORMAL                                0     79 (59.8%)   27 (20.5%)      0    \n  HIGH                                  0      5 (3.8%)      4 (3%)     1 (0.8%)\n  Missing                               0         0         1 (0.8%)       0    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEG &lt;- synthetic_cdisc_dataset(\"latest\", \"adeg\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADEG\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADEG &lt;- data[[\"ADEG\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_shift_by_arm(\n      label = \"Shift by Arm Table\",\n      dataname = \"ADEG\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        value_choices(ADEG, \"PARAMCD\"),\n        selected = \"HR\"\n      ),\n      visit_var = choices_selected(\n        value_choices(ADEG, \"AVISIT\"),\n        selected = \"POST-BASELINE MINIMUM\"\n      ),\n      aval_var = choices_selected(\n        variable_choices(ADEG, subset = \"ANRIND\"),\n        selected = \"ANRIND\", fixed = TRUE\n      ),\n      baseline_var = choices_selected(\n        variable_choices(ADEG, subset = \"BNRIND\"),\n        selected = \"BNRIND\", fixed = TRUE\n      ),\n      treatment_flag_var = choices_selected(\n        variable_choices(ADEG, subset = \"ONTRTFL\"),\n        selected = \"ONTRTFL\", fixed = TRUE\n      ),\n      treatment_flag = choices_selected(\n        value_choices(ADEG, \"ONTRTFL\"),\n        selected = \"Y\", fixed = TRUE\n      )\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:00 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ECG",
@@ -1187,7 +1187,7 @@
     "href": "tables/ADA/adat03.html",
     "title": "ADAT03",
     "section": "",
-    "text": "Data Setup\nStandard Table (μg/mL)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadab &lt;- df_explicit_na(adab)\nadpc &lt;- df_explicit_na(adpc)\n\n# Adjust zzz parameter\nmax_conc &lt;- 15\n\nadpc &lt;- adpc %&gt;% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)\n\nanl &lt;- adab %&gt;%\n  filter(., PARAM == \"ADA interpreted per sample result\") %&gt;%\n  select(-AVAL, AVALC, AVALU)\n\nanl &lt;- merge(anl, adpc, by = c(\"USUBJID\", \"NFRLT\")) %&gt;%\n  mutate(AVAL_LT = ifelse(AVAL &lt;= max_conc, TRUE, FALSE))\n\n\n\n\n\nCode# parameters in columns\nadat03_stats &lt;- c(\"n\", \"mean\", \"sd\", \"median\", \"min\", \"max\", \"cv\", \"geom_mean\", \"count_fraction\")\nadat03_lbls &lt;- c(\n  n = \"Total Number\\nof Measurable\\n Samples\",\n  mean = \"Mean\",\n  sd = \"SD\",\n  median = \"Median\",\n  min = \"Minimum\",\n  max = \"Maximum\",\n  cv = \"CV (%)\",\n  geom_mean = \"Geometric Mean\",\n  count_fraction = paste0(\"Samples with\\nConcentration\\n≤ \", max_conc, \"μg/mL\")\n)\nadat03_fmts &lt;- c(\n  n = \"xx.\",\n  mean = format_sigfig(3),\n  sd = format_sigfig(3),\n  median = format_sigfig(3),\n  min = format_sigfig(3),\n  max = format_sigfig(3),\n  cv = \"xx.x\",\n  geom_mean = format_sigfig(3),\n  count_fraction = format_count_fraction\n)\n\nafun_list &lt;- lapply(\n  1:9,\n  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = \"Overall\")\n)\n\n# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ARM\",\n    label_pos = \"topleft\",\n    split_label = \"Treatment Group\",\n    split_fun = drop_split_levels,\n    section_div = \"\"\n  ) %&gt;%\n  add_rowcounts() %&gt;%\n  split_rows_by(\n    var = \"VISIT\",\n    label_pos = \"topleft\",\n    split_label = \"Visit\",\n    split_fun = drop_split_levels,\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = c(rep(\"AVAL\", 8), \"AVAL_LT\"),\n    .stats = adat03_stats,\n    .labels = adat03_lbls,\n    .formats = adat03_fmts\n  ) %&gt;%\n  analyze_colvars(\n    afun_list,\n    nested = FALSE,\n    extra_args = list(\".labels\" = \"Overall\")\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\n\nmain_title(result) &lt;- paste(\n  \"Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\\n\n  Protocol:\", unique(adab$PARCAT1)[1]\n)\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adab$PARAMCD)[1])\nfnotes_at_path(result, rowpath = NULL, colpath = c(\"multivars\", \"AVAL\")) &lt;- \"Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value.\" \nfnotes_at_path(result, rowpath = NULL, colpath = c(\"multivars\", \"AVAL_LT\")) &lt;- \"In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]\" \n\nresult\n\nSummary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n\n  Protocol: A: Drug X Antibody\nAnalyte: R1800000\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                         Total Number                                                                         Samples with \nTreatment Group          of Measurable                                                                        Concentration\n  Visit                   Samples {1}    Mean    SD    Median   Minimum   Maximum   CV (%)   Geometric Mean   ≤ 15μg/mL {2}\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=536)                                                                                                          \n  Day 1                       402         0      0       0         0         0        NA           NA          402 (100%)  \n  Day 2                       134        16.2   1.63    16.2     12.6      19.9      10.0         16.1         39 (29.1%)  \n\nC: Combination (N=792)                                                                                                     \n  Day 1                       528         0      0       0         0         0        NA           NA          528 (100%)  \n  Day 2                       264        24.7   8.65    22.5     12.6      39.5      35.0         23.2         28 (10.6%)  \n\nOverall                      1328        6.54   11.0     0         0       39.5     167.5          NA          997 (75.1%) \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value.\n{2} - In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:43:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table (μg/mL)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadab &lt;- df_explicit_na(adab)\nadpc &lt;- df_explicit_na(adpc)\n\n# Adjust zzz parameter\nmax_conc &lt;- 15\n\nadpc &lt;- adpc %&gt;% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)\n\nanl &lt;- adab %&gt;%\n  filter(., PARAM == \"ADA interpreted per sample result\") %&gt;%\n  select(-AVAL, AVALC, AVALU)\n\nanl &lt;- merge(anl, adpc, by = c(\"USUBJID\", \"NFRLT\")) %&gt;%\n  mutate(AVAL_LT = ifelse(AVAL &lt;= max_conc, TRUE, FALSE))\n\n\n\n\n\nCode# parameters in columns\nadat03_stats &lt;- c(\"n\", \"mean\", \"sd\", \"median\", \"min\", \"max\", \"cv\", \"geom_mean\", \"count_fraction\")\nadat03_lbls &lt;- c(\n  n = \"Total Number\\nof Measurable\\n Samples\",\n  mean = \"Mean\",\n  sd = \"SD\",\n  median = \"Median\",\n  min = \"Minimum\",\n  max = \"Maximum\",\n  cv = \"CV (%)\",\n  geom_mean = \"Geometric Mean\",\n  count_fraction = paste0(\"Samples with\\nConcentration\\n≤ \", max_conc, \"μg/mL\")\n)\nadat03_fmts &lt;- c(\n  n = \"xx.\",\n  mean = format_sigfig(3),\n  sd = format_sigfig(3),\n  median = format_sigfig(3),\n  min = format_sigfig(3),\n  max = format_sigfig(3),\n  cv = \"xx.x\",\n  geom_mean = format_sigfig(3),\n  count_fraction = format_count_fraction\n)\n\nafun_list &lt;- lapply(\n  1:9,\n  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = \"Overall\")\n)\n\n# lyt creation\nlyt &lt;- basic_table() %&gt;%\n  split_rows_by(\n    var = \"ARM\",\n    label_pos = \"topleft\",\n    split_label = \"Treatment Group\",\n    split_fun = drop_split_levels,\n    section_div = \"\"\n  ) %&gt;%\n  add_rowcounts() %&gt;%\n  split_rows_by(\n    var = \"VISIT\",\n    label_pos = \"topleft\",\n    split_label = \"Visit\",\n    split_fun = drop_split_levels,\n    child_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars_in_cols(\n    vars = c(rep(\"AVAL\", 8), \"AVAL_LT\"),\n    .stats = adat03_stats,\n    .labels = adat03_lbls,\n    .formats = adat03_fmts\n  ) %&gt;%\n  analyze_colvars(\n    afun_list,\n    nested = FALSE,\n    extra_args = list(\".labels\" = \"Overall\")\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\n\nmain_title(result) &lt;- paste(\n  \"Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\\n\n  Protocol:\", unique(adab$PARCAT1)[1]\n)\nsubtitles(result) &lt;- paste(\"Analyte:\", unique(adab$PARAMCD)[1])\nfnotes_at_path(result, rowpath = NULL, colpath = c(\"multivars\", \"AVAL\")) &lt;- \"Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value.\" \nfnotes_at_path(result, rowpath = NULL, colpath = c(\"multivars\", \"AVAL_LT\")) &lt;- \"In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]\" \n\nresult\n\nSummary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n\n  Protocol: A: Drug X Antibody\nAnalyte: R1800000\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                         Total Number                                                                         Samples with \nTreatment Group          of Measurable                                                                        Concentration\n  Visit                   Samples {1}    Mean    SD    Median   Minimum   Maximum   CV (%)   Geometric Mean   ≤ 15μg/mL {2}\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X (N=536)                                                                                                          \n  Day 1                       402         0      0       0         0         0        NA           NA          402 (100%)  \n  Day 2                       134        16.2   1.63    16.2     12.6      19.9      10.0         16.1         39 (29.1%)  \n\nC: Combination (N=792)                                                                                                     \n  Day 1                       528         0      0       0         0         0        NA           NA          528 (100%)  \n  Day 2                       264        24.7   8.65    22.5     12.6      39.5      35.0         23.2         28 (10.6%)  \n\nOverall                      1328        6.54   11.0     0         0       39.5     167.5          NA          997 (75.1%) \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value.\n{2} - In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:13 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ADA",
@@ -1199,7 +1199,7 @@
     "href": "tables/ADA/adat04a.html",
     "title": "ADAT04A",
     "section": "",
-    "text": "Data Setup\nSummary of Treatment Emergent NAbs\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(tibble)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\ncombodf &lt;- tribble(\n  ~valname, ~label, ~levelcombo, ~exargs,\n  \"all_X\", \"All Drug X\", c(\"A: Drug X\", \"C: Combination\"), list(),\n  \"all_pt\", \"All Patients\", c(\"A: Drug X\", \"B: Placebo\", \"C: Combination\"), list()\n)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\n# Baseline Pts\nadab_b &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL == \"Y\",\n    ADABLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"NAB interpreted per sample result\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\",\n        \"NAB interpreted per sample result\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADABLPFL = ADABLPFL == \"Y\",\n    PADABLPFL = if (exists(\"ADA interpreted per sample result\", where = .)) {\n      `ADA interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    PNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"FALSE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADABLPFL = \"Baseline evaluable patients for ADA\",\n    PADABLPFL = \"Patients with a positive ADA sample at baseline\",\n    PNABBLFL = \"Patients with a positive NAb sample at baseline\",\n    NNABBLFL = \"Patient with no positive NAb samples at baseline\"\n  )\n\n# Post Baseline Treatment Enhanced NAb positive Pts\nadab_pb &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    ADPBLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"Treatment Emergent - Positive, Neutralizing Antibody\",\n      \"Treatment induced ADA, Neutralizing Antibody\",\n      \"Treatment enhanced ADA, Neutralizing Antibody\",\n      \"NAB interpreted per sample result\",\n      \"Treatment unaffected, Neutralizing Antibody\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\", \"NAB interpreted per sample result\",\n        \"Treatment Emergent - Positive, Neutralizing Antibody\",\n        \"Treatment induced ADA, Neutralizing Antibody\",\n        \"Treatment enhanced ADA, Neutralizing Antibody\",\n        \"Treatment unaffected, Neutralizing Antibody\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADPBLPFL = ADPBLPFL == \"Y\",\n    ADAPFL = if (exists(\"ADA interpreted per sample result\", where = .)) {\n      `ADA interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TENABPFL = if (exists(\"Treatment Emergent - Positive, Neutralizing Antibody\", where = .)) {\n      `Treatment Emergent - Positive, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TINPBFL = if (exists(\"Treatment induced ADA, Neutralizing Antibody\", where = .)) {\n      `Treatment induced ADA, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TENPBFL = if (exists(\"Treatment enhanced ADA, Neutralizing Antibody\", where = .)) {\n      `Treatment enhanced ADA, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NABNFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"FALSE\"\n    } else {\n      FALSE\n    },\n    TUNPBFL = if (exists(\"Treatment unaffected, Neutralizing Antibody\", where = .)) {\n      `Treatment unaffected, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients for ADA\",\n    ADAPFL = \"Patients positive for ADA\",\n    TENABPFL = \"Patients positive for Treatment Emergent NAb\",\n    TINPBFL = \"Treatment-induced NAb\",\n    TENPBFL = \"Treatment-enhanced NAb\",\n    NABNFL = \"Patients negative for NAb\",\n    TUNPBFL = \"Treatment unaffected\"\n  )\n\n\n\n\n\n\nCode# Layout for Baseline Prevalence of NAbs\nlyt_bl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADABLPFL\", \"PADABLPFL\"),\n    table_names = \"t1\",\n    .stats = \"count\",\n    var_labels = \"Baseline Prevalence of NAbs\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PNABBLFL\",\n    table_names = \"t2\",\n    .indent_mods = 1L\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NNABBLFL\",\n    .stats = \"count\",\n    table_names = \"t3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\n# Layout for incidence of NAbs\nlyt_pb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADPBLPFL\", \"ADAPFL\"),\n    .stats = \"count\",\n    var_labels = \"Incidence of Treatment Emergent NAbs\",\n    show_labels = \"visible\",\n    table_names = \"tb1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TENABPFL\",\n    table_names = \"tb2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"TINPBFL\", \"TENPBFL\"),\n    .stats = \"count\",\n    table_names = \"tb3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NABNFL\",\n    table_names = \"tb4\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TUNPBFL\",\n    .stats = \"count\",\n    table_names = \"tb5\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\nresult_1 &lt;- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)\nresult_2 &lt;- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)\n\n# Combine tables\nresult &lt;- rbind(result_1, result_2)\n\n# Change the column order\nresult &lt;- cbind_rtables(result[, 1], result[, 3]) %&gt;%\n  cbind_rtables(result[, 4]) %&gt;%\n  cbind_rtables(result[, 2]) %&gt;%\n  cbind_rtables(result[, 5])\n\nmain_title(result) &lt;- paste(\n  \"Baseline Prevalence and Incidence of Treatment Emergent NAbs\"\n)\nsubtitles(result) &lt;- paste(\"Protocol:\", unique(adab$PARCAT1)[1])\n\nmain_footer(result) &lt;- \"NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)\n  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)\n  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample\n  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample\n  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.\nTreatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nNumber of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.\nTreatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\"\n\nresult\n\nBaseline Prevalence and Incidence of Treatment Emergent NAbs\nProtocol: A: Drug X Antibody\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                    A: Drug X   C: Combination   &lt;Missing&gt;   B: Placebo   All Drug X\n                                                     (N=134)       (N=132)         (N=0)      (N=134)      (N=266)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBaseline Prevalence of NAbs                                                                                         \n  Baseline evaluable patients for ADA                  134           132             0           0           266    \n  Patients with a positive ADA sample at baseline      63             64             0           0           127    \nPatients with a positive NAb sample at baseline         0             0              0           0            0     \nPatient with no positive NAb samples at baseline        0             0              0           0            0     \nIncidence of Treatment Emergent NAbs                                                                                \n  Post-baseline evaluable patients for ADA             134           132             0           0           266    \n  Patients positive for ADA                            66             59             0           0           125    \nPatients positive for Treatment Emergent NAb            0             0              0           0            0     \nTreatment-induced NAb                                   0             0              0           0            0     \nTreatment-enhanced NAb                                  0             0              0           0            0     \nPatients negative for NAb                               0             0              0           0            0     \nTreatment unaffected                                    0             0              0           0            0     \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)\n  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)\n  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample\n  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample\n  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.\nTreatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nNumber of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.\nTreatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:43:44 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tibble_3.2.1          scda_0.1.6.9017       dplyr_1.1.4          \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nSummary of Treatment Emergent NAbs\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(tibble)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\")\n\ncombodf &lt;- tribble(\n  ~valname, ~label, ~levelcombo, ~exargs,\n  \"all_X\", \"All Drug X\", c(\"A: Drug X\", \"C: Combination\"), list(),\n  \"all_pt\", \"All Patients\", c(\"A: Drug X\", \"B: Placebo\", \"C: Combination\"), list()\n)\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\n# Baseline Pts\nadab_b &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL == \"Y\",\n    ADABLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"NAB interpreted per sample result\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU) %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\",\n        \"NAB interpreted per sample result\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADABLPFL = ADABLPFL == \"Y\",\n    PADABLPFL = if (exists(\"ADA interpreted per sample result\", where = .)) {\n      `ADA interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    PNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NNABBLFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"FALSE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADABLPFL = \"Baseline evaluable patients for ADA\",\n    PADABLPFL = \"Patients with a positive ADA sample at baseline\",\n    PNABBLFL = \"Patients with a positive NAb sample at baseline\",\n    NNABBLFL = \"Patient with no positive NAb samples at baseline\"\n  )\n\n# Post Baseline Treatment Enhanced NAb positive Pts\nadab_pb &lt;- df_explicit_na(adab) %&gt;%\n  filter(\n    ABLFL != \"Y\",\n    ADPBLPFL == \"Y\",\n    PARAM %in% c(\n      \"ADA interpreted per sample result\",\n      \"Treatment Emergent - Positive, Neutralizing Antibody\",\n      \"Treatment induced ADA, Neutralizing Antibody\",\n      \"Treatment enhanced ADA, Neutralizing Antibody\",\n      \"NAB interpreted per sample result\",\n      \"Treatment unaffected, Neutralizing Antibody\"\n    )\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  mutate(\n    across(\n      any_of(c(\n        \"ADA interpreted per sample result\", \"NAB interpreted per sample result\",\n        \"Treatment Emergent - Positive, Neutralizing Antibody\",\n        \"Treatment induced ADA, Neutralizing Antibody\",\n        \"Treatment enhanced ADA, Neutralizing Antibody\",\n        \"Treatment unaffected, Neutralizing Antibody\"\n      )),\n      as.logical\n    )\n  ) %&gt;%\n  mutate(\n    ADPBLPFL = ADPBLPFL == \"Y\",\n    ADAPFL = if (exists(\"ADA interpreted per sample result\", where = .)) {\n      `ADA interpreted per sample result` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TENABPFL = if (exists(\"Treatment Emergent - Positive, Neutralizing Antibody\", where = .)) {\n      `Treatment Emergent - Positive, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TINPBFL = if (exists(\"Treatment induced ADA, Neutralizing Antibody\", where = .)) {\n      `Treatment induced ADA, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    TENPBFL = if (exists(\"Treatment enhanced ADA, Neutralizing Antibody\", where = .)) {\n      `Treatment enhanced ADA, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    },\n    NABNFL = if (exists(\"NAB interpreted per sample result\", where = .)) {\n      `NAB interpreted per sample result` == \"FALSE\"\n    } else {\n      FALSE\n    },\n    TUNPBFL = if (exists(\"Treatment unaffected, Neutralizing Antibody\", where = .)) {\n      `Treatment unaffected, Neutralizing Antibody` == \"TRUE\"\n    } else {\n      FALSE\n    }\n  ) %&gt;%\n  var_relabel(\n    ADPBLPFL = \"Post-baseline evaluable patients for ADA\",\n    ADAPFL = \"Patients positive for ADA\",\n    TENABPFL = \"Patients positive for Treatment Emergent NAb\",\n    TINPBFL = \"Treatment-induced NAb\",\n    TENPBFL = \"Treatment-enhanced NAb\",\n    NABNFL = \"Patients negative for NAb\",\n    TUNPBFL = \"Treatment unaffected\"\n  )\n\n\n\n\n\n\nCode# Layout for Baseline Prevalence of NAbs\nlyt_bl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADABLPFL\", \"PADABLPFL\"),\n    table_names = \"t1\",\n    .stats = \"count\",\n    var_labels = \"Baseline Prevalence of NAbs\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"PNABBLFL\",\n    table_names = \"t2\",\n    .indent_mods = 1L\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NNABBLFL\",\n    .stats = \"count\",\n    table_names = \"t3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\n# Layout for incidence of NAbs\nlyt_pb &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_combo_levels(combodf)\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"ADPBLPFL\", \"ADAPFL\"),\n    .stats = \"count\",\n    var_labels = \"Incidence of Treatment Emergent NAbs\",\n    show_labels = \"visible\",\n    table_names = \"tb1\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TENABPFL\",\n    table_names = \"tb2\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = c(\"TINPBFL\", \"TENPBFL\"),\n    .stats = \"count\",\n    table_names = \"tb3\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"NABNFL\",\n    table_names = \"tb4\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = \"TUNPBFL\",\n    .stats = \"count\",\n    table_names = \"tb5\",\n    .indent_mods = 1L,\n    show_labels = \"hidden\"\n  )\n\nresult_1 &lt;- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)\nresult_2 &lt;- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)\n\n# Combine tables\nresult &lt;- rbind(result_1, result_2)\n\n# Change the column order\nresult &lt;- cbind_rtables(result[, 1], result[, 3]) %&gt;%\n  cbind_rtables(result[, 4]) %&gt;%\n  cbind_rtables(result[, 2]) %&gt;%\n  cbind_rtables(result[, 5])\n\nmain_title(result) &lt;- paste(\n  \"Baseline Prevalence and Incidence of Treatment Emergent NAbs\"\n)\nsubtitles(result) &lt;- paste(\"Protocol:\", unique(adab$PARCAT1)[1])\n\nmain_footer(result) &lt;- \"NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)\n  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)\n  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample\n  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample\n  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.\nTreatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nNumber of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.\nTreatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\"\n\nresult\n\nBaseline Prevalence and Incidence of Treatment Emergent NAbs\nProtocol: A: Drug X Antibody\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                    A: Drug X   C: Combination   &lt;Missing&gt;   B: Placebo   All Drug X\n                                                     (N=134)       (N=132)         (N=0)      (N=134)      (N=266)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nBaseline Prevalence of NAbs                                                                                         \n  Baseline evaluable patients for ADA                  134           132             0           0           266    \n  Patients with a positive ADA sample at baseline      63             64             0           0           127    \nPatients with a positive NAb sample at baseline         0             0              0           0            0     \nPatient with no positive NAb samples at baseline        0             0              0           0            0     \nIncidence of Treatment Emergent NAbs                                                                                \n  Post-baseline evaluable patients for ADA             134           132             0           0           266    \n  Patients positive for ADA                            66             59             0           0           125    \nPatients positive for Treatment Emergent NAb            0             0              0           0            0     \nTreatment-induced NAb                                   0             0              0           0            0     \nTreatment-enhanced NAb                                  0             0              0           0            0     \nPatients negative for NAb                               0             0              0           0            0     \nTreatment unaffected                                    0             0              0           0            0     \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)\n  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)\n  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample\n  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample\n  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.\nNumber of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.\nTreatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nNumber of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.\nTreatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:25 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tibble_3.2.1          scda_0.1.6.9017       dplyr_1.1.4          \n[4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[7] formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    stringi_1.8.3     broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "ADA",
@@ -1211,7 +1211,7 @@
     "href": "tables/disposition/dst01.html",
     "title": "DST01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Grouping of Reasons\nTable Adding Optional Rows\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nset.seed(1, kind = \"Mersenne-Twister\")\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# reorder EOSSTT factor levels so DISCONTINUED is the last level\nadsl &lt;- df_explicit_na(adsl) %&gt;%\n  mutate(EOSSTT = factor(EOSSTT, levels = c(\"COMPLETED\", \"ONGOING\", \"DISCONTINUED\")))\n\nadsl_gp_added &lt;- adsl %&gt;%\n  mutate(DCSREASGP = case_when(\n    DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\") ~ \"Safety\",\n    (DCSREAS != \"&lt;Missing&gt;\" & !DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\")) ~ \"Non-Safety\",\n    DCSREAS == \"&lt;Missing&gt;\" ~ \"&lt;Missing&gt;\"\n  ) %&gt;% factor(levels = c(\"Safety\", \"Non-Safety\", \"&lt;Missing&gt;\")))\n\nadsl_eotstt_added &lt;- adsl_gp_added %&gt;%\n  mutate(\n    EOTSTT = sample(\n      c(\"ONGOING\", \"COMPLETED\", \"DISCONTINUED\"),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% factor(levels = c(\"COMPLETED\", \"ONGOING\", \"DISCONTINUED\"))\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(\n    \"EOSSTT\",\n    show_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars(\n    \"DCSREAS\",\n    .stats = \"count_fraction\",\n    denom = \"N_col\",\n    show_labels = \"hidden\",\n    .indent_mods = c(count_fraction = 1L)\n  )\n\nresult1 &lt;- build_table(lyt = lyt, df = adsl)\nresult1\n\n                                  A: Drug X    B: Placebo   C: Combination   All Patients\n                                   (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————————\nCOMPLETED                         68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) \nONGOING                           24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) \nDISCONTINUED                      42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) \n  ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  \n  DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \n  LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  \n  PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  \n  PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   \n  WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  \n  WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(\n    \"EOSSTT\",\n    show_labels = \"hidden\"\n  ) %&gt;%\n  split_rows_by(\"DCSREASGP\", indent_mod = 1L) %&gt;%\n  analyze_vars(\n    \"DCSREAS\",\n    .stats = \"count_fraction\",\n    denom = \"N_col\",\n    show_labels = \"hidden\"\n  )\n\ntbl &lt;- build_table(lyt = lyt, df = adsl_gp_added)\nresult2 &lt;- prune_table(tbl) # remove rows containing all zeros\n\nresult2\n\n                                    A: Drug X    B: Placebo   C: Combination   All Patients\n                                     (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————\nCOMPLETED                           68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) \nONGOING                             24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) \nDISCONTINUED                        42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) \n  Safety                                                                                   \n    ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  \n    DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \n  Non-Safety                                                                               \n    LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  \n    PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  \n    PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   \n    WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  \n    WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(\n    \"EOTSTT\",\n    show_labels = \"hidden\"\n  )\n\ntbl &lt;- build_table(lyt = lyt, df = adsl_eotstt_added)\ntbl &lt;- prune_table(tbl) # remove rows containing all zeros\n\n# Combine tables\ncol_info(result2) &lt;- col_info(tbl)\nresult3 &lt;- rbind(result2, tbl)\n\nresult3\n\n                                    A: Drug X    B: Placebo   C: Combination   All Patients\n                                     (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————\nCOMPLETED                           68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) \nONGOING                             24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) \nDISCONTINUED                        42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) \n  Safety                                                                                   \n    ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  \n    DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \n  Non-Safety                                                                               \n    LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  \n    PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  \n    PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   \n    WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  \n    WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)  \nCOMPLETED                           46 (34.3%)   38 (28.4%)     41 (31.1%)     125 (31.2%) \nONGOING                             50 (37.3%)   51 (38.1%)     46 (34.8%)     147 (36.8%) \nDISCONTINUED                        38 (28.4%)   45 (33.6%)     45 (34.1%)     128 (32.0%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  set.seed(1, kind = \"Mersenne-Twister\")\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADSL &lt;- ADSL %&gt;%\n    mutate(\n      DCSREASGP = case_when(\n        DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\") ~ \"Safety\",\n        (DCSREAS != \"&lt;Missing&gt;\" & !DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\")) ~ \"Non-Safety\",\n        DCSREAS == \"&lt;Missing&gt;\" ~ \"&lt;Missing&gt;\"\n      ) %&gt;% as.factor(),\n      EOTSTT = sample(\n        c(\"ONGOING\", \"COMPLETED\", \"DISCONTINUED\"),\n        size = nrow(ADSL),\n        replace = TRUE\n      ) %&gt;% as.factor()\n    ) %&gt;%\n    col_relabel(\n      EOTSTT = \"End Of Treatment Status\"\n    )\n\n  date_vars_asl &lt;- names(ADSL)[vapply(ADSL, function(x) inherits(x, c(\"Date\", \"POSIXct\", \"POSIXlt\")), logical(1))]\n  demog_vars_asl &lt;- names(ADSL)[!(names(ADSL) %in% c(\"USUBJID\", \"STUDYID\", date_vars_asl))]\n})\ndatanames &lt;- \"ADSL\"\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\ndemog_vars_asl &lt;- data[[\"demog_vars_asl\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary(\n      label = \"Disposition Table\",\n      dataname = \"ADSL\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      summarize_vars = choices_selected(\n        variable_choices(ADSL, demog_vars_asl),\n        c(\"EOSSTT\", \"DCSREAS\", \"EOTSTT\")\n      ),\n      useNA = \"ifany\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:44:04 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Grouping of Reasons\nTable Adding Optional Rows\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nset.seed(1, kind = \"Mersenne-Twister\")\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# reorder EOSSTT factor levels so DISCONTINUED is the last level\nadsl &lt;- df_explicit_na(adsl) %&gt;%\n  mutate(EOSSTT = factor(EOSSTT, levels = c(\"COMPLETED\", \"ONGOING\", \"DISCONTINUED\")))\n\nadsl_gp_added &lt;- adsl %&gt;%\n  mutate(DCSREASGP = case_when(\n    DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\") ~ \"Safety\",\n    (DCSREAS != \"&lt;Missing&gt;\" & !DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\")) ~ \"Non-Safety\",\n    DCSREAS == \"&lt;Missing&gt;\" ~ \"&lt;Missing&gt;\"\n  ) %&gt;% factor(levels = c(\"Safety\", \"Non-Safety\", \"&lt;Missing&gt;\")))\n\nadsl_eotstt_added &lt;- adsl_gp_added %&gt;%\n  mutate(\n    EOTSTT = sample(\n      c(\"ONGOING\", \"COMPLETED\", \"DISCONTINUED\"),\n      size = nrow(adsl),\n      replace = TRUE\n    ) %&gt;% factor(levels = c(\"COMPLETED\", \"ONGOING\", \"DISCONTINUED\"))\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(\n    \"EOSSTT\",\n    show_labels = \"hidden\"\n  ) %&gt;%\n  analyze_vars(\n    \"DCSREAS\",\n    .stats = \"count_fraction\",\n    denom = \"N_col\",\n    show_labels = \"hidden\",\n    .indent_mods = c(count_fraction = 1L)\n  )\n\nresult1 &lt;- build_table(lyt = lyt, df = adsl)\nresult1\n\n                                  A: Drug X    B: Placebo   C: Combination   All Patients\n                                   (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————————\nCOMPLETED                         68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) \nONGOING                           24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) \nDISCONTINUED                      42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) \n  ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  \n  DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \n  LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  \n  PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  \n  PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   \n  WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  \n  WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(\n    \"EOSSTT\",\n    show_labels = \"hidden\"\n  ) %&gt;%\n  split_rows_by(\"DCSREASGP\", indent_mod = 1L) %&gt;%\n  analyze_vars(\n    \"DCSREAS\",\n    .stats = \"count_fraction\",\n    denom = \"N_col\",\n    show_labels = \"hidden\"\n  )\n\ntbl &lt;- build_table(lyt = lyt, df = adsl_gp_added)\nresult2 &lt;- prune_table(tbl) # remove rows containing all zeros\n\nresult2\n\n                                    A: Drug X    B: Placebo   C: Combination   All Patients\n                                     (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————\nCOMPLETED                           68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) \nONGOING                             24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) \nDISCONTINUED                        42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) \n  Safety                                                                                   \n    ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  \n    DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \n  Non-Safety                                                                               \n    LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  \n    PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  \n    PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   \n    WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  \n    WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\n    \"ACTARM\",\n    split_fun = add_overall_level(\"All Patients\", first = FALSE)\n  ) %&gt;%\n  count_occurrences(\n    \"EOTSTT\",\n    show_labels = \"hidden\"\n  )\n\ntbl &lt;- build_table(lyt = lyt, df = adsl_eotstt_added)\ntbl &lt;- prune_table(tbl) # remove rows containing all zeros\n\n# Combine tables\ncol_info(result2) &lt;- col_info(tbl)\nresult3 &lt;- rbind(result2, tbl)\n\nresult3\n\n                                    A: Drug X    B: Placebo   C: Combination   All Patients\n                                     (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————————————\nCOMPLETED                           68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) \nONGOING                             24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) \nDISCONTINUED                        42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) \n  Safety                                                                                   \n    ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  \n    DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) \n  Non-Safety                                                                               \n    LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  \n    PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  \n    PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   \n    WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  \n    WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)  \nCOMPLETED                           46 (34.3%)   38 (28.4%)     41 (31.1%)     125 (31.2%) \nONGOING                             50 (37.3%)   51 (38.1%)     46 (34.8%)     147 (36.8%) \nDISCONTINUED                        38 (28.4%)   45 (33.6%)     45 (34.1%)     128 (32.0%) \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  set.seed(1, kind = \"Mersenne-Twister\")\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADSL &lt;- ADSL %&gt;%\n    mutate(\n      DCSREASGP = case_when(\n        DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\") ~ \"Safety\",\n        (DCSREAS != \"&lt;Missing&gt;\" & !DCSREAS %in% c(\"ADVERSE EVENT\", \"DEATH\")) ~ \"Non-Safety\",\n        DCSREAS == \"&lt;Missing&gt;\" ~ \"&lt;Missing&gt;\"\n      ) %&gt;% as.factor(),\n      EOTSTT = sample(\n        c(\"ONGOING\", \"COMPLETED\", \"DISCONTINUED\"),\n        size = nrow(ADSL),\n        replace = TRUE\n      ) %&gt;% as.factor()\n    ) %&gt;%\n    col_relabel(\n      EOTSTT = \"End Of Treatment Status\"\n    )\n\n  date_vars_asl &lt;- names(ADSL)[vapply(ADSL, function(x) inherits(x, c(\"Date\", \"POSIXct\", \"POSIXlt\")), logical(1))]\n  demog_vars_asl &lt;- names(ADSL)[!(names(ADSL) %in% c(\"USUBJID\", \"STUDYID\", date_vars_asl))]\n})\ndatanames &lt;- \"ADSL\"\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\ndemog_vars_asl &lt;- data[[\"demog_vars_asl\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary(\n      label = \"Disposition Table\",\n      dataname = \"ADSL\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      summarize_vars = choices_selected(\n        variable_choices(ADSL, demog_vars_asl),\n        c(\"EOSSTT\", \"DCSREAS\", \"EOTSTT\")\n      ),\n      useNA = \"ifany\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:42:45 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Disposition",
@@ -1223,7 +1223,7 @@
     "href": "tables/disposition/pdt01.html",
     "title": "PDT01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\naddv &lt;- synthetic_cdisc_dataset(\"latest\", \"addv\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\naddv &lt;- df_explicit_na(addv)\n\naddv &lt;- addv %&gt;%\n  var_relabel(\n    DVDECOD = \"Category\",\n    DVTERM = \"Description\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one major protocol deviation\",\n      nonunique = \"Total number of major protocol deviations\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"DVDECOD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(addv$DVDECOD)\n  ) %&gt;%\n  count_occurrences(vars = \"DVTERM\") %&gt;%\n  append_varlabels(addv, \"DVTERM\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = addv, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(path = c(\"DVDECOD\", \"*\", \"DVTERM\"), scorefun = score_occurrences)\n\nresult\n\nCategory                                                              A: Drug X    B: Placebo   C: Combination\n  Description                                                          (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one major protocol deviation   28 (20.9%)   22 (16.4%)     18 (13.6%)  \nTotal number of major protocol deviations                                 49           35             35      \nEXCLUSION CRITERIA                                                                                            \n  Active or untreated or other excluded cns metastases                 2 (1.5%)     4 (3.0%)       1 (0.8%)   \n  Pregnancy criteria                                                   3 (2.2%)     3 (2.2%)       1 (0.8%)   \n  History of other malignancies within the last 5 years                3 (2.2%)     1 (0.7%)       1 (0.8%)   \n  Uncontrolled concurrent condition                                    2 (1.5%)        0           3 (2.3%)   \n  Other exclusion criteria                                             1 (0.7%)        0           2 (1.5%)   \n  Received prior prohibited therapy or medication                      2 (1.5%)     1 (0.7%)          0       \nINCLUSION CRITERIA                                                                                            \n  No signed ICF at study entry                                         5 (3.7%)        0           6 (4.5%)   \n  Ineligible cancer type or current cancer stage                       3 (2.2%)     2 (1.5%)       4 (3.0%)   \n  Inclusion lab values outside allowed limits                          4 (3.0%)     1 (0.7%)          0       \n  Does not meet prior therapy requirements                             2 (1.5%)     1 (0.7%)       1 (0.8%)   \n  Inclusion-related test not done/out of window                        1 (0.7%)     1 (0.7%)          0       \nMEDICATION                                                                                                    \n  Discontinued study drug for unspecified reason                       3 (2.2%)     2 (1.5%)       1 (0.8%)   \n  Significant deviation from planned dose                              3 (2.2%)     1 (0.7%)       2 (1.5%)   \n  Received incorrect study medication                                     0         3 (2.2%)       1 (0.8%)   \n  Received prohibited concomitant medication                           1 (0.7%)     2 (1.5%)       1 (0.8%)   \n  Dose missed or significantly out of window                           2 (1.5%)        0           1 (0.8%)   \nPROCEDURAL                                                                                                    \n  Eligibility-related test not done/out of window                      2 (1.5%)     4 (3.0%)       3 (2.3%)   \n  Omission of screening tumor assessment                               4 (3.0%)     1 (0.7%)       3 (2.3%)   \n  Missed assessment affecting safety/study outcomes                    2 (1.5%)     3 (2.2%)       1 (0.8%)   \n  Failure to sign updated ICF within two visits                        2 (1.5%)     1 (0.7%)       1 (0.8%)   \n  Missed 2 or more efficacy assessments                                1 (0.7%)     2 (1.5%)          0       \n  Omission of complete lab panel required by protocol                     0            0           1 (0.8%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADDV &lt;- synthetic_cdisc_dataset(\"latest\", \"addv\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADDV &lt;- df_explicit_na(ADDV)\n})\ndatanames &lt;- c(\"ADSL\", \"ADDV\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADDV &lt;- data[[\"ADDV\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Major Protocol Deviations Table\",\n      dataname = \"ADDV\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADDV, c(\"DVTERM\")),\n        selected = c(\"DVTERM\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADDV, c(\"DVDECOD\")),\n        selected = \"DVDECOD\"\n      ),\n      add_total = TRUE,\n      event_type = \"major protocol deviation\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:44:25 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\naddv &lt;- synthetic_cdisc_dataset(\"latest\", \"addv\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\naddv &lt;- df_explicit_na(addv)\n\naddv &lt;- addv %&gt;%\n  var_relabel(\n    DVDECOD = \"Category\",\n    DVTERM = \"Description\"\n  )\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one major protocol deviation\",\n      nonunique = \"Total number of major protocol deviations\"\n    )\n  ) %&gt;%\n  split_rows_by(\n    \"DVDECOD\",\n    split_fun = split_fun,\n    label_pos = \"topleft\",\n    split_label = obj_label(addv$DVDECOD)\n  ) %&gt;%\n  count_occurrences(vars = \"DVTERM\") %&gt;%\n  append_varlabels(addv, \"DVTERM\", indent = 1L)\n\nresult &lt;- build_table(lyt = lyt, df = addv, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(path = c(\"DVDECOD\", \"*\", \"DVTERM\"), scorefun = score_occurrences)\n\nresult\n\nCategory                                                              A: Drug X    B: Placebo   C: Combination\n  Description                                                          (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one major protocol deviation   28 (20.9%)   22 (16.4%)     18 (13.6%)  \nTotal number of major protocol deviations                                 49           35             35      \nEXCLUSION CRITERIA                                                                                            \n  Active or untreated or other excluded cns metastases                 2 (1.5%)     4 (3.0%)       1 (0.8%)   \n  Pregnancy criteria                                                   3 (2.2%)     3 (2.2%)       1 (0.8%)   \n  History of other malignancies within the last 5 years                3 (2.2%)     1 (0.7%)       1 (0.8%)   \n  Uncontrolled concurrent condition                                    2 (1.5%)        0           3 (2.3%)   \n  Other exclusion criteria                                             1 (0.7%)        0           2 (1.5%)   \n  Received prior prohibited therapy or medication                      2 (1.5%)     1 (0.7%)          0       \nINCLUSION CRITERIA                                                                                            \n  No signed ICF at study entry                                         5 (3.7%)        0           6 (4.5%)   \n  Ineligible cancer type or current cancer stage                       3 (2.2%)     2 (1.5%)       4 (3.0%)   \n  Inclusion lab values outside allowed limits                          4 (3.0%)     1 (0.7%)          0       \n  Does not meet prior therapy requirements                             2 (1.5%)     1 (0.7%)       1 (0.8%)   \n  Inclusion-related test not done/out of window                        1 (0.7%)     1 (0.7%)          0       \nMEDICATION                                                                                                    \n  Discontinued study drug for unspecified reason                       3 (2.2%)     2 (1.5%)       1 (0.8%)   \n  Significant deviation from planned dose                              3 (2.2%)     1 (0.7%)       2 (1.5%)   \n  Received incorrect study medication                                     0         3 (2.2%)       1 (0.8%)   \n  Received prohibited concomitant medication                           1 (0.7%)     2 (1.5%)       1 (0.8%)   \n  Dose missed or significantly out of window                           2 (1.5%)        0           1 (0.8%)   \nPROCEDURAL                                                                                                    \n  Eligibility-related test not done/out of window                      2 (1.5%)     4 (3.0%)       3 (2.3%)   \n  Omission of screening tumor assessment                               4 (3.0%)     1 (0.7%)       3 (2.3%)   \n  Missed assessment affecting safety/study outcomes                    2 (1.5%)     3 (2.2%)       1 (0.8%)   \n  Failure to sign updated ICF within two visits                        2 (1.5%)     1 (0.7%)       1 (0.8%)   \n  Missed 2 or more efficacy assessments                                1 (0.7%)     2 (1.5%)          0       \n  Omission of complete lab panel required by protocol                     0            0           1 (0.8%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADDV &lt;- synthetic_cdisc_dataset(\"latest\", \"addv\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADDV &lt;- df_explicit_na(ADDV)\n})\ndatanames &lt;- c(\"ADSL\", \"ADDV\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADDV &lt;- data[[\"ADDV\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Major Protocol Deviations Table\",\n      dataname = \"ADDV\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADDV, c(\"DVTERM\")),\n        selected = c(\"DVTERM\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADDV, c(\"DVDECOD\")),\n        selected = \"DVDECOD\"\n      ),\n      add_total = TRUE,\n      event_type = \"major protocol deviation\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:43:05 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Disposition",
@@ -1235,7 +1235,7 @@
     "href": "tables/adverse-events/aet05_all.html",
     "title": "AET05_ALL",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Setting Type of Confidence Interval\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\n# Create analysis dataset anl from the 2 parameters AEREPTTE & AETOT1\nanl_events &lt;- adaette %&gt;%\n  filter(PARAMCD == \"AETOT1\") %&gt;%\n  select(USUBJID, ARM, ARMCD, n_events = AVAL) %&gt;%\n  mutate(n_events = as.integer(n_events))\n\nanl_tte &lt;- adaette %&gt;%\n  filter(PARAMCD == \"AEREPTTE\") %&gt;%\n  select(USUBJID, ARM, ARMCD, AVAL)\n\nanl &lt;- full_join(anl_tte, anl_events, by = c(\"USUBJID\", \"ARM\", \"ARMCD\"))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(num_pt_year = 100)\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\nresult\n\n                                       A: Drug X         B: Placebo       C: Combination \n                                        (N=134)           (N=134)            (N=132)     \n—————————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk              337.9             331.8              341.6      \nNumber of adverse events observed         343               380                397       \nAE rate per 100 patient-years           101.51             114.51             116.22     \n95% CI                              (90.77, 112.25)   (103.00, 126.02)   (104.79, 127.65)\n\n\n\n\nThe type of confidence interval can be specified through the conf_type argument.\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(conf_type = \"exact\", num_pt_year = 100)\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\nresult\n\n                                       A: Drug X         B: Placebo       C: Combination \n                                        (N=134)           (N=134)            (N=132)     \n—————————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk              337.9             331.8              341.6      \nNumber of adverse events observed         343               380                397       \nAE rate per 100 patient-years           101.51             114.51             116.22     \n95% CI                              (91.05, 112.84)   (103.28, 126.62)   (105.07, 128.24)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  # Create analysis dataset anl from the 2 parameters AEREPTTE & AETOT1\n  anl_events &lt;- ADAETTE %&gt;%\n    filter(PARAMCD %in% c(\"AETOT1\", \"AETOT2\", \"AETOT3\")) %&gt;%\n    select(USUBJID, STUDYID, PARAMCD, ARM, ARMCD, n_events = AVAL) %&gt;%\n    mutate(n_events = as.integer(n_events))\n\n  anl_tte &lt;- ADAETTE %&gt;%\n    filter(PARAMCD == \"AEREPTTE\") %&gt;%\n    select(USUBJID, STUDYID, ARM, ARMCD, AVAL, AVALU)\n\n  ADAETTE &lt;- full_join(anl_tte, anl_events, by = c(\"USUBJID\", \"STUDYID\", \"ARM\", \"ARMCD\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_patyear(\n      label = \"AE Rate Adjusted for Patient-Years at Risk - All Occurrences Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARMCD\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADAETTE, \"PARAMCD\"),\n        selected = \"AETOT1\"\n      ),\n      events_var = choices_selected(\n        choices = variable_choices(ADAETTE, \"n_events\"),\n        selected = \"n_events\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:44:56 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Setting Type of Confidence Interval\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\n# Create analysis dataset anl from the 2 parameters AEREPTTE & AETOT1\nanl_events &lt;- adaette %&gt;%\n  filter(PARAMCD == \"AETOT1\") %&gt;%\n  select(USUBJID, ARM, ARMCD, n_events = AVAL) %&gt;%\n  mutate(n_events = as.integer(n_events))\n\nanl_tte &lt;- adaette %&gt;%\n  filter(PARAMCD == \"AEREPTTE\") %&gt;%\n  select(USUBJID, ARM, ARMCD, AVAL)\n\nanl &lt;- full_join(anl_tte, anl_events, by = c(\"USUBJID\", \"ARM\", \"ARMCD\"))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(num_pt_year = 100)\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\nresult\n\n                                       A: Drug X         B: Placebo       C: Combination \n                                        (N=134)           (N=134)            (N=132)     \n—————————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk              337.9             331.8              341.6      \nNumber of adverse events observed         343               380                397       \nAE rate per 100 patient-years           101.51             114.51             116.22     \n95% CI                              (90.77, 112.25)   (103.00, 126.02)   (104.79, 127.65)\n\n\n\n\nThe type of confidence interval can be specified through the conf_type argument.\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  estimate_incidence_rate(\n    vars = \"AVAL\",\n    n_events = \"n_events\",\n    control = control_incidence_rate(conf_type = \"exact\", num_pt_year = 100)\n  )\n\nresult &lt;- build_table(lyt, anl, alt_counts_df = adsl)\nresult\n\n                                       A: Drug X         B: Placebo       C: Combination \n                                        (N=134)           (N=134)            (N=132)     \n—————————————————————————————————————————————————————————————————————————————————————————\nTotal patient-years at risk              337.9             331.8              341.6      \nNumber of adverse events observed         343               380                397       \nAE rate per 100 patient-years           101.51             114.51             116.22     \n95% CI                              (91.05, 112.84)   (103.28, 126.62)   (105.07, 128.24)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  # Create analysis dataset anl from the 2 parameters AEREPTTE & AETOT1\n  anl_events &lt;- ADAETTE %&gt;%\n    filter(PARAMCD %in% c(\"AETOT1\", \"AETOT2\", \"AETOT3\")) %&gt;%\n    select(USUBJID, STUDYID, PARAMCD, ARM, ARMCD, n_events = AVAL) %&gt;%\n    mutate(n_events = as.integer(n_events))\n\n  anl_tte &lt;- ADAETTE %&gt;%\n    filter(PARAMCD == \"AEREPTTE\") %&gt;%\n    select(USUBJID, STUDYID, ARM, ARMCD, AVAL, AVALU)\n\n  ADAETTE &lt;- full_join(anl_tte, anl_events, by = c(\"USUBJID\", \"STUDYID\", \"ARM\", \"ARMCD\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_patyear(\n      label = \"AE Rate Adjusted for Patient-Years at Risk - All Occurrences Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARMCD\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADAETTE, \"PARAMCD\"),\n        selected = \"AETOT1\"\n      ),\n      events_var = choices_selected(\n        choices = variable_choices(ADAETTE, \"n_events\"),\n        selected = \"n_events\",\n        fixed = TRUE\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:43:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -1247,7 +1247,7 @@
     "href": "tables/adverse-events/aet06_smq.html",
     "title": "AET06_SMQ",
     "section": "",
-    "text": "Data Setup\nAdverse Events by Sex,  by SMQ and Preferred Term\nAdverse Events by Sex, by SMQ and  Preferred Term (with Customized Queries)\nAdverse Events by Other Baseline  Characteristics (e.g. Age Group)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(stringr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl_labels &lt;- var_labels(adsl)\nadae_labels &lt;- var_labels(adae)\n\nadsl &lt;- adsl %&gt;%\n  mutate(\n    AGE65 = case_when(\n      AGE &gt;= 65 ~ \"&gt;= 65\",\n      TRUE ~ \"&lt; 65\"\n    ),\n    AGE65 = factor(\n      AGE65,\n      levels = c(\"&lt; 65\", \"&gt;= 65\")\n    )\n  )\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AGE65 = case_when(\n      AGE &gt;= 65 ~ \"&gt;= 65\",\n      TRUE ~ \"&lt; 65\"\n    ),\n    AGE65 = factor(\n      AGE65,\n      levels = c(\"&lt; 65\", \"&gt;= 65\")\n    )\n  )\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing\n# levels. For details, refer to Teal and Study Data article.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n# Simulate a random AAG dataset.\naag &lt;- data.frame(\n  NAMVAR = c(\"SMQ01NAM\", \"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\", \"CQ01NAM\"),\n  SRCVAR = rep(\"AEDECOD\", 5),\n  GRPTYPE = c(\"SMQ\", \"SMQ\", \"SMQ\", \"CUSTOM\", \"CUSTOM\"),\n  REFID = c(1, 1, 2, 3, 3),\n  REFNAME = c(rep(\"C.1.1.1.3/B.2.2.3.1 AESI\", 2), \"non observed SMQ02NAM level\", rep(\"D.2.1.5.3/A.1.1.1.1 AESI\", 2)),\n  SCOPE = c(\"BROAD\", \"BROAD\", \"BROAD\", \"\", \"\"),\n  REFTERM = c(\"C.1.1.1.3\", \"B.2.2.3.1\", \"Z.9.9.9.9\", \"D.2.1.5.3\", \"A.1.1.1.1\"),\n  stringsAsFactors = FALSE\n)\n\n# Create summary AAG dataset (used for labelling).\n# Note it's important to incorporate SCOPE into the basket names for SMQs so as to\n# match the flags present in ADAE.\naag_summary &lt;- aag %&gt;%\n  select(NAMVAR, REFNAME, SCOPE) %&gt;%\n  unique() %&gt;%\n  mutate(\n    REFNAME_SCOPE = ifelse(SCOPE == \"\", REFNAME, paste0(REFNAME, \"(\", SCOPE, \")\"))\n  ) %&gt;%\n  rename(basket = NAMVAR, basket_name = REFNAME_SCOPE) %&gt;%\n  select(basket, basket_name)\n\n# Make a summary of the full ADAE based on AAG by using h_stack_by_baskets helper function\nadae_smq_all &lt;- h_stack_by_baskets(\n  df = adae,\n  aag_summary = aag_summary,\n  keys = c(\"STUDYID\", \"USUBJID\", \"ACTARMCD\", \"AEDECOD\", \"SEX\", \"AGE65\")\n)\n# Post-process adae_smq_all to keep only certain baskets of interest for variant 1\n# Not need to drop baskets for variant 2 as all baskets will be considered.\nbaskets_to_drop_variant &lt;- aag_summary$basket_name[!aag_summary$basket %in% c(\"SMQ01NAM\")]\n\n# Adjust the analysis dataset based on basket subset for variant 1.\nadae_smq_1 &lt;- adae_smq_all %&gt;%\n  filter(!adae_smq_all$SMQ %in% baskets_to_drop_variant) %&gt;%\n  mutate(\n    SMQ = tern::fct_discard(SMQ, discard = baskets_to_drop_variant)\n  )\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(unique = \"Total number of patients with at least one adverse event\")\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", drop = FALSE) %&gt;%\n  append_varlabels(adae_smq_1, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\n                                                                      ARM A                     ARM B                     ARM C         \nStandardized MedDRA Query                                        F            M            F            M            F            M     \n  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                                         \n  Total number of patients with at least one adverse event   47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)\n  Total number of events                                         79           40           95           44           81           60    \n    dcd B.2.2.3.1                                            30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n    dcd C.1.1.1.3                                            30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) {\n  !is(tr, \"ContentRow\") && all_zero_or_na(tr) && !grepl(\"Total number of\", obj_label(tr))\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(unique = \"Total number of patients with at least one adverse event\")\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", drop = FALSE) %&gt;%\n  append_varlabels(adae_smq_all, \"AEDECOD\", indent = 1L)\n\nscore_ae_pts &lt;- function(tt) {\n  count_vals &lt;- cell_values(tree_children(tt)[[\"USUBJID\"]])[[1]]\n  sum(matrix(unlist(count_vals), nrow = length(count_vals), byrow = TRUE)[, 1])\n}\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_smq_all,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = score_ae_pts) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\n                                                                      ARM A                     ARM B                     ARM C         \nStandardized MedDRA Query                                        F            M            F            M            F            M     \n  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     59 (74.7%)   36 (65.5%)   68 (82.9%)   35 (67.3%)   61 (87.1%)   47 (75.8%)\nD.2.1.5.3/A.1.1.1.1 AESI                                                                                                                \n  Total number of patients with at least one adverse event   45 (57.0%)   29 (52.7%)   54 (65.9%)   26 (50.0%)   50 (71.4%)   37 (59.7%)\n  Total number of events                                         78           48           91           43           88           74    \n  dcd D.2.1.5.3                                              26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n  dcd A.1.1.1.1                                              34 (43.0%)   16 (29.1%)   31 (37.8%)   14 (26.9%)   33 (47.1%)   30 (48.4%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                                         \n  Total number of patients with at least one adverse event   47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)\n  Total number of events                                         79           40           95           44           81           60    \n  dcd B.2.2.3.1                                              30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n  dcd C.1.1.1.3                                              30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\nnon observed SMQ02NAM level(BROAD)                                                                                                      \n  Total number of patients with at least one adverse event       0            0            0            0            0            0     \n  Total number of events                                         0            0            0            0            0            0     \n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_cols_by(\"AGE65\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(unique = \"Total number of patients with at least one adverse event\")\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", drop = FALSE) %&gt;%\n  append_varlabels(adae_smq_1, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\n                                                                   ARM A                ARM B                ARM C       \nStandardized MedDRA Query                                       &lt; 65      &gt;= 65      &lt; 65      &gt;= 65      &lt; 65      &gt;= 65\n  Dictionary-Derived Term                                     (N=134)     (N=0)    (N=134)     (N=0)    (N=131)     (N=1)\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     72 (53.7%)     0     79 (59.0%)     0     75 (57.3%)     0  \nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                          \n  Total number of patients with at least one adverse event   72 (53.7%)     0     79 (59.0%)     0     75 (57.3%)     0  \n  Total number of events                                        119         0        139         0        141         0  \n    dcd B.2.2.3.1                                            48 (35.8%)     0     54 (40.3%)     0     51 (38.9%)     0  \n    dcd C.1.1.1.3                                            43 (32.1%)     0     46 (34.3%)     0     43 (32.8%)     0  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n    mutate(\n      AGE65 = case_when(\n        AGE &gt;= 65 ~ \"&gt;= 65\",\n        TRUE ~ \"&lt; 65\"\n      ),\n      AGE65 = factor(\n        AGE65,\n        levels = c(\"&lt; 65\", \"&gt;= 65\")\n      )\n    )\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\") %&gt;%\n    mutate(\n      AGE65 = case_when(\n        AGE &gt;= 65 ~ \"&gt;= 65\",\n        TRUE ~ \"&lt; 65\"\n      ),\n      AGE65 = factor(\n        AGE65,\n        levels = c(\"&lt; 65\", \"&gt;= 65\")\n      )\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\n\nnames_baskets &lt;- grep(\"^(SMQ|CQ).*NAM$\", names(ADAE), value = TRUE)\nnames_scopes &lt;- grep(\"^SMQ.*SC$\", names(ADAE), value = TRUE)\n\ncs_baskets &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_baskets),\n  selected = names_baskets\n)\ncs_scopes &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_scopes),\n  selected = names_scopes,\n  fixed = TRUE\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_smq(\n      label = \"Adverse events by `SMQ` Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"SEX\", \"AGE65\")),\n        selected = c(\"ARM\", \"SEX\")\n      ),\n      add_total = FALSE,\n      baskets = cs_baskets,\n      scopes = cs_scopes,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, subset = c(\"AEDECOD\")),\n        selected = \"AEDECOD\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:45:36 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      stringr_1.5.1                   \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013            dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nAdverse Events by Sex,  by SMQ and Preferred Term\nAdverse Events by Sex, by SMQ and  Preferred Term (with Customized Queries)\nAdverse Events by Other Baseline  Characteristics (e.g. Age Group)\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\nlibrary(stringr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nadsl_labels &lt;- var_labels(adsl)\nadae_labels &lt;- var_labels(adae)\n\nadsl &lt;- adsl %&gt;%\n  mutate(\n    AGE65 = case_when(\n      AGE &gt;= 65 ~ \"&gt;= 65\",\n      TRUE ~ \"&lt; 65\"\n    ),\n    AGE65 = factor(\n      AGE65,\n      levels = c(\"&lt; 65\", \"&gt;= 65\")\n    )\n  )\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AGE65 = case_when(\n      AGE &gt;= 65 ~ \"&gt;= 65\",\n      TRUE ~ \"&lt; 65\"\n    ),\n    AGE65 = factor(\n      AGE65,\n      levels = c(\"&lt; 65\", \"&gt;= 65\")\n    )\n  )\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing\n# levels. For details, refer to Teal and Study Data article.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\n# Simulate a random AAG dataset.\naag &lt;- data.frame(\n  NAMVAR = c(\"SMQ01NAM\", \"SMQ01NAM\", \"SMQ02NAM\", \"CQ01NAM\", \"CQ01NAM\"),\n  SRCVAR = rep(\"AEDECOD\", 5),\n  GRPTYPE = c(\"SMQ\", \"SMQ\", \"SMQ\", \"CUSTOM\", \"CUSTOM\"),\n  REFID = c(1, 1, 2, 3, 3),\n  REFNAME = c(rep(\"C.1.1.1.3/B.2.2.3.1 AESI\", 2), \"non observed SMQ02NAM level\", rep(\"D.2.1.5.3/A.1.1.1.1 AESI\", 2)),\n  SCOPE = c(\"BROAD\", \"BROAD\", \"BROAD\", \"\", \"\"),\n  REFTERM = c(\"C.1.1.1.3\", \"B.2.2.3.1\", \"Z.9.9.9.9\", \"D.2.1.5.3\", \"A.1.1.1.1\"),\n  stringsAsFactors = FALSE\n)\n\n# Create summary AAG dataset (used for labelling).\n# Note it's important to incorporate SCOPE into the basket names for SMQs so as to\n# match the flags present in ADAE.\naag_summary &lt;- aag %&gt;%\n  select(NAMVAR, REFNAME, SCOPE) %&gt;%\n  unique() %&gt;%\n  mutate(\n    REFNAME_SCOPE = ifelse(SCOPE == \"\", REFNAME, paste0(REFNAME, \"(\", SCOPE, \")\"))\n  ) %&gt;%\n  rename(basket = NAMVAR, basket_name = REFNAME_SCOPE) %&gt;%\n  select(basket, basket_name)\n\n# Make a summary of the full ADAE based on AAG by using h_stack_by_baskets helper function\nadae_smq_all &lt;- h_stack_by_baskets(\n  df = adae,\n  aag_summary = aag_summary,\n  keys = c(\"STUDYID\", \"USUBJID\", \"ACTARMCD\", \"AEDECOD\", \"SEX\", \"AGE65\")\n)\n# Post-process adae_smq_all to keep only certain baskets of interest for variant 1\n# Not need to drop baskets for variant 2 as all baskets will be considered.\nbaskets_to_drop_variant &lt;- aag_summary$basket_name[!aag_summary$basket %in% c(\"SMQ01NAM\")]\n\n# Adjust the analysis dataset based on basket subset for variant 1.\nadae_smq_1 &lt;- adae_smq_all %&gt;%\n  filter(!adae_smq_all$SMQ %in% baskets_to_drop_variant) %&gt;%\n  mutate(\n    SMQ = tern::fct_discard(SMQ, discard = baskets_to_drop_variant)\n  )\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(unique = \"Total number of patients with at least one adverse event\")\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", drop = FALSE) %&gt;%\n  append_varlabels(adae_smq_1, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\n                                                                      ARM A                     ARM B                     ARM C         \nStandardized MedDRA Query                                        F            M            F            M            F            M     \n  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                                         \n  Total number of patients with at least one adverse event   47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)\n  Total number of events                                         79           40           95           44           81           60    \n    dcd B.2.2.3.1                                            30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n    dcd C.1.1.1.3                                            30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) {\n  !is(tr, \"ContentRow\") && all_zero_or_na(tr) && !grepl(\"Total number of\", obj_label(tr))\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_cols_by(\"SEX\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(unique = \"Total number of patients with at least one adverse event\")\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    ),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", drop = FALSE) %&gt;%\n  append_varlabels(adae_smq_all, \"AEDECOD\", indent = 1L)\n\nscore_ae_pts &lt;- function(tt) {\n  count_vals &lt;- cell_values(tree_children(tt)[[\"USUBJID\"]])[[1]]\n  sum(matrix(unlist(count_vals), nrow = length(count_vals), byrow = TRUE)[, 1])\n}\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_smq_all,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = score_ae_pts) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\n                                                                      ARM A                     ARM B                     ARM C         \nStandardized MedDRA Query                                        F            M            F            M            F            M     \n  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     59 (74.7%)   36 (65.5%)   68 (82.9%)   35 (67.3%)   61 (87.1%)   47 (75.8%)\nD.2.1.5.3/A.1.1.1.1 AESI                                                                                                                \n  Total number of patients with at least one adverse event   45 (57.0%)   29 (52.7%)   54 (65.9%)   26 (50.0%)   50 (71.4%)   37 (59.7%)\n  Total number of events                                         78           48           91           43           88           74    \n  dcd D.2.1.5.3                                              26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)\n  dcd A.1.1.1.1                                              34 (43.0%)   16 (29.1%)   31 (37.8%)   14 (26.9%)   33 (47.1%)   30 (48.4%)\nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                                         \n  Total number of patients with at least one adverse event   47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)\n  Total number of events                                         79           40           95           44           81           60    \n  dcd B.2.2.3.1                                              30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)\n  dcd C.1.1.1.3                                              30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)\nnon observed SMQ02NAM level(BROAD)                                                                                                      \n  Total number of patients with at least one adverse event       0            0            0            0            0            0     \n  Total number of events                                         0            0            0            0            0            0     \n\n\n\n\n\nCodecriteria_fun &lt;- function(tr) !is(tr, \"ContentRow\") && all_zero_or_na(tr)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARMCD\") %&gt;%\n  split_cols_by(\"AGE65\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\"),\n    .labels = c(unique = \"Total number of patients with at least one adverse event\")\n  ) %&gt;%\n  split_rows_by(\n    \"SMQ\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = trim_levels_in_group(\"AEDECOD\", drop_outlevs = FALSE),\n    label_pos = \"topleft\",\n    split_label = \"Standardized MedDRA Query\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one adverse event\",\n      nonunique = \"Total number of events\"\n    )\n  ) %&gt;%\n  count_occurrences(vars = \"AEDECOD\", drop = FALSE) %&gt;%\n  append_varlabels(adae_smq_1, \"AEDECOD\", indent = 1L)\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_smq_1,\n  alt_counts_df = adsl\n) %&gt;%\n  sort_at_path(path = c(\"SMQ\"), scorefun = cont_n_allcols) %&gt;%\n  sort_at_path(path = c(\"SMQ\", \"*\", \"AEDECOD\"), scorefun = score_occurrences, na.pos = \"last\") %&gt;%\n  trim_rows(criteria = criteria_fun)\n\nresult\n\n                                                                   ARM A                ARM B                ARM C       \nStandardized MedDRA Query                                       &lt; 65      &gt;= 65      &lt; 65      &gt;= 65      &lt; 65      &gt;= 65\n  Dictionary-Derived Term                                     (N=134)     (N=0)    (N=134)     (N=0)    (N=131)     (N=1)\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one adverse event     72 (53.7%)     0     79 (59.0%)     0     75 (57.3%)     0  \nC.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                          \n  Total number of patients with at least one adverse event   72 (53.7%)     0     79 (59.0%)     0     75 (57.3%)     0  \n  Total number of events                                        119         0        139         0        141         0  \n    dcd B.2.2.3.1                                            48 (35.8%)     0     54 (40.3%)     0     51 (38.9%)     0  \n    dcd C.1.1.1.3                                            43 (32.1%)     0     46 (34.3%)     0     43 (32.8%)     0  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n    mutate(\n      AGE65 = case_when(\n        AGE &gt;= 65 ~ \"&gt;= 65\",\n        TRUE ~ \"&lt; 65\"\n      ),\n      AGE65 = factor(\n        AGE65,\n        levels = c(\"&lt; 65\", \"&gt;= 65\")\n      )\n    )\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\") %&gt;%\n    mutate(\n      AGE65 = case_when(\n        AGE &gt;= 65 ~ \"&gt;= 65\",\n        TRUE ~ \"&lt; 65\"\n      ),\n      AGE65 = factor(\n        AGE65,\n        levels = c(\"&lt; 65\", \"&gt;= 65\")\n      )\n    )\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\n\nnames_baskets &lt;- grep(\"^(SMQ|CQ).*NAM$\", names(ADAE), value = TRUE)\nnames_scopes &lt;- grep(\"^SMQ.*SC$\", names(ADAE), value = TRUE)\n\ncs_baskets &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_baskets),\n  selected = names_baskets\n)\ncs_scopes &lt;- choices_selected(\n  choices = variable_choices(ADAE, subset = names_scopes),\n  selected = names_scopes,\n  fixed = TRUE\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_smq(\n      label = \"Adverse events by `SMQ` Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"SEX\", \"AGE65\")),\n        selected = c(\"ARM\", \"SEX\")\n      ),\n      add_total = FALSE,\n      baskets = cs_baskets,\n      scopes = cs_scopes,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, subset = c(\"AEDECOD\")),\n        selected = \"AEDECOD\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:44:13 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      stringr_1.5.1                   \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015            dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -1259,7 +1259,7 @@
     "href": "tables/adverse-events/aet04.html",
     "title": "AET04",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Fill-In  of Treatment Groups\nTable with Fill-In  of Grades\nTable with Collapsing  of Grades\nTable Using Worst Grade  Flags from ADAE\nTable with an Incidence Rate \\(\\geq\\) 40%, Totals Restricted\nTable with an Incidence Rate \\(\\geq\\) X%, Totals Unrestricted\nTable with an Incidence  Rate \\(\\geq\\) 58 Patients\nTable with a Difference in  Incidence Rate \\(\\geq\\) 10%\nTable with an Incidence Rate \\(\\geq\\) 5%, SOCs &lt; 5% Removed\nTable with an Incidence Rate \\(\\geq\\) 40%,  All SOCs w/o Preferred Terms Removed\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl) %&gt;% filter(TRT01A != \"&lt;Missing&gt;\")\nadae &lt;- df_explicit_na(adae) %&gt;%\n  var_relabel(\n    AEBODSYS = \"MedDRA System Organ Class\",\n    AEDECOD = \"MedDRA Preferred Term\"\n  ) %&gt;%\n  filter(\n    ANL01FL == \"Y\",\n    AETOXGR != \"&lt;Missing&gt;\"\n  )\n\n# Pre-Processing\ngrade_groups &lt;- list(\n  \"Grade 1-2\" = c(\"1\", \"2\"),\n  \"Grade 3-4\" = c(\"3\", \"4\"),\n  \"Grade 5\" = \"5\"\n)\nadae$TOTAL_VAR &lt;- \"- Any adverse events - \"\n\n# Helper function to avoid filtering also the first part of the table, where general information is given.\nmy_row_condition &lt;- function(row_fnc_condition) {\n  function(table_row) {\n    if (indent_mod(table_row) == 0) {\n      return(TRUE)\n    } else {\n      row_fnc_condition(table_row)\n    }\n  }\n}\n\n# Helper function to calculate sum from first nested row\nscore_all_sum &lt;- function(tt) {\n  cleaf &lt;- collect_leaves(tt)[[1]]\n  if (NROW(cleaf) == 0) {\n    stop(\"score_all_sum score function used at subtable [\", obj_name(tt), \"] that has no content.\")\n  }\n  sum(sapply(row_values(cleaf), function(cv) cv[1]))\n}\n\n# Raw table used by variant 8/10\nraw_table &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\") %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodeadae2 &lt;- adae %&gt;% filter(ACTARM == \"A: Drug X\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae2, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)       0              0       \n                            Grade 1-2        10 (7.5%)        0              0       \n                            1                5 (3.7%)         0              0       \n                            2                5 (3.7%)         0              0       \n                            Grade 3-4       26 (19.4%)        0              0       \n                            3                13 (9.7%)        0              0       \n                            4                13 (9.7%)        0              0       \n                            Grade 5         64 (47.8%)        0              0       \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)        0              0       \n                            Grade 1-2       68 (50.7%)        0              0       \n                            1               27 (20.1%)        0              0       \n                            2               41 (30.6%)        0              0       \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)        0              0       \n                            Grade 1-2       45 (33.6%)        0              0       \n                            1               45 (33.6%)        0              0       \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)        0              0       \n                            Grade 1-2       41 (30.6%)        0              0       \n                            2               41 (30.6%)        0              0       \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)        0              0       \n                            Grade 3-4       22 (16.4%)        0              0       \n                            3               22 (16.4%)        0              0       \n                            Grade 5         42 (31.3%)        0              0       \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)        0              0       \n                            Grade 5         42 (31.3%)        0              0       \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 3-4       38 (28.4%)        0              0       \n                            3               38 (28.4%)        0              0       \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)        0              0       \n                            Grade 1-2       23 (17.2%)        0              0       \n                            1               23 (17.2%)        0              0       \n                            Grade 3-4       39 (29.1%)        0              0       \n                            3               39 (29.1%)        0              0       \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)        0              0       \n                            Grade 3-4       39 (29.1%)        0              0       \n                            3               39 (29.1%)        0              0       \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 1-2       38 (28.4%)        0              0       \n                            1               38 (28.4%)        0              0       \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 5         38 (28.4%)        0              0       \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 5         38 (28.4%)        0              0       \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)        0              0       \n                            Grade 1-2       37 (27.6%)        0              0       \n                            1               37 (27.6%)        0              0       \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)        0              0       \n                            Grade 1-2       37 (27.6%)        0              0       \n                            1               37 (27.6%)        0              0       \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)        0              0       \n                            Grade 3-4       36 (26.9%)        0              0       \n                            4               36 (26.9%)        0              0       \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)        0              0       \n                            Grade 3-4       36 (26.9%)        0              0       \n                            4               36 (26.9%)        0              0       \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)        0              0       \n                            Grade 1-2       28 (20.9%)        0              0       \n                            2               28 (20.9%)        0              0       \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)        0              0       \n                            Grade 1-2       28 (20.9%)        0              0       \n                            2               28 (20.9%)        0              0       \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            1                    0            0              0       \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            2                    0            0              0       \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            4                    0            0              0       \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            1                    0            0              0       \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            1                    0            0              0       \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            3                    0            0              0       \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 5              0            0              0       \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            3                    0            0              0       \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 5              0            0              0       \n\n\n\n\n\nCodegrade_groups_1 &lt;- list(\n  \"Grade 1-2\" = c(\"1\", \"2\"),\n  \"Grade 3-5\" = c(\"3\", \"4\", \"5\")\n)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups_1,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups_1,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-5       90 (67.2%)    83 (61.9%)     93 (70.5%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            5               64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-5       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-5       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-5       64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            5               42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 3-5       42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            5               42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-5       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 3-5       38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            5               38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 3-5       38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            5               38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-5       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-5       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCode# No Worst Grade Flags found in the ADAE data set.\n\n\n\n\n\nCode# Simple wrapper to return subset ADAE to a threshold of xx%.\nget_adae_trimmed &lt;- function(adsl, adae, cutoff_rate) {\n  n_per_arm &lt;- adsl %&gt;%\n    dplyr::count(ACTARM)\n\n  anl_terms &lt;- adae %&gt;%\n    dplyr::group_by(ACTARM, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::count(\n      unique_terms = n_distinct(USUBJID)\n    ) %&gt;%\n    dplyr::select(-n) %&gt;%\n    dplyr::ungroup()\n\n  anl_terms &lt;- dplyr::left_join(\n    anl_terms,\n    n_per_arm,\n    by = \"ACTARM\"\n  ) %&gt;%\n    dplyr::mutate(\n      ae_rate = unique_terms / n\n    ) %&gt;%\n    dplyr::filter(ae_rate &gt;= cutoff_rate) %&gt;%\n    dplyr::select(AEDECOD) %&gt;%\n    unique()\n\n  anl &lt;- dplyr::left_join(\n    anl_terms,\n    adae,\n    by = \"AEDECOD\"\n  )\n  anl\n}\n\nadae6 &lt;- get_adae_trimmed(adsl, adae, cutoff_rate = 0.4)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae6, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCode# Variant 7 was not created.\n# With this variant, the SOC level is not trimmed (even if there are no terms left).\n\n\n\n\n\nCodecutoff &lt;- 58L\nrow_condition &lt;- has_count_in_any_col(atleast = cutoff, col_names = levels(adsl$ACTARM))\n\nresult &lt;- prune_table(raw_table, keep_content_rows(my_row_condition(row_condition)))\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodecutoff &lt;- 0.1\nrow_condition &lt;- has_fractions_difference(atleast = cutoff, col_names = levels(adsl$ACTARM))\n\nresult &lt;- prune_table(raw_table, keep_content_rows(my_row_condition(row_condition)))\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCode# Variant 10 was not done\n# With this variant, SOC levels above the threshold are still in the table even if\n# there are no terms left.\n\n\n\n\n\nCodecutoff &lt;- 0.4\nrow_condition &lt;- has_fraction_in_any_col(atleast = cutoff, col_names = levels(adsl$ACTARM))\n\nresult &lt;- prune_table(raw_table, keep_content_rows(my_row_condition(row_condition)))\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_by_grade(\n      label = \"Adverse Events by Grade Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETOXGR\", \"AESEV\")),\n        selected = \"AETOXGR\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:46:19 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Fill-In  of Treatment Groups\nTable with Fill-In  of Grades\nTable with Collapsing  of Grades\nTable Using Worst Grade  Flags from ADAE\nTable with an Incidence Rate \\(\\geq\\) 40%, Totals Restricted\nTable with an Incidence Rate \\(\\geq\\) X%, Totals Unrestricted\nTable with an Incidence  Rate \\(\\geq\\) 58 Patients\nTable with a Difference in  Incidence Rate \\(\\geq\\) 10%\nTable with an Incidence Rate \\(\\geq\\) 5%, SOCs &lt; 5% Removed\nTable with an Incidence Rate \\(\\geq\\) 40%,  All SOCs w/o Preferred Terms Removed\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl) %&gt;% filter(TRT01A != \"&lt;Missing&gt;\")\nadae &lt;- df_explicit_na(adae) %&gt;%\n  var_relabel(\n    AEBODSYS = \"MedDRA System Organ Class\",\n    AEDECOD = \"MedDRA Preferred Term\"\n  ) %&gt;%\n  filter(\n    ANL01FL == \"Y\",\n    AETOXGR != \"&lt;Missing&gt;\"\n  )\n\n# Pre-Processing\ngrade_groups &lt;- list(\n  \"Grade 1-2\" = c(\"1\", \"2\"),\n  \"Grade 3-4\" = c(\"3\", \"4\"),\n  \"Grade 5\" = \"5\"\n)\nadae$TOTAL_VAR &lt;- \"- Any adverse events - \"\n\n# Helper function to avoid filtering also the first part of the table, where general information is given.\nmy_row_condition &lt;- function(row_fnc_condition) {\n  function(table_row) {\n    if (indent_mod(table_row) == 0) {\n      return(TRUE)\n    } else {\n      row_fnc_condition(table_row)\n    }\n  }\n}\n\n# Helper function to calculate sum from first nested row\nscore_all_sum &lt;- function(tt) {\n  cleaf &lt;- collect_leaves(tt)[[1]]\n  if (NROW(cleaf) == 0) {\n    stop(\"score_all_sum score function used at subtable [\", obj_name(tt), \"] that has no content.\")\n  }\n  sum(sapply(row_values(cleaf), function(cv) cv[1]))\n}\n\n# Raw table used by variant 8/10\nraw_table &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\") %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodeadae2 &lt;- adae %&gt;% filter(ACTARM == \"A: Drug X\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae2, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)       0              0       \n                            Grade 1-2        10 (7.5%)        0              0       \n                            1                5 (3.7%)         0              0       \n                            2                5 (3.7%)         0              0       \n                            Grade 3-4       26 (19.4%)        0              0       \n                            3                13 (9.7%)        0              0       \n                            4                13 (9.7%)        0              0       \n                            Grade 5         64 (47.8%)        0              0       \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)        0              0       \n                            Grade 1-2       68 (50.7%)        0              0       \n                            1               27 (20.1%)        0              0       \n                            2               41 (30.6%)        0              0       \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)        0              0       \n                            Grade 1-2       45 (33.6%)        0              0       \n                            1               45 (33.6%)        0              0       \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)        0              0       \n                            Grade 1-2       41 (30.6%)        0              0       \n                            2               41 (30.6%)        0              0       \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)        0              0       \n                            Grade 3-4       22 (16.4%)        0              0       \n                            3               22 (16.4%)        0              0       \n                            Grade 5         42 (31.3%)        0              0       \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)        0              0       \n                            Grade 5         42 (31.3%)        0              0       \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 3-4       38 (28.4%)        0              0       \n                            3               38 (28.4%)        0              0       \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)        0              0       \n                            Grade 1-2       23 (17.2%)        0              0       \n                            1               23 (17.2%)        0              0       \n                            Grade 3-4       39 (29.1%)        0              0       \n                            3               39 (29.1%)        0              0       \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)        0              0       \n                            Grade 3-4       39 (29.1%)        0              0       \n                            3               39 (29.1%)        0              0       \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 1-2       38 (28.4%)        0              0       \n                            1               38 (28.4%)        0              0       \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 5         38 (28.4%)        0              0       \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)        0              0       \n                            Grade 5         38 (28.4%)        0              0       \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)        0              0       \n                            Grade 1-2       37 (27.6%)        0              0       \n                            1               37 (27.6%)        0              0       \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)        0              0       \n                            Grade 1-2       37 (27.6%)        0              0       \n                            1               37 (27.6%)        0              0       \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)        0              0       \n                            Grade 3-4       36 (26.9%)        0              0       \n                            4               36 (26.9%)        0              0       \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)        0              0       \n                            Grade 3-4       36 (26.9%)        0              0       \n                            4               36 (26.9%)        0              0       \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)        0              0       \n                            Grade 1-2       28 (20.9%)        0              0       \n                            2               28 (20.9%)        0              0       \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)        0              0       \n                            Grade 1-2       28 (20.9%)        0              0       \n                            2               28 (20.9%)        0              0       \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            1                    0            0              0       \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            2                    0            0              0       \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            4                    0            0              0       \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            1                    0            0              0       \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            1                    0            0              0       \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5              0            0              0       \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4            0            0              0       \n                            3                    0            0              0       \n                            4                    0            0              0       \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            3                    0            0              0       \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 5              0            0              0       \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 1-2            0            0              0       \n                            1                    0            0              0       \n                            2                    0            0              0       \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            3                    0            0              0       \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 5              0            0              0       \n\n\n\n\n\nCodegrade_groups_1 &lt;- list(\n  \"Grade 1-2\" = c(\"1\", \"2\"),\n  \"Grade 3-5\" = c(\"3\", \"4\", \"5\")\n)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups_1,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups_1,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-5       90 (67.2%)    83 (61.9%)     93 (70.5%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            5               64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-5       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-5       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-5       64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            5               42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 3-5       42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            5               42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-5       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 3-5       38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            5               38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 3-5       38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            5               38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-5       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-5       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCode# No Worst Grade Flags found in the ADAE data set.\n\n\n\n\n\nCode# Simple wrapper to return subset ADAE to a threshold of xx%.\nget_adae_trimmed &lt;- function(adsl, adae, cutoff_rate) {\n  n_per_arm &lt;- adsl %&gt;%\n    dplyr::count(ACTARM)\n\n  anl_terms &lt;- adae %&gt;%\n    dplyr::group_by(ACTARM, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::count(\n      unique_terms = n_distinct(USUBJID)\n    ) %&gt;%\n    dplyr::select(-n) %&gt;%\n    dplyr::ungroup()\n\n  anl_terms &lt;- dplyr::left_join(\n    anl_terms,\n    n_per_arm,\n    by = \"ACTARM\"\n  ) %&gt;%\n    dplyr::mutate(\n      ae_rate = unique_terms / n\n    ) %&gt;%\n    dplyr::filter(ae_rate &gt;= cutoff_rate) %&gt;%\n    dplyr::select(AEDECOD) %&gt;%\n    unique()\n\n  anl &lt;- dplyr::left_join(\n    anl_terms,\n    adae,\n    by = \"AEDECOD\"\n  )\n  anl\n}\n\nadae6 &lt;- get_adae_trimmed(adsl, adae, cutoff_rate = 0.4)\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    var = \"TOTAL_VAR\",\n    label_pos = \"hidden\",\n    child_labels = \"visible\",\n    indent_mod = -1L\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 7L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 6L\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = FALSE,\n    split_fun = drop_split_levels,\n    split_label = var_labels(adae)[[\"AEBODSYS\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    split_fun = add_overall_level(\"- Overall -\", trim = TRUE),\n    split_label = var_labels(adae)[[\"AEDECOD\"]],\n    label_pos = \"topleft\"\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"- Any Grade -\",\n    .indent_mods = 6L\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"AETOXGR\",\n    grade_groups = grade_groups,\n    .indent_mods = 5L\n  ) %&gt;%\n  append_topleft(\"                            Grade\")\n\nresult &lt;- lyt %&gt;%\n  build_table(adae6, alt_counts_df = adsl) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_all_sum,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCode# Variant 7 was not created.\n# With this variant, the SOC level is not trimmed (even if there are no terms left).\n\n\n\n\n\nCodecutoff &lt;- 58L\nrow_condition &lt;- has_count_in_any_col(atleast = cutoff, col_names = levels(adsl$ACTARM))\n\nresult &lt;- prune_table(raw_table, keep_content_rows(my_row_condition(row_condition)))\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCodecutoff &lt;- 0.1\nrow_condition &lt;- has_fractions_difference(atleast = cutoff, col_names = levels(adsl$ACTARM))\n\nresult &lt;- prune_table(raw_table, keep_content_rows(my_row_condition(row_condition)))\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\n\n\nCode# Variant 10 was not done\n# With this variant, SOC levels above the threshold are still in the table even if\n# there are no terms left.\n\n\n\n\n\nCodecutoff &lt;- 0.4\nrow_condition &lt;- has_fraction_in_any_col(atleast = cutoff, col_names = levels(adsl$ACTARM))\n\nresult &lt;- prune_table(raw_table, keep_content_rows(my_row_condition(row_condition)))\n\nresult\n\nMedDRA System Organ Class                                                            \n  MedDRA Preferred Term                      A: Drug X    B: Placebo   C: Combination\n                            Grade             (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————\n- Any adverse events -                                                               \n                            - Any Grade -   100 (74.6%)   98 (73.1%)    103 (78.0%)  \n                            Grade 1-2        10 (7.5%)    15 (11.2%)     10 (7.6%)   \n                            1                5 (3.7%)      7 (5.2%)       4 (3.0%)   \n                            2                5 (3.7%)      8 (6.0%)       6 (4.5%)   \n                            Grade 3-4       26 (19.4%)    31 (23.1%)     29 (22.0%)  \n                            3                13 (9.7%)    13 (9.7%)      14 (10.6%)  \n                            4                13 (9.7%)    18 (13.4%)     15 (11.4%)  \n                            Grade 5         64 (47.8%)    52 (38.8%)     64 (48.5%)  \ncl A.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            Grade 1-2       68 (50.7%)    58 (43.3%)     76 (57.6%)  \n                            1               27 (20.1%)    19 (14.2%)     34 (25.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \n  dcd A.1.1.1.1                                                                      \n                            - Any Grade -   45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            Grade 1-2       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n                            1               45 (33.6%)    31 (23.1%)     52 (39.4%)  \n  dcd A.1.1.1.2                                                                      \n                            - Any Grade -   41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            Grade 1-2       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n                            2               41 (30.6%)    39 (29.1%)     42 (31.8%)  \ncl B.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   62 (46.3%)    56 (41.8%)     74 (56.1%)  \n                            Grade 1-2       23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            1               23 (17.2%)    22 (16.4%)     28 (21.2%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \n  dcd B.2.2.3.1                                                                      \n                            - Any Grade -   38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            Grade 1-2       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n                            1               38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                                      \n                            - Any Grade -   39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            Grade 3-4       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n                            3               39 (29.1%)    34 (25.4%)     46 (34.8%)  \ncl D.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   64 (47.8%)    54 (40.3%)     68 (51.5%)  \n                            Grade 3-4       22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            3               22 (16.4%)    22 (16.4%)     22 (16.7%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.1.1                                                                      \n                            - Any Grade -   42 (31.3%)    32 (23.9%)     46 (34.8%)  \n                            Grade 5         42 (31.3%)    32 (23.9%)     46 (34.8%)  \n  dcd D.1.1.4.2                                                                      \n                            - Any Grade -   38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            Grade 3-4       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n                            3               38 (28.4%)    34 (25.4%)     40 (30.3%)  \ncl D.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  dcd D.2.1.5.3                                                                      \n                            - Any Grade -   37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            Grade 1-2       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n                            1               37 (27.6%)    46 (34.3%)     50 (37.9%)  \ncl C.2                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  dcd C.2.1.2.1                                                                      \n                            - Any Grade -   28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            Grade 1-2       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n                            2               28 (20.9%)    36 (26.9%)     48 (36.4%)  \ncl B.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  dcd B.1.1.1.1                                                                      \n                            - Any Grade -   38 (28.4%)    37 (27.6%)     36 (27.3%)  \n                            Grade 5         38 (28.4%)    37 (27.6%)     36 (27.3%)  \ncl C.1                                                                               \n  - Overall -                                                                        \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                                      \n                            - Any Grade -   36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            Grade 3-4       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n                            4               36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_by_grade(\n      label = \"Adverse Events by Grade Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETOXGR\", \"AESEV\")),\n        selected = \"AETOXGR\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:44:56 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -1271,7 +1271,7 @@
     "href": "tables/adverse-events/aet07.html",
     "title": "AET07",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Fill-In of Treatment Groups without Deaths\nteal App\nReproducibility\n\n\n\nFor illustrative purposes, we will pre-process AESDTH in ADAE so there are no deaths in arm A and concatenate AEBODSYS and AEDECOD as per GDSR output standards for AET07.\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_f &lt;- adae %&gt;%\n  mutate(\n    AESDTH = as.character(AESDTH), # characterizing AESDTH for next step\n    AESDTH = case_when(\n      ARM == \"A: Drug X\" ~ NA_character_, # For demonstration purpose only,\n      TRUE ~ AESDTH\n    ), # make \"A: Drug X\" as the arm without AE leading to death\n    AESDTH = as.factor(AESDTH),\n    SOC_PT = factor(paste(AEBODSYS, \"/\", AEDECOD))\n  ) %&gt;%\n  filter(AESDTH == \"Y\") %&gt;%\n  mutate(ARM = droplevels(ARM))\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = drop_split_levels) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = c(unique = \"Total number of deaths\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"SOC_PT\"\n  ) %&gt;%\n  append_topleft(\"MedDRA SOC and Preferred Term\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"SOC_PT\",\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  )\n\nresult\n\n                                B: Placebo   C: Combination\nMedDRA SOC and Preferred Term    (N=134)        (N=132)    \n———————————————————————————————————————————————————————————\nTotal number of deaths          70 (52.2%)     75 (56.8%)  \ncl D.1 / dcd D.1.1.1.1          42 (31.3%)     51 (38.6%)  \ncl B.1 / dcd B.1.1.1.1          49 (36.6%)     43 (32.6%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = c(unique = \"Total number of deaths\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"SOC_PT\"\n  ) %&gt;%\n  append_topleft(\"MedDRA SOC and Preferred Term\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"SOC_PT\",\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  )\n\nresult\n\n                                A: Drug X   B: Placebo   C: Combination\nMedDRA SOC and Preferred Term    (N=134)     (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————\nTotal number of deaths              0       70 (52.2%)     75 (56.8%)  \ncl D.1 / dcd D.1.1.1.1              0       42 (31.3%)     51 (38.6%)  \ncl B.1 / dcd B.1.1.1.1              0       49 (36.6%)     43 (32.6%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(\n      DTHFL = case_when(\n        !is.na(DTHDT) ~ \"Y\",\n        TRUE ~ \"\"\n      )\n    ) %&gt;%\n    col_relabel(\n      DTHFL = \"Subject Death Flag\"\n    )\n\n  ADAE &lt;- ADAE %&gt;%\n    mutate(\n      AESDTH = as.character(AESDTH), # characterizing AESDTH for next step\n      AESDTH = case_when(\n        ARM == \"A: Drug X\" ~ NA_character_, # For demonstration purpose only,\n        TRUE ~ AESDTH\n      ), # make \"A: Drug X\" as the arm without AE leading to death\n      AESDTH = as.factor(AESDTH),\n      SOC_PT = factor(paste(AEBODSYS, \"/\", AEDECOD))\n    ) %&gt;%\n    filter(AESDTH == \"Y\") %&gt;%\n    mutate(ARM = droplevels(ARM)) %&gt;%\n    col_relabel(SOC_PT = \"MedDRA SOC and Preferred Term\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\", \"SOC_PT\")),\n        selected = c(\"SOC_PT\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\", \"SOC_PT\")),\n        selected = NULL\n      ),\n      add_total = FALSE,\n      event_type = \"adverse event\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:46:56 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Fill-In of Treatment Groups without Deaths\nteal App\nReproducibility\n\n\n\nFor illustrative purposes, we will pre-process AESDTH in ADAE so there are no deaths in arm A and concatenate AEBODSYS and AEDECOD as per GDSR output standards for AET07.\n\nCodelibrary(dplyr)\nlibrary(tern)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae)\n\nadae_f &lt;- adae %&gt;%\n  mutate(\n    AESDTH = as.character(AESDTH), # characterizing AESDTH for next step\n    AESDTH = case_when(\n      ARM == \"A: Drug X\" ~ NA_character_, # For demonstration purpose only,\n      TRUE ~ AESDTH\n    ), # make \"A: Drug X\" as the arm without AE leading to death\n    AESDTH = as.factor(AESDTH),\n    SOC_PT = factor(paste(AEBODSYS, \"/\", AEDECOD))\n  ) %&gt;%\n  filter(AESDTH == \"Y\") %&gt;%\n  mutate(ARM = droplevels(ARM))\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\", split_fun = drop_split_levels) %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = c(unique = \"Total number of deaths\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"SOC_PT\"\n  ) %&gt;%\n  append_topleft(\"MedDRA SOC and Preferred Term\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"SOC_PT\",\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  )\n\nresult\n\n                                B: Placebo   C: Combination\nMedDRA SOC and Preferred Term    (N=134)        (N=132)    \n———————————————————————————————————————————————————————————\nTotal number of deaths          70 (52.2%)     75 (56.8%)  \ncl D.1 / dcd D.1.1.1.1          42 (31.3%)     51 (38.6%)  \ncl B.1 / dcd B.1.1.1.1          49 (36.6%)     43 (32.6%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = c(unique = \"Total number of deaths\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_occurrences(\n    vars = \"SOC_PT\"\n  ) %&gt;%\n  append_topleft(\"MedDRA SOC and Preferred Term\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adae_f,\n  alt_counts_df = adsl\n) %&gt;%\n  prune_table() %&gt;%\n  sort_at_path(\n    path = \"SOC_PT\",\n    scorefun = score_occurrences,\n    decreasing = TRUE\n  )\n\nresult\n\n                                A: Drug X   B: Placebo   C: Combination\nMedDRA SOC and Preferred Term    (N=134)     (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————\nTotal number of deaths              0       70 (52.2%)     75 (56.8%)  \ncl D.1 / dcd D.1.1.1.1              0       42 (31.3%)     51 (38.6%)  \ncl B.1 / dcd B.1.1.1.1              0       49 (36.6%)     43 (32.6%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAE &lt;- df_explicit_na(ADAE)\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(\n      DTHFL = case_when(\n        !is.na(DTHDT) ~ \"Y\",\n        TRUE ~ \"\"\n      )\n    ) %&gt;%\n    col_relabel(\n      DTHFL = \"Subject Death Flag\"\n    )\n\n  ADAE &lt;- ADAE %&gt;%\n    mutate(\n      AESDTH = as.character(AESDTH), # characterizing AESDTH for next step\n      AESDTH = case_when(\n        ARM == \"A: Drug X\" ~ NA_character_, # For demonstration purpose only,\n        TRUE ~ AESDTH\n      ), # make \"A: Drug X\" as the arm without AE leading to death\n      AESDTH = as.factor(AESDTH),\n      SOC_PT = factor(paste(AEBODSYS, \"/\", AEDECOD))\n    ) %&gt;%\n    filter(AESDTH == \"Y\") %&gt;%\n    mutate(ARM = droplevels(ARM)) %&gt;%\n    col_relabel(SOC_PT = \"MedDRA SOC and Preferred Term\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events(\n      label = \"Adverse Event Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\", \"SOC_PT\")),\n        selected = c(\"SOC_PT\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\", \"SOC_PT\")),\n        selected = NULL\n      ),\n      add_total = FALSE,\n      event_type = \"adverse event\"\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:45:33 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -1283,7 +1283,7 @@
     "href": "tables/adverse-events/aet03.html",
     "title": "AET03",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae) %&gt;%\n  var_relabel(\n    AEBODSYS = \"MedDRA System Organ Class\",\n    AEDECOD = \"MedDRA Preferred Term\"\n  ) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n\nFor illustrative purposes, we will create a new factor variable in ADAE, ASEV, with all intensity levels including \"LIFE THREATENING\".\n\nCodeadae &lt;- adae %&gt;% mutate(ASEV = as.character(AESEV))\nadae$ASEV[1:15] &lt;- \"LIFE THREATENING\"\nadae &lt;- adae %&gt;% mutate(ASEV = factor(ASEV, levels = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"LIFE THREATENING\")))\n\n\n\n\n\nCodegrade_groups &lt;- list(\"- Any Intensity -\" = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"LIFE THREATENING\"))\n\nsplit_fun &lt;- trim_levels_in_group\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_occurrences_by_grade(\n    var = \"ASEV\",\n    grade_groups = grade_groups\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun(\"ASEV\"),\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_occurrences_by_grade(\n    var = \"ASEV\",\n    grade_groups = grade_groups\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    indent_mod = -1L,\n    split_fun = split_fun(\"ASEV\"),\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEDECOD)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = c(\"- Any Intensity -\")\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"ASEV\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae, \"AESEV\", indent = 2L)\n\nresult &lt;- lyt %&gt;%\n  build_table(\n    adae,\n    alt_counts_df = adsl\n  ) %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                            \n  MedDRA Preferred Term      A: Drug X    B: Placebo   C: Combination\n    Severity/Intensity        (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————\n- Any Intensity -           100 (74.6%)   98 (73.1%)    103 (78.0%)  \nMILD                         5 (3.7%)      7 (5.2%)       4 (3.0%)   \nMODERATE                    17 (12.7%)    21 (15.7%)     20 (15.2%)  \nSEVERE                      77 (57.5%)    69 (51.5%)     77 (58.3%)  \nLIFE THREATENING             1 (0.7%)      1 (0.7%)       2 (1.5%)   \ncl A.1                                                               \n  - Any Intensity -         68 (50.7%)    58 (43.3%)     76 (57.6%)  \n  MILD                      27 (20.1%)    19 (14.2%)     34 (25.8%)  \n  MODERATE                  40 (29.9%)    39 (29.1%)     41 (31.1%)  \n  LIFE THREATENING           1 (0.7%)         0           1 (0.8%)   \n  dcd A.1.1.1.1                                                      \n    - Any Intensity -       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n    MILD                    45 (33.6%)    31 (23.1%)     51 (38.6%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \n  dcd A.1.1.1.2                                                      \n    - Any Intensity -       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n    MODERATE                40 (29.9%)    39 (29.1%)     41 (31.1%)  \n    LIFE THREATENING         1 (0.7%)         0           1 (0.8%)   \ncl B.2                                                               \n  - Any Intensity -         62 (46.3%)    56 (41.8%)     74 (56.1%)  \n  MILD                      23 (17.2%)    22 (16.4%)     28 (21.2%)  \n  MODERATE                  38 (28.4%)    34 (25.4%)     45 (34.1%)  \n  LIFE THREATENING           1 (0.7%)         0           1 (0.8%)   \n  dcd B.2.2.3.1                                                      \n    - Any Intensity -       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n    MILD                    38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                      \n    - Any Intensity -       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n    MODERATE                38 (28.4%)    34 (25.4%)     45 (34.1%)  \n    LIFE THREATENING         1 (0.7%)         0           1 (0.8%)   \ncl D.1                                                               \n  - Any Intensity -         64 (47.8%)    54 (40.3%)     68 (51.5%)  \n  MODERATE                  21 (15.7%)    22 (16.4%)     22 (16.7%)  \n  SEVERE                    42 (31.3%)    32 (23.9%)     44 (33.3%)  \n  LIFE THREATENING           1 (0.7%)         0           2 (1.5%)   \n  dcd D.1.1.1.1                                                      \n    - Any Intensity -       42 (31.3%)    32 (23.9%)     46 (34.8%)  \n    SEVERE                  42 (31.3%)    32 (23.9%)     45 (34.1%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \n  dcd D.1.1.4.2                                                      \n    - Any Intensity -       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n    MODERATE                37 (27.6%)    34 (25.4%)     39 (29.5%)  \n    LIFE THREATENING         1 (0.7%)         0           1 (0.8%)   \ncl D.2                                                               \n  - Any Intensity -         37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  MILD                      37 (27.6%)    46 (34.3%)     49 (37.1%)  \n  LIFE THREATENING               0            0           1 (0.8%)   \n  dcd D.2.1.5.3                                                      \n    - Any Intensity -       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n    MILD                    37 (27.6%)    46 (34.3%)     49 (37.1%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \ncl C.2                                                               \n  - Any Intensity -         28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  MODERATE                  28 (20.9%)    36 (26.9%)     47 (35.6%)  \n  LIFE THREATENING               0            0           1 (0.8%)   \n  dcd C.2.1.2.1                                                      \n    - Any Intensity -       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n    MODERATE                28 (20.9%)    36 (26.9%)     47 (35.6%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \ncl B.1                                                               \n  - Any Intensity -         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  SEVERE                    38 (28.4%)    36 (26.9%)     36 (27.3%)  \n  LIFE THREATENING               0         1 (0.7%)          0       \n  dcd B.1.1.1.1                                                      \n    - Any Intensity -       38 (28.4%)    37 (27.6%)     36 (27.3%)  \n    SEVERE                  38 (28.4%)    36 (26.9%)     36 (27.3%)  \n    LIFE THREATENING             0         1 (0.7%)          0       \ncl C.1                                                               \n  - Any Intensity -         36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  SEVERE                    36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                      \n    - Any Intensity -       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n    SEVERE                  36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_by_grade(\n      label = \"Adverse Events by Grade Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETOXGR\", \"AESEV\")),\n        selected = \"AESEV\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:47:28 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(adae) %&gt;%\n  var_relabel(\n    AEBODSYS = \"MedDRA System Organ Class\",\n    AEDECOD = \"MedDRA Preferred Term\"\n  ) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n\nFor illustrative purposes, we will create a new factor variable in ADAE, ASEV, with all intensity levels including \"LIFE THREATENING\".\n\nCodeadae &lt;- adae %&gt;% mutate(ASEV = as.character(AESEV))\nadae$ASEV[1:15] &lt;- \"LIFE THREATENING\"\nadae &lt;- adae %&gt;% mutate(ASEV = factor(ASEV, levels = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"LIFE THREATENING\")))\n\n\n\n\n\nCodegrade_groups &lt;- list(\"- Any Intensity -\" = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"LIFE THREATENING\"))\n\nsplit_fun &lt;- trim_levels_in_group\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_occurrences_by_grade(\n    var = \"ASEV\",\n    grade_groups = grade_groups\n  ) %&gt;%\n  split_rows_by(\n    \"AEBODSYS\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    split_fun = split_fun(\"ASEV\"),\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEBODSYS)\n  ) %&gt;%\n  summarize_occurrences_by_grade(\n    var = \"ASEV\",\n    grade_groups = grade_groups\n  ) %&gt;%\n  split_rows_by(\n    \"AEDECOD\",\n    child_labels = \"visible\",\n    nested = TRUE,\n    indent_mod = -1L,\n    split_fun = split_fun(\"ASEV\"),\n    label_pos = \"topleft\",\n    split_label = obj_label(adae$AEDECOD)\n  ) %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = c(\"- Any Intensity -\")\n  ) %&gt;%\n  count_occurrences_by_grade(\n    var = \"ASEV\",\n    .indent_mods = -1L\n  ) %&gt;%\n  append_varlabels(adae, \"AESEV\", indent = 2L)\n\nresult &lt;- lyt %&gt;%\n  build_table(\n    adae,\n    alt_counts_df = adsl\n  ) %&gt;%\n  sort_at_path(\n    path = \"AEBODSYS\",\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = cont_n_allcols,\n    decreasing = TRUE\n  )\n\nresult\n\nMedDRA System Organ Class                                            \n  MedDRA Preferred Term      A: Drug X    B: Placebo   C: Combination\n    Severity/Intensity        (N=134)      (N=134)        (N=132)    \n—————————————————————————————————————————————————————————————————————\n- Any Intensity -           100 (74.6%)   98 (73.1%)    103 (78.0%)  \nMILD                         5 (3.7%)      7 (5.2%)       4 (3.0%)   \nMODERATE                    17 (12.7%)    21 (15.7%)     20 (15.2%)  \nSEVERE                      77 (57.5%)    69 (51.5%)     77 (58.3%)  \nLIFE THREATENING             1 (0.7%)      1 (0.7%)       2 (1.5%)   \ncl A.1                                                               \n  - Any Intensity -         68 (50.7%)    58 (43.3%)     76 (57.6%)  \n  MILD                      27 (20.1%)    19 (14.2%)     34 (25.8%)  \n  MODERATE                  40 (29.9%)    39 (29.1%)     41 (31.1%)  \n  LIFE THREATENING           1 (0.7%)         0           1 (0.8%)   \n  dcd A.1.1.1.1                                                      \n    - Any Intensity -       45 (33.6%)    31 (23.1%)     52 (39.4%)  \n    MILD                    45 (33.6%)    31 (23.1%)     51 (38.6%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \n  dcd A.1.1.1.2                                                      \n    - Any Intensity -       41 (30.6%)    39 (29.1%)     42 (31.8%)  \n    MODERATE                40 (29.9%)    39 (29.1%)     41 (31.1%)  \n    LIFE THREATENING         1 (0.7%)         0           1 (0.8%)   \ncl B.2                                                               \n  - Any Intensity -         62 (46.3%)    56 (41.8%)     74 (56.1%)  \n  MILD                      23 (17.2%)    22 (16.4%)     28 (21.2%)  \n  MODERATE                  38 (28.4%)    34 (25.4%)     45 (34.1%)  \n  LIFE THREATENING           1 (0.7%)         0           1 (0.8%)   \n  dcd B.2.2.3.1                                                      \n    - Any Intensity -       38 (28.4%)    40 (29.9%)     45 (34.1%)  \n    MILD                    38 (28.4%)    40 (29.9%)     45 (34.1%)  \n  dcd B.2.1.2.1                                                      \n    - Any Intensity -       39 (29.1%)    34 (25.4%)     46 (34.8%)  \n    MODERATE                38 (28.4%)    34 (25.4%)     45 (34.1%)  \n    LIFE THREATENING         1 (0.7%)         0           1 (0.8%)   \ncl D.1                                                               \n  - Any Intensity -         64 (47.8%)    54 (40.3%)     68 (51.5%)  \n  MODERATE                  21 (15.7%)    22 (16.4%)     22 (16.7%)  \n  SEVERE                    42 (31.3%)    32 (23.9%)     44 (33.3%)  \n  LIFE THREATENING           1 (0.7%)         0           2 (1.5%)   \n  dcd D.1.1.1.1                                                      \n    - Any Intensity -       42 (31.3%)    32 (23.9%)     46 (34.8%)  \n    SEVERE                  42 (31.3%)    32 (23.9%)     45 (34.1%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \n  dcd D.1.1.4.2                                                      \n    - Any Intensity -       38 (28.4%)    34 (25.4%)     40 (30.3%)  \n    MODERATE                37 (27.6%)    34 (25.4%)     39 (29.5%)  \n    LIFE THREATENING         1 (0.7%)         0           1 (0.8%)   \ncl D.2                                                               \n  - Any Intensity -         37 (27.6%)    46 (34.3%)     50 (37.9%)  \n  MILD                      37 (27.6%)    46 (34.3%)     49 (37.1%)  \n  LIFE THREATENING               0            0           1 (0.8%)   \n  dcd D.2.1.5.3                                                      \n    - Any Intensity -       37 (27.6%)    46 (34.3%)     50 (37.9%)  \n    MILD                    37 (27.6%)    46 (34.3%)     49 (37.1%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \ncl C.2                                                               \n  - Any Intensity -         28 (20.9%)    36 (26.9%)     48 (36.4%)  \n  MODERATE                  28 (20.9%)    36 (26.9%)     47 (35.6%)  \n  LIFE THREATENING               0            0           1 (0.8%)   \n  dcd C.2.1.2.1                                                      \n    - Any Intensity -       28 (20.9%)    36 (26.9%)     48 (36.4%)  \n    MODERATE                28 (20.9%)    36 (26.9%)     47 (35.6%)  \n    LIFE THREATENING             0            0           1 (0.8%)   \ncl B.1                                                               \n  - Any Intensity -         38 (28.4%)    37 (27.6%)     36 (27.3%)  \n  SEVERE                    38 (28.4%)    36 (26.9%)     36 (27.3%)  \n  LIFE THREATENING               0         1 (0.7%)          0       \n  dcd B.1.1.1.1                                                      \n    - Any Intensity -       38 (28.4%)    37 (27.6%)     36 (27.3%)  \n    SEVERE                  38 (28.4%)    36 (26.9%)     36 (27.3%)  \n    LIFE THREATENING             0         1 (0.7%)          0       \ncl C.1                                                               \n  - Any Intensity -         36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  SEVERE                    36 (26.9%)    34 (25.4%)     36 (27.3%)  \n  dcd C.1.1.1.3                                                      \n    - Any Intensity -       36 (26.9%)    34 (25.4%)     36 (27.3%)  \n    SEVERE                  36 (26.9%)    34 (25.4%)     36 (27.3%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_by_grade(\n      label = \"Adverse Events by Grade Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETOXGR\", \"AESEV\")),\n        selected = \"AESEV\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:46:06 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -1295,7 +1295,7 @@
     "href": "tables/adverse-events/aet01.html",
     "title": "AET01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Medical  Concepts Section\nTable with  Modified Rows\nTable with Rows Counting  Events & Additional Sections\nteal App\nReproducibility\n\n\n\nTo illustrate, additional variables such as flags (TRUE/FALSE) for select AEs of interest and select AE baskets are added to the adae dataset.\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(\n  adae,\n  omit_columns = c(\"SMQ01NAM\", \"SMQ01SC\", \"SMQ02NAM\", \"SMQ02SC\", \"CQ01NAM\", \"STUDYID\", \"USUBJID\")\n)\n\nset.seed(99)\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AEDECOD = with_label(as.character(AEDECOD), \"Dictionary-Derived Term\"),\n    AESDTH = with_label(\n      sample(c(\"N\", \"Y\"), size = nrow(adae), replace = TRUE, prob = c(0.99, 0.01)),\n      \"Results in Death\"\n    ),\n    AEACN = with_label(\n      sample(\n        c(\"DOSE NOT CHANGED\", \"DOSE INCREASED\", \"DRUG INTERRUPTED\", \"DRUG WITHDRAWN\"),\n        size = nrow(adae),\n        replace = TRUE, prob = c(0.68, 0.02, 0.25, 0.05)\n      ),\n      \"Action Taken with Study Treatment\"\n    ),\n    FATAL = with_label(AESDTH == \"Y\", \"AE with fatal outcome\"),\n    SEV = with_label(AESEV == \"SEVERE\", \"Severe AE (at greatest intensity)\"),\n    SER = with_label(AESER == \"Y\", \"Serious AE\"),\n    SERWD = with_label(AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\", \"Serious AE leading to withdrawal from treatment\"),\n    SERDSM = with_label(\n      AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Serious AE leading to dose modification/interruption\"\n    ),\n    RELSER = with_label(AESER == \"Y\" & AEREL == \"Y\", \"Related Serious AE\"),\n    WD = with_label(AEACN == \"DRUG WITHDRAWN\", \"AE leading to withdrawal from treatment\"),\n    DSM = with_label(\n      AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"), \"AE leading to dose modification/interruption\"\n    ),\n    REL = with_label(AEREL == \"Y\", \"Related AE\"),\n    RELWD = with_label(AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\", \"Related AE leading to withdrawal from treatment\"),\n    RELDSM = with_label(\n      AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Related AE leading to dose modification/interruption\"\n    ),\n    CTC35 = with_label(AETOXGR %in% c(\"3\", \"4\", \"5\"), \"Grade 3-5 AE\"),\n    CTC45 = with_label(AETOXGR %in% c(\"4\", \"5\"), \"Grade 4/5 AE\"),\n    SMQ01 = with_label(SMQ01NAM != \"\", aesi_label(adae$SMQ01NAM, adae$SMQ01SC)),\n    SMQ02 = with_label(SMQ02NAM != \"\", aesi_label(adae$SMQ02NAM, adae$SMQ02SC)),\n    CQ01 = with_label(CQ01NAM != \"\", aesi_label(adae$CQ01NAM)),\n    USUBJID_AESEQ = paste(USUBJID, AESEQ, sep = \"@@\") # Create unique ID per AE in dataset.\n  ) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n\n\n\n\n\nCodeaesi_vars &lt;- c(\"FATAL\", \"SER\", \"SERWD\", \"SERDSM\", \"RELSER\", \"WD\", \"DSM\", \"REL\", \"RELWD\", \"RELDSM\", \"SEV\")\n\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  Serious AE leading to withdrawal from treatment             6 (4.5%)     12 (9.0%)       9 (6.8%)   \n  Serious AE leading to dose modification/interruption       36 (26.9%)    40 (29.9%)     47 (35.6%)  \n  Related Serious AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Related AE leading to withdrawal from treatment             10 (7.5%)     9 (6.7%)      12 (9.1%)   \n  Related AE leading to dose modification/interruption       44 (32.8%)    44 (32.8%)     51 (38.6%)  \n  Severe AE (at greatest intensity)                          77 (57.5%)    70 (52.2%)     79 (59.8%)  \n\n\n\n\n\nCodeaesi_vars &lt;- c(\"FATAL\", \"SER\", \"SERWD\", \"SERDSM\", \"RELSER\", \"WD\", \"DSM\", \"REL\", \"RELWD\", \"RELDSM\", \"CTC35\")\nbasket_vars &lt;- c(\"SMQ01\", \"SMQ02\", \"CQ01\")\n\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = basket_vars,\n    table_names = \"table_aesi\",\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  Serious AE leading to withdrawal from treatment             6 (4.5%)     12 (9.0%)       9 (6.8%)   \n  Serious AE leading to dose modification/interruption       36 (26.9%)    40 (29.9%)     47 (35.6%)  \n  Related Serious AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Related AE leading to withdrawal from treatment             10 (7.5%)     9 (6.7%)      12 (9.1%)   \n  Related AE leading to dose modification/interruption       44 (32.8%)    44 (32.8%)     51 (38.6%)  \n  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  \nTotal number of patients with at least one                                                            \n  C.1.1.1.3/B.2.2.3.1 AESI (BROAD)                           58 (43.3%)    60 (44.8%)     66 (50.0%)  \n  SMQ 02 Reference Name                                           0            0              0       \n  D.2.1.5.3/A.1.1.1.1 AESI                                   62 (46.3%)    61 (45.5%)     76 (57.6%)  \n\n\n\n\n\nCodeaesi_vars &lt;- c(\"FATAL\", \"SER\", \"WD\", \"REL\", \"CTC35\", \"CTC45\")\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"WITHDRAWAL BY SUBJECT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn informed consent\"),\n    table_names = \"tot_dscsreas_wd\"\n  )\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  )\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients withdrawn informed consent           1 (0.7%)      1 (0.7%)       1 (0.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  \n  Grade 4/5 AE                                               77 (57.5%)    70 (52.2%)     79 (59.8%)  \n\n\n\n\n\nCodecount_subj_vars &lt;- c(\"FATAL\", \"SER\", \"WD\", \"DSM\", \"REL\", \"CTC35\")\ncount_term_vars &lt;- c(\"SER\", \"DSM\", \"REL\", \"CTC35\", \"CTC45\")\ncount_ae_vars &lt;- c(\"SER\", \"DSM\", \"REL\", \"CTC35\", \"CTC45\")\n\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = count_subj_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"AEDECOD\",\n    flag_variables = count_term_vars,\n    .stats = \"count\",\n    .formats = c(count = \"xx\"),\n    table_names = \"table_term\",\n    var_labels = \"Total number of unique preferred terms which are\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID_AESEQ\",\n    flag_variables = count_ae_vars,\n    .stats = \"count\",\n    .formats = c(count = \"xx\"),\n    table_names = \"table_ae\",\n    var_labels = \"Total number of adverse events which are\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  \nTotal number of unique preferred terms which are                                                      \n  Serious AE                                                      4            4              4       \n  AE leading to dose modification/interruption                   10            10             10      \n  Related AE                                                      5            5              5       \n  Grade 3-5 AE                                                    5            5              5       \n  Grade 4/5 AE                                                    3            3              3       \nTotal number of adverse events which are                                                              \n  Serious AE                                                     204          194            245      \n  AE leading to dose modification/interruption                   123          135            158      \n  Related AE                                                     231          231            290      \n  Grade 3-5 AE                                                   249          229            277      \n  Grade 4/5 AE                                                   143          134            168      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  add_event_flags &lt;- function(dat) {\n    dat %&gt;%\n      mutate(\n        TMPFL_SER = AESER == \"Y\",\n        TMPFL_REL = AEREL == \"Y\",\n        TMPFL_GR5 = AETOXGR == \"5\",\n        TMP_SMQ01 = !is.na(SMQ01NAM),\n        TMP_SMQ02 = !is.na(SMQ02NAM),\n        TMP_CQ01 = !is.na(CQ01NAM)\n      ) %&gt;%\n      col_relabel(\n        TMPFL_SER = \"Serious AE\",\n        TMPFL_REL = \"Related AE\",\n        TMPFL_GR5 = \"Grade 5 AE\",\n        TMP_SMQ01 = aesi_label(dat[[\"SMQ01NAM\"]], dat[[\"SMQ01SC\"]]),\n        TMP_SMQ02 = aesi_label(dat[[\"SMQ02NAM\"]], dat[[\"SMQ02SC\"]]),\n        TMP_CQ01 = aesi_label(dat[[\"CQ01NAM\"]])\n      )\n  }\n\n  # Generating user-defined event flags.\n  ADAE &lt;- ADAE %&gt;% add_event_flags()\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\nae_anl_vars &lt;- names(ADAE)[startsWith(names(ADAE), \"TMPFL_\")]\naesi_vars &lt;- names(ADAE)[startsWith(names(ADAE), \"TMP_\")]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_summary(\n      label = \"Adverse Events Summary\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(\"ADSL\", c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      flag_var_anl = choices_selected(\n        choices = variable_choices(\"ADAE\", ae_anl_vars),\n        selected = ae_anl_vars[1],\n        keep_order = TRUE,\n        fixed = FALSE\n      ),\n      flag_var_aesi = choices_selected(\n        choices = variable_choices(\"ADAE\", aesi_vars),\n        selected = aesi_vars[1],\n        keep_order = TRUE,\n        fixed = FALSE\n      ),\n      add_total = TRUE\n    )\n  )\n)\n\nWarning in rlang::hash(list(data = data, modules = modules)):\n'package:teal.modules.clinical' may not be available when loading\n\nCodeshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:48:01 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Medical  Concepts Section\nTable with  Modified Rows\nTable with Rows Counting  Events & Additional Sections\nteal App\nReproducibility\n\n\n\nTo illustrate, additional variables such as flags (TRUE/FALSE) for select AEs of interest and select AE baskets are added to the adae dataset.\n\nCodelibrary(tern)\nlibrary(dplyr)\nlibrary(scda)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadae &lt;- df_explicit_na(\n  adae,\n  omit_columns = c(\"SMQ01NAM\", \"SMQ01SC\", \"SMQ02NAM\", \"SMQ02SC\", \"CQ01NAM\", \"STUDYID\", \"USUBJID\")\n)\n\nset.seed(99)\n\nadae &lt;- adae %&gt;%\n  mutate(\n    AEDECOD = with_label(as.character(AEDECOD), \"Dictionary-Derived Term\"),\n    AESDTH = with_label(\n      sample(c(\"N\", \"Y\"), size = nrow(adae), replace = TRUE, prob = c(0.99, 0.01)),\n      \"Results in Death\"\n    ),\n    AEACN = with_label(\n      sample(\n        c(\"DOSE NOT CHANGED\", \"DOSE INCREASED\", \"DRUG INTERRUPTED\", \"DRUG WITHDRAWN\"),\n        size = nrow(adae),\n        replace = TRUE, prob = c(0.68, 0.02, 0.25, 0.05)\n      ),\n      \"Action Taken with Study Treatment\"\n    ),\n    FATAL = with_label(AESDTH == \"Y\", \"AE with fatal outcome\"),\n    SEV = with_label(AESEV == \"SEVERE\", \"Severe AE (at greatest intensity)\"),\n    SER = with_label(AESER == \"Y\", \"Serious AE\"),\n    SERWD = with_label(AESER == \"Y\" & AEACN == \"DRUG WITHDRAWN\", \"Serious AE leading to withdrawal from treatment\"),\n    SERDSM = with_label(\n      AESER == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Serious AE leading to dose modification/interruption\"\n    ),\n    RELSER = with_label(AESER == \"Y\" & AEREL == \"Y\", \"Related Serious AE\"),\n    WD = with_label(AEACN == \"DRUG WITHDRAWN\", \"AE leading to withdrawal from treatment\"),\n    DSM = with_label(\n      AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"), \"AE leading to dose modification/interruption\"\n    ),\n    REL = with_label(AEREL == \"Y\", \"Related AE\"),\n    RELWD = with_label(AEREL == \"Y\" & AEACN == \"DRUG WITHDRAWN\", \"Related AE leading to withdrawal from treatment\"),\n    RELDSM = with_label(\n      AEREL == \"Y\" & AEACN %in% c(\"DRUG INTERRUPTED\", \"DOSE INCREASED\", \"DOSE REDUCED\"),\n      \"Related AE leading to dose modification/interruption\"\n    ),\n    CTC35 = with_label(AETOXGR %in% c(\"3\", \"4\", \"5\"), \"Grade 3-5 AE\"),\n    CTC45 = with_label(AETOXGR %in% c(\"4\", \"5\"), \"Grade 4/5 AE\"),\n    SMQ01 = with_label(SMQ01NAM != \"\", aesi_label(adae$SMQ01NAM, adae$SMQ01SC)),\n    SMQ02 = with_label(SMQ02NAM != \"\", aesi_label(adae$SMQ02NAM, adae$SMQ02SC)),\n    CQ01 = with_label(CQ01NAM != \"\", aesi_label(adae$CQ01NAM)),\n    USUBJID_AESEQ = paste(USUBJID, AESEQ, sep = \"@@\") # Create unique ID per AE in dataset.\n  ) %&gt;%\n  filter(ANL01FL == \"Y\")\n\n\n\n\n\n\nCodeaesi_vars &lt;- c(\"FATAL\", \"SER\", \"SERWD\", \"SERDSM\", \"RELSER\", \"WD\", \"DSM\", \"REL\", \"RELWD\", \"RELDSM\", \"SEV\")\n\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  Serious AE leading to withdrawal from treatment             6 (4.5%)     12 (9.0%)       9 (6.8%)   \n  Serious AE leading to dose modification/interruption       36 (26.9%)    40 (29.9%)     47 (35.6%)  \n  Related Serious AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Related AE leading to withdrawal from treatment             10 (7.5%)     9 (6.7%)      12 (9.1%)   \n  Related AE leading to dose modification/interruption       44 (32.8%)    44 (32.8%)     51 (38.6%)  \n  Severe AE (at greatest intensity)                          77 (57.5%)    70 (52.2%)     79 (59.8%)  \n\n\n\n\n\nCodeaesi_vars &lt;- c(\"FATAL\", \"SER\", \"SERWD\", \"SERDSM\", \"RELSER\", \"WD\", \"DSM\", \"REL\", \"RELWD\", \"RELDSM\", \"CTC35\")\nbasket_vars &lt;- c(\"SMQ01\", \"SMQ02\", \"CQ01\")\n\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = basket_vars,\n    table_names = \"table_aesi\",\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  Serious AE leading to withdrawal from treatment             6 (4.5%)     12 (9.0%)       9 (6.8%)   \n  Serious AE leading to dose modification/interruption       36 (26.9%)    40 (29.9%)     47 (35.6%)  \n  Related Serious AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Related AE leading to withdrawal from treatment             10 (7.5%)     9 (6.7%)      12 (9.1%)   \n  Related AE leading to dose modification/interruption       44 (32.8%)    44 (32.8%)     51 (38.6%)  \n  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  \nTotal number of patients with at least one                                                            \n  C.1.1.1.3/B.2.2.3.1 AESI (BROAD)                           58 (43.3%)    60 (44.8%)     66 (50.0%)  \n  SMQ 02 Reference Name                                           0            0              0       \n  D.2.1.5.3/A.1.1.1.1 AESI                                   62 (46.3%)    61 (45.5%)     76 (57.6%)  \n\n\n\n\n\nCodeaesi_vars &lt;- c(\"FATAL\", \"SER\", \"WD\", \"REL\", \"CTC35\", \"CTC45\")\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"WITHDRAWAL BY SUBJECT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn informed consent\"),\n    table_names = \"tot_dscsreas_wd\"\n  )\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = aesi_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  )\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients withdrawn informed consent           1 (0.7%)      1 (0.7%)       1 (0.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  \n  Grade 4/5 AE                                               77 (57.5%)    70 (52.2%)     79 (59.8%)  \n\n\n\n\n\nCodecount_subj_vars &lt;- c(\"FATAL\", \"SER\", \"WD\", \"DSM\", \"REL\", \"CTC35\")\ncount_term_vars &lt;- c(\"SER\", \"DSM\", \"REL\", \"CTC35\", \"CTC45\")\ncount_ae_vars &lt;- c(\"SER\", \"DSM\", \"REL\", \"CTC35\", \"CTC45\")\n\n# Layout for variables from adsl dataset.\nlyt_adsl &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DTHFL\" = \"Y\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of deaths\")\n  ) %&gt;%\n  count_patients_with_event(\n    \"USUBJID\",\n    filters = c(\"DCSREAS\" = \"ADVERSE EVENT\"),\n    denom = \"N_col\",\n    .labels = c(count_fraction = \"Total number of patients withdrawn from study due to an AE\"),\n    table_names = \"tot_wd\"\n  )\n\nresult_adsl &lt;- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)\n\n# Layout for variables from adae dataset.\nlyt_adae &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  analyze_num_patients(\n    vars = \"USUBJID\",\n    .stats = c(\"unique\", \"nonunique\"),\n    .labels = c(\n      unique = \"Total number of patients with at least one AE\",\n      nonunique = \"Total number of AEs\"\n    ),\n    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = \"xx\"),\n    show_labels = \"hidden\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID\",\n    flag_variables = count_subj_vars,\n    denom = \"N_col\",\n    var_labels = \"Total number of patients with at least one\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"AEDECOD\",\n    flag_variables = count_term_vars,\n    .stats = \"count\",\n    .formats = c(count = \"xx\"),\n    table_names = \"table_term\",\n    var_labels = \"Total number of unique preferred terms which are\",\n    show_labels = \"visible\"\n  ) %&gt;%\n  count_patients_with_flags(\n    \"USUBJID_AESEQ\",\n    flag_variables = count_ae_vars,\n    .stats = \"count\",\n    .formats = c(count = \"xx\"),\n    table_names = \"table_ae\",\n    var_labels = \"Total number of adverse events which are\",\n    show_labels = \"visible\"\n  )\n\nresult_adae &lt;- build_table(lyt_adae, df = adae, alt_counts_df = adsl)\n\n# Combine tables.\ncol_info(result_adsl) &lt;- col_info(result_adae)\nresult &lt;- rbind(\n  result_adae[1:2, ],\n  result_adsl,\n  result_adae[3:nrow(result_adae), ]\n)\n\nresult\n\n                                                              A: Drug X    B: Placebo   C: Combination\n                                                               (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  \nTotal number of AEs                                              502          480            604      \nTotal number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  \nTotal number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   \nTotal number of patients with at least one                                                            \n  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   \n  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  \n  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  \n  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  \n  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  \n  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  \nTotal number of unique preferred terms which are                                                      \n  Serious AE                                                      4            4              4       \n  AE leading to dose modification/interruption                   10            10             10      \n  Related AE                                                      5            5              5       \n  Grade 3-5 AE                                                    5            5              5       \n  Grade 4/5 AE                                                    3            3              3       \nTotal number of adverse events which are                                                              \n  Serious AE                                                     204          194            245      \n  AE leading to dose modification/interruption                   123          135            158      \n  Related AE                                                     231          231            290      \n  Grade 3-5 AE                                                   249          229            277      \n  Grade 4/5 AE                                                   143          134            168      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\n  add_event_flags &lt;- function(dat) {\n    dat %&gt;%\n      mutate(\n        TMPFL_SER = AESER == \"Y\",\n        TMPFL_REL = AEREL == \"Y\",\n        TMPFL_GR5 = AETOXGR == \"5\",\n        TMP_SMQ01 = !is.na(SMQ01NAM),\n        TMP_SMQ02 = !is.na(SMQ02NAM),\n        TMP_CQ01 = !is.na(CQ01NAM)\n      ) %&gt;%\n      col_relabel(\n        TMPFL_SER = \"Serious AE\",\n        TMPFL_REL = \"Related AE\",\n        TMPFL_GR5 = \"Grade 5 AE\",\n        TMP_SMQ01 = aesi_label(dat[[\"SMQ01NAM\"]], dat[[\"SMQ01SC\"]]),\n        TMP_SMQ02 = aesi_label(dat[[\"SMQ02NAM\"]], dat[[\"SMQ02SC\"]]),\n        TMP_CQ01 = aesi_label(dat[[\"CQ01NAM\"]])\n      )\n  }\n\n  # Generating user-defined event flags.\n  ADAE &lt;- ADAE %&gt;% add_event_flags()\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAE &lt;- data[[\"ADAE\"]]\nae_anl_vars &lt;- names(ADAE)[startsWith(names(ADAE), \"TMPFL_\")]\naesi_vars &lt;- names(ADAE)[startsWith(names(ADAE), \"TMP_\")]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_summary(\n      label = \"Adverse Events Summary\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(\n        choices = variable_choices(\"ADSL\", c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      flag_var_anl = choices_selected(\n        choices = variable_choices(\"ADAE\", ae_anl_vars),\n        selected = ae_anl_vars[1],\n        keep_order = TRUE,\n        fixed = FALSE\n      ),\n      flag_var_aesi = choices_selected(\n        choices = variable_choices(\"ADAE\", aesi_vars),\n        selected = aesi_vars[1],\n        keep_order = TRUE,\n        fixed = FALSE\n      ),\n      add_total = TRUE\n    )\n  )\n)\n\nWarning in rlang::hash(list(data = data, modules = modules)):\n'package:teal.modules.clinical' may not be available when loading\n\nCodeshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:46:37 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      scda_0.1.6.9017                 \n [9] dplyr_1.1.4                      tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -1307,7 +1307,7 @@
     "href": "tables/adverse-events/aet04_pi.html",
     "title": "AET04_PI",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable with Patients Treated with a Particular Treatment by Highest NCI  CTCAE Grade (specifying a treatment for selecting preferred terms)\nAdverse Events Reported in \\(\\geq\\) 5% of Patients by  Highest NCI CTCAE Grade (changing the threshold)\nAdverse Events Reported in \\(\\geq\\) 5% of Patients and \\(\\geq\\) 2% Difference Between Treatments by Highest  NCI CTCAE Grade (using more than one condition)\nAdverse Events Reported in \\(\\geq\\) 10% of Patients for Any Grade  or \\(\\geq\\) 5% of Patients for Grade 3-4 by Highest NCI CTCAE  Grade (using different condition depending on the grade)\nStandard Table with  Modified Grade Grouping\nStandard Table with  Overlapping Grade Groupings\nStandard Table without SOCs\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\npreprocess_adae &lt;- function(adae) {\n  adae %&gt;%\n    dplyr::group_by(ACTARM, USUBJID, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::summarize(\n      MAXAETOXGR = max(as.numeric(AETOXGR)),\n      .groups = \"drop\"\n    ) %&gt;%\n    dplyr::ungroup() %&gt;%\n    dplyr::mutate(\n      MAXAETOXGR = factor(MAXAETOXGR),\n      AEDECOD = droplevels(as.factor(AEDECOD))\n    )\n}\n\nfull_table_aet04_pi &lt;- function(adsl, adae_max) {\n  grade_groups &lt;- list(\n    \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n    \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n    \"Grade 5 (%)\" = \"5\"\n  )\n\n  col_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\n  basic_table() %&gt;%\n    split_cols_by(\"ACTARM\") %&gt;%\n    split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n    split_rows_by(\"AEBODSYS\",\n      child_labels = \"visible\", nested = FALSE, indent_mod = -1L,\n      split_fun = trim_levels_in_group(\"AEDECOD\")\n    ) %&gt;%\n    append_topleft(\"MedDRA System Organ Class\") %&gt;%\n    summarize_num_patients(\n      var = \"USUBJID\",\n      .stats = \"unique\",\n      .labels = \"Total number of patients with at least one adverse event\"\n    ) %&gt;%\n    analyze_vars(\n      \"AEDECOD\",\n      na.rm = FALSE,\n      denom = \"N_col\",\n      .stats = \"count_fraction\",\n      .formats = c(count_fraction = format_fraction_threshold(0.01))\n    ) %&gt;%\n    append_topleft(\"  MedDRA Preferred Term\") %&gt;%\n    build_table(adae_max, col_counts = col_counts)\n}\n\ncriteria_fun &lt;- function(tr) {\n  is(tr, \"ContentRow\")\n}\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae_max &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\") %&gt;%\n  preprocess_adae() %&gt;%\n  df_explicit_na()\n\n\n\n\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_10percent_any &lt;- has_fraction_in_any_col(atleast = 0.1, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_10percent_any))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ndcd A.1.1.1.2                    36               0              0             36               0              0             38               0              0     \ncl B.2                                                                                                                                                             \ndcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     \ndcd B.2.1.2.1                    37              37              0             33              33              0             39              39              0     \ncl D.1                                                                                                                                                             \ndcd D.1.1.1.1                    37               0             37             31               0             31             39               0             39     \ndcd D.1.1.4.2                    36              36              0             31              31              0             38              38              0     \ncl D.2                                                                                                                                                             \ndcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     \ncl B.1                                                                                                                                                             \ndcd B.1.1.1.1                    35               0             35             37               0             37             33               0             33     \ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \ncl C.1                                                                                                                                                             \ndcd C.1.1.1.3                    32              32              0             34              34              0             33              33              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses the “Any Grade” column for treatment A: Drug X with a filtering threshold at 0.37; AEs reported in greater than 37% of patients in treatment A: Drug X are shown. This can be changed by varying the parameter values in the has_fraction_in_cols function.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_37percent_any_drugx &lt;- has_fraction_in_cols(atleast = 0.37, col_indices = 1)\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_37percent_any_drugx))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ncl D.1                                                                                                                                                             \ndcd D.1.1.1.1                    37               0             37             31               0             31             39               0             39     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 with a filtering threshold at 0.40 to demonstrate the filtering ability; AEs reported in greater than 40% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.40, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ncl B.2                                                                                                                                                             \ndcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     \ncl D.2                                                                                                                                                             \ndcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     \ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 to filter for AEs reported in \\(\\geq\\) 30% of patients and AEs reported with a \\(\\geq\\) 15% difference between treatments to demonstrate the filtering ability; this can be changed by varying the parameter values in the has_fraction_in_any_col and has_fractions_difference functions.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_30percent_any &lt;- has_fraction_in_any_col(atleast = 0.3, col_indices = c(1, 4, 7))\n\nat_least_15percent_diff &lt;- has_fractions_difference(atleast = 0.15, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_30percent_any & at_least_15percent_diff))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example filters using column indices 1, 4, and 7 to include AEs reported in \\(\\geq\\) 40% of patients and using column indices 2, 5, and 8 to include grade 3-4 AEs reported in \\(\\geq\\) 20% of patients. These thresholds are chosen to demonstrate the filtering ability; they can be changed by varying parameter values in the has_fraction_in_any_col function.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 4, 7))\n\nat_least_20percent_g34 &lt;- has_fraction_in_any_col(atleast = 0.20, col_indices = c(2, 5, 8))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any | at_least_20percent_g34))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ncl B.2                                                                                                                                                             \ndcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     \ndcd B.2.1.2.1                    37              37              0             33              33              0             39              39              0     \ncl D.1                                                                                                                                                             \ndcd D.1.1.4.2                    36              36              0             31              31              0             38              38              0     \ncl D.2                                                                                                                                                             \ndcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     \ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \ncl C.1                                                                                                                                                             \ndcd C.1.1.1.3                    32              32              0             34              34              0             33              33              0     \n\n\n\n\nNote: User can specify/modify the grouping of grades, as shown in this example. In addition, the user needs to specify the column index for filtering the table. The current example uses the “Any Grade” columns with a filtering threshold at 0.40; this can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodegrade_groups &lt;- list(\n  \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n  \"Grade 1-2 (%)\" = c(\"1\", \"2\"),\n  \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n  \"Grade 5 (%)\" = \"5\"\n)\n\ncol_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\n\nfull_table &lt;- basic_table() %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    child_labels = \"visible\", nested = FALSE, indent_mod = -1L,\n    split_fun = trim_levels_in_group(\"AEDECOD\")\n  ) %&gt;%\n  append_topleft(\"MedDRA System Organ Class\") %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  analyze_vars(\n    \"AEDECOD\",\n    na.rm = FALSE,\n    denom = \"N_col\",\n    .stats = \"count_fraction\",\n    .formats = c(count_fraction = format_fraction_threshold(0.01))\n  ) %&gt;%\n  append_topleft(\"  MedDRA Preferred Term\") %&gt;%\n  build_table(adae_max, col_counts = col_counts) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 5, 9))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any))\n\nresult\n\n                                                     A: Drug X                                                    B: Placebo                                                  C: Combination                       \nMedDRA System Organ Class   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)         (N=134)        (N=134)        (N=134)         (N=134)         (N=134)        (N=134)        (N=132)         (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                                                                             \ndcd A.1.1.1.1                    37              37               0              0             34              34               0              0             48              48               0              0     \ncl B.2                                                                                                                                                                                                             \ndcd B.2.2.3.1                    36              36               0              0             40              40               0              0             39              39               0              0     \ncl D.2                                                                                                                                                                                                             \ndcd D.2.1.5.3                    35              35               0              0             43              43               0              0             43              43               0              0     \ncl C.2                                                                                                                                                                                                             \ndcd C.2.1.2.1                    26              26               0              0             36              36               0              0             42              42               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 5, and 9 with a filtering threshold at 0.40 to demonstrate the filtering ability; any grade AEs reported in greater than 40% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodegrade_groups &lt;- list(\n  \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n  \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n  \"Grade 3-5 (%)\" = c(\"3\", \"4\", \"5\"),\n  \"Grade 5 (%)\" = \"5\"\n)\n\ncol_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\nfull_table &lt;- basic_table() %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    child_labels = \"visible\", nested = FALSE, indent_mod = -1L,\n    split_fun = trim_levels_in_group(\"AEDECOD\")\n  ) %&gt;%\n  append_topleft(\"MedDRA System Organ Class\") %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  analyze_vars(\n    \"AEDECOD\",\n    na.rm = FALSE,\n    denom = \"N_col\",\n    .stats = \"count_fraction\",\n    .formats = c(count_fraction = format_fraction_threshold(0.01))\n  ) %&gt;%\n  append_topleft(\"  MedDRA Preferred Term\") %&gt;%\n  build_table(adae_max, col_counts = col_counts) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 5, 9))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any))\n\nresult\n\n                                                     A: Drug X                                                    B: Placebo                                                  C: Combination                       \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)         (N=134)        (N=134)        (N=134)         (N=134)         (N=134)        (N=134)        (N=132)         (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                                                                             \ndcd A.1.1.1.1                    37               0               0              0             34               0               0              0             48               0               0              0     \ncl B.2                                                                                                                                                                                                             \ndcd B.2.2.3.1                    36               0               0              0             40               0               0              0             39               0               0              0     \ncl D.2                                                                                                                                                                                                             \ndcd D.2.1.5.3                    35               0               0              0             43               0               0              0             43               0               0              0     \ncl C.2                                                                                                                                                                                                             \ndcd C.2.1.2.1                    26               0               0              0             36               0               0              0             42               0               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 with a filtering threshold at 0.20 to demonstrate the filtering ability; any grade AEs (Preferred Terms Only) reported in greater than 20% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodegrade_groups &lt;- list(\n  \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n  \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n  \"Grade 5 (%)\" = \"5\"\n)\n\ncol_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\n\nfull_table &lt;- basic_table() %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n  analyze_vars(\n    \"AEDECOD\",\n    na.rm = FALSE,\n    denom = \"N_col\",\n    .stats = \"count_fraction\",\n    .formats = c(count_fraction = format_fraction_threshold(0.01))\n  ) %&gt;%\n  append_topleft(\"MedDRA Preferred Term\") %&gt;%\n  build_table(adae_max, col_counts = col_counts)\n\nat_least_20percent_any &lt;- has_fraction_in_any_col(atleast = 0.2, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  prune_table(keep_rows(at_least_20percent_any)) %&gt;%\n  sort_at_path(\n    path = c(\"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nresult\n\n                                         A: Drug X                                    B: Placebo                                  C: Combination               \n                        Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\nMedDRA Preferred Term      (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ndcd D.2.1.5.3                35               0              0             43               0              0             43               0              0     \ndcd A.1.1.1.1                37               0              0             34               0              0             48               0              0     \ndcd B.2.2.3.1                36               0              0             40               0              0             39               0              0     \ndcd A.1.1.1.2                36               0              0             36               0              0             38               0              0     \ndcd B.2.1.2.1                37              37              0             33              33              0             39              39              0     \ndcd D.1.1.1.1                37               0             37             31               0             31             39               0             39     \ndcd D.1.1.4.2                36              36              0             31              31              0             38              38              0     \ndcd B.1.1.1.1                35               0             35             37               0             37             33               0             33     \ndcd C.2.1.2.1                26               0              0             36               0              0             42               0              0     \ndcd C.1.1.1.3                32              32              0             34              34              0             33              33              0     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_by_grade(\n      label = \"Adverse Events by Grade Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      col_by_grade = TRUE,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETOXGR\", \"AESEV\")),\n        selected = \"AETOXGR\"\n      ),\n      # List of column groups for grades used when `col_by_grade` = TRUE.\n      grading_groups = list(\n        `Any Grade (%)` = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n        `Grade 3-4 (%)` = c(\"3\", \"4\"),\n        `Grade 5 (%)` = \"5\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:48:40 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable with Patients Treated with a Particular Treatment by Highest NCI  CTCAE Grade (specifying a treatment for selecting preferred terms)\nAdverse Events Reported in \\(\\geq\\) 5% of Patients by  Highest NCI CTCAE Grade (changing the threshold)\nAdverse Events Reported in \\(\\geq\\) 5% of Patients and \\(\\geq\\) 2% Difference Between Treatments by Highest  NCI CTCAE Grade (using more than one condition)\nAdverse Events Reported in \\(\\geq\\) 10% of Patients for Any Grade  or \\(\\geq\\) 5% of Patients for Grade 3-4 by Highest NCI CTCAE  Grade (using different condition depending on the grade)\nStandard Table with  Modified Grade Grouping\nStandard Table with  Overlapping Grade Groupings\nStandard Table without SOCs\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\npreprocess_adae &lt;- function(adae) {\n  adae %&gt;%\n    dplyr::group_by(ACTARM, USUBJID, AEBODSYS, AEDECOD) %&gt;%\n    dplyr::summarize(\n      MAXAETOXGR = max(as.numeric(AETOXGR)),\n      .groups = \"drop\"\n    ) %&gt;%\n    dplyr::ungroup() %&gt;%\n    dplyr::mutate(\n      MAXAETOXGR = factor(MAXAETOXGR),\n      AEDECOD = droplevels(as.factor(AEDECOD))\n    )\n}\n\nfull_table_aet04_pi &lt;- function(adsl, adae_max) {\n  grade_groups &lt;- list(\n    \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n    \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n    \"Grade 5 (%)\" = \"5\"\n  )\n\n  col_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\n  basic_table() %&gt;%\n    split_cols_by(\"ACTARM\") %&gt;%\n    split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n    split_rows_by(\"AEBODSYS\",\n      child_labels = \"visible\", nested = FALSE, indent_mod = -1L,\n      split_fun = trim_levels_in_group(\"AEDECOD\")\n    ) %&gt;%\n    append_topleft(\"MedDRA System Organ Class\") %&gt;%\n    summarize_num_patients(\n      var = \"USUBJID\",\n      .stats = \"unique\",\n      .labels = \"Total number of patients with at least one adverse event\"\n    ) %&gt;%\n    analyze_vars(\n      \"AEDECOD\",\n      na.rm = FALSE,\n      denom = \"N_col\",\n      .stats = \"count_fraction\",\n      .formats = c(count_fraction = format_fraction_threshold(0.01))\n    ) %&gt;%\n    append_topleft(\"  MedDRA Preferred Term\") %&gt;%\n    build_table(adae_max, col_counts = col_counts)\n}\n\ncriteria_fun &lt;- function(tr) {\n  is(tr, \"ContentRow\")\n}\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadae_max &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\") %&gt;%\n  preprocess_adae() %&gt;%\n  df_explicit_na()\n\n\n\n\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_10percent_any &lt;- has_fraction_in_any_col(atleast = 0.1, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_10percent_any))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ndcd A.1.1.1.2                    36               0              0             36               0              0             38               0              0     \ncl B.2                                                                                                                                                             \ndcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     \ndcd B.2.1.2.1                    37              37              0             33              33              0             39              39              0     \ncl D.1                                                                                                                                                             \ndcd D.1.1.1.1                    37               0             37             31               0             31             39               0             39     \ndcd D.1.1.4.2                    36              36              0             31              31              0             38              38              0     \ncl D.2                                                                                                                                                             \ndcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     \ncl B.1                                                                                                                                                             \ndcd B.1.1.1.1                    35               0             35             37               0             37             33               0             33     \ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \ncl C.1                                                                                                                                                             \ndcd C.1.1.1.3                    32              32              0             34              34              0             33              33              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses the “Any Grade” column for treatment A: Drug X with a filtering threshold at 0.37; AEs reported in greater than 37% of patients in treatment A: Drug X are shown. This can be changed by varying the parameter values in the has_fraction_in_cols function.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_37percent_any_drugx &lt;- has_fraction_in_cols(atleast = 0.37, col_indices = 1)\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_37percent_any_drugx))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ncl D.1                                                                                                                                                             \ndcd D.1.1.1.1                    37               0             37             31               0             31             39               0             39     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 with a filtering threshold at 0.40 to demonstrate the filtering ability; AEs reported in greater than 40% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.40, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ncl B.2                                                                                                                                                             \ndcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     \ncl D.2                                                                                                                                                             \ndcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     \ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 to filter for AEs reported in \\(\\geq\\) 30% of patients and AEs reported with a \\(\\geq\\) 15% difference between treatments to demonstrate the filtering ability; this can be changed by varying the parameter values in the has_fraction_in_any_col and has_fractions_difference functions.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_30percent_any &lt;- has_fraction_in_any_col(atleast = 0.3, col_indices = c(1, 4, 7))\n\nat_least_15percent_diff &lt;- has_fractions_difference(atleast = 0.15, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_30percent_any & at_least_15percent_diff))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example filters using column indices 1, 4, and 7 to include AEs reported in \\(\\geq\\) 40% of patients and using column indices 2, 5, and 8 to include grade 3-4 AEs reported in \\(\\geq\\) 20% of patients. These thresholds are chosen to demonstrate the filtering ability; they can be changed by varying parameter values in the has_fraction_in_any_col function.\n\nCodefull_table &lt;- full_table_aet04_pi(adsl, adae_max) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 4, 7))\n\nat_least_20percent_g34 &lt;- has_fraction_in_any_col(atleast = 0.20, col_indices = c(2, 5, 8))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any | at_least_20percent_g34))\n\nresult\n\n                                             A: Drug X                                    B: Placebo                                  C: Combination               \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                             \ndcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     \ncl B.2                                                                                                                                                             \ndcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     \ndcd B.2.1.2.1                    37              37              0             33              33              0             39              39              0     \ncl D.1                                                                                                                                                             \ndcd D.1.1.4.2                    36              36              0             31              31              0             38              38              0     \ncl D.2                                                                                                                                                             \ndcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     \ncl C.2                                                                                                                                                             \ndcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     \ncl C.1                                                                                                                                                             \ndcd C.1.1.1.3                    32              32              0             34              34              0             33              33              0     \n\n\n\n\nNote: User can specify/modify the grouping of grades, as shown in this example. In addition, the user needs to specify the column index for filtering the table. The current example uses the “Any Grade” columns with a filtering threshold at 0.40; this can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodegrade_groups &lt;- list(\n  \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n  \"Grade 1-2 (%)\" = c(\"1\", \"2\"),\n  \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n  \"Grade 5 (%)\" = \"5\"\n)\n\ncol_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\n\nfull_table &lt;- basic_table() %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    child_labels = \"visible\", nested = FALSE, indent_mod = -1L,\n    split_fun = trim_levels_in_group(\"AEDECOD\")\n  ) %&gt;%\n  append_topleft(\"MedDRA System Organ Class\") %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  analyze_vars(\n    \"AEDECOD\",\n    na.rm = FALSE,\n    denom = \"N_col\",\n    .stats = \"count_fraction\",\n    .formats = c(count_fraction = format_fraction_threshold(0.01))\n  ) %&gt;%\n  append_topleft(\"  MedDRA Preferred Term\") %&gt;%\n  build_table(adae_max, col_counts = col_counts) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 5, 9))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any))\n\nresult\n\n                                                     A: Drug X                                                    B: Placebo                                                  C: Combination                       \nMedDRA System Organ Class   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)         (N=134)        (N=134)        (N=134)         (N=134)         (N=134)        (N=134)        (N=132)         (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                                                                             \ndcd A.1.1.1.1                    37              37               0              0             34              34               0              0             48              48               0              0     \ncl B.2                                                                                                                                                                                                             \ndcd B.2.2.3.1                    36              36               0              0             40              40               0              0             39              39               0              0     \ncl D.2                                                                                                                                                                                                             \ndcd D.2.1.5.3                    35              35               0              0             43              43               0              0             43              43               0              0     \ncl C.2                                                                                                                                                                                                             \ndcd C.2.1.2.1                    26              26               0              0             36              36               0              0             42              42               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 5, and 9 with a filtering threshold at 0.40 to demonstrate the filtering ability; any grade AEs reported in greater than 40% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodegrade_groups &lt;- list(\n  \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n  \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n  \"Grade 3-5 (%)\" = c(\"3\", \"4\", \"5\"),\n  \"Grade 5 (%)\" = \"5\"\n)\n\ncol_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\nfull_table &lt;- basic_table() %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n  split_rows_by(\"AEBODSYS\",\n    child_labels = \"visible\", nested = FALSE, indent_mod = -1L,\n    split_fun = trim_levels_in_group(\"AEDECOD\")\n  ) %&gt;%\n  append_topleft(\"MedDRA System Organ Class\") %&gt;%\n  summarize_num_patients(\n    var = \"USUBJID\",\n    .stats = \"unique\",\n    .labels = \"Total number of patients with at least one adverse event\"\n  ) %&gt;%\n  analyze_vars(\n    \"AEDECOD\",\n    na.rm = FALSE,\n    denom = \"N_col\",\n    .stats = \"count_fraction\",\n    .formats = c(count_fraction = format_fraction_threshold(0.01))\n  ) %&gt;%\n  append_topleft(\"  MedDRA Preferred Term\") %&gt;%\n  build_table(adae_max, col_counts = col_counts) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\"),\n    scorefun = score_occurrences_cont_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  ) %&gt;%\n  sort_at_path(\n    path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 5, 9)),\n    decreasing = TRUE\n  )\n\nat_least_40percent_any &lt;- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 5, 9))\n\nresult &lt;- full_table %&gt;%\n  trim_rows(criteria = criteria_fun) %&gt;%\n  prune_table(keep_rows(at_least_40percent_any))\n\nresult\n\n                                                     A: Drug X                                                    B: Placebo                                                  C: Combination                       \nMedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)\n  MedDRA Preferred Term        (N=134)         (N=134)         (N=134)        (N=134)        (N=134)         (N=134)         (N=134)        (N=134)        (N=132)         (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ncl A.1                                                                                                                                                                                                             \ndcd A.1.1.1.1                    37               0               0              0             34               0               0              0             48               0               0              0     \ncl B.2                                                                                                                                                                                                             \ndcd B.2.2.3.1                    36               0               0              0             40               0               0              0             39               0               0              0     \ncl D.2                                                                                                                                                                                                             \ndcd D.2.1.5.3                    35               0               0              0             43               0               0              0             43               0               0              0     \ncl C.2                                                                                                                                                                                                             \ndcd C.2.1.2.1                    26               0               0              0             36               0               0              0             42               0               0              0     \n\n\n\n\nNote: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 with a filtering threshold at 0.20 to demonstrate the filtering ability; any grade AEs (Preferred Terms Only) reported in greater than 20% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.\n\nCodegrade_groups &lt;- list(\n  \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n  \"Grade 3-4 (%)\" = c(\"3\", \"4\"),\n  \"Grade 5 (%)\" = \"5\"\n)\n\ncol_counts &lt;- rep(table(adsl$ACTARM), each = length(grade_groups))\n\nfull_table &lt;- basic_table() %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_cols_by_groups(\"MAXAETOXGR\", groups_list = grade_groups) %&gt;%\n  analyze_vars(\n    \"AEDECOD\",\n    na.rm = FALSE,\n    denom = \"N_col\",\n    .stats = \"count_fraction\",\n    .formats = c(count_fraction = format_fraction_threshold(0.01))\n  ) %&gt;%\n  append_topleft(\"MedDRA Preferred Term\") %&gt;%\n  build_table(adae_max, col_counts = col_counts)\n\nat_least_20percent_any &lt;- has_fraction_in_any_col(atleast = 0.2, col_indices = c(1, 4, 7))\n\nresult &lt;- full_table %&gt;%\n  prune_table(keep_rows(at_least_20percent_any)) %&gt;%\n  sort_at_path(\n    path = c(\"AEDECOD\"),\n    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),\n    decreasing = TRUE\n  )\n\nresult\n\n                                         A: Drug X                                    B: Placebo                                  C: Combination               \n                        Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)\nMedDRA Preferred Term      (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\ndcd D.2.1.5.3                35               0              0             43               0              0             43               0              0     \ndcd A.1.1.1.1                37               0              0             34               0              0             48               0              0     \ndcd B.2.2.3.1                36               0              0             40               0              0             39               0              0     \ndcd A.1.1.1.2                36               0              0             36               0              0             38               0              0     \ndcd B.2.1.2.1                37              37              0             33              33              0             39              39              0     \ndcd D.1.1.1.1                37               0             37             31               0             31             39               0             39     \ndcd D.1.1.4.2                36              36              0             31              31              0             38              38              0     \ndcd B.1.1.1.1                35               0             35             37               0             37             33               0             33     \ndcd C.2.1.2.1                26               0              0             36               0              0             42               0              0     \ndcd C.1.1.1.3                32              32              0             34              34              0             33              33              0     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAE &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADAE &lt;- data[[\"ADAE\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_events_by_grade(\n      label = \"Adverse Events by Grade Table\",\n      dataname = \"ADAE\",\n      arm_var = choices_selected(c(\"ARM\", \"ARMCD\"), \"ARM\"),\n      col_by_grade = TRUE,\n      llt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETERM\", \"AEDECOD\")),\n        selected = c(\"AEDECOD\")\n      ),\n      hlt = choices_selected(\n        choices = variable_choices(ADAE, c(\"AEBODSYS\", \"AESOC\")),\n        selected = \"AEBODSYS\"\n      ),\n      grade = choices_selected(\n        choices = variable_choices(ADAE, c(\"AETOXGR\", \"AESEV\")),\n        selected = \"AETOXGR\"\n      ),\n      # List of column groups for grades used when `col_by_grade` = TRUE.\n      grading_groups = list(\n        `Any Grade (%)` = c(\"1\", \"2\", \"3\", \"4\", \"5\"),\n        `Grade 3-4 (%)` = c(\"3\", \"4\"),\n        `Grade 5 (%)` = \"5\"\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:47:15 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Adverse Events",
@@ -1319,7 +1319,7 @@
     "href": "tables/vital-signs/vst01.html",
     "title": "VST01",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadvs &lt;- df_explicit_na(advs)\n\nadvs_label &lt;- var_labels(advs)\n\nadvs &lt;- advs %&gt;%\n  filter(\n    PARAMCD == \"DIABP\",\n    PARAM == \"Diastolic Blood Pressure\"\n  ) %&gt;%\n  mutate(\n    PARAMCD = droplevels(PARAMCD),\n    PARAM = droplevels(PARAM)\n  )\n\n# post-baseline\nadvs_pb &lt;- advs %&gt;%\n  filter(ABLFL != \"Y\", ABLFL2 != \"Y\")\n\nadvs_pb_max &lt;- advs_pb %&gt;%\n  group_by(PARAM, USUBJID) %&gt;%\n  arrange(desc(AVAL)) %&gt;%\n  slice(1) %&gt;%\n  ungroup() %&gt;%\n  mutate(AVISIT = \"Post-Baseline Maximum\")\n\nadvs_pb_min &lt;- advs_pb %&gt;%\n  group_by(PARAM, USUBJID) %&gt;%\n  arrange(AVAL) %&gt;%\n  slice(1) %&gt;%\n  ungroup() %&gt;%\n  mutate(AVISIT = \"Post-Baseline Minimum\")\n\nadvs_pb_last &lt;- advs_pb %&gt;%\n  group_by(PARAM, USUBJID) %&gt;%\n  arrange(desc(AVISITN)) %&gt;%\n  slice(1) %&gt;%\n  ungroup() %&gt;%\n  mutate(AVISIT = \"Post-Baseline Last\")\n\n# Please note that for real data, per ADaM Spec 1.1, the advs_f can be obtained by filtering on PARAMCD, then\n# ANL01FL == 'Y' or AVISIT in c('POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST')\n\nadvs_f &lt;- rbind(\n  advs,\n  advs_pb_last,\n  advs_pb_min,\n  advs_pb_max\n)\n\nadvs_f &lt;- advs_f %&gt;%\n  mutate(AVISIT = droplevels(AVISIT))\n\nvar_labels(advs_f) &lt;- advs_label\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = \"\\n\\nAnalysis Visit\") %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt = lyt, df = advs_f, alt_counts_df = adsl)\nresult\n\n                                   A: Drug X                        B: Placebo                      C: Combination         \n                                          Change from                       Change from                       Change from  \n                        Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   \nAnalysis Visit             (N=134)          (N=134)          (N=134)          (N=134)          (N=132)          (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nSCREENING                                                                                                                  \n  n                          134               0               134               0               132               0       \n  Mean (SD)             99.92 (17.99)       NE (NE)       101.88 (21.07)      NE (NE)       100.48 (19.07)      NE (NE)    \n  Median                    99.26              NE             100.22             NE             99.10              NE      \n  Min - Max             54.22 - 152.98      NE - NE       48.15 - 148.03      NE - NE       42.22 - 149.95      NE - NE    \nBASELINE                                                                                                                   \n  n                          134                               134                               132                       \n  Mean (SD)             96.50 (19.90)                     101.10 (19.87)                    102.77 (19.48)                 \n  Median                    96.05                             100.45                            102.01                     \n  Min - Max             44.28 - 136.59                    29.21 - 143.77                    49.37 - 153.50                 \nWEEK 1 DAY 8                                                                                                               \n  n                          134              134              134              134              132              132      \n  Mean (SD)             100.65 (18.79)    4.14 (26.93)    99.19 (19.36)    -1.92 (27.37)    97.14 (19.72)    -5.63 (25.95) \n  Median                    100.22            1.52            99.37            -2.79            94.19            -5.57     \n  Min - Max             57.60 - 147.46   -50.92 - 74.84   59.28 - 141.21   -64.50 - 70.93   50.98 - 142.47   -55.15 - 56.11\nWEEK 2 DAY 15                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             102.09 (19.62)    5.58 (29.38)    99.29 (21.11)    -1.81 (31.11)    99.95 (20.85)    -2.81 (29.10) \n  Median                    103.59            7.82            100.57           -1.77            102.52           -5.68     \n  Min - Max             54.73 - 150.85   -59.71 - 98.08   51.60 - 145.28   -71.00 - 92.43   37.06 - 138.92   -73.10 - 76.80\nWEEK 3 DAY 22                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             101.78 (19.54)    5.28 (27.16)    97.73 (19.85)    -3.37 (27.76)    99.86 (19.15)    -2.91 (27.25) \n  Median                    100.39            6.53            98.83            -5.79            100.06           -1.54     \n  Min - Max             47.68 - 162.22   -64.46 - 76.64   36.25 - 142.78   -70.23 - 84.74   53.80 - 146.37   -74.30 - 67.46\nWEEK 4 DAY 29                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             100.18 (20.18)    3.68 (27.21)    99.06 (17.75)    -2.04 (26.59)    99.27 (20.12)    -3.50 (27.62) \n  Median                    98.17             3.00            97.98            -1.89            99.61            -0.46     \n  Min - Max             48.52 - 153.41   -77.30 - 61.90   56.78 - 142.45   -82.96 - 89.87   46.87 - 146.12   -88.68 - 70.90\nWEEK 5 DAY 36                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             101.42 (18.83)    4.92 (28.02)    95.92 (19.90)    -5.19 (29.34)    97.73 (18.92)    -5.04 (26.64) \n  Median                    98.56             3.93            94.30            -7.18            98.09            -5.62     \n  Min - Max             62.66 - 155.52   -63.05 - 78.66   51.06 - 151.52   -78.07 - 72.91   51.48 - 157.27   -76.13 - 86.42\nPost-Baseline Last                                                                                                         \n  n                          134              134              134              134              132              132      \n  Mean (SD)             101.42 (18.83)    4.92 (28.02)    95.92 (19.90)    -5.19 (29.34)    97.73 (18.92)    -5.04 (26.64) \n  Median                    98.56             3.93            94.30            -7.18            98.09            -5.62     \n  Min - Max             62.66 - 155.52   -63.05 - 78.66   51.06 - 151.52   -78.07 - 72.91   51.48 - 157.27   -76.13 - 86.42\nPost-Baseline Minimum                                                                                                      \n  n                          134              134              134              134              132              132      \n  Mean (SD)             78.98 (11.94)    -17.52 (23.92)   75.77 (13.25)    -25.33 (25.39)   76.35 (13.13)    -26.41 (22.78)\n  Median                    80.14            -13.96           75.71            -26.70           76.26            -27.17    \n  Min - Max             47.68 - 110.54   -77.30 - 33.00   36.25 - 113.53   -82.96 - 69.23   37.06 - 112.35   -88.68 - 34.24\nPost-Baseline Maximum                                                                                                      \n  n                          134              134              134              134              132              132      \n  Mean (SD)             125.08 (12.28)   28.57 (23.20)    121.33 (12.01)   20.23 (23.64)    121.42 (12.09)   18.65 (22.74) \n  Median                    124.20           29.14            120.80           18.74            122.55           16.93     \n  Min - Max             94.06 - 162.22   -21.49 - 98.08   94.43 - 151.52   -34.95 - 92.43   79.39 - 157.27   -43.32 - 86.42\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADVS &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n  advs_label &lt;- col_labels(ADVS)\n\n  # post-baseline\n  advs_pb &lt;- ADVS %&gt;%\n    filter(\n      ABLFL != \"Y\",\n      ABLFL2 != \"Y\"\n    )\n\n  advs_pb_max &lt;- advs_pb %&gt;%\n    group_by(PARAM, USUBJID) %&gt;%\n    arrange(desc(AVAL)) %&gt;%\n    slice(1) %&gt;%\n    ungroup() %&gt;%\n    mutate(AVISIT = \"Post-Baseline Maximum\")\n\n  advs_pb_min &lt;- advs_pb %&gt;%\n    group_by(PARAM, USUBJID) %&gt;%\n    arrange(AVAL) %&gt;%\n    slice(1) %&gt;%\n    ungroup() %&gt;%\n    mutate(AVISIT = \"Post-Baseline Minimum\")\n\n  advs_pb_last &lt;- advs_pb %&gt;%\n    group_by(PARAM, USUBJID) %&gt;%\n    arrange(desc(AVISITN)) %&gt;%\n    slice(1) %&gt;%\n    ungroup() %&gt;%\n    mutate(AVISIT = \"Post-Baseline Last\")\n\n  ADVS &lt;- rbind(\n    ADVS,\n    advs_pb_last,\n    advs_pb_min,\n    advs_pb_max\n  ) %&gt;%\n    mutate(AVISIT = droplevels(AVISIT))\n\n  col_labels(ADVS) &lt;- advs_label\n})\ndatanames &lt;- c(\"ADSL\", \"ADVS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADVS &lt;- data[[\"ADVS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Vital Sign Results and Change from Baseline by Visit\",\n      dataname = \"ADVS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADVS, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\", \"PARAM\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADVS, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\", \"CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADVS, \"PARAMCD\", \"PARAM\"),\n        selected = \"DIABP\"\n      ),\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADVS\", \"AVAL\", selected = NULL))\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADVS-filter-ADVS_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:49:16 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadvs &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadvs &lt;- df_explicit_na(advs)\n\nadvs_label &lt;- var_labels(advs)\n\nadvs &lt;- advs %&gt;%\n  filter(\n    PARAMCD == \"DIABP\",\n    PARAM == \"Diastolic Blood Pressure\"\n  ) %&gt;%\n  mutate(\n    PARAMCD = droplevels(PARAMCD),\n    PARAM = droplevels(PARAM)\n  )\n\n# post-baseline\nadvs_pb &lt;- advs %&gt;%\n  filter(ABLFL != \"Y\", ABLFL2 != \"Y\")\n\nadvs_pb_max &lt;- advs_pb %&gt;%\n  group_by(PARAM, USUBJID) %&gt;%\n  arrange(desc(AVAL)) %&gt;%\n  slice(1) %&gt;%\n  ungroup() %&gt;%\n  mutate(AVISIT = \"Post-Baseline Maximum\")\n\nadvs_pb_min &lt;- advs_pb %&gt;%\n  group_by(PARAM, USUBJID) %&gt;%\n  arrange(AVAL) %&gt;%\n  slice(1) %&gt;%\n  ungroup() %&gt;%\n  mutate(AVISIT = \"Post-Baseline Minimum\")\n\nadvs_pb_last &lt;- advs_pb %&gt;%\n  group_by(PARAM, USUBJID) %&gt;%\n  arrange(desc(AVISITN)) %&gt;%\n  slice(1) %&gt;%\n  ungroup() %&gt;%\n  mutate(AVISIT = \"Post-Baseline Last\")\n\n# Please note that for real data, per ADaM Spec 1.1, the advs_f can be obtained by filtering on PARAMCD, then\n# ANL01FL == 'Y' or AVISIT in c('POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST')\n\nadvs_f &lt;- rbind(\n  advs,\n  advs_pb_last,\n  advs_pb_min,\n  advs_pb_max\n)\n\nadvs_f &lt;- advs_f %&gt;%\n  mutate(AVISIT = droplevels(AVISIT))\n\nvar_labels(advs_f) &lt;- advs_label\n\n\n\n\n\nCodesplit_fun &lt;- drop_split_levels\n\nafun &lt;- function(x, .var, .spl_context, ...) {\n  n_fun &lt;- sum(!is.na(x), na.rm = TRUE)\n  if (n_fun == 0) {\n    mean_sd_fun &lt;- c(NA, NA)\n    median_fun &lt;- NA\n    min_max_fun &lt;- c(NA, NA)\n  } else {\n    mean_sd_fun &lt;- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))\n    median_fun &lt;- median(x, na.rm = TRUE)\n    min_max_fun &lt;- c(min(x), max(x))\n  }\n  is_chg &lt;- .var == \"CHG\"\n  is_baseline &lt;- .spl_context$value[which(.spl_context$split == \"AVISIT\")] == \"BASELINE\"\n  if (is_baseline && is_chg) n_fun &lt;- mean_sd_fun &lt;- median_fun &lt;- min_max_fun &lt;- NULL\n\n  in_rows(\n    \"n\" = n_fun,\n    \"Mean (SD)\" = mean_sd_fun,\n    \"Median\" = median_fun,\n    \"Min - Max\" = min_max_fun,\n    .formats = list(\"n\" = \"xx\", \"Mean (SD)\" = \"xx.xx (xx.xx)\", \"Median\" = \"xx.xx\", \"Min - Max\" = \"xx.xx - xx.xx\"),\n    .format_na_strs = list(\"n\" = \"NE\", \"Mean (SD)\" = \"NE (NE)\", \"Median\" = \"NE\", \"Min - Max\" = \"NE - NE\")\n  )\n}\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = \"\\n\\nAnalysis Visit\") %&gt;%\n  split_cols_by_multivar(\n    vars = c(\"AVAL\", \"CHG\"),\n    varlabels = c(\"Value at Visit\", \"Change from\\nBaseline\")\n  ) %&gt;%\n  analyze_colvars(afun = afun)\n\nresult &lt;- build_table(lyt = lyt, df = advs_f, alt_counts_df = adsl)\nresult\n\n                                   A: Drug X                        B: Placebo                      C: Combination         \n                                          Change from                       Change from                       Change from  \n                        Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   \nAnalysis Visit             (N=134)          (N=134)          (N=134)          (N=134)          (N=132)          (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nSCREENING                                                                                                                  \n  n                          134               0               134               0               132               0       \n  Mean (SD)             99.92 (17.99)       NE (NE)       101.88 (21.07)      NE (NE)       100.48 (19.07)      NE (NE)    \n  Median                    99.26              NE             100.22             NE             99.10              NE      \n  Min - Max             54.22 - 152.98      NE - NE       48.15 - 148.03      NE - NE       42.22 - 149.95      NE - NE    \nBASELINE                                                                                                                   \n  n                          134                               134                               132                       \n  Mean (SD)             96.50 (19.90)                     101.10 (19.87)                    102.77 (19.48)                 \n  Median                    96.05                             100.45                            102.01                     \n  Min - Max             44.28 - 136.59                    29.21 - 143.77                    49.37 - 153.50                 \nWEEK 1 DAY 8                                                                                                               \n  n                          134              134              134              134              132              132      \n  Mean (SD)             100.65 (18.79)    4.14 (26.93)    99.19 (19.36)    -1.92 (27.37)    97.14 (19.72)    -5.63 (25.95) \n  Median                    100.22            1.52            99.37            -2.79            94.19            -5.57     \n  Min - Max             57.60 - 147.46   -50.92 - 74.84   59.28 - 141.21   -64.50 - 70.93   50.98 - 142.47   -55.15 - 56.11\nWEEK 2 DAY 15                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             102.09 (19.62)    5.58 (29.38)    99.29 (21.11)    -1.81 (31.11)    99.95 (20.85)    -2.81 (29.10) \n  Median                    103.59            7.82            100.57           -1.77            102.52           -5.68     \n  Min - Max             54.73 - 150.85   -59.71 - 98.08   51.60 - 145.28   -71.00 - 92.43   37.06 - 138.92   -73.10 - 76.80\nWEEK 3 DAY 22                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             101.78 (19.54)    5.28 (27.16)    97.73 (19.85)    -3.37 (27.76)    99.86 (19.15)    -2.91 (27.25) \n  Median                    100.39            6.53            98.83            -5.79            100.06           -1.54     \n  Min - Max             47.68 - 162.22   -64.46 - 76.64   36.25 - 142.78   -70.23 - 84.74   53.80 - 146.37   -74.30 - 67.46\nWEEK 4 DAY 29                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             100.18 (20.18)    3.68 (27.21)    99.06 (17.75)    -2.04 (26.59)    99.27 (20.12)    -3.50 (27.62) \n  Median                    98.17             3.00            97.98            -1.89            99.61            -0.46     \n  Min - Max             48.52 - 153.41   -77.30 - 61.90   56.78 - 142.45   -82.96 - 89.87   46.87 - 146.12   -88.68 - 70.90\nWEEK 5 DAY 36                                                                                                              \n  n                          134              134              134              134              132              132      \n  Mean (SD)             101.42 (18.83)    4.92 (28.02)    95.92 (19.90)    -5.19 (29.34)    97.73 (18.92)    -5.04 (26.64) \n  Median                    98.56             3.93            94.30            -7.18            98.09            -5.62     \n  Min - Max             62.66 - 155.52   -63.05 - 78.66   51.06 - 151.52   -78.07 - 72.91   51.48 - 157.27   -76.13 - 86.42\nPost-Baseline Last                                                                                                         \n  n                          134              134              134              134              132              132      \n  Mean (SD)             101.42 (18.83)    4.92 (28.02)    95.92 (19.90)    -5.19 (29.34)    97.73 (18.92)    -5.04 (26.64) \n  Median                    98.56             3.93            94.30            -7.18            98.09            -5.62     \n  Min - Max             62.66 - 155.52   -63.05 - 78.66   51.06 - 151.52   -78.07 - 72.91   51.48 - 157.27   -76.13 - 86.42\nPost-Baseline Minimum                                                                                                      \n  n                          134              134              134              134              132              132      \n  Mean (SD)             78.98 (11.94)    -17.52 (23.92)   75.77 (13.25)    -25.33 (25.39)   76.35 (13.13)    -26.41 (22.78)\n  Median                    80.14            -13.96           75.71            -26.70           76.26            -27.17    \n  Min - Max             47.68 - 110.54   -77.30 - 33.00   36.25 - 113.53   -82.96 - 69.23   37.06 - 112.35   -88.68 - 34.24\nPost-Baseline Maximum                                                                                                      \n  n                          134              134              134              134              132              132      \n  Mean (SD)             125.08 (12.28)   28.57 (23.20)    121.33 (12.01)   20.23 (23.64)    121.42 (12.09)   18.65 (22.74) \n  Median                    124.20           29.14            120.80           18.74            122.55           16.93     \n  Min - Max             94.06 - 162.22   -21.49 - 98.08   94.43 - 151.52   -34.95 - 92.43   79.39 - 157.27   -43.32 - 86.42\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADVS &lt;- synthetic_cdisc_dataset(\"latest\", \"advs\")\n  advs_label &lt;- col_labels(ADVS)\n\n  # post-baseline\n  advs_pb &lt;- ADVS %&gt;%\n    filter(\n      ABLFL != \"Y\",\n      ABLFL2 != \"Y\"\n    )\n\n  advs_pb_max &lt;- advs_pb %&gt;%\n    group_by(PARAM, USUBJID) %&gt;%\n    arrange(desc(AVAL)) %&gt;%\n    slice(1) %&gt;%\n    ungroup() %&gt;%\n    mutate(AVISIT = \"Post-Baseline Maximum\")\n\n  advs_pb_min &lt;- advs_pb %&gt;%\n    group_by(PARAM, USUBJID) %&gt;%\n    arrange(AVAL) %&gt;%\n    slice(1) %&gt;%\n    ungroup() %&gt;%\n    mutate(AVISIT = \"Post-Baseline Minimum\")\n\n  advs_pb_last &lt;- advs_pb %&gt;%\n    group_by(PARAM, USUBJID) %&gt;%\n    arrange(desc(AVISITN)) %&gt;%\n    slice(1) %&gt;%\n    ungroup() %&gt;%\n    mutate(AVISIT = \"Post-Baseline Last\")\n\n  ADVS &lt;- rbind(\n    ADVS,\n    advs_pb_last,\n    advs_pb_min,\n    advs_pb_max\n  ) %&gt;%\n    mutate(AVISIT = droplevels(AVISIT))\n\n  col_labels(ADVS) &lt;- advs_label\n})\ndatanames &lt;- c(\"ADSL\", \"ADVS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADVS &lt;- data[[\"ADVS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Vital Sign Results and Change from Baseline by Visit\",\n      dataname = \"ADVS\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADVS, c(\"PARAM\", \"AVISIT\")),\n        selected = c(\"AVISIT\", \"PARAM\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADVS, c(\"AVAL\", \"CHG\")),\n        selected = c(\"AVAL\", \"CHG\")\n      ),\n      useNA = \"ifany\",\n      paramcd = choices_selected(\n        choices = value_choices(ADVS, \"PARAMCD\", \"PARAM\"),\n        selected = \"DIABP\"\n      ),\n      parallel_vars = TRUE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADVS\", \"AVAL\", selected = NULL))\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: The 'plotly_relayout' event tied a source ID of\n'teal-main_ui-filter_panel-active-ADVS-filter-ADVS_AVAL-inputs-histogram_plot'\nis not registered. In order to obtain this event data, please add\n`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to\nobtain event data from.\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:47:50 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] callr_3.7.5              vctrs_0.6.5              pkgconfig_2.0.3         \n[13] fastmap_1.1.1            backports_1.4.1          ellipsis_0.3.2          \n[16] fontawesome_0.5.2        utf8_1.2.4               promises_1.2.1          \n[19] rmarkdown_2.26           ps_1.7.6                 purrr_1.0.2             \n[22] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[25] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[28] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[31] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[34] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[58] plotly_4.10.4            generics_0.1.3           chromote_0.2.0          \n[61] ggplot2_3.5.0            munsell_0.5.0            scales_1.3.0            \n[64] xtable_1.8-4             glue_1.7.0               emmeans_1.10.0          \n[67] lazyeval_0.2.2           tools_4.3.3              webshot2_0.1.1          \n[70] data.table_1.15.2        webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            grid_4.3.3               tidyr_1.3.1             \n[76] rbibutils_2.2.16         colorspace_2.1-0         nlme_3.1-164            \n[79] cli_3.6.2                fansi_1.0.6              viridisLite_0.4.2       \n[82] geepack_1.3.10           gtable_0.3.4             sass_0.4.8              \n[85] digest_0.6.34            TH.data_1.1-2            htmlwidgets_1.6.4       \n[88] memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4         \n[91] httr_1.4.7               shinyWidgets_0.8.2       mime_0.12               \n[94] MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Vital Signs",
@@ -1331,7 +1331,7 @@
     "href": "tables/safety/enrollment01.html",
     "title": "ENTXX",
     "section": "",
-    "text": "Data Setup\nENT01 – Enrollment by Region,  Country, and Investigator Number\nENT01A – Enrollment by Country  and Investigator Number\nENT02 – Enrollment by Region, Country,  and Investigator Number/Name\nENT02A – Enrollment by Country  and Investigator Number/Name\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(forcats)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\nadsl &lt;- adsl %&gt;%\n  mutate(\n    REGION1 = droplevels(REGION1),\n    COUNTRY = droplevels(COUNTRY),\n    INVID_INVNAM = paste(INVID, INVNAM, sep = \" / \")\n  ) %&gt;%\n  mutate(\n    INVID = factor(INVID),\n    SITEID = factor(SITEID),\n    INVNAM = factor(INVNAM),\n    INVID_INVNAM = factor(INVID_INVNAM)\n  ) %&gt;%\n  var_relabel(\n    INVID_INVNAM = \"Investigator Number/Name\",\n    REGION1 = \"Geographic Region 1\",\n    COUNTRY = \"Country\",\n    INVID = \"Investigator Identifier\"\n  ) %&gt;%\n  arrange(REGION1, COUNTRY, INVID)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"REGION1\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$REGION1)) %&gt;%\n  summarize_row_groups() %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID\", 2L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nGeographic Region 1                                                                  \n  Country                     A: Drug X    B: Placebo   C: Combination   All Patients\n    Investigator Identifier    (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————\nAfrica                         8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  NGA                          8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n    INV ID NGA-1              3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n    INV ID NGA-11             1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n    INV ID NGA-12                 0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n    INV ID NGA-17              2 (25%)         0              0            2 (7.7%)  \n    INV ID NGA-2              1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n    INV ID NGA-4              1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n    INV ID NGA-5                  0            0           1 (9.1%)        1 (3.8%)  \n    INV ID NGA-6                  0            0           1 (9.1%)        1 (3.8%)  \nAsia                          91 (67.9%)   94 (70.1%)     83 (62.9%)     268 (67.0%) \n  CHN                         74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n    INV ID CHN-1              21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n    INV ID CHN-10                 0         1 (1.2%)          0            1 (0.5%)  \n    INV ID CHN-11             12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n    INV ID CHN-12              4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n    INV ID CHN-13              2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n    INV ID CHN-14              4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-15              2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n    INV ID CHN-16                 0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n    INV ID CHN-17              4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n    INV ID CHN-18              1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n    INV ID CHN-2              9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n    INV ID CHN-3               5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n    INV ID CHN-4               3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-5               4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n    INV ID CHN-6               1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n    INV ID CHN-7                  0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n    INV ID CHN-8               1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n    INV ID CHN-9               1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \n  PAK                         12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n    INV ID PAK-1              2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n    INV ID PAK-11             4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n    INV ID PAK-12             2 (16.7%)        0              0            2 (6.5%)  \n    INV ID PAK-13              1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n    INV ID PAK-14              1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-15              1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-2               1 (8.3%)        0           2 (20%)         3 (9.7%)  \n    INV ID PAK-4                  0        1 (11.1%)       1 (10%)         2 (6.5%)  \n    INV ID PAK-5                  0            0           1 (10%)         1 (3.2%)  \n  JPN                          5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n    INV ID JPN-1               2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n    INV ID JPN-11                 0            0          3 (33.3%)       3 (16.7%)  \n    INV ID JPN-12                 0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-14                 0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-17                 0            0          2 (22.2%)       2 (11.1%)  \n    INV ID JPN-18              1 (20%)         0              0            1 (5.6%)  \n    INV ID JPN-2                  0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-3                  0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-5               1 (20%)         0          1 (11.1%)       2 (11.1%)  \n    INV ID JPN-6               1 (20%)         0              0            1 (5.6%)  \nEurasia                        5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  RUS                          5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n    INV ID RUS-1               1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-11                 0         2 (25%)       2 (33.3%)       4 (21.1%)  \n    INV ID RUS-12                 0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-13              1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-14                 0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-16              1 (20%)     1 (12.5%)          0           2 (10.5%)  \n    INV ID RUS-18              1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-2                  0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-3                  0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-4                  0         2 (25%)       1 (16.7%)       3 (15.8%)  \n    INV ID RUS-5                  0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-6               1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-7                  0            0          1 (16.7%)        1 (5.3%)  \nEurope                         4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  GBR                          4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n    INV ID GBR-1               2 (50%)         0           1 (50%)        3 (33.3%)  \n    INV ID GBR-11                 0        1 (33.3%)       1 (50%)        2 (22.2%)  \n    INV ID GBR-13              1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-15              1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-17                 0        1 (33.3%)          0           1 (11.1%)  \n    INV ID GBR-6                  0        1 (33.3%)          0           1 (11.1%)  \nNorth America                 13 (9.7%)    15 (11.2%)     20 (15.2%)      48 (12.0%) \n  USA                         10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n    INV ID USA-1               1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n    INV ID USA-11              4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n    INV ID USA-12              1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n    INV ID USA-14              1 (10%)         0              0            1 (2.5%)  \n    INV ID USA-15                 0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-17              1 (10%)      1 (7.7%)          0             2 (5%)   \n    INV ID USA-19                 0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-2                  0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-3               1 (10%)         0           1 (5.9%)         2 (5%)   \n    INV ID USA-4                  0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-5                  0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-6                  0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-8                  0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-9               1 (10%)         0              0            1 (2.5%)  \n  CAN                          3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n    INV ID CAN-1              1 (33.3%)     1 (50%)           0            2 (25%)   \n    INV ID CAN-11             1 (33.3%)        0          1 (33.3%)        2 (25%)   \n    INV ID CAN-14             1 (33.3%)        0              0           1 (12.5%)  \n    INV ID CAN-4                  0         1 (50%)       1 (33.3%)        2 (25%)   \n    INV ID CAN-5                  0            0          1 (33.3%)       1 (12.5%)  \nSouth America                 13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  BRA                         13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n    INV ID BRA-1              4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n    INV ID BRA-11             4 (30.8%)        0           4 (40%)        8 (26.7%)  \n    INV ID BRA-12                 0        1 (14.3%)          0            1 (3.3%)  \n    INV ID BRA-13              1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-14              1 (7.7%)        0           1 (10%)         2 (6.7%)  \n    INV ID BRA-15              1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-2               1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n    INV ID BRA-3                  0        2 (28.6%)          0            2 (6.7%)  \n    INV ID BRA-4                  0        1 (14.3%)       1 (10%)         2 (6.7%)  \n    INV ID BRA-5                  0            0           1 (10%)         1 (3.3%)  \n    INV ID BRA-6               1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-7                  0            0           1 (10%)         1 (3.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID\", 1L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nCountry                     A: Drug X    B: Placebo   C: Combination   All Patients\n  Investigator Identifier    (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————\nCHN                         74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n  INV ID CHN-1              21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n  INV ID CHN-10                 0         1 (1.2%)          0            1 (0.5%)  \n  INV ID CHN-11             12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n  INV ID CHN-12              4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n  INV ID CHN-13              2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n  INV ID CHN-14              4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-15              2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n  INV ID CHN-16                 0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n  INV ID CHN-17              4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n  INV ID CHN-18              1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n  INV ID CHN-2              9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n  INV ID CHN-3               5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n  INV ID CHN-4               3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-5               4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n  INV ID CHN-6               1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n  INV ID CHN-7                  0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n  INV ID CHN-8               1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n  INV ID CHN-9               1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \nUSA                         10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n  INV ID USA-1               1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n  INV ID USA-11              4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n  INV ID USA-12              1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n  INV ID USA-14              1 (10%)         0              0            1 (2.5%)  \n  INV ID USA-15                 0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-17              1 (10%)      1 (7.7%)          0             2 (5%)   \n  INV ID USA-19                 0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-2                  0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-3               1 (10%)         0           1 (5.9%)         2 (5%)   \n  INV ID USA-4                  0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-5                  0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-6                  0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-8                  0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-9               1 (10%)         0              0            1 (2.5%)  \nBRA                         13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  INV ID BRA-1              4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n  INV ID BRA-11             4 (30.8%)        0           4 (40%)        8 (26.7%)  \n  INV ID BRA-12                 0        1 (14.3%)          0            1 (3.3%)  \n  INV ID BRA-13              1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-14              1 (7.7%)        0           1 (10%)         2 (6.7%)  \n  INV ID BRA-15              1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-2               1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n  INV ID BRA-3                  0        2 (28.6%)          0            2 (6.7%)  \n  INV ID BRA-4                  0        1 (14.3%)       1 (10%)         2 (6.7%)  \n  INV ID BRA-5                  0            0           1 (10%)         1 (3.3%)  \n  INV ID BRA-6               1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-7                  0            0           1 (10%)         1 (3.3%)  \nPAK                         12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n  INV ID PAK-1              2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n  INV ID PAK-11             4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n  INV ID PAK-12             2 (16.7%)        0              0            2 (6.5%)  \n  INV ID PAK-13              1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n  INV ID PAK-14              1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-15              1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-2               1 (8.3%)        0           2 (20%)         3 (9.7%)  \n  INV ID PAK-4                  0        1 (11.1%)       1 (10%)         2 (6.5%)  \n  INV ID PAK-5                  0            0           1 (10%)         1 (3.2%)  \nNGA                          8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  INV ID NGA-1              3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n  INV ID NGA-11             1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n  INV ID NGA-12                 0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n  INV ID NGA-17              2 (25%)         0              0            2 (7.7%)  \n  INV ID NGA-2              1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n  INV ID NGA-4              1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n  INV ID NGA-5                  0            0           1 (9.1%)        1 (3.8%)  \n  INV ID NGA-6                  0            0           1 (9.1%)        1 (3.8%)  \nRUS                          5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  INV ID RUS-1               1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-11                 0         2 (25%)       2 (33.3%)       4 (21.1%)  \n  INV ID RUS-12                 0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-13              1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-14                 0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-16              1 (20%)     1 (12.5%)          0           2 (10.5%)  \n  INV ID RUS-18              1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-2                  0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-3                  0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-4                  0         2 (25%)       1 (16.7%)       3 (15.8%)  \n  INV ID RUS-5                  0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-6               1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-7                  0            0          1 (16.7%)        1 (5.3%)  \nJPN                          5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n  INV ID JPN-1               2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n  INV ID JPN-11                 0            0          3 (33.3%)       3 (16.7%)  \n  INV ID JPN-12                 0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-14                 0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-17                 0            0          2 (22.2%)       2 (11.1%)  \n  INV ID JPN-18              1 (20%)         0              0            1 (5.6%)  \n  INV ID JPN-2                  0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-3                  0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-5               1 (20%)         0          1 (11.1%)       2 (11.1%)  \n  INV ID JPN-6               1 (20%)         0              0            1 (5.6%)  \nGBR                          4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  INV ID GBR-1               2 (50%)         0           1 (50%)        3 (33.3%)  \n  INV ID GBR-11                 0        1 (33.3%)       1 (50%)        2 (22.2%)  \n  INV ID GBR-13              1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-15              1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-17                 0        1 (33.3%)          0           1 (11.1%)  \n  INV ID GBR-6                  0        1 (33.3%)          0           1 (11.1%)  \nCAN                          3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n  INV ID CAN-1              1 (33.3%)     1 (50%)           0            2 (25%)   \n  INV ID CAN-11             1 (33.3%)        0          1 (33.3%)        2 (25%)   \n  INV ID CAN-14             1 (33.3%)        0              0           1 (12.5%)  \n  INV ID CAN-4                  0         1 (50%)       1 (33.3%)        2 (25%)   \n  INV ID CAN-5                  0            0          1 (33.3%)       1 (12.5%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"REGION1\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$REGION1)) %&gt;%\n  summarize_row_groups() %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID_INVNAM\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID_INVNAM\", 2L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nGeographic Region 1                                                                         \n  Country                            A: Drug X    B: Placebo   C: Combination   All Patients\n    Investigator Number/Name          (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————\nAfrica                                8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  NGA                                 8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n    INV ID NGA-1 / Dr. NGA-1 Doe     3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n    INV ID NGA-11 / Dr. NGA-11 Doe   1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n    INV ID NGA-12 / Dr. NGA-12 Doe       0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n    INV ID NGA-17 / Dr. NGA-17 Doe    2 (25%)         0              0            2 (7.7%)  \n    INV ID NGA-2 / Dr. NGA-2 Doe     1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n    INV ID NGA-4 / Dr. NGA-4 Doe     1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n    INV ID NGA-5 / Dr. NGA-5 Doe         0            0           1 (9.1%)        1 (3.8%)  \n    INV ID NGA-6 / Dr. NGA-6 Doe         0            0           1 (9.1%)        1 (3.8%)  \nAsia                                 91 (67.9%)   94 (70.1%)     83 (62.9%)     268 (67.0%) \n  CHN                                74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n    INV ID CHN-1 / Dr. CHN-1 Doe     21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n    INV ID CHN-10 / Dr. CHN-10 Doe       0         1 (1.2%)          0            1 (0.5%)  \n    INV ID CHN-11 / Dr. CHN-11 Doe   12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n    INV ID CHN-12 / Dr. CHN-12 Doe    4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n    INV ID CHN-13 / Dr. CHN-13 Doe    2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n    INV ID CHN-14 / Dr. CHN-14 Doe    4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-15 / Dr. CHN-15 Doe    2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n    INV ID CHN-16 / Dr. CHN-16 Doe       0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n    INV ID CHN-17 / Dr. CHN-17 Doe    4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n    INV ID CHN-18 / Dr. CHN-18 Doe    1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n    INV ID CHN-2 / Dr. CHN-2 Doe     9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n    INV ID CHN-3 / Dr. CHN-3 Doe      5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n    INV ID CHN-4 / Dr. CHN-4 Doe      3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-5 / Dr. CHN-5 Doe      4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n    INV ID CHN-6 / Dr. CHN-6 Doe      1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n    INV ID CHN-7 / Dr. CHN-7 Doe         0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n    INV ID CHN-8 / Dr. CHN-8 Doe      1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n    INV ID CHN-9 / Dr. CHN-9 Doe      1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \n  PAK                                12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n    INV ID PAK-1 / Dr. PAK-1 Doe     2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n    INV ID PAK-11 / Dr. PAK-11 Doe   4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n    INV ID PAK-12 / Dr. PAK-12 Doe   2 (16.7%)        0              0            2 (6.5%)  \n    INV ID PAK-13 / Dr. PAK-13 Doe    1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n    INV ID PAK-14 / Dr. PAK-14 Doe    1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-15 / Dr. PAK-15 Doe    1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-2 / Dr. PAK-2 Doe      1 (8.3%)        0           2 (20%)         3 (9.7%)  \n    INV ID PAK-4 / Dr. PAK-4 Doe         0        1 (11.1%)       1 (10%)         2 (6.5%)  \n    INV ID PAK-5 / Dr. PAK-5 Doe         0            0           1 (10%)         1 (3.2%)  \n  JPN                                 5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n    INV ID JPN-1 / Dr. JPN-1 Doe      2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n    INV ID JPN-11 / Dr. JPN-11 Doe       0            0          3 (33.3%)       3 (16.7%)  \n    INV ID JPN-12 / Dr. JPN-12 Doe       0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-14 / Dr. JPN-14 Doe       0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-17 / Dr. JPN-17 Doe       0            0          2 (22.2%)       2 (11.1%)  \n    INV ID JPN-18 / Dr. JPN-18 Doe    1 (20%)         0              0            1 (5.6%)  \n    INV ID JPN-2 / Dr. JPN-2 Doe         0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-3 / Dr. JPN-3 Doe         0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-5 / Dr. JPN-5 Doe      1 (20%)         0          1 (11.1%)       2 (11.1%)  \n    INV ID JPN-6 / Dr. JPN-6 Doe      1 (20%)         0              0            1 (5.6%)  \nEurasia                               5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  RUS                                 5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n    INV ID RUS-1 / Dr. RUS-1 Doe      1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-11 / Dr. RUS-11 Doe       0         2 (25%)       2 (33.3%)       4 (21.1%)  \n    INV ID RUS-12 / Dr. RUS-12 Doe       0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-13 / Dr. RUS-13 Doe    1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-14 / Dr. RUS-14 Doe       0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-16 / Dr. RUS-16 Doe    1 (20%)     1 (12.5%)          0           2 (10.5%)  \n    INV ID RUS-18 / Dr. RUS-18 Doe    1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-2 / Dr. RUS-2 Doe         0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-3 / Dr. RUS-3 Doe         0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-4 / Dr. RUS-4 Doe         0         2 (25%)       1 (16.7%)       3 (15.8%)  \n    INV ID RUS-5 / Dr. RUS-5 Doe         0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-6 / Dr. RUS-6 Doe      1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-7 / Dr. RUS-7 Doe         0            0          1 (16.7%)        1 (5.3%)  \nEurope                                4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  GBR                                 4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n    INV ID GBR-1 / Dr. GBR-1 Doe      2 (50%)         0           1 (50%)        3 (33.3%)  \n    INV ID GBR-11 / Dr. GBR-11 Doe       0        1 (33.3%)       1 (50%)        2 (22.2%)  \n    INV ID GBR-13 / Dr. GBR-13 Doe    1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-15 / Dr. GBR-15 Doe    1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-17 / Dr. GBR-17 Doe       0        1 (33.3%)          0           1 (11.1%)  \n    INV ID GBR-6 / Dr. GBR-6 Doe         0        1 (33.3%)          0           1 (11.1%)  \nNorth America                        13 (9.7%)    15 (11.2%)     20 (15.2%)      48 (12.0%) \n  USA                                10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n    INV ID USA-1 / Dr. USA-1 Doe      1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n    INV ID USA-11 / Dr. USA-11 Doe    4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n    INV ID USA-12 / Dr. USA-12 Doe    1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n    INV ID USA-14 / Dr. USA-14 Doe    1 (10%)         0              0            1 (2.5%)  \n    INV ID USA-15 / Dr. USA-15 Doe       0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-17 / Dr. USA-17 Doe    1 (10%)      1 (7.7%)          0             2 (5%)   \n    INV ID USA-19 / Dr. USA-19 Doe       0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-2 / Dr. USA-2 Doe         0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-3 / Dr. USA-3 Doe      1 (10%)         0           1 (5.9%)         2 (5%)   \n    INV ID USA-4 / Dr. USA-4 Doe         0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-5 / Dr. USA-5 Doe         0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-6 / Dr. USA-6 Doe         0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-8 / Dr. USA-8 Doe         0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-9 / Dr. USA-9 Doe      1 (10%)         0              0            1 (2.5%)  \n  CAN                                 3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n    INV ID CAN-1 / Dr. CAN-1 Doe     1 (33.3%)     1 (50%)           0            2 (25%)   \n    INV ID CAN-11 / Dr. CAN-11 Doe   1 (33.3%)        0          1 (33.3%)        2 (25%)   \n    INV ID CAN-14 / Dr. CAN-14 Doe   1 (33.3%)        0              0           1 (12.5%)  \n    INV ID CAN-4 / Dr. CAN-4 Doe         0         1 (50%)       1 (33.3%)        2 (25%)   \n    INV ID CAN-5 / Dr. CAN-5 Doe         0            0          1 (33.3%)       1 (12.5%)  \nSouth America                        13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  BRA                                13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n    INV ID BRA-1 / Dr. BRA-1 Doe     4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n    INV ID BRA-11 / Dr. BRA-11 Doe   4 (30.8%)        0           4 (40%)        8 (26.7%)  \n    INV ID BRA-12 / Dr. BRA-12 Doe       0        1 (14.3%)          0            1 (3.3%)  \n    INV ID BRA-13 / Dr. BRA-13 Doe    1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-14 / Dr. BRA-14 Doe    1 (7.7%)        0           1 (10%)         2 (6.7%)  \n    INV ID BRA-15 / Dr. BRA-15 Doe    1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-2 / Dr. BRA-2 Doe      1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n    INV ID BRA-3 / Dr. BRA-3 Doe         0        2 (28.6%)          0            2 (6.7%)  \n    INV ID BRA-4 / Dr. BRA-4 Doe         0        1 (14.3%)       1 (10%)         2 (6.7%)  \n    INV ID BRA-5 / Dr. BRA-5 Doe         0            0           1 (10%)         1 (3.3%)  \n    INV ID BRA-6 / Dr. BRA-6 Doe      1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-7 / Dr. BRA-7 Doe         0            0           1 (10%)         1 (3.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID_INVNAM\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID_INVNAM\", 1L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nCountry                            A: Drug X    B: Placebo   C: Combination   All Patients\n  Investigator Number/Name          (N=134)      (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————————————\nCHN                                74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n  INV ID CHN-1 / Dr. CHN-1 Doe     21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n  INV ID CHN-10 / Dr. CHN-10 Doe       0         1 (1.2%)          0            1 (0.5%)  \n  INV ID CHN-11 / Dr. CHN-11 Doe   12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n  INV ID CHN-12 / Dr. CHN-12 Doe    4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n  INV ID CHN-13 / Dr. CHN-13 Doe    2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n  INV ID CHN-14 / Dr. CHN-14 Doe    4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-15 / Dr. CHN-15 Doe    2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n  INV ID CHN-16 / Dr. CHN-16 Doe       0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n  INV ID CHN-17 / Dr. CHN-17 Doe    4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n  INV ID CHN-18 / Dr. CHN-18 Doe    1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n  INV ID CHN-2 / Dr. CHN-2 Doe     9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n  INV ID CHN-3 / Dr. CHN-3 Doe      5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n  INV ID CHN-4 / Dr. CHN-4 Doe      3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-5 / Dr. CHN-5 Doe      4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n  INV ID CHN-6 / Dr. CHN-6 Doe      1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n  INV ID CHN-7 / Dr. CHN-7 Doe         0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n  INV ID CHN-8 / Dr. CHN-8 Doe      1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n  INV ID CHN-9 / Dr. CHN-9 Doe      1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \nUSA                                10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n  INV ID USA-1 / Dr. USA-1 Doe      1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n  INV ID USA-11 / Dr. USA-11 Doe    4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n  INV ID USA-12 / Dr. USA-12 Doe    1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n  INV ID USA-14 / Dr. USA-14 Doe    1 (10%)         0              0            1 (2.5%)  \n  INV ID USA-15 / Dr. USA-15 Doe       0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-17 / Dr. USA-17 Doe    1 (10%)      1 (7.7%)          0             2 (5%)   \n  INV ID USA-19 / Dr. USA-19 Doe       0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-2 / Dr. USA-2 Doe         0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-3 / Dr. USA-3 Doe      1 (10%)         0           1 (5.9%)         2 (5%)   \n  INV ID USA-4 / Dr. USA-4 Doe         0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-5 / Dr. USA-5 Doe         0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-6 / Dr. USA-6 Doe         0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-8 / Dr. USA-8 Doe         0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-9 / Dr. USA-9 Doe      1 (10%)         0              0            1 (2.5%)  \nBRA                                13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  INV ID BRA-1 / Dr. BRA-1 Doe     4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n  INV ID BRA-11 / Dr. BRA-11 Doe   4 (30.8%)        0           4 (40%)        8 (26.7%)  \n  INV ID BRA-12 / Dr. BRA-12 Doe       0        1 (14.3%)          0            1 (3.3%)  \n  INV ID BRA-13 / Dr. BRA-13 Doe    1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-14 / Dr. BRA-14 Doe    1 (7.7%)        0           1 (10%)         2 (6.7%)  \n  INV ID BRA-15 / Dr. BRA-15 Doe    1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-2 / Dr. BRA-2 Doe      1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n  INV ID BRA-3 / Dr. BRA-3 Doe         0        2 (28.6%)          0            2 (6.7%)  \n  INV ID BRA-4 / Dr. BRA-4 Doe         0        1 (14.3%)       1 (10%)         2 (6.7%)  \n  INV ID BRA-5 / Dr. BRA-5 Doe         0            0           1 (10%)         1 (3.3%)  \n  INV ID BRA-6 / Dr. BRA-6 Doe      1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-7 / Dr. BRA-7 Doe         0            0           1 (10%)         1 (3.3%)  \nPAK                                12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n  INV ID PAK-1 / Dr. PAK-1 Doe     2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n  INV ID PAK-11 / Dr. PAK-11 Doe   4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n  INV ID PAK-12 / Dr. PAK-12 Doe   2 (16.7%)        0              0            2 (6.5%)  \n  INV ID PAK-13 / Dr. PAK-13 Doe    1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n  INV ID PAK-14 / Dr. PAK-14 Doe    1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-15 / Dr. PAK-15 Doe    1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-2 / Dr. PAK-2 Doe      1 (8.3%)        0           2 (20%)         3 (9.7%)  \n  INV ID PAK-4 / Dr. PAK-4 Doe         0        1 (11.1%)       1 (10%)         2 (6.5%)  \n  INV ID PAK-5 / Dr. PAK-5 Doe         0            0           1 (10%)         1 (3.2%)  \nNGA                                 8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  INV ID NGA-1 / Dr. NGA-1 Doe     3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n  INV ID NGA-11 / Dr. NGA-11 Doe   1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n  INV ID NGA-12 / Dr. NGA-12 Doe       0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n  INV ID NGA-17 / Dr. NGA-17 Doe    2 (25%)         0              0            2 (7.7%)  \n  INV ID NGA-2 / Dr. NGA-2 Doe     1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n  INV ID NGA-4 / Dr. NGA-4 Doe     1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n  INV ID NGA-5 / Dr. NGA-5 Doe         0            0           1 (9.1%)        1 (3.8%)  \n  INV ID NGA-6 / Dr. NGA-6 Doe         0            0           1 (9.1%)        1 (3.8%)  \nRUS                                 5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  INV ID RUS-1 / Dr. RUS-1 Doe      1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-11 / Dr. RUS-11 Doe       0         2 (25%)       2 (33.3%)       4 (21.1%)  \n  INV ID RUS-12 / Dr. RUS-12 Doe       0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-13 / Dr. RUS-13 Doe    1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-14 / Dr. RUS-14 Doe       0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-16 / Dr. RUS-16 Doe    1 (20%)     1 (12.5%)          0           2 (10.5%)  \n  INV ID RUS-18 / Dr. RUS-18 Doe    1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-2 / Dr. RUS-2 Doe         0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-3 / Dr. RUS-3 Doe         0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-4 / Dr. RUS-4 Doe         0         2 (25%)       1 (16.7%)       3 (15.8%)  \n  INV ID RUS-5 / Dr. RUS-5 Doe         0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-6 / Dr. RUS-6 Doe      1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-7 / Dr. RUS-7 Doe         0            0          1 (16.7%)        1 (5.3%)  \nJPN                                 5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n  INV ID JPN-1 / Dr. JPN-1 Doe      2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n  INV ID JPN-11 / Dr. JPN-11 Doe       0            0          3 (33.3%)       3 (16.7%)  \n  INV ID JPN-12 / Dr. JPN-12 Doe       0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-14 / Dr. JPN-14 Doe       0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-17 / Dr. JPN-17 Doe       0            0          2 (22.2%)       2 (11.1%)  \n  INV ID JPN-18 / Dr. JPN-18 Doe    1 (20%)         0              0            1 (5.6%)  \n  INV ID JPN-2 / Dr. JPN-2 Doe         0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-3 / Dr. JPN-3 Doe         0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-5 / Dr. JPN-5 Doe      1 (20%)         0          1 (11.1%)       2 (11.1%)  \n  INV ID JPN-6 / Dr. JPN-6 Doe      1 (20%)         0              0            1 (5.6%)  \nGBR                                 4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  INV ID GBR-1 / Dr. GBR-1 Doe      2 (50%)         0           1 (50%)        3 (33.3%)  \n  INV ID GBR-11 / Dr. GBR-11 Doe       0        1 (33.3%)       1 (50%)        2 (22.2%)  \n  INV ID GBR-13 / Dr. GBR-13 Doe    1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-15 / Dr. GBR-15 Doe    1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-17 / Dr. GBR-17 Doe       0        1 (33.3%)          0           1 (11.1%)  \n  INV ID GBR-6 / Dr. GBR-6 Doe         0        1 (33.3%)          0           1 (11.1%)  \nCAN                                 3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n  INV ID CAN-1 / Dr. CAN-1 Doe     1 (33.3%)     1 (50%)           0            2 (25%)   \n  INV ID CAN-11 / Dr. CAN-11 Doe   1 (33.3%)        0          1 (33.3%)        2 (25%)   \n  INV ID CAN-14 / Dr. CAN-14 Doe   1 (33.3%)        0              0           1 (12.5%)  \n  INV ID CAN-4 / Dr. CAN-4 Doe         0         1 (50%)       1 (33.3%)        2 (25%)   \n  INV ID CAN-5 / Dr. CAN-5 Doe         0            0          1 (33.3%)       1 (12.5%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nNote that for this module application, only the variables passed into by_vars are used when row_groups is selected. Variables passed into analyze_vars are additionally used when row_groups is deselected.\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(forcats)\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n\n  adsl_labels &lt;- col_labels(ADSL)\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(\n      REGION1 = droplevels(REGION1),\n      COUNTRY = droplevels(COUNTRY),\n      INVID_INVNAM = paste(INVID, INVNAM, sep = \" / \")\n    ) %&gt;%\n    arrange(REGION1, COUNTRY, INVID) %&gt;%\n    mutate(\n      INVID = factor(INVID),\n      SITEID = factor(SITEID),\n      INVNAM = factor(INVNAM),\n      INVID_INVNAM = factor(INVID_INVNAM)\n    )\n\n  col_labels(ADSL) &lt;- c(adsl_labels, c(INVID_INVNAM = \"Investigator Number/Name\"))\n})\ndatanames &lt;- \"ADSL\"\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Enrollment Table\",\n      dataname = \"ADSL\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADSL, c(\"REGION1\", \"COUNTRY\", \"SITEID\", \"INVID\", \"INVNAM\", \"INVID_INVNAM\")),\n        selected = c(\"REGION1\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADSL, c(\"REGION1\", \"COUNTRY\", \"SITEID\", \"INVID\", \"INVNAM\", \"INVID_INVNAM\")),\n        selected = c(\"SITEID\")\n      ),\n      denominator = choices_selected(\n        choices = c(\"n\", \"N\", \"omit\"),\n        selected = \"N\",\n        fixed = TRUE\n      ),\n      useNA = \"ifany\",\n      row_groups = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:50:02 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            scda_0.1.6.9017                 \n[13] forcats_1.0.0                    dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nENT01 – Enrollment by Region,  Country, and Investigator Number\nENT01A – Enrollment by Country  and Investigator Number\nENT02 – Enrollment by Region, Country,  and Investigator Number/Name\nENT02A – Enrollment by Country  and Investigator Number/Name\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(forcats)\nlibrary(scda)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\n\nadsl &lt;- adsl %&gt;%\n  mutate(\n    REGION1 = droplevels(REGION1),\n    COUNTRY = droplevels(COUNTRY),\n    INVID_INVNAM = paste(INVID, INVNAM, sep = \" / \")\n  ) %&gt;%\n  mutate(\n    INVID = factor(INVID),\n    SITEID = factor(SITEID),\n    INVNAM = factor(INVNAM),\n    INVID_INVNAM = factor(INVID_INVNAM)\n  ) %&gt;%\n  var_relabel(\n    INVID_INVNAM = \"Investigator Number/Name\",\n    REGION1 = \"Geographic Region 1\",\n    COUNTRY = \"Country\",\n    INVID = \"Investigator Identifier\"\n  ) %&gt;%\n  arrange(REGION1, COUNTRY, INVID)\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"REGION1\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$REGION1)) %&gt;%\n  summarize_row_groups() %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID\", 2L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nGeographic Region 1                                                                  \n  Country                     A: Drug X    B: Placebo   C: Combination   All Patients\n    Investigator Identifier    (N=134)      (N=134)        (N=132)         (N=400)   \n—————————————————————————————————————————————————————————————————————————————————————\nAfrica                         8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  NGA                          8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n    INV ID NGA-1              3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n    INV ID NGA-11             1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n    INV ID NGA-12                 0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n    INV ID NGA-17              2 (25%)         0              0            2 (7.7%)  \n    INV ID NGA-2              1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n    INV ID NGA-4              1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n    INV ID NGA-5                  0            0           1 (9.1%)        1 (3.8%)  \n    INV ID NGA-6                  0            0           1 (9.1%)        1 (3.8%)  \nAsia                          91 (67.9%)   94 (70.1%)     83 (62.9%)     268 (67.0%) \n  CHN                         74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n    INV ID CHN-1              21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n    INV ID CHN-10                 0         1 (1.2%)          0            1 (0.5%)  \n    INV ID CHN-11             12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n    INV ID CHN-12              4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n    INV ID CHN-13              2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n    INV ID CHN-14              4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-15              2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n    INV ID CHN-16                 0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n    INV ID CHN-17              4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n    INV ID CHN-18              1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n    INV ID CHN-2              9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n    INV ID CHN-3               5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n    INV ID CHN-4               3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-5               4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n    INV ID CHN-6               1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n    INV ID CHN-7                  0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n    INV ID CHN-8               1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n    INV ID CHN-9               1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \n  PAK                         12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n    INV ID PAK-1              2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n    INV ID PAK-11             4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n    INV ID PAK-12             2 (16.7%)        0              0            2 (6.5%)  \n    INV ID PAK-13              1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n    INV ID PAK-14              1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-15              1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-2               1 (8.3%)        0           2 (20%)         3 (9.7%)  \n    INV ID PAK-4                  0        1 (11.1%)       1 (10%)         2 (6.5%)  \n    INV ID PAK-5                  0            0           1 (10%)         1 (3.2%)  \n  JPN                          5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n    INV ID JPN-1               2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n    INV ID JPN-11                 0            0          3 (33.3%)       3 (16.7%)  \n    INV ID JPN-12                 0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-14                 0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-17                 0            0          2 (22.2%)       2 (11.1%)  \n    INV ID JPN-18              1 (20%)         0              0            1 (5.6%)  \n    INV ID JPN-2                  0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-3                  0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-5               1 (20%)         0          1 (11.1%)       2 (11.1%)  \n    INV ID JPN-6               1 (20%)         0              0            1 (5.6%)  \nEurasia                        5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  RUS                          5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n    INV ID RUS-1               1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-11                 0         2 (25%)       2 (33.3%)       4 (21.1%)  \n    INV ID RUS-12                 0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-13              1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-14                 0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-16              1 (20%)     1 (12.5%)          0           2 (10.5%)  \n    INV ID RUS-18              1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-2                  0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-3                  0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-4                  0         2 (25%)       1 (16.7%)       3 (15.8%)  \n    INV ID RUS-5                  0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-6               1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-7                  0            0          1 (16.7%)        1 (5.3%)  \nEurope                         4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  GBR                          4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n    INV ID GBR-1               2 (50%)         0           1 (50%)        3 (33.3%)  \n    INV ID GBR-11                 0        1 (33.3%)       1 (50%)        2 (22.2%)  \n    INV ID GBR-13              1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-15              1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-17                 0        1 (33.3%)          0           1 (11.1%)  \n    INV ID GBR-6                  0        1 (33.3%)          0           1 (11.1%)  \nNorth America                 13 (9.7%)    15 (11.2%)     20 (15.2%)      48 (12.0%) \n  USA                         10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n    INV ID USA-1               1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n    INV ID USA-11              4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n    INV ID USA-12              1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n    INV ID USA-14              1 (10%)         0              0            1 (2.5%)  \n    INV ID USA-15                 0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-17              1 (10%)      1 (7.7%)          0             2 (5%)   \n    INV ID USA-19                 0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-2                  0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-3               1 (10%)         0           1 (5.9%)         2 (5%)   \n    INV ID USA-4                  0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-5                  0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-6                  0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-8                  0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-9               1 (10%)         0              0            1 (2.5%)  \n  CAN                          3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n    INV ID CAN-1              1 (33.3%)     1 (50%)           0            2 (25%)   \n    INV ID CAN-11             1 (33.3%)        0          1 (33.3%)        2 (25%)   \n    INV ID CAN-14             1 (33.3%)        0              0           1 (12.5%)  \n    INV ID CAN-4                  0         1 (50%)       1 (33.3%)        2 (25%)   \n    INV ID CAN-5                  0            0          1 (33.3%)       1 (12.5%)  \nSouth America                 13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  BRA                         13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n    INV ID BRA-1              4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n    INV ID BRA-11             4 (30.8%)        0           4 (40%)        8 (26.7%)  \n    INV ID BRA-12                 0        1 (14.3%)          0            1 (3.3%)  \n    INV ID BRA-13              1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-14              1 (7.7%)        0           1 (10%)         2 (6.7%)  \n    INV ID BRA-15              1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-2               1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n    INV ID BRA-3                  0        2 (28.6%)          0            2 (6.7%)  \n    INV ID BRA-4                  0        1 (14.3%)       1 (10%)         2 (6.7%)  \n    INV ID BRA-5                  0            0           1 (10%)         1 (3.3%)  \n    INV ID BRA-6               1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-7                  0            0           1 (10%)         1 (3.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID\", 1L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nCountry                     A: Drug X    B: Placebo   C: Combination   All Patients\n  Investigator Identifier    (N=134)      (N=134)        (N=132)         (N=400)   \n———————————————————————————————————————————————————————————————————————————————————\nCHN                         74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n  INV ID CHN-1              21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n  INV ID CHN-10                 0         1 (1.2%)          0            1 (0.5%)  \n  INV ID CHN-11             12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n  INV ID CHN-12              4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n  INV ID CHN-13              2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n  INV ID CHN-14              4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-15              2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n  INV ID CHN-16                 0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n  INV ID CHN-17              4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n  INV ID CHN-18              1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n  INV ID CHN-2              9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n  INV ID CHN-3               5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n  INV ID CHN-4               3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-5               4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n  INV ID CHN-6               1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n  INV ID CHN-7                  0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n  INV ID CHN-8               1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n  INV ID CHN-9               1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \nUSA                         10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n  INV ID USA-1               1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n  INV ID USA-11              4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n  INV ID USA-12              1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n  INV ID USA-14              1 (10%)         0              0            1 (2.5%)  \n  INV ID USA-15                 0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-17              1 (10%)      1 (7.7%)          0             2 (5%)   \n  INV ID USA-19                 0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-2                  0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-3               1 (10%)         0           1 (5.9%)         2 (5%)   \n  INV ID USA-4                  0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-5                  0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-6                  0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-8                  0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-9               1 (10%)         0              0            1 (2.5%)  \nBRA                         13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  INV ID BRA-1              4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n  INV ID BRA-11             4 (30.8%)        0           4 (40%)        8 (26.7%)  \n  INV ID BRA-12                 0        1 (14.3%)          0            1 (3.3%)  \n  INV ID BRA-13              1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-14              1 (7.7%)        0           1 (10%)         2 (6.7%)  \n  INV ID BRA-15              1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-2               1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n  INV ID BRA-3                  0        2 (28.6%)          0            2 (6.7%)  \n  INV ID BRA-4                  0        1 (14.3%)       1 (10%)         2 (6.7%)  \n  INV ID BRA-5                  0            0           1 (10%)         1 (3.3%)  \n  INV ID BRA-6               1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-7                  0            0           1 (10%)         1 (3.3%)  \nPAK                         12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n  INV ID PAK-1              2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n  INV ID PAK-11             4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n  INV ID PAK-12             2 (16.7%)        0              0            2 (6.5%)  \n  INV ID PAK-13              1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n  INV ID PAK-14              1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-15              1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-2               1 (8.3%)        0           2 (20%)         3 (9.7%)  \n  INV ID PAK-4                  0        1 (11.1%)       1 (10%)         2 (6.5%)  \n  INV ID PAK-5                  0            0           1 (10%)         1 (3.2%)  \nNGA                          8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  INV ID NGA-1              3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n  INV ID NGA-11             1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n  INV ID NGA-12                 0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n  INV ID NGA-17              2 (25%)         0              0            2 (7.7%)  \n  INV ID NGA-2              1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n  INV ID NGA-4              1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n  INV ID NGA-5                  0            0           1 (9.1%)        1 (3.8%)  \n  INV ID NGA-6                  0            0           1 (9.1%)        1 (3.8%)  \nRUS                          5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  INV ID RUS-1               1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-11                 0         2 (25%)       2 (33.3%)       4 (21.1%)  \n  INV ID RUS-12                 0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-13              1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-14                 0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-16              1 (20%)     1 (12.5%)          0           2 (10.5%)  \n  INV ID RUS-18              1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-2                  0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-3                  0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-4                  0         2 (25%)       1 (16.7%)       3 (15.8%)  \n  INV ID RUS-5                  0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-6               1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-7                  0            0          1 (16.7%)        1 (5.3%)  \nJPN                          5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n  INV ID JPN-1               2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n  INV ID JPN-11                 0            0          3 (33.3%)       3 (16.7%)  \n  INV ID JPN-12                 0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-14                 0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-17                 0            0          2 (22.2%)       2 (11.1%)  \n  INV ID JPN-18              1 (20%)         0              0            1 (5.6%)  \n  INV ID JPN-2                  0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-3                  0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-5               1 (20%)         0          1 (11.1%)       2 (11.1%)  \n  INV ID JPN-6               1 (20%)         0              0            1 (5.6%)  \nGBR                          4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  INV ID GBR-1               2 (50%)         0           1 (50%)        3 (33.3%)  \n  INV ID GBR-11                 0        1 (33.3%)       1 (50%)        2 (22.2%)  \n  INV ID GBR-13              1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-15              1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-17                 0        1 (33.3%)          0           1 (11.1%)  \n  INV ID GBR-6                  0        1 (33.3%)          0           1 (11.1%)  \nCAN                          3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n  INV ID CAN-1              1 (33.3%)     1 (50%)           0            2 (25%)   \n  INV ID CAN-11             1 (33.3%)        0          1 (33.3%)        2 (25%)   \n  INV ID CAN-14             1 (33.3%)        0              0           1 (12.5%)  \n  INV ID CAN-4                  0         1 (50%)       1 (33.3%)        2 (25%)   \n  INV ID CAN-5                  0            0          1 (33.3%)       1 (12.5%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"REGION1\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$REGION1)) %&gt;%\n  summarize_row_groups() %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID_INVNAM\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID_INVNAM\", 2L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nGeographic Region 1                                                                         \n  Country                            A: Drug X    B: Placebo   C: Combination   All Patients\n    Investigator Number/Name          (N=134)      (N=134)        (N=132)         (N=400)   \n————————————————————————————————————————————————————————————————————————————————————————————\nAfrica                                8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  NGA                                 8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n    INV ID NGA-1 / Dr. NGA-1 Doe     3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n    INV ID NGA-11 / Dr. NGA-11 Doe   1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n    INV ID NGA-12 / Dr. NGA-12 Doe       0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n    INV ID NGA-17 / Dr. NGA-17 Doe    2 (25%)         0              0            2 (7.7%)  \n    INV ID NGA-2 / Dr. NGA-2 Doe     1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n    INV ID NGA-4 / Dr. NGA-4 Doe     1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n    INV ID NGA-5 / Dr. NGA-5 Doe         0            0           1 (9.1%)        1 (3.8%)  \n    INV ID NGA-6 / Dr. NGA-6 Doe         0            0           1 (9.1%)        1 (3.8%)  \nAsia                                 91 (67.9%)   94 (70.1%)     83 (62.9%)     268 (67.0%) \n  CHN                                74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n    INV ID CHN-1 / Dr. CHN-1 Doe     21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n    INV ID CHN-10 / Dr. CHN-10 Doe       0         1 (1.2%)          0            1 (0.5%)  \n    INV ID CHN-11 / Dr. CHN-11 Doe   12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n    INV ID CHN-12 / Dr. CHN-12 Doe    4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n    INV ID CHN-13 / Dr. CHN-13 Doe    2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n    INV ID CHN-14 / Dr. CHN-14 Doe    4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-15 / Dr. CHN-15 Doe    2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n    INV ID CHN-16 / Dr. CHN-16 Doe       0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n    INV ID CHN-17 / Dr. CHN-17 Doe    4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n    INV ID CHN-18 / Dr. CHN-18 Doe    1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n    INV ID CHN-2 / Dr. CHN-2 Doe     9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n    INV ID CHN-3 / Dr. CHN-3 Doe      5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n    INV ID CHN-4 / Dr. CHN-4 Doe      3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n    INV ID CHN-5 / Dr. CHN-5 Doe      4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n    INV ID CHN-6 / Dr. CHN-6 Doe      1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n    INV ID CHN-7 / Dr. CHN-7 Doe         0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n    INV ID CHN-8 / Dr. CHN-8 Doe      1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n    INV ID CHN-9 / Dr. CHN-9 Doe      1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \n  PAK                                12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n    INV ID PAK-1 / Dr. PAK-1 Doe     2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n    INV ID PAK-11 / Dr. PAK-11 Doe   4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n    INV ID PAK-12 / Dr. PAK-12 Doe   2 (16.7%)        0              0            2 (6.5%)  \n    INV ID PAK-13 / Dr. PAK-13 Doe    1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n    INV ID PAK-14 / Dr. PAK-14 Doe    1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-15 / Dr. PAK-15 Doe    1 (8.3%)        0              0            1 (3.2%)  \n    INV ID PAK-2 / Dr. PAK-2 Doe      1 (8.3%)        0           2 (20%)         3 (9.7%)  \n    INV ID PAK-4 / Dr. PAK-4 Doe         0        1 (11.1%)       1 (10%)         2 (6.5%)  \n    INV ID PAK-5 / Dr. PAK-5 Doe         0            0           1 (10%)         1 (3.2%)  \n  JPN                                 5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n    INV ID JPN-1 / Dr. JPN-1 Doe      2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n    INV ID JPN-11 / Dr. JPN-11 Doe       0            0          3 (33.3%)       3 (16.7%)  \n    INV ID JPN-12 / Dr. JPN-12 Doe       0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-14 / Dr. JPN-14 Doe       0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-17 / Dr. JPN-17 Doe       0            0          2 (22.2%)       2 (11.1%)  \n    INV ID JPN-18 / Dr. JPN-18 Doe    1 (20%)         0              0            1 (5.6%)  \n    INV ID JPN-2 / Dr. JPN-2 Doe         0            0          1 (11.1%)        1 (5.6%)  \n    INV ID JPN-3 / Dr. JPN-3 Doe         0         1 (25%)           0            1 (5.6%)  \n    INV ID JPN-5 / Dr. JPN-5 Doe      1 (20%)         0          1 (11.1%)       2 (11.1%)  \n    INV ID JPN-6 / Dr. JPN-6 Doe      1 (20%)         0              0            1 (5.6%)  \nEurasia                               5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  RUS                                 5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n    INV ID RUS-1 / Dr. RUS-1 Doe      1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-11 / Dr. RUS-11 Doe       0         2 (25%)       2 (33.3%)       4 (21.1%)  \n    INV ID RUS-12 / Dr. RUS-12 Doe       0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-13 / Dr. RUS-13 Doe    1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-14 / Dr. RUS-14 Doe       0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-16 / Dr. RUS-16 Doe    1 (20%)     1 (12.5%)          0           2 (10.5%)  \n    INV ID RUS-18 / Dr. RUS-18 Doe    1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-2 / Dr. RUS-2 Doe         0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-3 / Dr. RUS-3 Doe         0            0          1 (16.7%)        1 (5.3%)  \n    INV ID RUS-4 / Dr. RUS-4 Doe         0         2 (25%)       1 (16.7%)       3 (15.8%)  \n    INV ID RUS-5 / Dr. RUS-5 Doe         0        1 (12.5%)          0            1 (5.3%)  \n    INV ID RUS-6 / Dr. RUS-6 Doe      1 (20%)         0              0            1 (5.3%)  \n    INV ID RUS-7 / Dr. RUS-7 Doe         0            0          1 (16.7%)        1 (5.3%)  \nEurope                                4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  GBR                                 4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n    INV ID GBR-1 / Dr. GBR-1 Doe      2 (50%)         0           1 (50%)        3 (33.3%)  \n    INV ID GBR-11 / Dr. GBR-11 Doe       0        1 (33.3%)       1 (50%)        2 (22.2%)  \n    INV ID GBR-13 / Dr. GBR-13 Doe    1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-15 / Dr. GBR-15 Doe    1 (25%)         0              0           1 (11.1%)  \n    INV ID GBR-17 / Dr. GBR-17 Doe       0        1 (33.3%)          0           1 (11.1%)  \n    INV ID GBR-6 / Dr. GBR-6 Doe         0        1 (33.3%)          0           1 (11.1%)  \nNorth America                        13 (9.7%)    15 (11.2%)     20 (15.2%)      48 (12.0%) \n  USA                                10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n    INV ID USA-1 / Dr. USA-1 Doe      1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n    INV ID USA-11 / Dr. USA-11 Doe    4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n    INV ID USA-12 / Dr. USA-12 Doe    1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n    INV ID USA-14 / Dr. USA-14 Doe    1 (10%)         0              0            1 (2.5%)  \n    INV ID USA-15 / Dr. USA-15 Doe       0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-17 / Dr. USA-17 Doe    1 (10%)      1 (7.7%)          0             2 (5%)   \n    INV ID USA-19 / Dr. USA-19 Doe       0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-2 / Dr. USA-2 Doe         0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-3 / Dr. USA-3 Doe      1 (10%)         0           1 (5.9%)         2 (5%)   \n    INV ID USA-4 / Dr. USA-4 Doe         0         1 (7.7%)       1 (5.9%)         2 (5%)   \n    INV ID USA-5 / Dr. USA-5 Doe         0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-6 / Dr. USA-6 Doe         0         1 (7.7%)          0            1 (2.5%)  \n    INV ID USA-8 / Dr. USA-8 Doe         0            0           1 (5.9%)        1 (2.5%)  \n    INV ID USA-9 / Dr. USA-9 Doe      1 (10%)         0              0            1 (2.5%)  \n  CAN                                 3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n    INV ID CAN-1 / Dr. CAN-1 Doe     1 (33.3%)     1 (50%)           0            2 (25%)   \n    INV ID CAN-11 / Dr. CAN-11 Doe   1 (33.3%)        0          1 (33.3%)        2 (25%)   \n    INV ID CAN-14 / Dr. CAN-14 Doe   1 (33.3%)        0              0           1 (12.5%)  \n    INV ID CAN-4 / Dr. CAN-4 Doe         0         1 (50%)       1 (33.3%)        2 (25%)   \n    INV ID CAN-5 / Dr. CAN-5 Doe         0            0          1 (33.3%)       1 (12.5%)  \nSouth America                        13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  BRA                                13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n    INV ID BRA-1 / Dr. BRA-1 Doe     4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n    INV ID BRA-11 / Dr. BRA-11 Doe   4 (30.8%)        0           4 (40%)        8 (26.7%)  \n    INV ID BRA-12 / Dr. BRA-12 Doe       0        1 (14.3%)          0            1 (3.3%)  \n    INV ID BRA-13 / Dr. BRA-13 Doe    1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-14 / Dr. BRA-14 Doe    1 (7.7%)        0           1 (10%)         2 (6.7%)  \n    INV ID BRA-15 / Dr. BRA-15 Doe    1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-2 / Dr. BRA-2 Doe      1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n    INV ID BRA-3 / Dr. BRA-3 Doe         0        2 (28.6%)          0            2 (6.7%)  \n    INV ID BRA-4 / Dr. BRA-4 Doe         0        1 (14.3%)       1 (10%)         2 (6.7%)  \n    INV ID BRA-5 / Dr. BRA-5 Doe         0            0           1 (10%)         1 (3.3%)  \n    INV ID BRA-6 / Dr. BRA-6 Doe      1 (7.7%)        0              0            1 (3.3%)  \n    INV ID BRA-7 / Dr. BRA-7 Doe         0            0           1 (10%)         1 (3.3%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  add_overall_col(label = \"All Patients\") %&gt;%\n  split_rows_by(\"COUNTRY\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adsl$COUNTRY)) %&gt;%\n  summarize_row_groups() %&gt;%\n  analyze_vars(\"INVID_INVNAM\", .stats = \"count_fraction\") %&gt;%\n  append_varlabels(adsl, \"INVID_INVNAM\", 1L)\n\nresult &lt;- build_table(lyt, adsl) %&gt;%\n  prune_table()\n\nresult\n\nCountry                            A: Drug X    B: Placebo   C: Combination   All Patients\n  Investigator Number/Name          (N=134)      (N=134)        (N=132)         (N=400)   \n——————————————————————————————————————————————————————————————————————————————————————————\nCHN                                74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) \n  INV ID CHN-1 / Dr. CHN-1 Doe     21 (28.4%)   20 (24.7%)      16 (25%)        57 (26%)  \n  INV ID CHN-10 / Dr. CHN-10 Doe       0         1 (1.2%)          0            1 (0.5%)  \n  INV ID CHN-11 / Dr. CHN-11 Doe   12 (16.2%)   20 (24.7%)      16 (25%)       48 (21.9%) \n  INV ID CHN-12 / Dr. CHN-12 Doe    4 (5.4%)     3 (3.7%)       1 (1.6%)        8 (3.7%)  \n  INV ID CHN-13 / Dr. CHN-13 Doe    2 (2.7%)     6 (7.4%)          0            8 (3.7%)  \n  INV ID CHN-14 / Dr. CHN-14 Doe    4 (5.4%)     2 (2.5%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-15 / Dr. CHN-15 Doe    2 (2.7%)        0           4 (6.2%)        6 (2.7%)  \n  INV ID CHN-16 / Dr. CHN-16 Doe       0         3 (3.7%)       3 (4.7%)        6 (2.7%)  \n  INV ID CHN-17 / Dr. CHN-17 Doe    4 (5.4%)     4 (4.9%)       3 (4.7%)        11 (5%)   \n  INV ID CHN-18 / Dr. CHN-18 Doe    1 (1.4%)        0           2 (3.1%)        3 (1.4%)  \n  INV ID CHN-2 / Dr. CHN-2 Doe     9 (12.2%)     4 (4.9%)       3 (4.7%)       16 (7.3%)  \n  INV ID CHN-3 / Dr. CHN-3 Doe      5 (6.8%)     1 (1.2%)       5 (7.8%)        11 (5%)   \n  INV ID CHN-4 / Dr. CHN-4 Doe      3 (4.1%)     3 (3.7%)       3 (4.7%)        9 (4.1%)  \n  INV ID CHN-5 / Dr. CHN-5 Doe      4 (5.4%)     3 (3.7%)       4 (6.2%)        11 (5%)   \n  INV ID CHN-6 / Dr. CHN-6 Doe      1 (1.4%)     3 (3.7%)          0            4 (1.8%)  \n  INV ID CHN-7 / Dr. CHN-7 Doe         0         5 (6.2%)       1 (1.6%)        6 (2.7%)  \n  INV ID CHN-8 / Dr. CHN-8 Doe      1 (1.4%)     1 (1.2%)          0            2 (0.9%)  \n  INV ID CHN-9 / Dr. CHN-9 Doe      1 (1.4%)     2 (2.5%)          0            3 (1.4%)  \nUSA                                10 (7.5%)    13 (9.7%)      17 (12.9%)      40 (10.0%) \n  INV ID USA-1 / Dr. USA-1 Doe      1 (10%)     4 (30.8%)      5 (29.4%)        10 (25%)  \n  INV ID USA-11 / Dr. USA-11 Doe    4 (40%)     2 (15.4%)      3 (17.6%)       9 (22.5%)  \n  INV ID USA-12 / Dr. USA-12 Doe    1 (10%)     2 (15.4%)      3 (17.6%)        6 (15%)   \n  INV ID USA-14 / Dr. USA-14 Doe    1 (10%)         0              0            1 (2.5%)  \n  INV ID USA-15 / Dr. USA-15 Doe       0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-17 / Dr. USA-17 Doe    1 (10%)      1 (7.7%)          0             2 (5%)   \n  INV ID USA-19 / Dr. USA-19 Doe       0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-2 / Dr. USA-2 Doe         0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-3 / Dr. USA-3 Doe      1 (10%)         0           1 (5.9%)         2 (5%)   \n  INV ID USA-4 / Dr. USA-4 Doe         0         1 (7.7%)       1 (5.9%)         2 (5%)   \n  INV ID USA-5 / Dr. USA-5 Doe         0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-6 / Dr. USA-6 Doe         0         1 (7.7%)          0            1 (2.5%)  \n  INV ID USA-8 / Dr. USA-8 Doe         0            0           1 (5.9%)        1 (2.5%)  \n  INV ID USA-9 / Dr. USA-9 Doe      1 (10%)         0              0            1 (2.5%)  \nBRA                                13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  \n  INV ID BRA-1 / Dr. BRA-1 Doe     4 (30.8%)    2 (28.6%)       2 (20%)        8 (26.7%)  \n  INV ID BRA-11 / Dr. BRA-11 Doe   4 (30.8%)        0           4 (40%)        8 (26.7%)  \n  INV ID BRA-12 / Dr. BRA-12 Doe       0        1 (14.3%)          0            1 (3.3%)  \n  INV ID BRA-13 / Dr. BRA-13 Doe    1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-14 / Dr. BRA-14 Doe    1 (7.7%)        0           1 (10%)         2 (6.7%)  \n  INV ID BRA-15 / Dr. BRA-15 Doe    1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-2 / Dr. BRA-2 Doe      1 (7.7%)    1 (14.3%)          0            2 (6.7%)  \n  INV ID BRA-3 / Dr. BRA-3 Doe         0        2 (28.6%)          0            2 (6.7%)  \n  INV ID BRA-4 / Dr. BRA-4 Doe         0        1 (14.3%)       1 (10%)         2 (6.7%)  \n  INV ID BRA-5 / Dr. BRA-5 Doe         0            0           1 (10%)         1 (3.3%)  \n  INV ID BRA-6 / Dr. BRA-6 Doe      1 (7.7%)        0              0            1 (3.3%)  \n  INV ID BRA-7 / Dr. BRA-7 Doe         0            0           1 (10%)         1 (3.3%)  \nPAK                                12 (9.0%)     9 (6.7%)      10 (7.6%)       31 (7.8%)  \n  INV ID PAK-1 / Dr. PAK-1 Doe     2 (16.7%)    4 (44.4%)       4 (40%)        10 (32.3%) \n  INV ID PAK-11 / Dr. PAK-11 Doe   4 (33.3%)    3 (33.3%)       2 (20%)         9 (29%)   \n  INV ID PAK-12 / Dr. PAK-12 Doe   2 (16.7%)        0              0            2 (6.5%)  \n  INV ID PAK-13 / Dr. PAK-13 Doe    1 (8.3%)    1 (11.1%)          0            2 (6.5%)  \n  INV ID PAK-14 / Dr. PAK-14 Doe    1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-15 / Dr. PAK-15 Doe    1 (8.3%)        0              0            1 (3.2%)  \n  INV ID PAK-2 / Dr. PAK-2 Doe      1 (8.3%)        0           2 (20%)         3 (9.7%)  \n  INV ID PAK-4 / Dr. PAK-4 Doe         0        1 (11.1%)       1 (10%)         2 (6.5%)  \n  INV ID PAK-5 / Dr. PAK-5 Doe         0            0           1 (10%)         1 (3.2%)  \nNGA                                 8 (6.0%)     7 (5.2%)      11 (8.3%)       26 (6.5%)  \n  INV ID NGA-1 / Dr. NGA-1 Doe     3 (37.5%)    3 (42.9%)      4 (36.4%)       10 (38.5%) \n  INV ID NGA-11 / Dr. NGA-11 Doe   1 (12.5%)    2 (28.6%)      3 (27.3%)       6 (23.1%)  \n  INV ID NGA-12 / Dr. NGA-12 Doe       0        1 (14.3%)       1 (9.1%)        2 (7.7%)  \n  INV ID NGA-17 / Dr. NGA-17 Doe    2 (25%)         0              0            2 (7.7%)  \n  INV ID NGA-2 / Dr. NGA-2 Doe     1 (12.5%)    1 (14.3%)          0            2 (7.7%)  \n  INV ID NGA-4 / Dr. NGA-4 Doe     1 (12.5%)        0           1 (9.1%)        2 (7.7%)  \n  INV ID NGA-5 / Dr. NGA-5 Doe         0            0           1 (9.1%)        1 (3.8%)  \n  INV ID NGA-6 / Dr. NGA-6 Doe         0            0           1 (9.1%)        1 (3.8%)  \nRUS                                 5 (3.7%)     8 (6.0%)       6 (4.5%)       19 (4.8%)  \n  INV ID RUS-1 / Dr. RUS-1 Doe      1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-11 / Dr. RUS-11 Doe       0         2 (25%)       2 (33.3%)       4 (21.1%)  \n  INV ID RUS-12 / Dr. RUS-12 Doe       0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-13 / Dr. RUS-13 Doe    1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-14 / Dr. RUS-14 Doe       0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-16 / Dr. RUS-16 Doe    1 (20%)     1 (12.5%)          0           2 (10.5%)  \n  INV ID RUS-18 / Dr. RUS-18 Doe    1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-2 / Dr. RUS-2 Doe         0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-3 / Dr. RUS-3 Doe         0            0          1 (16.7%)        1 (5.3%)  \n  INV ID RUS-4 / Dr. RUS-4 Doe         0         2 (25%)       1 (16.7%)       3 (15.8%)  \n  INV ID RUS-5 / Dr. RUS-5 Doe         0        1 (12.5%)          0            1 (5.3%)  \n  INV ID RUS-6 / Dr. RUS-6 Doe      1 (20%)         0              0            1 (5.3%)  \n  INV ID RUS-7 / Dr. RUS-7 Doe         0            0          1 (16.7%)        1 (5.3%)  \nJPN                                 5 (3.7%)     4 (3.0%)       9 (6.8%)       18 (4.5%)  \n  INV ID JPN-1 / Dr. JPN-1 Doe      2 (40%)      2 (50%)       1 (11.1%)       5 (27.8%)  \n  INV ID JPN-11 / Dr. JPN-11 Doe       0            0          3 (33.3%)       3 (16.7%)  \n  INV ID JPN-12 / Dr. JPN-12 Doe       0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-14 / Dr. JPN-14 Doe       0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-17 / Dr. JPN-17 Doe       0            0          2 (22.2%)       2 (11.1%)  \n  INV ID JPN-18 / Dr. JPN-18 Doe    1 (20%)         0              0            1 (5.6%)  \n  INV ID JPN-2 / Dr. JPN-2 Doe         0            0          1 (11.1%)        1 (5.6%)  \n  INV ID JPN-3 / Dr. JPN-3 Doe         0         1 (25%)           0            1 (5.6%)  \n  INV ID JPN-5 / Dr. JPN-5 Doe      1 (20%)         0          1 (11.1%)       2 (11.1%)  \n  INV ID JPN-6 / Dr. JPN-6 Doe      1 (20%)         0              0            1 (5.6%)  \nGBR                                 4 (3.0%)     3 (2.2%)       2 (1.5%)        9 (2.2%)  \n  INV ID GBR-1 / Dr. GBR-1 Doe      2 (50%)         0           1 (50%)        3 (33.3%)  \n  INV ID GBR-11 / Dr. GBR-11 Doe       0        1 (33.3%)       1 (50%)        2 (22.2%)  \n  INV ID GBR-13 / Dr. GBR-13 Doe    1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-15 / Dr. GBR-15 Doe    1 (25%)         0              0           1 (11.1%)  \n  INV ID GBR-17 / Dr. GBR-17 Doe       0        1 (33.3%)          0           1 (11.1%)  \n  INV ID GBR-6 / Dr. GBR-6 Doe         0        1 (33.3%)          0           1 (11.1%)  \nCAN                                 3 (2.2%)     2 (1.5%)       3 (2.3%)        8 (2.0%)  \n  INV ID CAN-1 / Dr. CAN-1 Doe     1 (33.3%)     1 (50%)           0            2 (25%)   \n  INV ID CAN-11 / Dr. CAN-11 Doe   1 (33.3%)        0          1 (33.3%)        2 (25%)   \n  INV ID CAN-14 / Dr. CAN-14 Doe   1 (33.3%)        0              0           1 (12.5%)  \n  INV ID CAN-4 / Dr. CAN-4 Doe         0         1 (50%)       1 (33.3%)        2 (25%)   \n  INV ID CAN-5 / Dr. CAN-5 Doe         0            0          1 (33.3%)       1 (12.5%)  \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nNote that for this module application, only the variables passed into by_vars are used when row_groups is selected. Variables passed into analyze_vars are additionally used when row_groups is deselected.\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(dplyr)\n  library(forcats)\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n\n  adsl_labels &lt;- col_labels(ADSL)\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(\n      REGION1 = droplevels(REGION1),\n      COUNTRY = droplevels(COUNTRY),\n      INVID_INVNAM = paste(INVID, INVNAM, sep = \" / \")\n    ) %&gt;%\n    arrange(REGION1, COUNTRY, INVID) %&gt;%\n    mutate(\n      INVID = factor(INVID),\n      SITEID = factor(SITEID),\n      INVNAM = factor(INVNAM),\n      INVID_INVNAM = factor(INVID_INVNAM)\n    )\n\n  col_labels(ADSL) &lt;- c(adsl_labels, c(INVID_INVNAM = \"Investigator Number/Name\"))\n})\ndatanames &lt;- \"ADSL\"\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_summary_by(\n      label = \"Enrollment Table\",\n      dataname = \"ADSL\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      by_vars = choices_selected(\n        choices = variable_choices(ADSL, c(\"REGION1\", \"COUNTRY\", \"SITEID\", \"INVID\", \"INVNAM\", \"INVID_INVNAM\")),\n        selected = c(\"REGION1\")\n      ),\n      summarize_vars = choices_selected(\n        choices = variable_choices(ADSL, c(\"REGION1\", \"COUNTRY\", \"SITEID\", \"INVID\", \"INVNAM\", \"INVID_INVNAM\")),\n        selected = c(\"SITEID\")\n      ),\n      denominator = choices_selected(\n        choices = c(\"n\", \"N\", \"omit\"),\n        selected = \"N\",\n        fixed = TRUE\n      ),\n      useNA = \"ifany\",\n      row_groups = TRUE\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:48:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            scda_0.1.6.9017                 \n[13] forcats_1.0.0                    dplyr_1.1.4                     \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] rlang_1.1.3              sass_0.4.8               tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Safety",
@@ -1343,7 +1343,7 @@
     "href": "tables/lab-results/lbt13.html",
     "title": "LBT13",
     "section": "",
-    "text": "Data Setup\nStandard Table (Low)\nStandard Table (High)\nTable Without Patients with  Missing Baseline (Low)\nTable with Missing Baseline  Considered as Grade 0 (High)\nTable with Fill-In  of Grades (High)\nteal App\nReproducibility\n\n\n\nPlease note that for each variant, the adlb dataset needs to be filtered on correct flags like WGRLOVFL, WGRHIVFL, et al., otherwise the layout function will not return the correct counts. There is an option to create a record for a lab test where no record is found at that visit. If you specified add_derived_type = \"PHANTOM\" & dtype_phantom_cond, you don’t have to use the h_adsl_adlb_merge_using_worst_flag function to preprocess your adlb dataset. Otherwise please follow the pre-processing steps below before applying the layout functions.\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n# Please note that in real clinical data, population flag like SAFFL, and parameter category like PARCAT2 needs to be\n# selected properly.\nadsl_f &lt;- adsl %&gt;% filter(SAFFL == \"Y\")\nadlb &lt;- adlb %&gt;% filter(PARAMCD == \"CRP\" & SAFFL == \"Y\")\n\n\n\n\nNote that the worst laboratory flag (below WGRLOVFL) must be selected appropriately to match the direction of abnormality (here Low). New grouping variables ATOXGR_GP and BTOXGR_GP are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRLOVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                             \n  Visit                                                                    \n    NCI CTCAE Grade at Visit      A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade     (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————\nCRP                                                                        \n  WEEK 1 DAY 8                                                             \n    Not Low (n)                      116           110            101      \n      Not Low                    103 (88.8%)   91 (82.7%)      88 (87.1%)  \n      1                           3 (2.6%)      5 (4.5%)        6 (5.9%)   \n      2                           4 (3.4%)      9 (8.2%)        2 (2.0%)   \n      3                           3 (2.6%)      4 (3.6%)        4 (4.0%)   \n      4                           3 (2.6%)      1 (0.9%)        1 (1.0%)   \n    1 (n)                             4             7              5       \n      Not Low                     3 (75.0%)     7 (100%)       4 (80.0%)   \n      3                           1 (25.0%)         0          1 (20.0%)   \n    2 (n)                             5             6              11      \n      Not Low                     5 (100%)      5 (83.3%)      8 (72.7%)   \n      1                               0             0           1 (9.1%)   \n      3                               0             0          2 (18.2%)   \n      4                               0         1 (16.7%)          0       \n    3 (n)                             6             8              5       \n      Not Low                     6 (100%)      7 (87.5%)       5 (100%)   \n      3                               0         1 (12.5%)          0       \n    4 (n)                             3             3              10      \n      Not Low                     2 (66.7%)     3 (100%)       7 (70.0%)   \n      1                               0             0          2 (20.0%)   \n      2                           1 (33.3%)         0              0       \n      4                               0             0          1 (10.0%)   \n  WEEK 2 DAY 15                                                            \n    Not Low (n)                      106           111            116      \n      Not Low                    93 (87.7%)    93 (83.8%)     100 (86.2%)  \n      1                           3 (2.8%)      4 (3.6%)        8 (6.9%)   \n      2                           5 (4.7%)      7 (6.3%)        2 (1.7%)   \n      3                           2 (1.9%)      5 (4.5%)        6 (5.2%)   \n      4                           3 (2.8%)      2 (1.8%)           0       \n    1 (n)                            15             9              2       \n      Not Low                    13 (86.7%)     8 (88.9%)      1 (50.0%)   \n      2                               0         1 (11.1%)          0       \n      3                           2 (13.3%)         0              0       \n      4                               0             0          1 (50.0%)   \n    2 (n)                             3             3              6       \n      Not Low                     3 (100%)      3 (100%)       4 (66.7%)   \n      1                               0             0          1 (16.7%)   \n      3                               0             0          1 (16.7%)   \n    3 (n)                             7             7              6       \n      Not Low                     7 (100%)      6 (85.7%)      5 (83.3%)   \n      2                               0         1 (14.3%)          0       \n      4                               0             0          1 (16.7%)   \n    4 (n)                             3             4              2       \n      Not Low                     3 (100%)      3 (75.0%)       2 (100%)   \n      1                               0         1 (25.0%)          0       \n  WEEK 3 DAY 22                                                            \n    Not Low (n)                      119           110            114      \n      Not Low                    104 (87.4%)   92 (83.6%)      94 (82.5%)  \n      1                           3 (2.5%)      4 (3.6%)        9 (7.9%)   \n      2                           5 (4.2%)      7 (6.4%)        2 (1.8%)   \n      3                           4 (3.4%)      5 (4.5%)        7 (6.1%)   \n      4                           3 (2.5%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             9             9              6       \n      Not Low                     9 (100%)      7 (77.8%)       6 (100%)   \n      2                               0         2 (22.2%)          0       \n    2 (n)                             1             7              3       \n      Not Low                     1 (100%)      6 (85.7%)       3 (100%)   \n      1                               0         1 (14.3%)          0       \n    3 (n)                             4             5              4       \n      Not Low                     4 (100%)      5 (100%)        4 (100%)   \n    4 (n)                             1             3              5       \n      Not Low                     1 (100%)      3 (100%)        5 (100%)   \n  WEEK 4 DAY 29                                                            \n    Not Low (n)                      101           119            111      \n      Not Low                    89 (88.1%)    100 (84.0%)     96 (86.5%)  \n      1                           3 (3.0%)      4 (3.4%)        6 (5.4%)   \n      2                           4 (4.0%)      8 (6.7%)        1 (0.9%)   \n      3                           2 (2.0%)      5 (4.2%)        6 (5.4%)   \n      4                           3 (3.0%)      2 (1.7%)        2 (1.8%)   \n    1 (n)                            10             5              6       \n      Not Low                     9 (90.0%)     5 (100%)       5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                           1 (10.0%)         0              0       \n    2 (n)                            13             4              6       \n      Not Low                     13 (100%)     3 (75.0%)      5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                               0         1 (25.0%)          0       \n    3 (n)                             8             2              6       \n      Not Low                     6 (75.0%)     1 (50.0%)      5 (83.3%)   \n      1                               0         1 (50.0%)          0       \n      2                               0             0          1 (16.7%)   \n      3                           2 (25.0%)         0              0       \n    4 (n)                             2             4              3       \n      Not Low                     2 (100%)      4 (100%)       1 (33.3%)   \n      1                               0             0          1 (33.3%)   \n      3                               0             0          1 (33.3%)   \n  WEEK 5 DAY 36                                                            \n    Not Low (n)                      115           112            111      \n      Not Low                    102 (88.7%)   94 (83.9%)      96 (86.5%)  \n      1                           2 (1.7%)      4 (3.6%)        6 (5.4%)   \n      2                           5 (4.3%)      7 (6.2%)        2 (1.8%)   \n      3                           3 (2.6%)      5 (4.5%)        5 (4.5%)   \n      4                           3 (2.6%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             5             7              6       \n      Not Low                     5 (100%)      4 (57.1%)      5 (83.3%)   \n      1                               0         1 (14.3%)      1 (16.7%)   \n      2                               0         2 (28.6%)          0       \n    2 (n)                             5            11              6       \n      Not Low                     5 (100%)      11 (100%)       6 (100%)   \n    3 (n)                             8             2              7       \n      Not Low                     6 (75.0%)     2 (100%)       4 (57.1%)   \n      1                           1 (12.5%)         0          1 (14.3%)   \n      3                           1 (12.5%)         0          2 (28.6%)   \n    4 (n)                             1             2              2       \n      Not Low                     1 (100%)      2 (100%)       1 (50.0%)   \n      1                               0             0          1 (50.0%)   \n\n\n\n\nNote that the worst laboratory flag (below WGRHIVFL) must be selected appropriately to match the direction of abnormality (here High). New grouping variables ATOXGR_GP and BTOXGR_GP are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRHIVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                            \n  Visit                                                                   \n    NCI CTCAE Grade at Visit     A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade    (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————\nCRP                                                                       \n  WEEK 1 DAY 8                                                            \n    Not High (n)                    111           110            112      \n      Not High                   93 (83.8%)   92 (83.6%)      98 (87.5%)  \n      1                           8 (7.2%)     7 (6.4%)        5 (4.5%)   \n      2                           2 (1.8%)     6 (5.5%)        7 (6.2%)   \n      3                           6 (5.4%)     3 (2.7%)        2 (1.8%)   \n      4                           2 (1.8%)     2 (1.8%)           0       \n    1 (n)                            8            11              6       \n      Not High                   7 (87.5%)     11 (100%)      5 (83.3%)   \n      3                          1 (12.5%)         0          1 (16.7%)   \n    2 (n)                            6             4              5       \n      Not High                    6 (100%)     1 (25.0%)      4 (80.0%)   \n      1                              0         3 (75.0%)      1 (20.0%)   \n    3 (n)                            8             3              4       \n      Not High                   7 (87.5%)     3 (100%)        4 (100%)   \n      4                          1 (12.5%)         0              0       \n    4 (n)                            1             6              5       \n      Not High                    1 (100%)     5 (83.3%)      4 (80.0%)   \n      1                              0             0          1 (20.0%)   \n      3                              0         1 (16.7%)          0       \n  WEEK 2 DAY 15                                                           \n    Not High (n)                    117           113            113      \n      Not High                   99 (84.6%)   95 (84.1%)     100 (88.5%)  \n      1                           7 (6.0%)     6 (5.3%)        4 (3.5%)   \n      2                           2 (1.7%)     6 (5.3%)        6 (5.3%)   \n      3                           6 (5.1%)     4 (3.5%)        3 (2.7%)   \n      4                           3 (2.6%)     2 (1.8%)           0       \n    1 (n)                            6             9              7       \n      Not High                   5 (83.3%)     8 (88.9%)      6 (85.7%)   \n      1                              0         1 (11.1%)          0       \n      2                              0             0          1 (14.3%)   \n      3                          1 (16.7%)         0              0       \n    2 (n)                            4             4              6       \n      Not High                    4 (100%)     4 (100%)       3 (50.0%)   \n      1                              0             0          3 (50.0%)   \n    3 (n)                            5             3              5       \n      Not High                   4 (80.0%)     2 (66.7%)       5 (100%)   \n      1                          1 (20.0%)     1 (33.3%)          0       \n    4 (n)                            2             5              1       \n      Not High                    2 (100%)     3 (60.0%)       1 (100%)   \n      1                              0         2 (40.0%)          0       \n  WEEK 3 DAY 22                                                           \n    Not High (n)                    110           111            114      \n      Not High                   92 (83.6%)   94 (84.7%)      98 (86.0%)  \n      1                           8 (7.3%)     9 (8.1%)        7 (6.1%)   \n      2                           2 (1.8%)     5 (4.5%)        6 (5.3%)   \n      3                           5 (4.5%)     2 (1.8%)        3 (2.6%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            7             8              7       \n      Not High                    7 (100%)     5 (62.5%)      6 (85.7%)   \n      1                              0         1 (12.5%)          0       \n      2                              0         1 (12.5%)      1 (14.3%)   \n      3                              0         1 (12.5%)          0       \n    2 (n)                            9             7              5       \n      Not High                    9 (100%)     6 (85.7%)       5 (100%)   \n      3                              0         1 (14.3%)          0       \n    3 (n)                            6             3              4       \n      Not High                   5 (83.3%)     2 (66.7%)       4 (100%)   \n      3                          1 (16.7%)         0              0       \n      4                              0         1 (33.3%)          0       \n    4 (n)                            2             5              2       \n      Not High                   1 (50.0%)     5 (100%)        2 (100%)   \n      3                          1 (50.0%)         0              0       \n  WEEK 4 DAY 29                                                           \n    Not High (n)                    115           117            113      \n      Not High                   97 (84.3%)   101 (86.3%)     99 (87.6%)  \n      1                           8 (7.0%)     7 (6.0%)        6 (5.3%)   \n      2                           2 (1.7%)     6 (5.1%)        5 (4.4%)   \n      3                           7 (6.1%)     3 (2.6%)        3 (2.7%)   \n      4                           1 (0.9%)         0              0       \n    1 (n)                            4             3              11      \n      Not High                    4 (100%)     2 (66.7%)      10 (90.9%)  \n      2                              0             0           1 (9.1%)   \n      4                              0         1 (33.3%)          0       \n    2 (n)                            6             4              2       \n      Not High                    6 (100%)     3 (75.0%)       2 (100%)   \n      1                              0         1 (25.0%)          0       \n    3 (n)                            6             7              4       \n      Not High                   5 (83.3%)     4 (57.1%)       4 (100%)   \n      1                              0         1 (14.3%)          0       \n      3                              0         1 (14.3%)          0       \n      4                          1 (16.7%)     1 (14.3%)          0       \n    4 (n)                            3             3              2       \n      Not High                   2 (66.7%)     2 (66.7%)          0       \n      1                              0         1 (33.3%)      1 (50.0%)   \n      2                              0             0          1 (50.0%)   \n      4                          1 (33.3%)         0              0       \n  WEEK 5 DAY 36                                                           \n    Not High (n)                    111           111            119      \n      Not High                   95 (85.6%)   95 (85.6%)     104 (87.4%)  \n      1                           6 (5.4%)     8 (7.2%)        6 (5.0%)   \n      2                           1 (0.9%)     5 (4.5%)        6 (5.0%)   \n      3                           6 (5.4%)     2 (1.8%)        3 (2.5%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            8            11              5       \n      Not High                   7 (87.5%)     9 (81.8%)      4 (80.0%)   \n      1                              0         1 (9.1%)           0       \n      2                              0             0          1 (20.0%)   \n      3                          1 (12.5%)     1 (9.1%)           0       \n    2 (n)                            7             6              3       \n      Not High                   5 (71.4%)     3 (50.0%)      2 (66.7%)   \n      1                          1 (14.3%)     1 (16.7%)      1 (33.3%)   \n      2                          1 (14.3%)     1 (16.7%)          0       \n      4                              0         1 (16.7%)          0       \n    3 (n)                            3             3              4       \n      Not High                    3 (100%)     3 (100%)        4 (100%)   \n    4 (n)                            5             3              1       \n      Not High                   4 (80.0%)     2 (66.7%)       1 (100%)   \n      1                          1 (20.0%)         0              0       \n      3                              0         1 (33.3%)          0       \n\n\n\n\nNote that missing baseline values are filtered out in the pre-processing step.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRLOVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  filter(BTOXGR != \"&lt;Missing&gt;\") %&gt;% # filter out missing baseline grade\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                             \n  Visit                                                                    \n    NCI CTCAE Grade at Visit      A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade     (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————\nCRP                                                                        \n  WEEK 1 DAY 8                                                             \n    Not Low (n)                      116           110            101      \n      Not Low                    103 (88.8%)   91 (82.7%)      88 (87.1%)  \n      1                           3 (2.6%)      5 (4.5%)        6 (5.9%)   \n      2                           4 (3.4%)      9 (8.2%)        2 (2.0%)   \n      3                           3 (2.6%)      4 (3.6%)        4 (4.0%)   \n      4                           3 (2.6%)      1 (0.9%)        1 (1.0%)   \n    1 (n)                             4             7              5       \n      Not Low                     3 (75.0%)     7 (100%)       4 (80.0%)   \n      3                           1 (25.0%)         0          1 (20.0%)   \n    2 (n)                             5             6              11      \n      Not Low                     5 (100%)      5 (83.3%)      8 (72.7%)   \n      1                               0             0           1 (9.1%)   \n      3                               0             0          2 (18.2%)   \n      4                               0         1 (16.7%)          0       \n    3 (n)                             6             8              5       \n      Not Low                     6 (100%)      7 (87.5%)       5 (100%)   \n      3                               0         1 (12.5%)          0       \n    4 (n)                             3             3              10      \n      Not Low                     2 (66.7%)     3 (100%)       7 (70.0%)   \n      1                               0             0          2 (20.0%)   \n      2                           1 (33.3%)         0              0       \n      4                               0             0          1 (10.0%)   \n  WEEK 2 DAY 15                                                            \n    Not Low (n)                      106           111            116      \n      Not Low                    93 (87.7%)    93 (83.8%)     100 (86.2%)  \n      1                           3 (2.8%)      4 (3.6%)        8 (6.9%)   \n      2                           5 (4.7%)      7 (6.3%)        2 (1.7%)   \n      3                           2 (1.9%)      5 (4.5%)        6 (5.2%)   \n      4                           3 (2.8%)      2 (1.8%)           0       \n    1 (n)                            15             9              2       \n      Not Low                    13 (86.7%)     8 (88.9%)      1 (50.0%)   \n      2                               0         1 (11.1%)          0       \n      3                           2 (13.3%)         0              0       \n      4                               0             0          1 (50.0%)   \n    2 (n)                             3             3              6       \n      Not Low                     3 (100%)      3 (100%)       4 (66.7%)   \n      1                               0             0          1 (16.7%)   \n      3                               0             0          1 (16.7%)   \n    3 (n)                             7             7              6       \n      Not Low                     7 (100%)      6 (85.7%)      5 (83.3%)   \n      2                               0         1 (14.3%)          0       \n      4                               0             0          1 (16.7%)   \n    4 (n)                             3             4              2       \n      Not Low                     3 (100%)      3 (75.0%)       2 (100%)   \n      1                               0         1 (25.0%)          0       \n  WEEK 3 DAY 22                                                            \n    Not Low (n)                      119           110            114      \n      Not Low                    104 (87.4%)   92 (83.6%)      94 (82.5%)  \n      1                           3 (2.5%)      4 (3.6%)        9 (7.9%)   \n      2                           5 (4.2%)      7 (6.4%)        2 (1.8%)   \n      3                           4 (3.4%)      5 (4.5%)        7 (6.1%)   \n      4                           3 (2.5%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             9             9              6       \n      Not Low                     9 (100%)      7 (77.8%)       6 (100%)   \n      2                               0         2 (22.2%)          0       \n    2 (n)                             1             7              3       \n      Not Low                     1 (100%)      6 (85.7%)       3 (100%)   \n      1                               0         1 (14.3%)          0       \n    3 (n)                             4             5              4       \n      Not Low                     4 (100%)      5 (100%)        4 (100%)   \n    4 (n)                             1             3              5       \n      Not Low                     1 (100%)      3 (100%)        5 (100%)   \n  WEEK 4 DAY 29                                                            \n    Not Low (n)                      101           119            111      \n      Not Low                    89 (88.1%)    100 (84.0%)     96 (86.5%)  \n      1                           3 (3.0%)      4 (3.4%)        6 (5.4%)   \n      2                           4 (4.0%)      8 (6.7%)        1 (0.9%)   \n      3                           2 (2.0%)      5 (4.2%)        6 (5.4%)   \n      4                           3 (3.0%)      2 (1.7%)        2 (1.8%)   \n    1 (n)                            10             5              6       \n      Not Low                     9 (90.0%)     5 (100%)       5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                           1 (10.0%)         0              0       \n    2 (n)                            13             4              6       \n      Not Low                     13 (100%)     3 (75.0%)      5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                               0         1 (25.0%)          0       \n    3 (n)                             8             2              6       \n      Not Low                     6 (75.0%)     1 (50.0%)      5 (83.3%)   \n      1                               0         1 (50.0%)          0       \n      2                               0             0          1 (16.7%)   \n      3                           2 (25.0%)         0              0       \n    4 (n)                             2             4              3       \n      Not Low                     2 (100%)      4 (100%)       1 (33.3%)   \n      1                               0             0          1 (33.3%)   \n      3                               0             0          1 (33.3%)   \n  WEEK 5 DAY 36                                                            \n    Not Low (n)                      115           112            111      \n      Not Low                    102 (88.7%)   94 (83.9%)      96 (86.5%)  \n      1                           2 (1.7%)      4 (3.6%)        6 (5.4%)   \n      2                           5 (4.3%)      7 (6.2%)        2 (1.8%)   \n      3                           3 (2.6%)      5 (4.5%)        5 (4.5%)   \n      4                           3 (2.6%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             5             7              6       \n      Not Low                     5 (100%)      4 (57.1%)      5 (83.3%)   \n      1                               0         1 (14.3%)      1 (16.7%)   \n      2                               0         2 (28.6%)          0       \n    2 (n)                             5            11              6       \n      Not Low                     5 (100%)      11 (100%)       6 (100%)   \n    3 (n)                             8             2              7       \n      Not Low                     6 (75.0%)     2 (100%)       4 (57.1%)   \n      1                           1 (12.5%)         0          1 (14.3%)   \n      3                           1 (12.5%)         0          2 (28.6%)   \n    4 (n)                             1             2              2       \n      Not Low                     1 (100%)      2 (100%)       1 (50.0%)   \n      1                               0             0          1 (50.0%)   \n\n\n\n\nNote that when BTOXGR is missing, the grouping variable BTOXGR_GP is now \"Not High\" instead of \"Missing\" compared to Standard Table (High).\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRHIVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4, \"&lt;Missing&gt;\") ~ \"Not High\", # Missing BTOXGR now grouped to \"Not High\"\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                            \n  Visit                                                                   \n    NCI CTCAE Grade at Visit     A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade    (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————\nCRP                                                                       \n  WEEK 1 DAY 8                                                            \n    Not High (n)                    111           110            112      \n      Not High                   93 (83.8%)   92 (83.6%)      98 (87.5%)  \n      1                           8 (7.2%)     7 (6.4%)        5 (4.5%)   \n      2                           2 (1.8%)     6 (5.5%)        7 (6.2%)   \n      3                           6 (5.4%)     3 (2.7%)        2 (1.8%)   \n      4                           2 (1.8%)     2 (1.8%)           0       \n    1 (n)                            8            11              6       \n      Not High                   7 (87.5%)     11 (100%)      5 (83.3%)   \n      3                          1 (12.5%)         0          1 (16.7%)   \n    2 (n)                            6             4              5       \n      Not High                    6 (100%)     1 (25.0%)      4 (80.0%)   \n      1                              0         3 (75.0%)      1 (20.0%)   \n    3 (n)                            8             3              4       \n      Not High                   7 (87.5%)     3 (100%)        4 (100%)   \n      4                          1 (12.5%)         0              0       \n    4 (n)                            1             6              5       \n      Not High                    1 (100%)     5 (83.3%)      4 (80.0%)   \n      1                              0             0          1 (20.0%)   \n      3                              0         1 (16.7%)          0       \n  WEEK 2 DAY 15                                                           \n    Not High (n)                    117           113            113      \n      Not High                   99 (84.6%)   95 (84.1%)     100 (88.5%)  \n      1                           7 (6.0%)     6 (5.3%)        4 (3.5%)   \n      2                           2 (1.7%)     6 (5.3%)        6 (5.3%)   \n      3                           6 (5.1%)     4 (3.5%)        3 (2.7%)   \n      4                           3 (2.6%)     2 (1.8%)           0       \n    1 (n)                            6             9              7       \n      Not High                   5 (83.3%)     8 (88.9%)      6 (85.7%)   \n      1                              0         1 (11.1%)          0       \n      2                              0             0          1 (14.3%)   \n      3                          1 (16.7%)         0              0       \n    2 (n)                            4             4              6       \n      Not High                    4 (100%)     4 (100%)       3 (50.0%)   \n      1                              0             0          3 (50.0%)   \n    3 (n)                            5             3              5       \n      Not High                   4 (80.0%)     2 (66.7%)       5 (100%)   \n      1                          1 (20.0%)     1 (33.3%)          0       \n    4 (n)                            2             5              1       \n      Not High                    2 (100%)     3 (60.0%)       1 (100%)   \n      1                              0         2 (40.0%)          0       \n  WEEK 3 DAY 22                                                           \n    Not High (n)                    110           111            114      \n      Not High                   92 (83.6%)   94 (84.7%)      98 (86.0%)  \n      1                           8 (7.3%)     9 (8.1%)        7 (6.1%)   \n      2                           2 (1.8%)     5 (4.5%)        6 (5.3%)   \n      3                           5 (4.5%)     2 (1.8%)        3 (2.6%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            7             8              7       \n      Not High                    7 (100%)     5 (62.5%)      6 (85.7%)   \n      1                              0         1 (12.5%)          0       \n      2                              0         1 (12.5%)      1 (14.3%)   \n      3                              0         1 (12.5%)          0       \n    2 (n)                            9             7              5       \n      Not High                    9 (100%)     6 (85.7%)       5 (100%)   \n      3                              0         1 (14.3%)          0       \n    3 (n)                            6             3              4       \n      Not High                   5 (83.3%)     2 (66.7%)       4 (100%)   \n      3                          1 (16.7%)         0              0       \n      4                              0         1 (33.3%)          0       \n    4 (n)                            2             5              2       \n      Not High                   1 (50.0%)     5 (100%)        2 (100%)   \n      3                          1 (50.0%)         0              0       \n  WEEK 4 DAY 29                                                           \n    Not High (n)                    115           117            113      \n      Not High                   97 (84.3%)   101 (86.3%)     99 (87.6%)  \n      1                           8 (7.0%)     7 (6.0%)        6 (5.3%)   \n      2                           2 (1.7%)     6 (5.1%)        5 (4.4%)   \n      3                           7 (6.1%)     3 (2.6%)        3 (2.7%)   \n      4                           1 (0.9%)         0              0       \n    1 (n)                            4             3              11      \n      Not High                    4 (100%)     2 (66.7%)      10 (90.9%)  \n      2                              0             0           1 (9.1%)   \n      4                              0         1 (33.3%)          0       \n    2 (n)                            6             4              2       \n      Not High                    6 (100%)     3 (75.0%)       2 (100%)   \n      1                              0         1 (25.0%)          0       \n    3 (n)                            6             7              4       \n      Not High                   5 (83.3%)     4 (57.1%)       4 (100%)   \n      1                              0         1 (14.3%)          0       \n      3                              0         1 (14.3%)          0       \n      4                          1 (16.7%)     1 (14.3%)          0       \n    4 (n)                            3             3              2       \n      Not High                   2 (66.7%)     2 (66.7%)          0       \n      1                              0         1 (33.3%)      1 (50.0%)   \n      2                              0             0          1 (50.0%)   \n      4                          1 (33.3%)         0              0       \n  WEEK 5 DAY 36                                                           \n    Not High (n)                    111           111            119      \n      Not High                   95 (85.6%)   95 (85.6%)     104 (87.4%)  \n      1                           6 (5.4%)     8 (7.2%)        6 (5.0%)   \n      2                           1 (0.9%)     5 (4.5%)        6 (5.0%)   \n      3                           6 (5.4%)     2 (1.8%)        3 (2.5%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            8            11              5       \n      Not High                   7 (87.5%)     9 (81.8%)      4 (80.0%)   \n      1                              0         1 (9.1%)           0       \n      2                              0             0          1 (20.0%)   \n      3                          1 (12.5%)     1 (9.1%)           0       \n    2 (n)                            7             6              3       \n      Not High                   5 (71.4%)     3 (50.0%)      2 (66.7%)   \n      1                          1 (14.3%)     1 (16.7%)      1 (33.3%)   \n      2                          1 (14.3%)     1 (16.7%)          0       \n      4                              0         1 (16.7%)          0       \n    3 (n)                            3             3              4       \n      Not High                    3 (100%)     3 (100%)        4 (100%)   \n    4 (n)                            5             3              1       \n      Not High                   4 (80.0%)     2 (66.7%)       1 (100%)   \n      1                          1 (20.0%)         0              0       \n      3                              0         1 (33.3%)          0       \n\n\n\n\nPre-processing is the same as for Standard Table (High), but in order to keep all levels, the drop argument in count_occurrences is set to FALSE.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRHIVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = keep_split_levels(c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = FALSE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                            \n  Visit                                                                   \n    NCI CTCAE Grade at Visit     A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade    (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————\nCRP                                                                       \n  WEEK 1 DAY 8                                                            \n    Not High (n)                    111           110            112      \n      Not High                   93 (83.8%)   92 (83.6%)      98 (87.5%)  \n      1                           8 (7.2%)     7 (6.4%)        5 (4.5%)   \n      2                           2 (1.8%)     6 (5.5%)        7 (6.2%)   \n      3                           6 (5.4%)     3 (2.7%)        2 (1.8%)   \n      4                           2 (1.8%)     2 (1.8%)           0       \n      Missing                        0             0              0       \n    1 (n)                            8            11              6       \n      Not High                   7 (87.5%)     11 (100%)      5 (83.3%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                          1 (12.5%)         0          1 (16.7%)   \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            6             4              5       \n      Not High                    6 (100%)     1 (25.0%)      4 (80.0%)   \n      1                              0         3 (75.0%)      1 (20.0%)   \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            8             3              4       \n      Not High                   7 (87.5%)     3 (100%)        4 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                          1 (12.5%)         0              0       \n      Missing                        0             0              0       \n    4 (n)                            1             6              5       \n      Not High                    1 (100%)     5 (83.3%)      4 (80.0%)   \n      1                              0             0          1 (20.0%)   \n      2                              0             0              0       \n      3                              0         1 (16.7%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 2 DAY 15                                                           \n    Not High (n)                    117           113            113      \n      Not High                   99 (84.6%)   95 (84.1%)     100 (88.5%)  \n      1                           7 (6.0%)     6 (5.3%)        4 (3.5%)   \n      2                           2 (1.7%)     6 (5.3%)        6 (5.3%)   \n      3                           6 (5.1%)     4 (3.5%)        3 (2.7%)   \n      4                           3 (2.6%)     2 (1.8%)           0       \n      Missing                        0             0              0       \n    1 (n)                            6             9              7       \n      Not High                   5 (83.3%)     8 (88.9%)      6 (85.7%)   \n      1                              0         1 (11.1%)          0       \n      2                              0             0          1 (14.3%)   \n      3                          1 (16.7%)         0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            4             4              6       \n      Not High                    4 (100%)     4 (100%)       3 (50.0%)   \n      1                              0             0          3 (50.0%)   \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            5             3              5       \n      Not High                   4 (80.0%)     2 (66.7%)       5 (100%)   \n      1                          1 (20.0%)     1 (33.3%)          0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    4 (n)                            2             5              1       \n      Not High                    2 (100%)     3 (60.0%)       1 (100%)   \n      1                              0         2 (40.0%)          0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 3 DAY 22                                                           \n    Not High (n)                    110           111            114      \n      Not High                   92 (83.6%)   94 (84.7%)      98 (86.0%)  \n      1                           8 (7.3%)     9 (8.1%)        7 (6.1%)   \n      2                           2 (1.8%)     5 (4.5%)        6 (5.3%)   \n      3                           5 (4.5%)     2 (1.8%)        3 (2.6%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n      Missing                        0             0              0       \n    1 (n)                            7             8              7       \n      Not High                    7 (100%)     5 (62.5%)      6 (85.7%)   \n      1                              0         1 (12.5%)          0       \n      2                              0         1 (12.5%)      1 (14.3%)   \n      3                              0         1 (12.5%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            9             7              5       \n      Not High                    9 (100%)     6 (85.7%)       5 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0         1 (14.3%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            6             3              4       \n      Not High                   5 (83.3%)     2 (66.7%)       4 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                          1 (16.7%)         0              0       \n      4                              0         1 (33.3%)          0       \n      Missing                        0             0              0       \n    4 (n)                            2             5              2       \n      Not High                   1 (50.0%)     5 (100%)        2 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                          1 (50.0%)         0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 4 DAY 29                                                           \n    Not High (n)                    115           117            113      \n      Not High                   97 (84.3%)   101 (86.3%)     99 (87.6%)  \n      1                           8 (7.0%)     7 (6.0%)        6 (5.3%)   \n      2                           2 (1.7%)     6 (5.1%)        5 (4.4%)   \n      3                           7 (6.1%)     3 (2.6%)        3 (2.7%)   \n      4                           1 (0.9%)         0              0       \n      Missing                        0             0              0       \n    1 (n)                            4             3              11      \n      Not High                    4 (100%)     2 (66.7%)      10 (90.9%)  \n      1                              0             0              0       \n      2                              0             0           1 (9.1%)   \n      3                              0             0              0       \n      4                              0         1 (33.3%)          0       \n      Missing                        0             0              0       \n    2 (n)                            6             4              2       \n      Not High                    6 (100%)     3 (75.0%)       2 (100%)   \n      1                              0         1 (25.0%)          0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            6             7              4       \n      Not High                   5 (83.3%)     4 (57.1%)       4 (100%)   \n      1                              0         1 (14.3%)          0       \n      2                              0             0              0       \n      3                              0         1 (14.3%)          0       \n      4                          1 (16.7%)     1 (14.3%)          0       \n      Missing                        0             0              0       \n    4 (n)                            3             3              2       \n      Not High                   2 (66.7%)     2 (66.7%)          0       \n      1                              0         1 (33.3%)      1 (50.0%)   \n      2                              0             0          1 (50.0%)   \n      3                              0             0              0       \n      4                          1 (33.3%)         0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 5 DAY 36                                                           \n    Not High (n)                    111           111            119      \n      Not High                   95 (85.6%)   95 (85.6%)     104 (87.4%)  \n      1                           6 (5.4%)     8 (7.2%)        6 (5.0%)   \n      2                           1 (0.9%)     5 (4.5%)        6 (5.0%)   \n      3                           6 (5.4%)     2 (1.8%)        3 (2.5%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n      Missing                        0             0              0       \n    1 (n)                            8            11              5       \n      Not High                   7 (87.5%)     9 (81.8%)      4 (80.0%)   \n      1                              0         1 (9.1%)           0       \n      2                              0             0          1 (20.0%)   \n      3                          1 (12.5%)     1 (9.1%)           0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            7             6              3       \n      Not High                   5 (71.4%)     3 (50.0%)      2 (66.7%)   \n      1                          1 (14.3%)     1 (16.7%)      1 (33.3%)   \n      2                          1 (14.3%)     1 (16.7%)          0       \n      3                              0             0              0       \n      4                              0         1 (16.7%)          0       \n      Missing                        0             0              0       \n    3 (n)                            3             3              4       \n      Not High                    3 (100%)     3 (100%)        4 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    4 (n)                            5             3              1       \n      Not High                   4 (80.0%)     2 (66.7%)       1 (100%)   \n      1                          1 (20.0%)         0              0       \n      2                              0             0              0       \n      3                              0         1 (33.3%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_shift_by_grade(\n      label = \"Grade Laboratory Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      ),\n      worst_flag_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"WGRLOVFL\", \"WGRLOFL\", \"WGRHIVFL\", \"WGRHIFL\")),\n        selected = c(\"WGRLOVFL\")\n      ),\n      worst_flag_indicator = choices_selected(\n        value_choices(ADLB, \"WGRLOVFL\"),\n        selected = \"Y\", fixed = TRUE\n      ),\n      anl_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"ATOXGR\")),\n        selected = c(\"ATOXGR\"),\n        fixed = TRUE\n      ),\n      base_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"BTOXGR\")),\n        selected = c(\"BTOXGR\"),\n        fixed = TRUE\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:50:44 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table (Low)\nStandard Table (High)\nTable Without Patients with  Missing Baseline (Low)\nTable with Missing Baseline  Considered as Grade 0 (High)\nTable with Fill-In  of Grades (High)\nteal App\nReproducibility\n\n\n\nPlease note that for each variant, the adlb dataset needs to be filtered on correct flags like WGRLOVFL, WGRHIVFL, et al., otherwise the layout function will not return the correct counts. There is an option to create a record for a lab test where no record is found at that visit. If you specified add_derived_type = \"PHANTOM\" & dtype_phantom_cond, you don’t have to use the h_adsl_adlb_merge_using_worst_flag function to preprocess your adlb dataset. Otherwise please follow the pre-processing steps below before applying the layout functions.\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n# Please note that in real clinical data, population flag like SAFFL, and parameter category like PARCAT2 needs to be\n# selected properly.\nadsl_f &lt;- adsl %&gt;% filter(SAFFL == \"Y\")\nadlb &lt;- adlb %&gt;% filter(PARAMCD == \"CRP\" & SAFFL == \"Y\")\n\n\n\n\nNote that the worst laboratory flag (below WGRLOVFL) must be selected appropriately to match the direction of abnormality (here Low). New grouping variables ATOXGR_GP and BTOXGR_GP are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRLOVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                             \n  Visit                                                                    \n    NCI CTCAE Grade at Visit      A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade     (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————\nCRP                                                                        \n  WEEK 1 DAY 8                                                             \n    Not Low (n)                      116           110            101      \n      Not Low                    103 (88.8%)   91 (82.7%)      88 (87.1%)  \n      1                           3 (2.6%)      5 (4.5%)        6 (5.9%)   \n      2                           4 (3.4%)      9 (8.2%)        2 (2.0%)   \n      3                           3 (2.6%)      4 (3.6%)        4 (4.0%)   \n      4                           3 (2.6%)      1 (0.9%)        1 (1.0%)   \n    1 (n)                             4             7              5       \n      Not Low                     3 (75.0%)     7 (100%)       4 (80.0%)   \n      3                           1 (25.0%)         0          1 (20.0%)   \n    2 (n)                             5             6              11      \n      Not Low                     5 (100%)      5 (83.3%)      8 (72.7%)   \n      1                               0             0           1 (9.1%)   \n      3                               0             0          2 (18.2%)   \n      4                               0         1 (16.7%)          0       \n    3 (n)                             6             8              5       \n      Not Low                     6 (100%)      7 (87.5%)       5 (100%)   \n      3                               0         1 (12.5%)          0       \n    4 (n)                             3             3              10      \n      Not Low                     2 (66.7%)     3 (100%)       7 (70.0%)   \n      1                               0             0          2 (20.0%)   \n      2                           1 (33.3%)         0              0       \n      4                               0             0          1 (10.0%)   \n  WEEK 2 DAY 15                                                            \n    Not Low (n)                      106           111            116      \n      Not Low                    93 (87.7%)    93 (83.8%)     100 (86.2%)  \n      1                           3 (2.8%)      4 (3.6%)        8 (6.9%)   \n      2                           5 (4.7%)      7 (6.3%)        2 (1.7%)   \n      3                           2 (1.9%)      5 (4.5%)        6 (5.2%)   \n      4                           3 (2.8%)      2 (1.8%)           0       \n    1 (n)                            15             9              2       \n      Not Low                    13 (86.7%)     8 (88.9%)      1 (50.0%)   \n      2                               0         1 (11.1%)          0       \n      3                           2 (13.3%)         0              0       \n      4                               0             0          1 (50.0%)   \n    2 (n)                             3             3              6       \n      Not Low                     3 (100%)      3 (100%)       4 (66.7%)   \n      1                               0             0          1 (16.7%)   \n      3                               0             0          1 (16.7%)   \n    3 (n)                             7             7              6       \n      Not Low                     7 (100%)      6 (85.7%)      5 (83.3%)   \n      2                               0         1 (14.3%)          0       \n      4                               0             0          1 (16.7%)   \n    4 (n)                             3             4              2       \n      Not Low                     3 (100%)      3 (75.0%)       2 (100%)   \n      1                               0         1 (25.0%)          0       \n  WEEK 3 DAY 22                                                            \n    Not Low (n)                      119           110            114      \n      Not Low                    104 (87.4%)   92 (83.6%)      94 (82.5%)  \n      1                           3 (2.5%)      4 (3.6%)        9 (7.9%)   \n      2                           5 (4.2%)      7 (6.4%)        2 (1.8%)   \n      3                           4 (3.4%)      5 (4.5%)        7 (6.1%)   \n      4                           3 (2.5%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             9             9              6       \n      Not Low                     9 (100%)      7 (77.8%)       6 (100%)   \n      2                               0         2 (22.2%)          0       \n    2 (n)                             1             7              3       \n      Not Low                     1 (100%)      6 (85.7%)       3 (100%)   \n      1                               0         1 (14.3%)          0       \n    3 (n)                             4             5              4       \n      Not Low                     4 (100%)      5 (100%)        4 (100%)   \n    4 (n)                             1             3              5       \n      Not Low                     1 (100%)      3 (100%)        5 (100%)   \n  WEEK 4 DAY 29                                                            \n    Not Low (n)                      101           119            111      \n      Not Low                    89 (88.1%)    100 (84.0%)     96 (86.5%)  \n      1                           3 (3.0%)      4 (3.4%)        6 (5.4%)   \n      2                           4 (4.0%)      8 (6.7%)        1 (0.9%)   \n      3                           2 (2.0%)      5 (4.2%)        6 (5.4%)   \n      4                           3 (3.0%)      2 (1.7%)        2 (1.8%)   \n    1 (n)                            10             5              6       \n      Not Low                     9 (90.0%)     5 (100%)       5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                           1 (10.0%)         0              0       \n    2 (n)                            13             4              6       \n      Not Low                     13 (100%)     3 (75.0%)      5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                               0         1 (25.0%)          0       \n    3 (n)                             8             2              6       \n      Not Low                     6 (75.0%)     1 (50.0%)      5 (83.3%)   \n      1                               0         1 (50.0%)          0       \n      2                               0             0          1 (16.7%)   \n      3                           2 (25.0%)         0              0       \n    4 (n)                             2             4              3       \n      Not Low                     2 (100%)      4 (100%)       1 (33.3%)   \n      1                               0             0          1 (33.3%)   \n      3                               0             0          1 (33.3%)   \n  WEEK 5 DAY 36                                                            \n    Not Low (n)                      115           112            111      \n      Not Low                    102 (88.7%)   94 (83.9%)      96 (86.5%)  \n      1                           2 (1.7%)      4 (3.6%)        6 (5.4%)   \n      2                           5 (4.3%)      7 (6.2%)        2 (1.8%)   \n      3                           3 (2.6%)      5 (4.5%)        5 (4.5%)   \n      4                           3 (2.6%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             5             7              6       \n      Not Low                     5 (100%)      4 (57.1%)      5 (83.3%)   \n      1                               0         1 (14.3%)      1 (16.7%)   \n      2                               0         2 (28.6%)          0       \n    2 (n)                             5            11              6       \n      Not Low                     5 (100%)      11 (100%)       6 (100%)   \n    3 (n)                             8             2              7       \n      Not Low                     6 (75.0%)     2 (100%)       4 (57.1%)   \n      1                           1 (12.5%)         0          1 (14.3%)   \n      3                           1 (12.5%)         0          2 (28.6%)   \n    4 (n)                             1             2              2       \n      Not Low                     1 (100%)      2 (100%)       1 (50.0%)   \n      1                               0             0          1 (50.0%)   \n\n\n\n\nNote that the worst laboratory flag (below WGRHIVFL) must be selected appropriately to match the direction of abnormality (here High). New grouping variables ATOXGR_GP and BTOXGR_GP are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRHIVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                            \n  Visit                                                                   \n    NCI CTCAE Grade at Visit     A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade    (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————\nCRP                                                                       \n  WEEK 1 DAY 8                                                            \n    Not High (n)                    111           110            112      \n      Not High                   93 (83.8%)   92 (83.6%)      98 (87.5%)  \n      1                           8 (7.2%)     7 (6.4%)        5 (4.5%)   \n      2                           2 (1.8%)     6 (5.5%)        7 (6.2%)   \n      3                           6 (5.4%)     3 (2.7%)        2 (1.8%)   \n      4                           2 (1.8%)     2 (1.8%)           0       \n    1 (n)                            8            11              6       \n      Not High                   7 (87.5%)     11 (100%)      5 (83.3%)   \n      3                          1 (12.5%)         0          1 (16.7%)   \n    2 (n)                            6             4              5       \n      Not High                    6 (100%)     1 (25.0%)      4 (80.0%)   \n      1                              0         3 (75.0%)      1 (20.0%)   \n    3 (n)                            8             3              4       \n      Not High                   7 (87.5%)     3 (100%)        4 (100%)   \n      4                          1 (12.5%)         0              0       \n    4 (n)                            1             6              5       \n      Not High                    1 (100%)     5 (83.3%)      4 (80.0%)   \n      1                              0             0          1 (20.0%)   \n      3                              0         1 (16.7%)          0       \n  WEEK 2 DAY 15                                                           \n    Not High (n)                    117           113            113      \n      Not High                   99 (84.6%)   95 (84.1%)     100 (88.5%)  \n      1                           7 (6.0%)     6 (5.3%)        4 (3.5%)   \n      2                           2 (1.7%)     6 (5.3%)        6 (5.3%)   \n      3                           6 (5.1%)     4 (3.5%)        3 (2.7%)   \n      4                           3 (2.6%)     2 (1.8%)           0       \n    1 (n)                            6             9              7       \n      Not High                   5 (83.3%)     8 (88.9%)      6 (85.7%)   \n      1                              0         1 (11.1%)          0       \n      2                              0             0          1 (14.3%)   \n      3                          1 (16.7%)         0              0       \n    2 (n)                            4             4              6       \n      Not High                    4 (100%)     4 (100%)       3 (50.0%)   \n      1                              0             0          3 (50.0%)   \n    3 (n)                            5             3              5       \n      Not High                   4 (80.0%)     2 (66.7%)       5 (100%)   \n      1                          1 (20.0%)     1 (33.3%)          0       \n    4 (n)                            2             5              1       \n      Not High                    2 (100%)     3 (60.0%)       1 (100%)   \n      1                              0         2 (40.0%)          0       \n  WEEK 3 DAY 22                                                           \n    Not High (n)                    110           111            114      \n      Not High                   92 (83.6%)   94 (84.7%)      98 (86.0%)  \n      1                           8 (7.3%)     9 (8.1%)        7 (6.1%)   \n      2                           2 (1.8%)     5 (4.5%)        6 (5.3%)   \n      3                           5 (4.5%)     2 (1.8%)        3 (2.6%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            7             8              7       \n      Not High                    7 (100%)     5 (62.5%)      6 (85.7%)   \n      1                              0         1 (12.5%)          0       \n      2                              0         1 (12.5%)      1 (14.3%)   \n      3                              0         1 (12.5%)          0       \n    2 (n)                            9             7              5       \n      Not High                    9 (100%)     6 (85.7%)       5 (100%)   \n      3                              0         1 (14.3%)          0       \n    3 (n)                            6             3              4       \n      Not High                   5 (83.3%)     2 (66.7%)       4 (100%)   \n      3                          1 (16.7%)         0              0       \n      4                              0         1 (33.3%)          0       \n    4 (n)                            2             5              2       \n      Not High                   1 (50.0%)     5 (100%)        2 (100%)   \n      3                          1 (50.0%)         0              0       \n  WEEK 4 DAY 29                                                           \n    Not High (n)                    115           117            113      \n      Not High                   97 (84.3%)   101 (86.3%)     99 (87.6%)  \n      1                           8 (7.0%)     7 (6.0%)        6 (5.3%)   \n      2                           2 (1.7%)     6 (5.1%)        5 (4.4%)   \n      3                           7 (6.1%)     3 (2.6%)        3 (2.7%)   \n      4                           1 (0.9%)         0              0       \n    1 (n)                            4             3              11      \n      Not High                    4 (100%)     2 (66.7%)      10 (90.9%)  \n      2                              0             0           1 (9.1%)   \n      4                              0         1 (33.3%)          0       \n    2 (n)                            6             4              2       \n      Not High                    6 (100%)     3 (75.0%)       2 (100%)   \n      1                              0         1 (25.0%)          0       \n    3 (n)                            6             7              4       \n      Not High                   5 (83.3%)     4 (57.1%)       4 (100%)   \n      1                              0         1 (14.3%)          0       \n      3                              0         1 (14.3%)          0       \n      4                          1 (16.7%)     1 (14.3%)          0       \n    4 (n)                            3             3              2       \n      Not High                   2 (66.7%)     2 (66.7%)          0       \n      1                              0         1 (33.3%)      1 (50.0%)   \n      2                              0             0          1 (50.0%)   \n      4                          1 (33.3%)         0              0       \n  WEEK 5 DAY 36                                                           \n    Not High (n)                    111           111            119      \n      Not High                   95 (85.6%)   95 (85.6%)     104 (87.4%)  \n      1                           6 (5.4%)     8 (7.2%)        6 (5.0%)   \n      2                           1 (0.9%)     5 (4.5%)        6 (5.0%)   \n      3                           6 (5.4%)     2 (1.8%)        3 (2.5%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            8            11              5       \n      Not High                   7 (87.5%)     9 (81.8%)      4 (80.0%)   \n      1                              0         1 (9.1%)           0       \n      2                              0             0          1 (20.0%)   \n      3                          1 (12.5%)     1 (9.1%)           0       \n    2 (n)                            7             6              3       \n      Not High                   5 (71.4%)     3 (50.0%)      2 (66.7%)   \n      1                          1 (14.3%)     1 (16.7%)      1 (33.3%)   \n      2                          1 (14.3%)     1 (16.7%)          0       \n      4                              0         1 (16.7%)          0       \n    3 (n)                            3             3              4       \n      Not High                    3 (100%)     3 (100%)        4 (100%)   \n    4 (n)                            5             3              1       \n      Not High                   4 (80.0%)     2 (66.7%)       1 (100%)   \n      1                          1 (20.0%)         0              0       \n      3                              0         1 (33.3%)          0       \n\n\n\n\nNote that missing baseline values are filtered out in the pre-processing step.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRLOVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  filter(BTOXGR != \"&lt;Missing&gt;\") %&gt;% # filter out missing baseline grade\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                             \n  Visit                                                                    \n    NCI CTCAE Grade at Visit      A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade     (N=134)       (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————\nCRP                                                                        \n  WEEK 1 DAY 8                                                             \n    Not Low (n)                      116           110            101      \n      Not Low                    103 (88.8%)   91 (82.7%)      88 (87.1%)  \n      1                           3 (2.6%)      5 (4.5%)        6 (5.9%)   \n      2                           4 (3.4%)      9 (8.2%)        2 (2.0%)   \n      3                           3 (2.6%)      4 (3.6%)        4 (4.0%)   \n      4                           3 (2.6%)      1 (0.9%)        1 (1.0%)   \n    1 (n)                             4             7              5       \n      Not Low                     3 (75.0%)     7 (100%)       4 (80.0%)   \n      3                           1 (25.0%)         0          1 (20.0%)   \n    2 (n)                             5             6              11      \n      Not Low                     5 (100%)      5 (83.3%)      8 (72.7%)   \n      1                               0             0           1 (9.1%)   \n      3                               0             0          2 (18.2%)   \n      4                               0         1 (16.7%)          0       \n    3 (n)                             6             8              5       \n      Not Low                     6 (100%)      7 (87.5%)       5 (100%)   \n      3                               0         1 (12.5%)          0       \n    4 (n)                             3             3              10      \n      Not Low                     2 (66.7%)     3 (100%)       7 (70.0%)   \n      1                               0             0          2 (20.0%)   \n      2                           1 (33.3%)         0              0       \n      4                               0             0          1 (10.0%)   \n  WEEK 2 DAY 15                                                            \n    Not Low (n)                      106           111            116      \n      Not Low                    93 (87.7%)    93 (83.8%)     100 (86.2%)  \n      1                           3 (2.8%)      4 (3.6%)        8 (6.9%)   \n      2                           5 (4.7%)      7 (6.3%)        2 (1.7%)   \n      3                           2 (1.9%)      5 (4.5%)        6 (5.2%)   \n      4                           3 (2.8%)      2 (1.8%)           0       \n    1 (n)                            15             9              2       \n      Not Low                    13 (86.7%)     8 (88.9%)      1 (50.0%)   \n      2                               0         1 (11.1%)          0       \n      3                           2 (13.3%)         0              0       \n      4                               0             0          1 (50.0%)   \n    2 (n)                             3             3              6       \n      Not Low                     3 (100%)      3 (100%)       4 (66.7%)   \n      1                               0             0          1 (16.7%)   \n      3                               0             0          1 (16.7%)   \n    3 (n)                             7             7              6       \n      Not Low                     7 (100%)      6 (85.7%)      5 (83.3%)   \n      2                               0         1 (14.3%)          0       \n      4                               0             0          1 (16.7%)   \n    4 (n)                             3             4              2       \n      Not Low                     3 (100%)      3 (75.0%)       2 (100%)   \n      1                               0         1 (25.0%)          0       \n  WEEK 3 DAY 22                                                            \n    Not Low (n)                      119           110            114      \n      Not Low                    104 (87.4%)   92 (83.6%)      94 (82.5%)  \n      1                           3 (2.5%)      4 (3.6%)        9 (7.9%)   \n      2                           5 (4.2%)      7 (6.4%)        2 (1.8%)   \n      3                           4 (3.4%)      5 (4.5%)        7 (6.1%)   \n      4                           3 (2.5%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             9             9              6       \n      Not Low                     9 (100%)      7 (77.8%)       6 (100%)   \n      2                               0         2 (22.2%)          0       \n    2 (n)                             1             7              3       \n      Not Low                     1 (100%)      6 (85.7%)       3 (100%)   \n      1                               0         1 (14.3%)          0       \n    3 (n)                             4             5              4       \n      Not Low                     4 (100%)      5 (100%)        4 (100%)   \n    4 (n)                             1             3              5       \n      Not Low                     1 (100%)      3 (100%)        5 (100%)   \n  WEEK 4 DAY 29                                                            \n    Not Low (n)                      101           119            111      \n      Not Low                    89 (88.1%)    100 (84.0%)     96 (86.5%)  \n      1                           3 (3.0%)      4 (3.4%)        6 (5.4%)   \n      2                           4 (4.0%)      8 (6.7%)        1 (0.9%)   \n      3                           2 (2.0%)      5 (4.2%)        6 (5.4%)   \n      4                           3 (3.0%)      2 (1.7%)        2 (1.8%)   \n    1 (n)                            10             5              6       \n      Not Low                     9 (90.0%)     5 (100%)       5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                           1 (10.0%)         0              0       \n    2 (n)                            13             4              6       \n      Not Low                     13 (100%)     3 (75.0%)      5 (83.3%)   \n      1                               0             0          1 (16.7%)   \n      2                               0         1 (25.0%)          0       \n    3 (n)                             8             2              6       \n      Not Low                     6 (75.0%)     1 (50.0%)      5 (83.3%)   \n      1                               0         1 (50.0%)          0       \n      2                               0             0          1 (16.7%)   \n      3                           2 (25.0%)         0              0       \n    4 (n)                             2             4              3       \n      Not Low                     2 (100%)      4 (100%)       1 (33.3%)   \n      1                               0             0          1 (33.3%)   \n      3                               0             0          1 (33.3%)   \n  WEEK 5 DAY 36                                                            \n    Not Low (n)                      115           112            111      \n      Not Low                    102 (88.7%)   94 (83.9%)      96 (86.5%)  \n      1                           2 (1.7%)      4 (3.6%)        6 (5.4%)   \n      2                           5 (4.3%)      7 (6.2%)        2 (1.8%)   \n      3                           3 (2.6%)      5 (4.5%)        5 (4.5%)   \n      4                           3 (2.6%)      2 (1.8%)        2 (1.8%)   \n    1 (n)                             5             7              6       \n      Not Low                     5 (100%)      4 (57.1%)      5 (83.3%)   \n      1                               0         1 (14.3%)      1 (16.7%)   \n      2                               0         2 (28.6%)          0       \n    2 (n)                             5            11              6       \n      Not Low                     5 (100%)      11 (100%)       6 (100%)   \n    3 (n)                             8             2              7       \n      Not Low                     6 (75.0%)     2 (100%)       4 (57.1%)   \n      1                           1 (12.5%)         0          1 (14.3%)   \n      3                           1 (12.5%)         0          2 (28.6%)   \n    4 (n)                             1             2              2       \n      Not Low                     1 (100%)      2 (100%)       1 (50.0%)   \n      1                               0             0          1 (50.0%)   \n\n\n\n\nNote that when BTOXGR is missing, the grouping variable BTOXGR_GP is now \"Not High\" instead of \"Missing\" compared to Standard Table (High).\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRHIVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4, \"&lt;Missing&gt;\") ~ \"Not High\", # Missing BTOXGR now grouped to \"Not High\"\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = TRUE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                            \n  Visit                                                                   \n    NCI CTCAE Grade at Visit     A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade    (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————\nCRP                                                                       \n  WEEK 1 DAY 8                                                            \n    Not High (n)                    111           110            112      \n      Not High                   93 (83.8%)   92 (83.6%)      98 (87.5%)  \n      1                           8 (7.2%)     7 (6.4%)        5 (4.5%)   \n      2                           2 (1.8%)     6 (5.5%)        7 (6.2%)   \n      3                           6 (5.4%)     3 (2.7%)        2 (1.8%)   \n      4                           2 (1.8%)     2 (1.8%)           0       \n    1 (n)                            8            11              6       \n      Not High                   7 (87.5%)     11 (100%)      5 (83.3%)   \n      3                          1 (12.5%)         0          1 (16.7%)   \n    2 (n)                            6             4              5       \n      Not High                    6 (100%)     1 (25.0%)      4 (80.0%)   \n      1                              0         3 (75.0%)      1 (20.0%)   \n    3 (n)                            8             3              4       \n      Not High                   7 (87.5%)     3 (100%)        4 (100%)   \n      4                          1 (12.5%)         0              0       \n    4 (n)                            1             6              5       \n      Not High                    1 (100%)     5 (83.3%)      4 (80.0%)   \n      1                              0             0          1 (20.0%)   \n      3                              0         1 (16.7%)          0       \n  WEEK 2 DAY 15                                                           \n    Not High (n)                    117           113            113      \n      Not High                   99 (84.6%)   95 (84.1%)     100 (88.5%)  \n      1                           7 (6.0%)     6 (5.3%)        4 (3.5%)   \n      2                           2 (1.7%)     6 (5.3%)        6 (5.3%)   \n      3                           6 (5.1%)     4 (3.5%)        3 (2.7%)   \n      4                           3 (2.6%)     2 (1.8%)           0       \n    1 (n)                            6             9              7       \n      Not High                   5 (83.3%)     8 (88.9%)      6 (85.7%)   \n      1                              0         1 (11.1%)          0       \n      2                              0             0          1 (14.3%)   \n      3                          1 (16.7%)         0              0       \n    2 (n)                            4             4              6       \n      Not High                    4 (100%)     4 (100%)       3 (50.0%)   \n      1                              0             0          3 (50.0%)   \n    3 (n)                            5             3              5       \n      Not High                   4 (80.0%)     2 (66.7%)       5 (100%)   \n      1                          1 (20.0%)     1 (33.3%)          0       \n    4 (n)                            2             5              1       \n      Not High                    2 (100%)     3 (60.0%)       1 (100%)   \n      1                              0         2 (40.0%)          0       \n  WEEK 3 DAY 22                                                           \n    Not High (n)                    110           111            114      \n      Not High                   92 (83.6%)   94 (84.7%)      98 (86.0%)  \n      1                           8 (7.3%)     9 (8.1%)        7 (6.1%)   \n      2                           2 (1.8%)     5 (4.5%)        6 (5.3%)   \n      3                           5 (4.5%)     2 (1.8%)        3 (2.6%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            7             8              7       \n      Not High                    7 (100%)     5 (62.5%)      6 (85.7%)   \n      1                              0         1 (12.5%)          0       \n      2                              0         1 (12.5%)      1 (14.3%)   \n      3                              0         1 (12.5%)          0       \n    2 (n)                            9             7              5       \n      Not High                    9 (100%)     6 (85.7%)       5 (100%)   \n      3                              0         1 (14.3%)          0       \n    3 (n)                            6             3              4       \n      Not High                   5 (83.3%)     2 (66.7%)       4 (100%)   \n      3                          1 (16.7%)         0              0       \n      4                              0         1 (33.3%)          0       \n    4 (n)                            2             5              2       \n      Not High                   1 (50.0%)     5 (100%)        2 (100%)   \n      3                          1 (50.0%)         0              0       \n  WEEK 4 DAY 29                                                           \n    Not High (n)                    115           117            113      \n      Not High                   97 (84.3%)   101 (86.3%)     99 (87.6%)  \n      1                           8 (7.0%)     7 (6.0%)        6 (5.3%)   \n      2                           2 (1.7%)     6 (5.1%)        5 (4.4%)   \n      3                           7 (6.1%)     3 (2.6%)        3 (2.7%)   \n      4                           1 (0.9%)         0              0       \n    1 (n)                            4             3              11      \n      Not High                    4 (100%)     2 (66.7%)      10 (90.9%)  \n      2                              0             0           1 (9.1%)   \n      4                              0         1 (33.3%)          0       \n    2 (n)                            6             4              2       \n      Not High                    6 (100%)     3 (75.0%)       2 (100%)   \n      1                              0         1 (25.0%)          0       \n    3 (n)                            6             7              4       \n      Not High                   5 (83.3%)     4 (57.1%)       4 (100%)   \n      1                              0         1 (14.3%)          0       \n      3                              0         1 (14.3%)          0       \n      4                          1 (16.7%)     1 (14.3%)          0       \n    4 (n)                            3             3              2       \n      Not High                   2 (66.7%)     2 (66.7%)          0       \n      1                              0         1 (33.3%)      1 (50.0%)   \n      2                              0             0          1 (50.0%)   \n      4                          1 (33.3%)         0              0       \n  WEEK 5 DAY 36                                                           \n    Not High (n)                    111           111            119      \n      Not High                   95 (85.6%)   95 (85.6%)     104 (87.4%)  \n      1                           6 (5.4%)     8 (7.2%)        6 (5.0%)   \n      2                           1 (0.9%)     5 (4.5%)        6 (5.0%)   \n      3                           6 (5.4%)     2 (1.8%)        3 (2.5%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n    1 (n)                            8            11              5       \n      Not High                   7 (87.5%)     9 (81.8%)      4 (80.0%)   \n      1                              0         1 (9.1%)           0       \n      2                              0             0          1 (20.0%)   \n      3                          1 (12.5%)     1 (9.1%)           0       \n    2 (n)                            7             6              3       \n      Not High                   5 (71.4%)     3 (50.0%)      2 (66.7%)   \n      1                          1 (14.3%)     1 (16.7%)      1 (33.3%)   \n      2                          1 (14.3%)     1 (16.7%)          0       \n      4                              0         1 (16.7%)          0       \n    3 (n)                            3             3              4       \n      Not High                    3 (100%)     3 (100%)        4 (100%)   \n    4 (n)                            5             3              1       \n      Not High                   4 (80.0%)     2 (66.7%)       1 (100%)   \n      1                          1 (20.0%)         0              0       \n      3                              0         1 (33.3%)          0       \n\n\n\n\nPre-processing is the same as for Standard Table (High), but in order to keep all levels, the drop argument in count_occurrences is set to FALSE.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIVFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- adsl_f %&gt;%\n  h_adsl_adlb_merge_using_worst_flag(\n    adlb_f,\n    worst_flag = c(\"WGRHIVFL\" = \"Y\"),\n    by_visit = TRUE,\n    no_fillin_visits = c(\"SCREENING\", \"BASELINE\", \"UNSCHEDULED\")\n  )\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    )\n  )\n\nadlb_out &lt;- adlb_out %&gt;% mutate(\n  AVISIT = forcats::fct_reorder(AVISIT, AVISITN),\n  ATOXGR_GP = factor(ATOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n  BTOXGR_GP = factor(BTOXGR_GP, levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n)\n\nadlb_out &lt;- adlb_out %&gt;%\n  var_relabel(\n    PARAMCD = \"Parameter Code\",\n    AVISIT = \"Visit\",\n    ATOXGR_GP = \"NCI CTCAE Grade at Visit\",\n    BTOXGR_GP = \"Baseline NCI CTCAE Grade\"\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAMCD\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$PARAMCD)\n  ) %&gt;%\n  split_rows_by(\n    \"AVISIT\",\n    split_fun = drop_split_levels, label_pos = \"topleft\", split_label = obj_label(adlb_out$AVISIT)\n  ) %&gt;%\n  split_rows_by(\n    \"ATOXGR_GP\",\n    split_fun = keep_split_levels(c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\")),\n    label_pos = \"topleft\",\n    split_label = obj_label(adlb_out$ATOXGR_GP)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\")) %&gt;%\n  count_occurrences(\"BTOXGR_GP\", denom = \"n\", drop = FALSE) %&gt;%\n  append_varlabels(adlb_out, \"BTOXGR_GP\", indent = 3L) %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter Code                                                            \n  Visit                                                                   \n    NCI CTCAE Grade at Visit     A: Drug X    B: Placebo    C: Combination\n      Baseline NCI CTCAE Grade    (N=134)       (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————\nCRP                                                                       \n  WEEK 1 DAY 8                                                            \n    Not High (n)                    111           110            112      \n      Not High                   93 (83.8%)   92 (83.6%)      98 (87.5%)  \n      1                           8 (7.2%)     7 (6.4%)        5 (4.5%)   \n      2                           2 (1.8%)     6 (5.5%)        7 (6.2%)   \n      3                           6 (5.4%)     3 (2.7%)        2 (1.8%)   \n      4                           2 (1.8%)     2 (1.8%)           0       \n      Missing                        0             0              0       \n    1 (n)                            8            11              6       \n      Not High                   7 (87.5%)     11 (100%)      5 (83.3%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                          1 (12.5%)         0          1 (16.7%)   \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            6             4              5       \n      Not High                    6 (100%)     1 (25.0%)      4 (80.0%)   \n      1                              0         3 (75.0%)      1 (20.0%)   \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            8             3              4       \n      Not High                   7 (87.5%)     3 (100%)        4 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                          1 (12.5%)         0              0       \n      Missing                        0             0              0       \n    4 (n)                            1             6              5       \n      Not High                    1 (100%)     5 (83.3%)      4 (80.0%)   \n      1                              0             0          1 (20.0%)   \n      2                              0             0              0       \n      3                              0         1 (16.7%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 2 DAY 15                                                           \n    Not High (n)                    117           113            113      \n      Not High                   99 (84.6%)   95 (84.1%)     100 (88.5%)  \n      1                           7 (6.0%)     6 (5.3%)        4 (3.5%)   \n      2                           2 (1.7%)     6 (5.3%)        6 (5.3%)   \n      3                           6 (5.1%)     4 (3.5%)        3 (2.7%)   \n      4                           3 (2.6%)     2 (1.8%)           0       \n      Missing                        0             0              0       \n    1 (n)                            6             9              7       \n      Not High                   5 (83.3%)     8 (88.9%)      6 (85.7%)   \n      1                              0         1 (11.1%)          0       \n      2                              0             0          1 (14.3%)   \n      3                          1 (16.7%)         0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            4             4              6       \n      Not High                    4 (100%)     4 (100%)       3 (50.0%)   \n      1                              0             0          3 (50.0%)   \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            5             3              5       \n      Not High                   4 (80.0%)     2 (66.7%)       5 (100%)   \n      1                          1 (20.0%)     1 (33.3%)          0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    4 (n)                            2             5              1       \n      Not High                    2 (100%)     3 (60.0%)       1 (100%)   \n      1                              0         2 (40.0%)          0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 3 DAY 22                                                           \n    Not High (n)                    110           111            114      \n      Not High                   92 (83.6%)   94 (84.7%)      98 (86.0%)  \n      1                           8 (7.3%)     9 (8.1%)        7 (6.1%)   \n      2                           2 (1.8%)     5 (4.5%)        6 (5.3%)   \n      3                           5 (4.5%)     2 (1.8%)        3 (2.6%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n      Missing                        0             0              0       \n    1 (n)                            7             8              7       \n      Not High                    7 (100%)     5 (62.5%)      6 (85.7%)   \n      1                              0         1 (12.5%)          0       \n      2                              0         1 (12.5%)      1 (14.3%)   \n      3                              0         1 (12.5%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            9             7              5       \n      Not High                    9 (100%)     6 (85.7%)       5 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0         1 (14.3%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            6             3              4       \n      Not High                   5 (83.3%)     2 (66.7%)       4 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                          1 (16.7%)         0              0       \n      4                              0         1 (33.3%)          0       \n      Missing                        0             0              0       \n    4 (n)                            2             5              2       \n      Not High                   1 (50.0%)     5 (100%)        2 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                          1 (50.0%)         0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 4 DAY 29                                                           \n    Not High (n)                    115           117            113      \n      Not High                   97 (84.3%)   101 (86.3%)     99 (87.6%)  \n      1                           8 (7.0%)     7 (6.0%)        6 (5.3%)   \n      2                           2 (1.7%)     6 (5.1%)        5 (4.4%)   \n      3                           7 (6.1%)     3 (2.6%)        3 (2.7%)   \n      4                           1 (0.9%)         0              0       \n      Missing                        0             0              0       \n    1 (n)                            4             3              11      \n      Not High                    4 (100%)     2 (66.7%)      10 (90.9%)  \n      1                              0             0              0       \n      2                              0             0           1 (9.1%)   \n      3                              0             0              0       \n      4                              0         1 (33.3%)          0       \n      Missing                        0             0              0       \n    2 (n)                            6             4              2       \n      Not High                    6 (100%)     3 (75.0%)       2 (100%)   \n      1                              0         1 (25.0%)          0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    3 (n)                            6             7              4       \n      Not High                   5 (83.3%)     4 (57.1%)       4 (100%)   \n      1                              0         1 (14.3%)          0       \n      2                              0             0              0       \n      3                              0         1 (14.3%)          0       \n      4                          1 (16.7%)     1 (14.3%)          0       \n      Missing                        0             0              0       \n    4 (n)                            3             3              2       \n      Not High                   2 (66.7%)     2 (66.7%)          0       \n      1                              0         1 (33.3%)      1 (50.0%)   \n      2                              0             0          1 (50.0%)   \n      3                              0             0              0       \n      4                          1 (33.3%)         0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n  WEEK 5 DAY 36                                                           \n    Not High (n)                    111           111            119      \n      Not High                   95 (85.6%)   95 (85.6%)     104 (87.4%)  \n      1                           6 (5.4%)     8 (7.2%)        6 (5.0%)   \n      2                           1 (0.9%)     5 (4.5%)        6 (5.0%)   \n      3                           6 (5.4%)     2 (1.8%)        3 (2.5%)   \n      4                           3 (2.7%)     1 (0.9%)           0       \n      Missing                        0             0              0       \n    1 (n)                            8            11              5       \n      Not High                   7 (87.5%)     9 (81.8%)      4 (80.0%)   \n      1                              0         1 (9.1%)           0       \n      2                              0             0          1 (20.0%)   \n      3                          1 (12.5%)     1 (9.1%)           0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    2 (n)                            7             6              3       \n      Not High                   5 (71.4%)     3 (50.0%)      2 (66.7%)   \n      1                          1 (14.3%)     1 (16.7%)      1 (33.3%)   \n      2                          1 (14.3%)     1 (16.7%)          0       \n      3                              0             0              0       \n      4                              0         1 (16.7%)          0       \n      Missing                        0             0              0       \n    3 (n)                            3             3              4       \n      Not High                    3 (100%)     3 (100%)        4 (100%)   \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    4 (n)                            5             3              1       \n      Not High                   4 (80.0%)     2 (66.7%)       1 (100%)   \n      1                          1 (20.0%)         0              0       \n      2                              0             0              0       \n      3                              0         1 (33.3%)          0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n    Missing (n)                      0             0              0       \n      Not High                       0             0              0       \n      1                              0             0              0       \n      2                              0             0              0       \n      3                              0             0              0       \n      4                              0             0              0       \n      Missing                        0             0              0       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_shift_by_grade(\n      label = \"Grade Laboratory Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      ),\n      worst_flag_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"WGRLOVFL\", \"WGRLOFL\", \"WGRHIVFL\", \"WGRHIFL\")),\n        selected = c(\"WGRLOVFL\")\n      ),\n      worst_flag_indicator = choices_selected(\n        value_choices(ADLB, \"WGRLOVFL\"),\n        selected = \"Y\", fixed = TRUE\n      ),\n      anl_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"ATOXGR\")),\n        selected = c(\"ATOXGR\"),\n        fixed = TRUE\n      ),\n      base_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"BTOXGR\")),\n        selected = c(\"BTOXGR\"),\n        fixed = TRUE\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:49:17 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1355,7 +1355,7 @@
     "href": "tables/lab-results/lbt12.html",
     "title": "LBT12",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nanl &lt;- adhy\nanl$APERIODC &lt;- as.factor(anl$APERIODC) # to ensure the table is built even if there is no patients after filtering\nanl$ACTARM &lt;- as.factor(anl$ACTARM) # to ensure the table is built even if there is no patients after filtering\n\nanl &lt;- anl %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    PARAMCD %in% c(\"ASTPULN\", \"ALTPULN\", \"ALTASTPU\") & AVISIT == \"POST-BASELINE\"\n  ) %&gt;%\n  mutate(\n    ARM_AVALC = factor(\n      case_when(\n        AVALC == \"Y\" ~ as.character(ACTARM),\n        TRUE ~ \"Criteria not met\"\n      ),\n      levels = c(levels(anl$ACTARM), \"Criteria not met\")\n    ),\n    PARAM = factor(\n      case_when(\n        PARAMCD == \"ASTPULN\" ~ \"AST &gt;3x ULN\",\n        PARAMCD == \"ALTPULN\" ~ \"ALT &gt;3x ULN\",\n        PARAMCD == \"ALTASTPU\" ~ \"AST &gt;3x ULN or ALT &gt;x3 ULN\"\n      ),\n      levels = c(\"AST &gt;3x ULN\", \"ALT &gt;3x ULN\", \"AST &gt;3x ULN or ALT &gt;x3 ULN\")\n    ),\n    TITLE = factor(\"First Elevated Result Occurring During\")\n  )\n\nanl &lt;- df_explicit_na(anl)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"TITLE\") %&gt;%\n  split_cols_by(\"APERIODC\") %&gt;%\n  split_rows_by(\"PARAM\") %&gt;%\n  split_rows_by(\"ACTARM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\"ARM_AVALC\", .stats = \"fraction\", denom = \"n\", drop = TRUE) %&gt;%\n  build_table(anl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  dplyr::if_else(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                              First Elevated Result Occurring During  \n                                  PERIOD 1              PERIOD 2      \n——————————————————————————————————————————————————————————————————————\nAST &gt;3x ULN                                                           \n  A: Drug X                     11/134 (8.2%)        21/134 (15.7%)   \n  B: Placebo                   17/134 (12.7%)        16/134 (11.9%)   \n  C: Combination                10/132 (7.6%)         8/132 (6.1%)    \nALT &gt;3x ULN                                                           \n  A: Drug X                    14/134 (10.4%)         8/134 (6.0%)    \n  B: Placebo                   24/134 (17.9%)        18/134 (13.4%)   \n  C: Combination               16/132 (12.1%)         10/132 (7.6%)   \nAST &gt;3x ULN or ALT &gt;x3 ULN                                            \n  A: Drug X                    14/134 (10.4%)        15/134 (11.2%)   \n  B: Placebo                    8/134 (6.0%)          13/134 (9.7%)   \n  C: Combination                11/132 (8.3%)         12/132 (9.1%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:51:07 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nanl &lt;- adhy\nanl$APERIODC &lt;- as.factor(anl$APERIODC) # to ensure the table is built even if there is no patients after filtering\nanl$ACTARM &lt;- as.factor(anl$ACTARM) # to ensure the table is built even if there is no patients after filtering\n\nanl &lt;- anl %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    PARAMCD %in% c(\"ASTPULN\", \"ALTPULN\", \"ALTASTPU\") & AVISIT == \"POST-BASELINE\"\n  ) %&gt;%\n  mutate(\n    ARM_AVALC = factor(\n      case_when(\n        AVALC == \"Y\" ~ as.character(ACTARM),\n        TRUE ~ \"Criteria not met\"\n      ),\n      levels = c(levels(anl$ACTARM), \"Criteria not met\")\n    ),\n    PARAM = factor(\n      case_when(\n        PARAMCD == \"ASTPULN\" ~ \"AST &gt;3x ULN\",\n        PARAMCD == \"ALTPULN\" ~ \"ALT &gt;3x ULN\",\n        PARAMCD == \"ALTASTPU\" ~ \"AST &gt;3x ULN or ALT &gt;x3 ULN\"\n      ),\n      levels = c(\"AST &gt;3x ULN\", \"ALT &gt;3x ULN\", \"AST &gt;3x ULN or ALT &gt;x3 ULN\")\n    ),\n    TITLE = factor(\"First Elevated Result Occurring During\")\n  )\n\nanl &lt;- df_explicit_na(anl)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"TITLE\") %&gt;%\n  split_cols_by(\"APERIODC\") %&gt;%\n  split_rows_by(\"PARAM\") %&gt;%\n  split_rows_by(\"ACTARM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\"ARM_AVALC\", .stats = \"fraction\", denom = \"n\", drop = TRUE) %&gt;%\n  build_table(anl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  dplyr::if_else(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                              First Elevated Result Occurring During  \n                                  PERIOD 1              PERIOD 2      \n——————————————————————————————————————————————————————————————————————\nAST &gt;3x ULN                                                           \n  A: Drug X                     11/134 (8.2%)        21/134 (15.7%)   \n  B: Placebo                   17/134 (12.7%)        16/134 (11.9%)   \n  C: Combination                10/132 (7.6%)         8/132 (6.1%)    \nALT &gt;3x ULN                                                           \n  A: Drug X                    14/134 (10.4%)         8/134 (6.0%)    \n  B: Placebo                   24/134 (17.9%)        18/134 (13.4%)   \n  C: Combination               16/132 (12.1%)         10/132 (7.6%)   \nAST &gt;3x ULN or ALT &gt;x3 ULN                                            \n  A: Drug X                    14/134 (10.4%)        15/134 (11.2%)   \n  B: Placebo                    8/134 (6.0%)          13/134 (9.7%)   \n  C: Combination                11/132 (8.3%)         12/132 (9.1%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:49:40 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1367,7 +1367,7 @@
     "href": "tables/lab-results/lbt11_bl.html",
     "title": "LBT11_BL",
     "section": "",
-    "text": "Data Setup\nTime-To-Event Summary\nTime-To-Event Summary  Selecting Sections to Display\nTime-To-Event Summary  with Stratified Analysis\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\nadsl_safl &lt;- dplyr::filter(adsl, .data$SAFFL == \"Y\")\n\nanl &lt;- adaette %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    PARAMCD == \"HYSTTEBL\"\n  ) %&gt;%\n  dplyr::mutate(\n    AVAL = .data$AVAL * dplyr::case_when( # convert to days, if possible\n      .data$AVALU == \"WEEKS\" ~ 7,\n      .data$AVALU == \"MONTHS\" ~ 30.4375,\n      .data$AVALU == \"YEARS\" ~ 365,\n      TRUE ~ 1\n    ),\n    AVALU = factor(dplyr::case_when(\n      .data$AVALU %in% c(\"WEEKS\", \"MONTHS\", \"YEARS\") ~ factor(\"DAYS\"),\n      TRUE ~ .data$AVALU\n    ), levels = \"DAYS\"),\n    is_event = CNSR == 0, # this will be a LLT event\n    event_grp = factor(\n      dplyr::case_when(\n        CNSR == 0 ~ \"Patients with LLT event (%)\",\n        CNSR == 1 ~ \"Patients without LLT event (%)\"\n      ),\n      levels = c(\n        \"Patients with LLT event (%)\",\n        \"Patients without LLT event (%)\"\n      )\n    )\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   \nPatients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               721.0      \n    95% CI                              NA            (681.0, NA)        (691.0, NA)   \n  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    \n  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.5013             0.6413     \n  Hazard Ratio                                            1.27               0.84      \n  95% CI                                              (0.63, 2.56)       (0.40, 1.75)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   \nPatients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               721.0      \n    95% CI                              NA            (681.0, NA)        (691.0, NA)   \n  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    \n  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}\n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodestrata &lt;- c(\"RACE\", \"SEX\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = paste0(\"Stratified By: \", paste(strata, collapse = \", \")),\n    strata = strata,\n    table_names = \"coxph_stratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   \nPatients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               721.0      \n    95% CI                              NA            (681.0, NA)        (691.0, NA)   \n  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    \n  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.5013             0.6413     \n  Hazard Ratio                                            1.27               0.84      \n  95% CI                                              (0.63, 2.56)       (0.40, 1.75)  \nStratified By: RACE, SEX                                                               \n  p-value (log-rank)                                     0.3886             0.7970     \n  Hazard Ratio                                            1.39               0.91      \n  95% CI                                              (0.65, 2.97)       (0.43, 1.93)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  ADSL &lt;- filter(ADSL, .data$SAFFL == \"Y\")\n  ADAETTE &lt;- filter(ADAETTE, .data$SAFFL == \"Y\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To LLT Event Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADAETTE, \"PARAMCD\", \"PARAM\"),\n        \"HYSTTEBL\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"RACE\", \"SEX\")),\n        c(\"RACE\", \"SEX\")\n      ),\n      time_points = choices_selected(0, NULL),\n      event_desc_var = choices_selected(\"EVNTDESC\", \"EVNTDESC\", fixed = TRUE)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:51:37 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nTime-To-Event Summary\nTime-To-Event Summary  Selecting Sections to Display\nTime-To-Event Summary  with Stratified Analysis\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\nadsl_safl &lt;- dplyr::filter(adsl, .data$SAFFL == \"Y\")\n\nanl &lt;- adaette %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    PARAMCD == \"HYSTTEBL\"\n  ) %&gt;%\n  dplyr::mutate(\n    AVAL = .data$AVAL * dplyr::case_when( # convert to days, if possible\n      .data$AVALU == \"WEEKS\" ~ 7,\n      .data$AVALU == \"MONTHS\" ~ 30.4375,\n      .data$AVALU == \"YEARS\" ~ 365,\n      TRUE ~ 1\n    ),\n    AVALU = factor(dplyr::case_when(\n      .data$AVALU %in% c(\"WEEKS\", \"MONTHS\", \"YEARS\") ~ factor(\"DAYS\"),\n      TRUE ~ .data$AVALU\n    ), levels = \"DAYS\"),\n    is_event = CNSR == 0, # this will be a LLT event\n    event_grp = factor(\n      dplyr::case_when(\n        CNSR == 0 ~ \"Patients with LLT event (%)\",\n        CNSR == 1 ~ \"Patients without LLT event (%)\"\n      ),\n      levels = c(\n        \"Patients with LLT event (%)\",\n        \"Patients without LLT event (%)\"\n      )\n    )\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   \nPatients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               721.0      \n    95% CI                              NA            (681.0, NA)        (691.0, NA)   \n  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    \n  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.5013             0.6413     \n  Hazard Ratio                                            1.27               0.84      \n  95% CI                                              (0.63, 2.56)       (0.40, 1.75)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   \nPatients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               721.0      \n    95% CI                              NA            (681.0, NA)        (691.0, NA)   \n  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    \n  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}\n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodestrata &lt;- c(\"RACE\", \"SEX\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = paste0(\"Stratified By: \", paste(strata, collapse = \", \")),\n    strata = strata,\n    table_names = \"coxph_stratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   \nPatients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               721.0      \n    95% CI                              NA            (681.0, NA)        (691.0, NA)   \n  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    \n  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.5013             0.6413     \n  Hazard Ratio                                            1.27               0.84      \n  95% CI                                              (0.63, 2.56)       (0.40, 1.75)  \nStratified By: RACE, SEX                                                               \n  p-value (log-rank)                                     0.3886             0.7970     \n  Hazard Ratio                                            1.39               0.91      \n  95% CI                                              (0.65, 2.97)       (0.43, 1.93)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  ADSL &lt;- filter(ADSL, .data$SAFFL == \"Y\")\n  ADAETTE &lt;- filter(ADAETTE, .data$SAFFL == \"Y\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To LLT Event Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADAETTE, \"PARAMCD\", \"PARAM\"),\n        \"HYSTTEBL\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"RACE\", \"SEX\")),\n        c(\"RACE\", \"SEX\")\n      ),\n      time_points = choices_selected(0, NULL),\n      event_desc_var = choices_selected(\"EVNTDESC\", \"EVNTDESC\", fixed = TRUE)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:50:09 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1379,7 +1379,7 @@
     "href": "tables/lab-results/lbt11.html",
     "title": "LBT11",
     "section": "",
-    "text": "Data Setup\nTime-To-Event Summary\nTime-To-Event Summary  Selecting Sections to Display\nTime-To-Event Summary  with Stratified Analysis\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\nadsl_safl &lt;- dplyr::filter(adsl, .data$SAFFL == \"Y\")\n\nanl &lt;- adaette %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    PARAMCD == \"HYSTTEUL\"\n  ) %&gt;%\n  dplyr::mutate(\n    AVAL = .data$AVAL * dplyr::case_when( # convert to days, if possible\n      .data$AVALU == \"WEEKS\" ~ 7,\n      .data$AVALU == \"MONTHS\" ~ 30.4375,\n      .data$AVALU == \"YEARS\" ~ 365,\n      TRUE ~ 1\n    ),\n    AVALU = factor(dplyr::case_when(\n      .data$AVALU %in% c(\"WEEKS\", \"MONTHS\", \"YEARS\") ~ factor(\"DAYS\"),\n      TRUE ~ .data$AVALU\n    ), levels = \"DAYS\"),\n    is_event = CNSR == 0, # this will be a LLT event\n    event_grp = factor(\n      dplyr::case_when(\n        CNSR == 0 ~ \"Patients with LLT event (%)\",\n        CNSR == 1 ~ \"Patients without LLT event (%)\"\n      ),\n      levels = c(\n        \"Patients with LLT event (%)\",\n        \"Patients without LLT event (%)\"\n      )\n    )\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    \nPatients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               728.0      \n    95% CI                              NA            (724.0, NA)             NA       \n  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  \n  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.0295             0.1687     \n  Hazard Ratio                                            0.43               0.58      \n  95% CI                                              (0.19, 0.94)       (0.26, 1.27)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n{2} - Censored observation: range minimum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    \nPatients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               728.0      \n    95% CI                              NA            (724.0, NA)             NA       \n  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  \n  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}\n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n{2} - Censored observation: range minimum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodestrata &lt;- c(\"RACE\", \"SEX\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = paste0(\"Stratified By: \", paste(strata, collapse = \", \")),\n    strata = strata,\n    table_names = \"coxph_stratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    \nPatients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               728.0      \n    95% CI                              NA            (724.0, NA)             NA       \n  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  \n  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.0295             0.1687     \n  Hazard Ratio                                            0.43               0.58      \n  95% CI                                              (0.19, 0.94)       (0.26, 1.27)  \nStratified By: RACE, SEX                                                               \n  p-value (log-rank)                                     0.0858             0.1605     \n  Hazard Ratio                                            0.51               0.57      \n  95% CI                                              (0.23, 1.12)       (0.26, 1.26)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n{2} - Censored observation: range minimum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  ADSL &lt;- filter(ADSL, .data$SAFFL == \"Y\")\n  ADAETTE &lt;- filter(ADAETTE, .data$SAFFL == \"Y\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To LLT Event Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        variable_choices(data[[\"ADSL\"]], c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(data[[\"ADAETTE\"]], \"PARAMCD\", \"PARAM\"),\n        \"HYSTTEUL\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(data[[\"ADSL\"]], c(\"RACE\", \"SEX\")),\n        c(\"RACE\", \"SEX\")\n      ),\n      time_points = choices_selected(0, NULL),\n      event_desc_var = choices_selected(\"EVNTDESC\", \"EVNTDESC\", fixed = TRUE)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:52:09 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nTime-To-Event Summary\nTime-To-Event Summary  Selecting Sections to Display\nTime-To-Event Summary  with Stratified Analysis\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadaette &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadaette &lt;- df_explicit_na(adaette)\n\nadsl_safl &lt;- dplyr::filter(adsl, .data$SAFFL == \"Y\")\n\nanl &lt;- adaette %&gt;%\n  dplyr::filter(\n    SAFFL == \"Y\",\n    PARAMCD == \"HYSTTEUL\"\n  ) %&gt;%\n  dplyr::mutate(\n    AVAL = .data$AVAL * dplyr::case_when( # convert to days, if possible\n      .data$AVALU == \"WEEKS\" ~ 7,\n      .data$AVALU == \"MONTHS\" ~ 30.4375,\n      .data$AVALU == \"YEARS\" ~ 365,\n      TRUE ~ 1\n    ),\n    AVALU = factor(dplyr::case_when(\n      .data$AVALU %in% c(\"WEEKS\", \"MONTHS\", \"YEARS\") ~ factor(\"DAYS\"),\n      TRUE ~ .data$AVALU\n    ), levels = \"DAYS\"),\n    is_event = CNSR == 0, # this will be a LLT event\n    event_grp = factor(\n      dplyr::case_when(\n        CNSR == 0 ~ \"Patients with LLT event (%)\",\n        CNSR == 1 ~ \"Patients without LLT event (%)\"\n      ),\n      levels = c(\n        \"Patients with LLT event (%)\",\n        \"Patients without LLT event (%)\"\n      )\n    )\n  )\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    \nPatients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               728.0      \n    95% CI                              NA            (724.0, NA)             NA       \n  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  \n  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.0295             0.1687     \n  Hazard Ratio                                            0.43               0.58      \n  95% CI                                              (0.19, 0.94)       (0.26, 1.27)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n{2} - Censored observation: range minimum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    \nPatients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               728.0      \n    95% CI                              NA            (724.0, NA)             NA       \n  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  \n  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}\n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n{2} - Censored observation: range minimum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\n\n\nCodestrata &lt;- c(\"RACE\", \"SEX\")\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %&gt;%\n  count_occurrences(vars = \"event_grp\") %&gt;%\n  surv_time(\n    vars = \"AVAL\",\n    var_labels = paste0(\"Time to 1st LLT Event (\", levels(anl$AVALU), \")\"),\n    is_event = \"is_event\",\n    table_names = \"time_to_event\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = \"Unstratified Analysis\",\n    control = control_coxph(pval_method = \"log-rank\"),\n    table_names = \"coxph_unstratified\"\n  ) %&gt;%\n  coxph_pairwise(\n    vars = \"AVAL\",\n    is_event = \"is_event\",\n    var_labels = paste0(\"Stratified By: \", paste(strata, collapse = \", \")),\n    strata = strata,\n    table_names = \"coxph_stratified\"\n  )\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_safl)\nresult\n\n                                      ARM A              ARM B              ARM C      \n                                     (N=134)            (N=134)            (N=132)     \n———————————————————————————————————————————————————————————————————————————————————————\nPatients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    \nPatients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   \nTime to 1st LLT Event (DAYS)                                                           \n  Median                                NA                 NA               728.0      \n    95% CI                              NA            (724.0, NA)             NA       \n  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  \n  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}\nUnstratified Analysis                                                                  \n  p-value (log-rank)                                     0.0295             0.1687     \n  Hazard Ratio                                            0.43               0.58      \n  95% CI                                              (0.19, 0.94)       (0.26, 1.27)  \nStratified By: RACE, SEX                                                               \n  p-value (log-rank)                                     0.0858             0.1605     \n  Hazard Ratio                                            0.51               0.57      \n  95% CI                                              (0.23, 1.12)       (0.26, 1.26)  \n———————————————————————————————————————————————————————————————————————————————————————\n\n{1} - Censored observations: range minimum & maximum\n{2} - Censored observation: range minimum\n———————————————————————————————————————————————————————————————————————————————————————\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADAETTE &lt;- synthetic_cdisc_dataset(\"latest\", \"adaette\")\n\n  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\n  ADSL &lt;- df_explicit_na(ADSL)\n  ADAETTE &lt;- df_explicit_na(ADAETTE)\n\n  ADSL &lt;- filter(ADSL, .data$SAFFL == \"Y\")\n  ADAETTE &lt;- filter(ADAETTE, .data$SAFFL == \"Y\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADAETTE\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADAETTE &lt;- data[[\"ADAETTE\"]]\narm_ref_comp &lt;- list(\n  ACTARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  ),\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  )\n)\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_tte(\n      label = \"Time To LLT Event Table\",\n      dataname = \"ADAETTE\",\n      arm_var = choices_selected(\n        variable_choices(data[[\"ADSL\"]], c(\"ARM\", \"ARMCD\", \"ACTARMCD\")),\n        \"ARM\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(data[[\"ADAETTE\"]], \"PARAMCD\", \"PARAM\"),\n        \"HYSTTEUL\"\n      ),\n      strata_var = choices_selected(\n        variable_choices(data[[\"ADSL\"]], c(\"RACE\", \"SEX\")),\n        c(\"RACE\", \"SEX\")\n      ),\n      time_points = choices_selected(0, NULL),\n      event_desc_var = choices_selected(\"EVNTDESC\", \"EVNTDESC\", fixed = TRUE)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:50:40 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1391,7 +1391,7 @@
     "href": "tables/lab-results/lbt06.html",
     "title": "LBT06",
     "section": "",
-    "text": "Data Setup\nStandard Table for  Single Lab Test\nStandard Table for  Multiple Lab Tests\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n# Please note that df_explict_na has a na_level argument defaulting to \"&lt;Missing&gt;\",\n# Please don't change the na_level to anything other than NA, empty string or the default \"&lt;Missing&gt;\".\n\nadlb_f &lt;- adlb %&gt;%\n  dplyr::filter(ABLFL != \"Y\") %&gt;%\n  dplyr::filter(!(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %&gt;%\n  dplyr::mutate(AVISIT = droplevels(AVISIT)) %&gt;%\n  var_relabel(AVISIT = \"Visit\")\n\nadlb_f_crp &lt;- adlb_f %&gt;% dplyr::filter(PARAMCD == \"CRP\")\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f_crp$AVISIT)) %&gt;%\n  count_abnormal_by_baseline(\n    \"ANRIND\",\n    abnormal = c(Low = \"LOW\", High = \"HIGH\"),\n    .indent_mods = 4L\n  ) %&gt;%\n  append_varlabels(adlb_f_crp, \"ANRIND\", indent = 1L) %&gt;%\n  append_topleft(\"    Baseline Status\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f_crp,\n  alt_counts_df = adsl\n) %&gt;%\n  trim_rows()\n\nresult\n\nVisit                                                                                  \n  Analysis Reference Range Indicator     A: Drug X        B: Placebo     C: Combination\n    Baseline Status                       (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8                                                                           \n  Low                                                                                  \n            Not low                    16/119 (13.4%)   22/113 (19.5%)   24/112 (21.4%)\n            Low                         2/15 (13.3%)     2/21 (9.5%)       7/20 (35%)  \n            Total                      18/134 (13.4%)   24/134 (17.9%)   31/132 (23.5%)\n  High                                                                                 \n            Not high                   21/114 (18.4%)   20/112 (17.9%)   17/115 (14.8%)\n            High                         2/20 (10%)      4/22 (18.2%)     3/17 (17.6%) \n            Total                      23/134 (17.2%)   24/134 (17.9%)   20/132 (15.2%)\nWEEK 2 DAY 15                                                                          \n  Low                                                                                  \n            Not low                    26/119 (21.8%)   20/113 (17.7%)   12/112 (10.7%)\n            Low                         2/15 (13.3%)     3/21 (14.3%)      4/20 (20%)  \n            Total                      28/134 (20.9%)   23/134 (17.2%)   16/132 (12.1%)\n  High                                                                                 \n            Not high                   15/114 (13.2%)   17/112 (15.2%)    15/115 (13%) \n            High                         2/20 (10%)      4/22 (18.2%)     4/17 (23.5%) \n            Total                      17/134 (12.7%)   21/134 (15.7%)   19/132 (14.4%)\nWEEK 3 DAY 22                                                                          \n  Low                                                                                  \n            Not low                    15/119 (12.6%)   21/113 (18.6%)   18/112 (16.1%)\n            Low                             0/15         3/21 (14.3%)         0/20     \n            Total                      15/134 (11.2%)   24/134 (17.9%)   18/132 (13.6%)\n  High                                                                                 \n            Not high                   22/114 (19.3%)   18/112 (16.1%)   17/115 (14.8%)\n            High                         2/20 (10%)      5/22 (22.7%)     1/17 (5.9%)  \n            Total                      24/134 (17.9%)   23/134 (17.2%)   18/132 (13.6%)\nWEEK 4 DAY 29                                                                          \n  Low                                                                                  \n            Not low                    30/119 (25.2%)   13/113 (11.5%)   16/112 (14.3%)\n            Low                          3/15 (20%)      2/21 (9.5%)       5/20 (25%)  \n            Total                      33/134 (24.6%)   15/134 (11.2%)   21/132 (15.9%)\n  High                                                                                 \n            Not high                   17/114 (14.9%)   11/112 (9.8%)    16/115 (13.9%)\n            High                         2/20 (10%)      6/22 (27.3%)     3/17 (17.6%) \n            Total                      19/134 (14.2%)   17/134 (12.7%)   19/132 (14.4%)\nWEEK 5 DAY 36                                                                          \n  Low                                                                                  \n            Not low                    17/119 (14.3%)   19/113 (16.8%)   16/112 (14.3%)\n            Low                         2/15 (13.3%)     3/21 (14.3%)      5/20 (25%)  \n            Total                      19/134 (14.2%)   22/134 (16.4%)   21/132 (15.9%)\n  High                                                                                 \n            Not high                   19/114 (16.7%)   17/112 (15.2%)   11/115 (9.6%) \n            High                         4/20 (20%)      6/22 (27.3%)     2/17 (11.8%) \n            Total                      23/134 (17.2%)   23/134 (17.2%)   13/132 (9.8%) \n\n\n\n\n\nCode# The following code generates one large table for multiple lab tests.\n# If separate tables are needed for each lab test per GDSR standard, use the code for \"single lab test\".\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$PARAM)) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$AVISIT)) %&gt;%\n  count_abnormal_by_baseline(\n    \"ANRIND\",\n    abnormal = c(Low = \"LOW\", High = \"HIGH\"),\n    .indent_mods = 4L\n  ) %&gt;%\n  append_varlabels(adlb_f, \"ANRIND\", indent = 2L) %&gt;%\n  append_topleft(\"      Baseline Status\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f,\n  alt_counts_df = adsl\n) %&gt;%\n  trim_rows()\n\nresult\n\nParameter                                                                                \n  Visit                                                                                  \n    Analysis Reference Range Indicator     A: Drug X        B: Placebo     C: Combination\n      Baseline Status                       (N=134)          (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                     \n  WEEK 1 DAY 8                                                                           \n    Low                                                                                  \n              Not low                    19/113 (16.8%)   13/117 (11.1%)   14/123 (11.4%)\n              Low                         2/21 (9.5%)      1/17 (5.9%)      1/9 (11.1%)  \n              Total                      21/134 (15.7%)   14/134 (10.4%)   15/132 (11.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 2 DAY 15                                                                          \n    Low                                                                                  \n              Not low                     17/113 (15%)    13/117 (11.1%)   13/123 (10.6%)\n              Low                         2/21 (9.5%)      2/17 (11.8%)     4/9 (44.4%)  \n              Total                      19/134 (14.2%)   15/134 (11.2%)   17/132 (12.9%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 3 DAY 22                                                                          \n    Low                                                                                  \n              Not low                    10/113 (8.8%)    20/117 (17.1%)   21/123 (17.1%)\n              Low                          4/21 (19%)      4/17 (23.5%)     2/9 (22.2%)  \n              Total                      14/134 (10.4%)   24/134 (17.9%)   23/132 (17.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 4 DAY 29                                                                          \n    Low                                                                                  \n              Not low                    15/113 (13.3%)   19/117 (16.2%)   18/123 (14.6%)\n              Low                         2/21 (9.5%)      3/17 (17.6%)     1/9 (11.1%)  \n              Total                      17/134 (12.7%)   22/134 (16.4%)   19/132 (14.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 5 DAY 36                                                                          \n    Low                                                                                  \n              Not low                     17/113 (15%)    23/117 (19.7%)   18/123 (14.6%)\n              Low                             0/21         1/17 (5.9%)      1/9 (11.1%)  \n              Total                      17/134 (12.7%)   24/134 (17.9%)   19/132 (14.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \nC-Reactive Protein Measurement                                                           \n  WEEK 1 DAY 8                                                                           \n    Low                                                                                  \n              Not low                    16/119 (13.4%)   22/113 (19.5%)   24/112 (21.4%)\n              Low                         2/15 (13.3%)     2/21 (9.5%)       7/20 (35%)  \n              Total                      18/134 (13.4%)   24/134 (17.9%)   31/132 (23.5%)\n    High                                                                                 \n              Not high                   21/114 (18.4%)   20/112 (17.9%)   17/115 (14.8%)\n              High                         2/20 (10%)      4/22 (18.2%)     3/17 (17.6%) \n              Total                      23/134 (17.2%)   24/134 (17.9%)   20/132 (15.2%)\n  WEEK 2 DAY 15                                                                          \n    Low                                                                                  \n              Not low                    26/119 (21.8%)   20/113 (17.7%)   12/112 (10.7%)\n              Low                         2/15 (13.3%)     3/21 (14.3%)      4/20 (20%)  \n              Total                      28/134 (20.9%)   23/134 (17.2%)   16/132 (12.1%)\n    High                                                                                 \n              Not high                   15/114 (13.2%)   17/112 (15.2%)    15/115 (13%) \n              High                         2/20 (10%)      4/22 (18.2%)     4/17 (23.5%) \n              Total                      17/134 (12.7%)   21/134 (15.7%)   19/132 (14.4%)\n  WEEK 3 DAY 22                                                                          \n    Low                                                                                  \n              Not low                    15/119 (12.6%)   21/113 (18.6%)   18/112 (16.1%)\n              Low                             0/15         3/21 (14.3%)         0/20     \n              Total                      15/134 (11.2%)   24/134 (17.9%)   18/132 (13.6%)\n    High                                                                                 \n              Not high                   22/114 (19.3%)   18/112 (16.1%)   17/115 (14.8%)\n              High                         2/20 (10%)      5/22 (22.7%)     1/17 (5.9%)  \n              Total                      24/134 (17.9%)   23/134 (17.2%)   18/132 (13.6%)\n  WEEK 4 DAY 29                                                                          \n    Low                                                                                  \n              Not low                    30/119 (25.2%)   13/113 (11.5%)   16/112 (14.3%)\n              Low                          3/15 (20%)      2/21 (9.5%)       5/20 (25%)  \n              Total                      33/134 (24.6%)   15/134 (11.2%)   21/132 (15.9%)\n    High                                                                                 \n              Not high                   17/114 (14.9%)   11/112 (9.8%)    16/115 (13.9%)\n              High                         2/20 (10%)      6/22 (27.3%)     3/17 (17.6%) \n              Total                      19/134 (14.2%)   17/134 (12.7%)   19/132 (14.4%)\n  WEEK 5 DAY 36                                                                          \n    Low                                                                                  \n              Not low                    17/119 (14.3%)   19/113 (16.8%)   16/112 (14.3%)\n              Low                         2/15 (13.3%)     3/21 (14.3%)      5/20 (25%)  \n              Total                      19/134 (14.2%)   22/134 (16.4%)   21/132 (15.9%)\n    High                                                                                 \n              Not high                   19/114 (16.7%)   17/112 (15.2%)   11/115 (9.6%) \n              High                         4/20 (20%)      6/22 (27.3%)     2/17 (11.8%) \n              Total                      23/134 (17.2%)   23/134 (17.2%)   13/132 (9.8%) \nImmunoglobulin A Measurement                                                             \n  WEEK 1 DAY 8                                                                           \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   18/119 (15.1%)   20/116 (17.2%)   14/113 (12.4%)\n              High                        1/15 (6.7%)      5/18 (27.8%)     4/19 (21.1%) \n              Total                      19/134 (14.2%)   25/134 (18.7%)   18/132 (13.6%)\n  WEEK 2 DAY 15                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   16/119 (13.4%)   13/116 (11.2%)   12/113 (10.6%)\n              High                        1/15 (6.7%)      1/18 (5.6%)      2/19 (10.5%) \n              Total                      17/134 (12.7%)   14/134 (10.4%)   14/132 (10.6%)\n  WEEK 3 DAY 22                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   17/119 (14.3%)   15/116 (12.9%)   21/113 (18.6%)\n              High                            0/15         1/18 (5.6%)      1/19 (5.3%)  \n              Total                      17/134 (12.7%)   16/134 (11.9%)   22/132 (16.7%)\n  WEEK 4 DAY 29                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                    19/119 (16%)    16/116 (13.8%)   19/113 (16.8%)\n              High                        4/15 (26.7%)     5/18 (27.8%)     3/19 (15.8%) \n              Total                      23/134 (17.2%)   21/134 (15.7%)   22/132 (16.7%)\n  WEEK 5 DAY 36                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   16/119 (13.4%)   21/116 (18.1%)   20/113 (17.7%)\n              High                        1/15 (6.7%)      3/18 (16.7%)         0/19     \n              Total                      17/134 (12.7%)   24/134 (17.9%)   20/132 (15.2%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:52:24 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table for  Single Lab Test\nStandard Table for  Multiple Lab Tests\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(tern)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n# Please note that df_explict_na has a na_level argument defaulting to \"&lt;Missing&gt;\",\n# Please don't change the na_level to anything other than NA, empty string or the default \"&lt;Missing&gt;\".\n\nadlb_f &lt;- adlb %&gt;%\n  dplyr::filter(ABLFL != \"Y\") %&gt;%\n  dplyr::filter(!(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %&gt;%\n  dplyr::mutate(AVISIT = droplevels(AVISIT)) %&gt;%\n  var_relabel(AVISIT = \"Visit\")\n\nadlb_f_crp &lt;- adlb_f %&gt;% dplyr::filter(PARAMCD == \"CRP\")\n\n\n\n\n\nCode# Define the split function\nsplit_fun &lt;- drop_split_levels\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f_crp$AVISIT)) %&gt;%\n  count_abnormal_by_baseline(\n    \"ANRIND\",\n    abnormal = c(Low = \"LOW\", High = \"HIGH\"),\n    .indent_mods = 4L\n  ) %&gt;%\n  append_varlabels(adlb_f_crp, \"ANRIND\", indent = 1L) %&gt;%\n  append_topleft(\"    Baseline Status\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f_crp,\n  alt_counts_df = adsl\n) %&gt;%\n  trim_rows()\n\nresult\n\nVisit                                                                                  \n  Analysis Reference Range Indicator     A: Drug X        B: Placebo     C: Combination\n    Baseline Status                       (N=134)          (N=134)          (N=132)    \n———————————————————————————————————————————————————————————————————————————————————————\nWEEK 1 DAY 8                                                                           \n  Low                                                                                  \n            Not low                    16/119 (13.4%)   22/113 (19.5%)   24/112 (21.4%)\n            Low                         2/15 (13.3%)     2/21 (9.5%)       7/20 (35%)  \n            Total                      18/134 (13.4%)   24/134 (17.9%)   31/132 (23.5%)\n  High                                                                                 \n            Not high                   21/114 (18.4%)   20/112 (17.9%)   17/115 (14.8%)\n            High                         2/20 (10%)      4/22 (18.2%)     3/17 (17.6%) \n            Total                      23/134 (17.2%)   24/134 (17.9%)   20/132 (15.2%)\nWEEK 2 DAY 15                                                                          \n  Low                                                                                  \n            Not low                    26/119 (21.8%)   20/113 (17.7%)   12/112 (10.7%)\n            Low                         2/15 (13.3%)     3/21 (14.3%)      4/20 (20%)  \n            Total                      28/134 (20.9%)   23/134 (17.2%)   16/132 (12.1%)\n  High                                                                                 \n            Not high                   15/114 (13.2%)   17/112 (15.2%)    15/115 (13%) \n            High                         2/20 (10%)      4/22 (18.2%)     4/17 (23.5%) \n            Total                      17/134 (12.7%)   21/134 (15.7%)   19/132 (14.4%)\nWEEK 3 DAY 22                                                                          \n  Low                                                                                  \n            Not low                    15/119 (12.6%)   21/113 (18.6%)   18/112 (16.1%)\n            Low                             0/15         3/21 (14.3%)         0/20     \n            Total                      15/134 (11.2%)   24/134 (17.9%)   18/132 (13.6%)\n  High                                                                                 \n            Not high                   22/114 (19.3%)   18/112 (16.1%)   17/115 (14.8%)\n            High                         2/20 (10%)      5/22 (22.7%)     1/17 (5.9%)  \n            Total                      24/134 (17.9%)   23/134 (17.2%)   18/132 (13.6%)\nWEEK 4 DAY 29                                                                          \n  Low                                                                                  \n            Not low                    30/119 (25.2%)   13/113 (11.5%)   16/112 (14.3%)\n            Low                          3/15 (20%)      2/21 (9.5%)       5/20 (25%)  \n            Total                      33/134 (24.6%)   15/134 (11.2%)   21/132 (15.9%)\n  High                                                                                 \n            Not high                   17/114 (14.9%)   11/112 (9.8%)    16/115 (13.9%)\n            High                         2/20 (10%)      6/22 (27.3%)     3/17 (17.6%) \n            Total                      19/134 (14.2%)   17/134 (12.7%)   19/132 (14.4%)\nWEEK 5 DAY 36                                                                          \n  Low                                                                                  \n            Not low                    17/119 (14.3%)   19/113 (16.8%)   16/112 (14.3%)\n            Low                         2/15 (13.3%)     3/21 (14.3%)      5/20 (25%)  \n            Total                      19/134 (14.2%)   22/134 (16.4%)   21/132 (15.9%)\n  High                                                                                 \n            Not high                   19/114 (16.7%)   17/112 (15.2%)   11/115 (9.6%) \n            High                         4/20 (20%)      6/22 (27.3%)     2/17 (11.8%) \n            Total                      23/134 (17.2%)   23/134 (17.2%)   13/132 (9.8%) \n\n\n\n\n\nCode# The following code generates one large table for multiple lab tests.\n# If separate tables are needed for each lab test per GDSR standard, use the code for \"single lab test\".\n\nlyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARM\") %&gt;%\n  split_rows_by(\"PARAM\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$PARAM)) %&gt;%\n  split_rows_by(\"AVISIT\", split_fun = split_fun, label_pos = \"topleft\", split_label = obj_label(adlb_f$AVISIT)) %&gt;%\n  count_abnormal_by_baseline(\n    \"ANRIND\",\n    abnormal = c(Low = \"LOW\", High = \"HIGH\"),\n    .indent_mods = 4L\n  ) %&gt;%\n  append_varlabels(adlb_f, \"ANRIND\", indent = 2L) %&gt;%\n  append_topleft(\"      Baseline Status\")\n\nresult &lt;- build_table(\n  lyt = lyt,\n  df = adlb_f,\n  alt_counts_df = adsl\n) %&gt;%\n  trim_rows()\n\nresult\n\nParameter                                                                                \n  Visit                                                                                  \n    Analysis Reference Range Indicator     A: Drug X        B: Placebo     C: Combination\n      Baseline Status                       (N=134)          (N=134)          (N=132)    \n—————————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                     \n  WEEK 1 DAY 8                                                                           \n    Low                                                                                  \n              Not low                    19/113 (16.8%)   13/117 (11.1%)   14/123 (11.4%)\n              Low                         2/21 (9.5%)      1/17 (5.9%)      1/9 (11.1%)  \n              Total                      21/134 (15.7%)   14/134 (10.4%)   15/132 (11.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 2 DAY 15                                                                          \n    Low                                                                                  \n              Not low                     17/113 (15%)    13/117 (11.1%)   13/123 (10.6%)\n              Low                         2/21 (9.5%)      2/17 (11.8%)     4/9 (44.4%)  \n              Total                      19/134 (14.2%)   15/134 (11.2%)   17/132 (12.9%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 3 DAY 22                                                                          \n    Low                                                                                  \n              Not low                    10/113 (8.8%)    20/117 (17.1%)   21/123 (17.1%)\n              Low                          4/21 (19%)      4/17 (23.5%)     2/9 (22.2%)  \n              Total                      14/134 (10.4%)   24/134 (17.9%)   23/132 (17.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 4 DAY 29                                                                          \n    Low                                                                                  \n              Not low                    15/113 (13.3%)   19/117 (16.2%)   18/123 (14.6%)\n              Low                         2/21 (9.5%)      3/17 (17.6%)     1/9 (11.1%)  \n              Total                      17/134 (12.7%)   22/134 (16.4%)   19/132 (14.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n  WEEK 5 DAY 36                                                                          \n    Low                                                                                  \n              Not low                     17/113 (15%)    23/117 (19.7%)   18/123 (14.6%)\n              Low                             0/21         1/17 (5.9%)      1/9 (11.1%)  \n              Total                      17/134 (12.7%)   24/134 (17.9%)   19/132 (14.4%)\n    High                                                                                 \n              Not high                       0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \nC-Reactive Protein Measurement                                                           \n  WEEK 1 DAY 8                                                                           \n    Low                                                                                  \n              Not low                    16/119 (13.4%)   22/113 (19.5%)   24/112 (21.4%)\n              Low                         2/15 (13.3%)     2/21 (9.5%)       7/20 (35%)  \n              Total                      18/134 (13.4%)   24/134 (17.9%)   31/132 (23.5%)\n    High                                                                                 \n              Not high                   21/114 (18.4%)   20/112 (17.9%)   17/115 (14.8%)\n              High                         2/20 (10%)      4/22 (18.2%)     3/17 (17.6%) \n              Total                      23/134 (17.2%)   24/134 (17.9%)   20/132 (15.2%)\n  WEEK 2 DAY 15                                                                          \n    Low                                                                                  \n              Not low                    26/119 (21.8%)   20/113 (17.7%)   12/112 (10.7%)\n              Low                         2/15 (13.3%)     3/21 (14.3%)      4/20 (20%)  \n              Total                      28/134 (20.9%)   23/134 (17.2%)   16/132 (12.1%)\n    High                                                                                 \n              Not high                   15/114 (13.2%)   17/112 (15.2%)    15/115 (13%) \n              High                         2/20 (10%)      4/22 (18.2%)     4/17 (23.5%) \n              Total                      17/134 (12.7%)   21/134 (15.7%)   19/132 (14.4%)\n  WEEK 3 DAY 22                                                                          \n    Low                                                                                  \n              Not low                    15/119 (12.6%)   21/113 (18.6%)   18/112 (16.1%)\n              Low                             0/15         3/21 (14.3%)         0/20     \n              Total                      15/134 (11.2%)   24/134 (17.9%)   18/132 (13.6%)\n    High                                                                                 \n              Not high                   22/114 (19.3%)   18/112 (16.1%)   17/115 (14.8%)\n              High                         2/20 (10%)      5/22 (22.7%)     1/17 (5.9%)  \n              Total                      24/134 (17.9%)   23/134 (17.2%)   18/132 (13.6%)\n  WEEK 4 DAY 29                                                                          \n    Low                                                                                  \n              Not low                    30/119 (25.2%)   13/113 (11.5%)   16/112 (14.3%)\n              Low                          3/15 (20%)      2/21 (9.5%)       5/20 (25%)  \n              Total                      33/134 (24.6%)   15/134 (11.2%)   21/132 (15.9%)\n    High                                                                                 \n              Not high                   17/114 (14.9%)   11/112 (9.8%)    16/115 (13.9%)\n              High                         2/20 (10%)      6/22 (27.3%)     3/17 (17.6%) \n              Total                      19/134 (14.2%)   17/134 (12.7%)   19/132 (14.4%)\n  WEEK 5 DAY 36                                                                          \n    Low                                                                                  \n              Not low                    17/119 (14.3%)   19/113 (16.8%)   16/112 (14.3%)\n              Low                         2/15 (13.3%)     3/21 (14.3%)      5/20 (25%)  \n              Total                      19/134 (14.2%)   22/134 (16.4%)   21/132 (15.9%)\n    High                                                                                 \n              Not high                   19/114 (16.7%)   17/112 (15.2%)   11/115 (9.6%) \n              High                         4/20 (20%)      6/22 (27.3%)     2/17 (11.8%) \n              Total                      23/134 (17.2%)   23/134 (17.2%)   13/132 (9.8%) \nImmunoglobulin A Measurement                                                             \n  WEEK 1 DAY 8                                                                           \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   18/119 (15.1%)   20/116 (17.2%)   14/113 (12.4%)\n              High                        1/15 (6.7%)      5/18 (27.8%)     4/19 (21.1%) \n              Total                      19/134 (14.2%)   25/134 (18.7%)   18/132 (13.6%)\n  WEEK 2 DAY 15                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   16/119 (13.4%)   13/116 (11.2%)   12/113 (10.6%)\n              High                        1/15 (6.7%)      1/18 (5.6%)      2/19 (10.5%) \n              Total                      17/134 (12.7%)   14/134 (10.4%)   14/132 (10.6%)\n  WEEK 3 DAY 22                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   17/119 (14.3%)   15/116 (12.9%)   21/113 (18.6%)\n              High                            0/15         1/18 (5.6%)      1/19 (5.3%)  \n              Total                      17/134 (12.7%)   16/134 (11.9%)   22/132 (16.7%)\n  WEEK 4 DAY 29                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                    19/119 (16%)    16/116 (13.8%)   19/113 (16.8%)\n              High                        4/15 (26.7%)     5/18 (27.8%)     3/19 (15.8%) \n              Total                      23/134 (17.2%)   21/134 (15.7%)   22/132 (16.7%)\n  WEEK 5 DAY 36                                                                          \n    Low                                                                                  \n              Not low                        0/134            0/134            0/132     \n              Total                          0/134            0/134            0/132     \n    High                                                                                 \n              Not high                   16/119 (13.4%)   21/116 (18.1%)   20/113 (17.7%)\n              High                        1/15 (6.7%)      3/18 (16.7%)         0/19     \n              Total                      17/134 (12.7%)   24/134 (17.9%)   20/132 (15.2%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:50:55 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1403,7 +1403,7 @@
     "href": "tables/lab-results/lbt08.html",
     "title": "LBT08",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb &lt;- adlb %&gt;%\n  mutate(\n    GRADDR = case_when(\n      PARAMCD == \"ALT\" ~ \"L\",\n      PARAMCD == \"CRP\" ~ \"B\",\n      PARAMCD == \"IGA\" ~ \"H\"\n    )\n  ) %&gt;%\n  filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")\n\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\ndf &lt;- h_adlb_worsen(\n  adlb,\n  worst_flag_low = c(\"WGRLOFL\" = \"Y\"),\n  worst_flag_high = c(\"WGRHIFL\" = \"Y\"),\n  direction_var = \"GRADDR\"\n)\n\nattributes(df$GRADDR) &lt;- list(\"label\" = \"Direction of Abnormality\")\n\n\n\n\nNote: The direction of the shift table for each lab test is based on metadata and NCI CTCAE specifications. In addition, the worst laboratory flags must be selected appropriately to match the direction of abnormality. For example, if any lab requires a shift table for both directions, then both worst_flag_low and worst_flag_high must be specified in h_adlb_worsen. If all labs requires a shift table for only one direction, then the matching worst lab flag variable must be selected in h_adlb_worsen.\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\") %&gt;%\n  split_rows_by(\"PARAMCD\", label_pos = \"topleft\", split_label = obj_label(df$PARAMCD)) %&gt;%\n  split_rows_by(\"GRADDR\", label_pos = \"topleft\", split_label = obj_label(df$GRADDR)) %&gt;%\n  count_abnormal_lab_worsen_by_baseline(\n    var = \"ATOXGR\",\n    variables = list(\n      id = \"USUBJID\",\n      baseline_var = \"BTOXGR\",\n      direction_var = \"GRADDR\"\n    )\n  ) %&gt;%\n  append_topleft(\"    Highest NCI CTCAE Grade\") %&gt;%\n  build_table(df = df, alt_counts_df = adsl)\n\nresult\n\nParameter Code                                                                \n  Direction of Abnormality        ARM A            ARM B            ARM C     \n    Highest NCI CTCAE Grade      (N=134)          (N=134)          (N=132)    \n——————————————————————————————————————————————————————————————————————————————\nALT                                                                           \n  Low                                                                         \n    1                         11/113 (9.7%)     9/117 (7.7%)    15/123 (12.2%)\n    2                         15/119 (12.6%)   23/123 (18.7%)   16/127 (12.6%)\n    3                         15/127 (11.8%)   22/128 (17.2%)   14/128 (10.9%)\n    4                         17/130 (13.1%)   10/131 (7.6%)    18/130 (13.8%)\n    Any                       58/130 (44.6%)   64/131 (48.9%)   63/130 (48.5%)\nCRP                                                                           \n  High                                                                        \n    1                         18/114 (15.8%)   18/112 (16.1%)   19/115 (16.5%)\n    2                         20/122 (16.4%)   13/122 (10.7%)   14/122 (11.5%)\n    3                         23/124 (18.5%)   14/128 (10.9%)   20/129 (15.5%)\n    4                         12/131 (9.2%)    20/132 (15.2%)   11/132 (8.3%) \n    Any                       73/131 (55.7%)   65/132 (49.2%)   64/132 (48.5%)\n  Low                                                                         \n    1                         20/119 (16.8%)   18/113 (15.9%)   11/112 (9.8%) \n    2                         24/122 (19.7%)   21/118 (17.8%)    17/121 (14%) \n    3                         26/127 (20.5%)   20/127 (15.7%)   22/123 (17.9%)\n    4                         10/131 (7.6%)    16/132 (12.1%)   21/130 (16.2%)\n    Any                       80/131 (61.1%)   75/132 (56.8%)   71/130 (54.6%)\nIGA                                                                           \n  High                                                                        \n    1                         18/119 (15.1%)   11/116 (9.5%)    15/113 (13.3%)\n    2                         15/124 (12.1%)   23/120 (19.2%)   19/115 (16.5%)\n    3                         21/128 (16.4%)   21/124 (16.9%)   20/120 (16.7%)\n    4                         12/132 (9.1%)    12/129 (9.3%)     9/131 (6.9%) \n    Any                        66/132 (50%)    67/129 (51.9%)   63/131 (48.1%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:52:46 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb &lt;- adlb %&gt;%\n  mutate(\n    GRADDR = case_when(\n      PARAMCD == \"ALT\" ~ \"L\",\n      PARAMCD == \"CRP\" ~ \"B\",\n      PARAMCD == \"IGA\" ~ \"H\"\n    )\n  ) %&gt;%\n  filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")\n\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\ndf &lt;- h_adlb_worsen(\n  adlb,\n  worst_flag_low = c(\"WGRLOFL\" = \"Y\"),\n  worst_flag_high = c(\"WGRHIFL\" = \"Y\"),\n  direction_var = \"GRADDR\"\n)\n\nattributes(df$GRADDR) &lt;- list(\"label\" = \"Direction of Abnormality\")\n\n\n\n\nNote: The direction of the shift table for each lab test is based on metadata and NCI CTCAE specifications. In addition, the worst laboratory flags must be selected appropriately to match the direction of abnormality. For example, if any lab requires a shift table for both directions, then both worst_flag_low and worst_flag_high must be specified in h_adlb_worsen. If all labs requires a shift table for only one direction, then the matching worst lab flag variable must be selected in h_adlb_worsen.\n\nCoderesult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ARMCD\") %&gt;%\n  split_rows_by(\"PARAMCD\", label_pos = \"topleft\", split_label = obj_label(df$PARAMCD)) %&gt;%\n  split_rows_by(\"GRADDR\", label_pos = \"topleft\", split_label = obj_label(df$GRADDR)) %&gt;%\n  count_abnormal_lab_worsen_by_baseline(\n    var = \"ATOXGR\",\n    variables = list(\n      id = \"USUBJID\",\n      baseline_var = \"BTOXGR\",\n      direction_var = \"GRADDR\"\n    )\n  ) %&gt;%\n  append_topleft(\"    Highest NCI CTCAE Grade\") %&gt;%\n  build_table(df = df, alt_counts_df = adsl)\n\nresult\n\nParameter Code                                                                \n  Direction of Abnormality        ARM A            ARM B            ARM C     \n    Highest NCI CTCAE Grade      (N=134)          (N=134)          (N=132)    \n——————————————————————————————————————————————————————————————————————————————\nALT                                                                           \n  Low                                                                         \n    1                         11/113 (9.7%)     9/117 (7.7%)    15/123 (12.2%)\n    2                         15/119 (12.6%)   23/123 (18.7%)   16/127 (12.6%)\n    3                         15/127 (11.8%)   22/128 (17.2%)   14/128 (10.9%)\n    4                         17/130 (13.1%)   10/131 (7.6%)    18/130 (13.8%)\n    Any                       58/130 (44.6%)   64/131 (48.9%)   63/130 (48.5%)\nCRP                                                                           \n  High                                                                        \n    1                         18/114 (15.8%)   18/112 (16.1%)   19/115 (16.5%)\n    2                         20/122 (16.4%)   13/122 (10.7%)   14/122 (11.5%)\n    3                         23/124 (18.5%)   14/128 (10.9%)   20/129 (15.5%)\n    4                         12/131 (9.2%)    20/132 (15.2%)   11/132 (8.3%) \n    Any                       73/131 (55.7%)   65/132 (49.2%)   64/132 (48.5%)\n  Low                                                                         \n    1                         20/119 (16.8%)   18/113 (15.9%)   11/112 (9.8%) \n    2                         24/122 (19.7%)   21/118 (17.8%)    17/121 (14%) \n    3                         26/127 (20.5%)   20/127 (15.7%)   22/123 (17.9%)\n    4                         10/131 (7.6%)    16/132 (12.1%)   21/130 (16.2%)\n    Any                       80/131 (61.1%)   75/132 (56.8%)   71/130 (54.6%)\nIGA                                                                           \n  High                                                                        \n    1                         18/119 (15.1%)   11/116 (9.5%)    15/113 (13.3%)\n    2                         15/124 (12.1%)   23/120 (19.2%)   19/115 (16.5%)\n    3                         21/128 (16.4%)   21/124 (16.9%)   20/120 (16.7%)\n    4                         12/132 (9.1%)    12/129 (9.3%)     9/131 (6.9%) \n    Any                        66/132 (50%)    67/129 (51.9%)   63/131 (48.1%)\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:51:17 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1415,7 +1415,7 @@
     "href": "tables/lab-results/lbt14.html",
     "title": "LBT14",
     "section": "",
-    "text": "Data Setup\nStandard Table (High)\nStandard Table (Low)\nTable Without Patients with  Missing Baseline (High)\nTable with Missing Baseline  Considered as Grade 0 (Low)\nTable with Fill-In of Grades\nteal App\nReproducibility\n\n\n\nPlease note that for each variant, the adlb dataset needs to be filtered on correct flags like WGRLOFL, WGRHIFL, et al., otherwise the layout function will not return the correct counts. There is an option to create a record for a lab test where no record is found at that visit. If you specified add_derived_type = \"PHANTOM\" & dtype_phantom_cond, you don’t have to use the h_adsl_adlb_merge_using_worst_flag function to preprocess your adlb dataset. Otherwise please follow the pre-processing steps below before applying the layout functions.\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n# Please note that in real clinical data, population flag like SAFFL, and parameter category like PARCAT2 needs to be\n# selected properly.\nadsl_f &lt;- adsl %&gt;% filter(SAFFL == \"Y\")\nadlb &lt;- adlb %&gt;% filter(SAFFL == \"Y\")\n\n\n\n\nNote that the worst laboratory flag (below WGRHIFL) must be selected appropriately in the pre-processing step. New grouping variables ATOXGR_GP (post-baseline) and BTOXGR_GP (baseline) are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRHIFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not High                                   134          134            132      \n              Not High                        134 (100%)   134 (100%)     132 (100%)  \nC-Reactive Protein Measurement                                                        \n      Not High                                   114          112            115      \n              Not High                        45 (33.6%)   55 (41.0%)     57 (43.2%)  \n              1                               18 (13.4%)   18 (13.4%)     19 (14.4%)  \n              2                               19 (14.2%)   10 (7.5%)      11 (8.3%)   \n              3                               22 (16.4%)   13 (9.7%)      20 (15.2%)  \n              4                               10 (7.5%)    16 (11.9%)      8 (6.1%)   \n      1                                           8            10             7       \n              Not High                         5 (3.7%)     2 (1.5%)       2 (1.5%)   \n              1                                   0         1 (0.7%)          0       \n              2                                1 (0.7%)     3 (2.2%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     3 (2.2%)       2 (1.5%)   \n      2                                           2            6              7       \n              Not High                         1 (0.7%)     4 (3.0%)       4 (3.0%)   \n              1                                   0         1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0            0           1 (0.8%)   \n      3                                           7            4              3       \n              Not High                         3 (2.2%)        0           2 (1.5%)   \n              1                                2 (1.5%)     1 (0.7%)       1 (0.8%)   \n              2                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n      4                                           3            2              0       \n              Not High                         1 (0.7%)        0              0       \n              1                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)        0              0       \nImmunoglobulin A Measurement                                                          \n      Not High                                   119          116            113      \n              Not High                        55 (41.0%)   54 (40.3%)     51 (38.6%)  \n              1                               18 (13.4%)   11 (8.2%)      15 (11.4%)  \n              2                               14 (10.4%)   22 (16.4%)     19 (14.4%)  \n              3                               20 (14.9%)   19 (14.2%)     20 (15.2%)  \n              4                               12 (9.0%)    10 (7.5%)       8 (6.1%)   \n      1                                           5            4              2       \n              Not High                         4 (3.0%)     1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0         2 (1.5%)          0       \n      2                                           4            4              5       \n              Not High                         2 (1.5%)     3 (2.2%)       3 (2.3%)   \n              1                                1 (0.7%)        0           2 (1.5%)   \n              3                                1 (0.7%)        0              0       \n              4                                   0         1 (0.7%)          0       \n      3                                           4            5              11      \n              Not High                         3 (2.2%)     1 (0.7%)       6 (4.5%)   \n              1                                   0         1 (0.7%)       1 (0.8%)   \n              2                                1 (0.7%)        0              0       \n              3                                   0         2 (1.5%)       3 (2.3%)   \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      4                                           2            5              1       \n              Not High                            0         3 (2.2%)       1 (0.8%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n\n\n\n\nNote that the worst laboratory flag (below WGRLOFL) must be selected appropriately in the pre-processing step. New grouping variables ATOXGR_GP (post-baseline) and BTOXGR_GP (baseline) are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRLOFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not Low                                    113          117            123      \n              Not Low                         56 (41.8%)   55 (41.0%)     62 (47.0%)  \n              1                               11 (8.2%)     9 (6.7%)      15 (11.4%)  \n              2                               15 (11.2%)   23 (17.2%)     16 (12.1%)  \n              3                               15 (11.2%)   21 (15.7%)     13 (9.8%)   \n              4                               16 (11.9%)    9 (6.7%)      17 (12.9%)  \n      1                                           6            6              4       \n              Not Low                          3 (2.2%)     5 (3.7%)       3 (2.3%)   \n              1                                3 (2.2%)        0              0       \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      2                                           8            5              1       \n              Not Low                          5 (3.7%)     3 (2.2%)          0       \n              1                                   0         1 (0.7%)          0       \n              2                                2 (1.5%)        0              0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           3            3              2       \n              Not Low                             0         1 (0.7%)          0       \n              1                                1 (0.7%)        0              0       \n              2                                1 (0.7%)        0           1 (0.8%)   \n              3                                1 (0.7%)     2 (1.5%)       1 (0.8%)   \n      4                                           4            3              2       \n              Not Low                          4 (3.0%)     1 (0.7%)       1 (0.8%)   \n              1                                   0         1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \nC-Reactive Protein Measurement                                                        \n      Not Low                                    119          113            112      \n              Not Low                         41 (30.6%)   42 (31.3%)     50 (37.9%)  \n              1                               20 (14.9%)   18 (13.4%)     11 (8.3%)   \n              2                               24 (17.9%)   20 (14.9%)     14 (10.6%)  \n              3                               25 (18.7%)   18 (13.4%)     21 (15.9%)  \n              4                                9 (6.7%)    15 (11.2%)     16 (12.1%)  \n      1                                           3            5              9       \n              Not Low                          2 (1.5%)     2 (1.5%)       2 (1.5%)   \n              2                                   0         1 (0.7%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0         1 (0.7%)       4 (3.0%)   \n      2                                           5            9              2       \n              Not Low                          3 (2.2%)     4 (3.0%)       1 (0.8%)   \n              1                                1 (0.7%)     4 (3.0%)          0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           4            5              7       \n              Not Low                          1 (0.7%)     4 (3.0%)       2 (1.5%)   \n              1                                   0            0           1 (0.8%)   \n              2                                   0            0           1 (0.8%)   \n              3                                3 (2.2%)     1 (0.7%)       2 (1.5%)   \n              4                                   0            0           1 (0.8%)   \n      4                                           3            2              2       \n              Not Low                          3 (2.2%)     1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \n              4                                   0            0           1 (0.8%)   \nImmunoglobulin A Measurement                                                          \n      Not Low                                    134          134            132      \n              Not Low                         134 (100%)   134 (100%)     132 (100%)  \n\n\n\n\nNote that missing baseline values are filtered out in the pre-processing step.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRHIFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  filter(BTOXGR != \"&lt;Missing&gt;\") %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not High                                   134          134            132      \n              Not High                        134 (100%)   134 (100%)     132 (100%)  \nC-Reactive Protein Measurement                                                        \n      Not High                                   114          112            115      \n              Not High                        45 (33.6%)   55 (41.0%)     57 (43.2%)  \n              1                               18 (13.4%)   18 (13.4%)     19 (14.4%)  \n              2                               19 (14.2%)   10 (7.5%)      11 (8.3%)   \n              3                               22 (16.4%)   13 (9.7%)      20 (15.2%)  \n              4                               10 (7.5%)    16 (11.9%)      8 (6.1%)   \n      1                                           8            10             7       \n              Not High                         5 (3.7%)     2 (1.5%)       2 (1.5%)   \n              1                                   0         1 (0.7%)          0       \n              2                                1 (0.7%)     3 (2.2%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     3 (2.2%)       2 (1.5%)   \n      2                                           2            6              7       \n              Not High                         1 (0.7%)     4 (3.0%)       4 (3.0%)   \n              1                                   0         1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0            0           1 (0.8%)   \n      3                                           7            4              3       \n              Not High                         3 (2.2%)        0           2 (1.5%)   \n              1                                2 (1.5%)     1 (0.7%)       1 (0.8%)   \n              2                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n      4                                           3            2              0       \n              Not High                         1 (0.7%)        0              0       \n              1                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)        0              0       \nImmunoglobulin A Measurement                                                          \n      Not High                                   119          116            113      \n              Not High                        55 (41.0%)   54 (40.3%)     51 (38.6%)  \n              1                               18 (13.4%)   11 (8.2%)      15 (11.4%)  \n              2                               14 (10.4%)   22 (16.4%)     19 (14.4%)  \n              3                               20 (14.9%)   19 (14.2%)     20 (15.2%)  \n              4                               12 (9.0%)    10 (7.5%)       8 (6.1%)   \n      1                                           5            4              2       \n              Not High                         4 (3.0%)     1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0         2 (1.5%)          0       \n      2                                           4            4              5       \n              Not High                         2 (1.5%)     3 (2.2%)       3 (2.3%)   \n              1                                1 (0.7%)        0           2 (1.5%)   \n              3                                1 (0.7%)        0              0       \n              4                                   0         1 (0.7%)          0       \n      3                                           4            5              11      \n              Not High                         3 (2.2%)     1 (0.7%)       6 (4.5%)   \n              1                                   0         1 (0.7%)       1 (0.8%)   \n              2                                1 (0.7%)        0              0       \n              3                                   0         2 (1.5%)       3 (2.3%)   \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      4                                           2            5              1       \n              Not High                            0         3 (2.2%)       1 (0.8%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n\n\n\n\nNote that when BTOXGR is missing, the grouping variable BTOXGR_GP now is \"Not Low\" instead of \"Missing\" compared to Standard Table (Low).\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRLOFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4, \"&lt;Missing&gt;\") ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not Low                                    113          117            123      \n              Not Low                         56 (41.8%)   55 (41.0%)     62 (47.0%)  \n              1                               11 (8.2%)     9 (6.7%)      15 (11.4%)  \n              2                               15 (11.2%)   23 (17.2%)     16 (12.1%)  \n              3                               15 (11.2%)   21 (15.7%)     13 (9.8%)   \n              4                               16 (11.9%)    9 (6.7%)      17 (12.9%)  \n      1                                           6            6              4       \n              Not Low                          3 (2.2%)     5 (3.7%)       3 (2.3%)   \n              1                                3 (2.2%)        0              0       \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      2                                           8            5              1       \n              Not Low                          5 (3.7%)     3 (2.2%)          0       \n              1                                   0         1 (0.7%)          0       \n              2                                2 (1.5%)        0              0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           3            3              2       \n              Not Low                             0         1 (0.7%)          0       \n              1                                1 (0.7%)        0              0       \n              2                                1 (0.7%)        0           1 (0.8%)   \n              3                                1 (0.7%)     2 (1.5%)       1 (0.8%)   \n      4                                           4            3              2       \n              Not Low                          4 (3.0%)     1 (0.7%)       1 (0.8%)   \n              1                                   0         1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \nC-Reactive Protein Measurement                                                        \n      Not Low                                    119          113            112      \n              Not Low                         41 (30.6%)   42 (31.3%)     50 (37.9%)  \n              1                               20 (14.9%)   18 (13.4%)     11 (8.3%)   \n              2                               24 (17.9%)   20 (14.9%)     14 (10.6%)  \n              3                               25 (18.7%)   18 (13.4%)     21 (15.9%)  \n              4                                9 (6.7%)    15 (11.2%)     16 (12.1%)  \n      1                                           3            5              9       \n              Not Low                          2 (1.5%)     2 (1.5%)       2 (1.5%)   \n              2                                   0         1 (0.7%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0         1 (0.7%)       4 (3.0%)   \n      2                                           5            9              2       \n              Not Low                          3 (2.2%)     4 (3.0%)       1 (0.8%)   \n              1                                1 (0.7%)     4 (3.0%)          0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           4            5              7       \n              Not Low                          1 (0.7%)     4 (3.0%)       2 (1.5%)   \n              1                                   0            0           1 (0.8%)   \n              2                                   0            0           1 (0.8%)   \n              3                                3 (2.2%)     1 (0.7%)       2 (1.5%)   \n              4                                   0            0           1 (0.8%)   \n      4                                           3            2              2       \n              Not Low                          3 (2.2%)     1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \n              4                                   0            0           1 (0.8%)   \nImmunoglobulin A Measurement                                                          \n      Not Low                                    134          134            132      \n              Not Low                         134 (100%)   134 (100%)     132 (100%)  \n\n\n\n\nPre-processing is the same as Standard Table (High), but in order to keep all levels, prune_table() is not applied.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRHIFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not High                                   134          134            132      \n              Not High                        134 (100%)   134 (100%)     132 (100%)  \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      1                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      2                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      3                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      4                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      Missing                                     0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \nC-Reactive Protein Measurement                                                        \n      Not High                                   114          112            115      \n              Not High                        45 (33.6%)   55 (41.0%)     57 (43.2%)  \n              1                               18 (13.4%)   18 (13.4%)     19 (14.4%)  \n              2                               19 (14.2%)   10 (7.5%)      11 (8.3%)   \n              3                               22 (16.4%)   13 (9.7%)      20 (15.2%)  \n              4                               10 (7.5%)    16 (11.9%)      8 (6.1%)   \n              Missing                             0            0              0       \n      1                                           8            10             7       \n              Not High                         5 (3.7%)     2 (1.5%)       2 (1.5%)   \n              1                                   0         1 (0.7%)          0       \n              2                                1 (0.7%)     3 (2.2%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     3 (2.2%)       2 (1.5%)   \n              Missing                             0            0              0       \n      2                                           2            6              7       \n              Not High                         1 (0.7%)     4 (3.0%)       4 (3.0%)   \n              1                                   0         1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0            0              0       \n              4                                   0            0           1 (0.8%)   \n              Missing                             0            0              0       \n      3                                           7            4              3       \n              Not High                         3 (2.2%)        0           2 (1.5%)   \n              1                                2 (1.5%)     1 (0.7%)       1 (0.8%)   \n              2                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n              Missing                             0            0              0       \n      4                                           3            2              0       \n              Not High                         1 (0.7%)        0              0       \n              1                                   0         1 (0.7%)          0       \n              2                                   0            0              0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)        0              0       \n              Missing                             0            0              0       \n      Missing                                     0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \nImmunoglobulin A Measurement                                                          \n      Not High                                   119          116            113      \n              Not High                        55 (41.0%)   54 (40.3%)     51 (38.6%)  \n              1                               18 (13.4%)   11 (8.2%)      15 (11.4%)  \n              2                               14 (10.4%)   22 (16.4%)     19 (14.4%)  \n              3                               20 (14.9%)   19 (14.2%)     20 (15.2%)  \n              4                               12 (9.0%)    10 (7.5%)       8 (6.1%)   \n              Missing                             0            0              0       \n      1                                           5            4              2       \n              Not High                         4 (3.0%)     1 (0.7%)       2 (1.5%)   \n              1                                   0            0              0       \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0         2 (1.5%)          0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      2                                           4            4              5       \n              Not High                         2 (1.5%)     3 (2.2%)       3 (2.3%)   \n              1                                1 (0.7%)        0           2 (1.5%)   \n              2                                   0            0              0       \n              3                                1 (0.7%)        0              0       \n              4                                   0         1 (0.7%)          0       \n              Missing                             0            0              0       \n      3                                           4            5              11      \n              Not High                         3 (2.2%)     1 (0.7%)       6 (4.5%)   \n              1                                   0         1 (0.7%)       1 (0.8%)   \n              2                                1 (0.7%)        0              0       \n              3                                   0         2 (1.5%)       3 (2.3%)   \n              4                                   0         1 (0.7%)       1 (0.8%)   \n              Missing                             0            0              0       \n      4                                           2            5              1       \n              Not High                            0         3 (2.2%)       1 (0.8%)   \n              1                                   0            0              0       \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0            0              0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n              Missing                             0            0              0       \n      Missing                                     0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_shift_by_grade(\n      label = \"Grade Laboratory Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      ),\n      worst_flag_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"WGRLOVFL\", \"WGRLOFL\", \"WGRHIVFL\", \"WGRHIFL\")),\n        selected = c(\"WGRHIFL\")\n      ),\n      worst_flag_indicator = choices_selected(\n        value_choices(ADLB, \"WGRLOVFL\"),\n        selected = \"Y\", fixed = TRUE\n      ),\n      anl_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"ATOXGR\")),\n        selected = c(\"ATOXGR\"),\n        fixed = TRUE\n      ),\n      base_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"BTOXGR\")),\n        selected = c(\"BTOXGR\"),\n        fixed = TRUE\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:53:23 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9013            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table (High)\nStandard Table (Low)\nTable Without Patients with  Missing Baseline (High)\nTable with Missing Baseline  Considered as Grade 0 (Low)\nTable with Fill-In of Grades\nteal App\nReproducibility\n\n\n\nPlease note that for each variant, the adlb dataset needs to be filtered on correct flags like WGRLOFL, WGRHIFL, et al., otherwise the layout function will not return the correct counts. There is an option to create a record for a lab test where no record is found at that visit. If you specified add_derived_type = \"PHANTOM\" & dtype_phantom_cond, you don’t have to use the h_adsl_adlb_merge_using_worst_flag function to preprocess your adlb dataset. Otherwise please follow the pre-processing steps below before applying the layout functions.\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\n# Please note that in real clinical data, population flag like SAFFL, and parameter category like PARCAT2 needs to be\n# selected properly.\nadsl_f &lt;- adsl %&gt;% filter(SAFFL == \"Y\")\nadlb &lt;- adlb %&gt;% filter(SAFFL == \"Y\")\n\n\n\n\nNote that the worst laboratory flag (below WGRHIFL) must be selected appropriately in the pre-processing step. New grouping variables ATOXGR_GP (post-baseline) and BTOXGR_GP (baseline) are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRHIFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not High                                   134          134            132      \n              Not High                        134 (100%)   134 (100%)     132 (100%)  \nC-Reactive Protein Measurement                                                        \n      Not High                                   114          112            115      \n              Not High                        45 (33.6%)   55 (41.0%)     57 (43.2%)  \n              1                               18 (13.4%)   18 (13.4%)     19 (14.4%)  \n              2                               19 (14.2%)   10 (7.5%)      11 (8.3%)   \n              3                               22 (16.4%)   13 (9.7%)      20 (15.2%)  \n              4                               10 (7.5%)    16 (11.9%)      8 (6.1%)   \n      1                                           8            10             7       \n              Not High                         5 (3.7%)     2 (1.5%)       2 (1.5%)   \n              1                                   0         1 (0.7%)          0       \n              2                                1 (0.7%)     3 (2.2%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     3 (2.2%)       2 (1.5%)   \n      2                                           2            6              7       \n              Not High                         1 (0.7%)     4 (3.0%)       4 (3.0%)   \n              1                                   0         1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0            0           1 (0.8%)   \n      3                                           7            4              3       \n              Not High                         3 (2.2%)        0           2 (1.5%)   \n              1                                2 (1.5%)     1 (0.7%)       1 (0.8%)   \n              2                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n      4                                           3            2              0       \n              Not High                         1 (0.7%)        0              0       \n              1                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)        0              0       \nImmunoglobulin A Measurement                                                          \n      Not High                                   119          116            113      \n              Not High                        55 (41.0%)   54 (40.3%)     51 (38.6%)  \n              1                               18 (13.4%)   11 (8.2%)      15 (11.4%)  \n              2                               14 (10.4%)   22 (16.4%)     19 (14.4%)  \n              3                               20 (14.9%)   19 (14.2%)     20 (15.2%)  \n              4                               12 (9.0%)    10 (7.5%)       8 (6.1%)   \n      1                                           5            4              2       \n              Not High                         4 (3.0%)     1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0         2 (1.5%)          0       \n      2                                           4            4              5       \n              Not High                         2 (1.5%)     3 (2.2%)       3 (2.3%)   \n              1                                1 (0.7%)        0           2 (1.5%)   \n              3                                1 (0.7%)        0              0       \n              4                                   0         1 (0.7%)          0       \n      3                                           4            5              11      \n              Not High                         3 (2.2%)     1 (0.7%)       6 (4.5%)   \n              1                                   0         1 (0.7%)       1 (0.8%)   \n              2                                1 (0.7%)        0              0       \n              3                                   0         2 (1.5%)       3 (2.3%)   \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      4                                           2            5              1       \n              Not High                            0         3 (2.2%)       1 (0.8%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n\n\n\n\nNote that the worst laboratory flag (below WGRLOFL) must be selected appropriately in the pre-processing step. New grouping variables ATOXGR_GP (post-baseline) and BTOXGR_GP (baseline) are created to display the correct output.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRLOFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not Low                                    113          117            123      \n              Not Low                         56 (41.8%)   55 (41.0%)     62 (47.0%)  \n              1                               11 (8.2%)     9 (6.7%)      15 (11.4%)  \n              2                               15 (11.2%)   23 (17.2%)     16 (12.1%)  \n              3                               15 (11.2%)   21 (15.7%)     13 (9.8%)   \n              4                               16 (11.9%)    9 (6.7%)      17 (12.9%)  \n      1                                           6            6              4       \n              Not Low                          3 (2.2%)     5 (3.7%)       3 (2.3%)   \n              1                                3 (2.2%)        0              0       \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      2                                           8            5              1       \n              Not Low                          5 (3.7%)     3 (2.2%)          0       \n              1                                   0         1 (0.7%)          0       \n              2                                2 (1.5%)        0              0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           3            3              2       \n              Not Low                             0         1 (0.7%)          0       \n              1                                1 (0.7%)        0              0       \n              2                                1 (0.7%)        0           1 (0.8%)   \n              3                                1 (0.7%)     2 (1.5%)       1 (0.8%)   \n      4                                           4            3              2       \n              Not Low                          4 (3.0%)     1 (0.7%)       1 (0.8%)   \n              1                                   0         1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \nC-Reactive Protein Measurement                                                        \n      Not Low                                    119          113            112      \n              Not Low                         41 (30.6%)   42 (31.3%)     50 (37.9%)  \n              1                               20 (14.9%)   18 (13.4%)     11 (8.3%)   \n              2                               24 (17.9%)   20 (14.9%)     14 (10.6%)  \n              3                               25 (18.7%)   18 (13.4%)     21 (15.9%)  \n              4                                9 (6.7%)    15 (11.2%)     16 (12.1%)  \n      1                                           3            5              9       \n              Not Low                          2 (1.5%)     2 (1.5%)       2 (1.5%)   \n              2                                   0         1 (0.7%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0         1 (0.7%)       4 (3.0%)   \n      2                                           5            9              2       \n              Not Low                          3 (2.2%)     4 (3.0%)       1 (0.8%)   \n              1                                1 (0.7%)     4 (3.0%)          0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           4            5              7       \n              Not Low                          1 (0.7%)     4 (3.0%)       2 (1.5%)   \n              1                                   0            0           1 (0.8%)   \n              2                                   0            0           1 (0.8%)   \n              3                                3 (2.2%)     1 (0.7%)       2 (1.5%)   \n              4                                   0            0           1 (0.8%)   \n      4                                           3            2              2       \n              Not Low                          3 (2.2%)     1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \n              4                                   0            0           1 (0.8%)   \nImmunoglobulin A Measurement                                                          \n      Not Low                                    134          134            132      \n              Not Low                         134 (100%)   134 (100%)     132 (100%)  \n\n\n\n\nNote that missing baseline values are filtered out in the pre-processing step.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRHIFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  filter(BTOXGR != \"&lt;Missing&gt;\") %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not High                                   134          134            132      \n              Not High                        134 (100%)   134 (100%)     132 (100%)  \nC-Reactive Protein Measurement                                                        \n      Not High                                   114          112            115      \n              Not High                        45 (33.6%)   55 (41.0%)     57 (43.2%)  \n              1                               18 (13.4%)   18 (13.4%)     19 (14.4%)  \n              2                               19 (14.2%)   10 (7.5%)      11 (8.3%)   \n              3                               22 (16.4%)   13 (9.7%)      20 (15.2%)  \n              4                               10 (7.5%)    16 (11.9%)      8 (6.1%)   \n      1                                           8            10             7       \n              Not High                         5 (3.7%)     2 (1.5%)       2 (1.5%)   \n              1                                   0         1 (0.7%)          0       \n              2                                1 (0.7%)     3 (2.2%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     3 (2.2%)       2 (1.5%)   \n      2                                           2            6              7       \n              Not High                         1 (0.7%)     4 (3.0%)       4 (3.0%)   \n              1                                   0         1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0            0           1 (0.8%)   \n      3                                           7            4              3       \n              Not High                         3 (2.2%)        0           2 (1.5%)   \n              1                                2 (1.5%)     1 (0.7%)       1 (0.8%)   \n              2                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n      4                                           3            2              0       \n              Not High                         1 (0.7%)        0              0       \n              1                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)        0              0       \nImmunoglobulin A Measurement                                                          \n      Not High                                   119          116            113      \n              Not High                        55 (41.0%)   54 (40.3%)     51 (38.6%)  \n              1                               18 (13.4%)   11 (8.2%)      15 (11.4%)  \n              2                               14 (10.4%)   22 (16.4%)     19 (14.4%)  \n              3                               20 (14.9%)   19 (14.2%)     20 (15.2%)  \n              4                               12 (9.0%)    10 (7.5%)       8 (6.1%)   \n      1                                           5            4              2       \n              Not High                         4 (3.0%)     1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0         2 (1.5%)          0       \n      2                                           4            4              5       \n              Not High                         2 (1.5%)     3 (2.2%)       3 (2.3%)   \n              1                                1 (0.7%)        0           2 (1.5%)   \n              3                                1 (0.7%)        0              0       \n              4                                   0         1 (0.7%)          0       \n      3                                           4            5              11      \n              Not High                         3 (2.2%)     1 (0.7%)       6 (4.5%)   \n              1                                   0         1 (0.7%)       1 (0.8%)   \n              2                                1 (0.7%)        0              0       \n              3                                   0         2 (1.5%)       3 (2.3%)   \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      4                                           2            5              1       \n              Not High                            0         3 (2.2%)       1 (0.8%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n\n\n\n\nNote that when BTOXGR is missing, the grouping variable BTOXGR_GP now is \"Not Low\" instead of \"Missing\" compared to Standard Table (Low).\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRLOFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRLOFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, 1, 2, 3, 4) ~ \"Not Low\",\n      ATOXGR == -1 ~ \"1\",\n      ATOXGR == -2 ~ \"2\",\n      ATOXGR == -3 ~ \"3\",\n      ATOXGR == -4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, 1, 2, 3, 4, \"&lt;Missing&gt;\") ~ \"Not Low\",\n      BTOXGR == -1 ~ \"1\",\n      BTOXGR == -2 ~ \"2\",\n      BTOXGR == -3 ~ \"3\",\n      BTOXGR == -4 ~ \"4\"\n    ), levels = c(\"Not Low\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not Low                                    113          117            123      \n              Not Low                         56 (41.8%)   55 (41.0%)     62 (47.0%)  \n              1                               11 (8.2%)     9 (6.7%)      15 (11.4%)  \n              2                               15 (11.2%)   23 (17.2%)     16 (12.1%)  \n              3                               15 (11.2%)   21 (15.7%)     13 (9.8%)   \n              4                               16 (11.9%)    9 (6.7%)      17 (12.9%)  \n      1                                           6            6              4       \n              Not Low                          3 (2.2%)     5 (3.7%)       3 (2.3%)   \n              1                                3 (2.2%)        0              0       \n              4                                   0         1 (0.7%)       1 (0.8%)   \n      2                                           8            5              1       \n              Not Low                          5 (3.7%)     3 (2.2%)          0       \n              1                                   0         1 (0.7%)          0       \n              2                                2 (1.5%)        0              0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           3            3              2       \n              Not Low                             0         1 (0.7%)          0       \n              1                                1 (0.7%)        0              0       \n              2                                1 (0.7%)        0           1 (0.8%)   \n              3                                1 (0.7%)     2 (1.5%)       1 (0.8%)   \n      4                                           4            3              2       \n              Not Low                          4 (3.0%)     1 (0.7%)       1 (0.8%)   \n              1                                   0         1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \nC-Reactive Protein Measurement                                                        \n      Not Low                                    119          113            112      \n              Not Low                         41 (30.6%)   42 (31.3%)     50 (37.9%)  \n              1                               20 (14.9%)   18 (13.4%)     11 (8.3%)   \n              2                               24 (17.9%)   20 (14.9%)     14 (10.6%)  \n              3                               25 (18.7%)   18 (13.4%)     21 (15.9%)  \n              4                                9 (6.7%)    15 (11.2%)     16 (12.1%)  \n      1                                           3            5              9       \n              Not Low                          2 (1.5%)     2 (1.5%)       2 (1.5%)   \n              2                                   0         1 (0.7%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                   0         1 (0.7%)       4 (3.0%)   \n      2                                           5            9              2       \n              Not Low                          3 (2.2%)     4 (3.0%)       1 (0.8%)   \n              1                                1 (0.7%)     4 (3.0%)          0       \n              3                                   0         1 (0.7%)       1 (0.8%)   \n              4                                1 (0.7%)        0              0       \n      3                                           4            5              7       \n              Not Low                          1 (0.7%)     4 (3.0%)       2 (1.5%)   \n              1                                   0            0           1 (0.8%)   \n              2                                   0            0           1 (0.8%)   \n              3                                3 (2.2%)     1 (0.7%)       2 (1.5%)   \n              4                                   0            0           1 (0.8%)   \n      4                                           3            2              2       \n              Not Low                          3 (2.2%)     1 (0.7%)          0       \n              2                                   0         1 (0.7%)          0       \n              3                                   0            0           1 (0.8%)   \n              4                                   0            0           1 (0.8%)   \nImmunoglobulin A Measurement                                                          \n      Not Low                                    134          134            132      \n              Not Low                         134 (100%)   134 (100%)     132 (100%)  \n\n\n\n\nPre-processing is the same as Standard Table (High), but in order to keep all levels, prune_table() is not applied.\n\nCodeadlb_f &lt;- adlb %&gt;% filter(WGRHIFL == \"Y\")\n\n# Please note the step below can be skipped if you are using DTYPE PHANTOM\nadlb_out &lt;- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c(\"WGRHIFL\" = \"Y\"))\n\n# Create new grouping variables ATOXGR_GP, BTOXGR_GP\nadlb_out &lt;- adlb_out %&gt;%\n  mutate(\n    ATOXGR_GP = factor(case_when(\n      ATOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      ATOXGR == 1 ~ \"1\",\n      ATOXGR == 2 ~ \"2\",\n      ATOXGR == 3 ~ \"3\",\n      ATOXGR == 4 ~ \"4\",\n      ATOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  ) %&gt;%\n  mutate(\n    BTOXGR_GP = factor(case_when(\n      BTOXGR %in% c(0, -1, -2, -3, -4) ~ \"Not High\",\n      BTOXGR == 1 ~ \"1\",\n      BTOXGR == 2 ~ \"2\",\n      BTOXGR == 3 ~ \"3\",\n      BTOXGR == 4 ~ \"4\",\n      BTOXGR == \"&lt;Missing&gt;\" ~ \"Missing\"\n    ), levels = c(\"Not High\", \"1\", \"2\", \"3\", \"4\", \"Missing\"))\n  )\n\nresult &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    split_fun = drop_split_levels,\n    label_pos = \"topleft\",\n    split_label = \"Parameter\"\n  ) %&gt;%\n  split_rows_by(\n    \"BTOXGR_GP\",\n    label_pos = \"topleft\",\n    split_label = \"    Baseline NCI-CTCAE Grade\",\n    indent_mod = 2L\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = c(\"unique_count\"), unique_count_suffix = FALSE) %&gt;%\n  count_occurrences_by_grade(\"ATOXGR_GP\", denom = \"n\", drop = FALSE, .indent_mods = 3L) %&gt;%\n  append_topleft(\"              Post-baseline NCI-CTCAE Grade\") %&gt;%\n  build_table(df = adlb_out, alt_counts_df = adsl_f)\n\nresult\n\nParameter                                                                             \n      Baseline NCI-CTCAE Grade                A: Drug X    B: Placebo   C: Combination\n              Post-baseline NCI-CTCAE Grade    (N=134)      (N=134)        (N=132)    \n——————————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement                                                  \n      Not High                                   134          134            132      \n              Not High                        134 (100%)   134 (100%)     132 (100%)  \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      1                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      2                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      3                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      4                                           0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      Missing                                     0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \nC-Reactive Protein Measurement                                                        \n      Not High                                   114          112            115      \n              Not High                        45 (33.6%)   55 (41.0%)     57 (43.2%)  \n              1                               18 (13.4%)   18 (13.4%)     19 (14.4%)  \n              2                               19 (14.2%)   10 (7.5%)      11 (8.3%)   \n              3                               22 (16.4%)   13 (9.7%)      20 (15.2%)  \n              4                               10 (7.5%)    16 (11.9%)      8 (6.1%)   \n              Missing                             0            0              0       \n      1                                           8            10             7       \n              Not High                         5 (3.7%)     2 (1.5%)       2 (1.5%)   \n              1                                   0         1 (0.7%)          0       \n              2                                1 (0.7%)     3 (2.2%)       3 (2.3%)   \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     3 (2.2%)       2 (1.5%)   \n              Missing                             0            0              0       \n      2                                           2            6              7       \n              Not High                         1 (0.7%)     4 (3.0%)       4 (3.0%)   \n              1                                   0         1 (0.7%)       2 (1.5%)   \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0            0              0       \n              4                                   0            0           1 (0.8%)   \n              Missing                             0            0              0       \n      3                                           7            4              3       \n              Not High                         3 (2.2%)        0           2 (1.5%)   \n              1                                2 (1.5%)     1 (0.7%)       1 (0.8%)   \n              2                                   0         1 (0.7%)          0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n              Missing                             0            0              0       \n      4                                           3            2              0       \n              Not High                         1 (0.7%)        0              0       \n              1                                   0         1 (0.7%)          0       \n              2                                   0            0              0       \n              3                                1 (0.7%)     1 (0.7%)          0       \n              4                                1 (0.7%)        0              0       \n              Missing                             0            0              0       \n      Missing                                     0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \nImmunoglobulin A Measurement                                                          \n      Not High                                   119          116            113      \n              Not High                        55 (41.0%)   54 (40.3%)     51 (38.6%)  \n              1                               18 (13.4%)   11 (8.2%)      15 (11.4%)  \n              2                               14 (10.4%)   22 (16.4%)     19 (14.4%)  \n              3                               20 (14.9%)   19 (14.2%)     20 (15.2%)  \n              4                               12 (9.0%)    10 (7.5%)       8 (6.1%)   \n              Missing                             0            0              0       \n      1                                           5            4              2       \n              Not High                         4 (3.0%)     1 (0.7%)       2 (1.5%)   \n              1                                   0            0              0       \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0         2 (1.5%)          0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n      2                                           4            4              5       \n              Not High                         2 (1.5%)     3 (2.2%)       3 (2.3%)   \n              1                                1 (0.7%)        0           2 (1.5%)   \n              2                                   0            0              0       \n              3                                1 (0.7%)        0              0       \n              4                                   0         1 (0.7%)          0       \n              Missing                             0            0              0       \n      3                                           4            5              11      \n              Not High                         3 (2.2%)     1 (0.7%)       6 (4.5%)   \n              1                                   0         1 (0.7%)       1 (0.8%)   \n              2                                1 (0.7%)        0              0       \n              3                                   0         2 (1.5%)       3 (2.3%)   \n              4                                   0         1 (0.7%)       1 (0.8%)   \n              Missing                             0            0              0       \n      4                                           2            5              1       \n              Not High                            0         3 (2.2%)       1 (0.8%)   \n              1                                   0            0              0       \n              2                                1 (0.7%)     1 (0.7%)          0       \n              3                                   0            0              0       \n              4                                1 (0.7%)     1 (0.7%)          0       \n              Missing                             0            0              0       \n      Missing                                     0            0              0       \n              Not High                            0            0              0       \n              1                                   0            0              0       \n              2                                   0            0              0       \n              3                                   0            0              0       \n              4                                   0            0              0       \n              Missing                             0            0              0       \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_shift_by_grade(\n      label = \"Grade Laboratory Abnormality Table\",\n      dataname = \"ADLB\",\n      arm_var = choices_selected(\n        choices = variable_choices(ADSL, subset = c(\"ARM\", \"ARMCD\")),\n        selected = \"ARM\"\n      ),\n      paramcd = choices_selected(\n        choices = value_choices(ADLB, \"PARAMCD\", \"PARAM\"),\n        selected = \"ALT\"\n      ),\n      worst_flag_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"WGRLOVFL\", \"WGRLOFL\", \"WGRHIVFL\", \"WGRHIFL\")),\n        selected = c(\"WGRHIFL\")\n      ),\n      worst_flag_indicator = choices_selected(\n        value_choices(ADLB, \"WGRLOVFL\"),\n        selected = \"Y\", fixed = TRUE\n      ),\n      anl_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"ATOXGR\")),\n        selected = c(\"ATOXGR\"),\n        fixed = TRUE\n      ),\n      base_toxgrade_var = choices_selected(\n        choices = variable_choices(ADLB, subset = c(\"BTOXGR\")),\n        selected = c(\"BTOXGR\"),\n        fixed = TRUE\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:51:54 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      tern_0.9.3.9024                 \n [9] rtables_0.6.6.9011               magrittr_2.0.3                  \n[11] formatters_0.5.5.9015            dplyr_1.1.4                     \n[13] scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1427,7 +1427,7 @@
     "href": "tables/lab-results/lbt05.html",
     "title": "LBT05",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Showing  All Categories\nTable with  Study-Specific  MLAs\nTable of Lab Abnormalities Showing  All Categories But Only for Parameter  Codes with At Least One Abnormality\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\nqntls &lt;- adlb %&gt;%\n  group_by(PARAMCD) %&gt;%\n  summarise(as_tibble(t(quantile(AVAL, probs = c(0.1, 0.9)))), .groups = \"drop_last\") %&gt;%\n  rename(q1 = 2, q2 = 3)\n\nadlb &lt;- adlb %&gt;%\n  left_join(qntls, by = \"PARAMCD\")\n\nset.seed(1)\n\n# Modify ANRIND and create AVALCAT1/PARCAT2\n# PARCAT2 is just used for filtering, but in order to be the\n# filtering as realistic as possible, will create the variable.\nadlb &lt;- adlb %&gt;%\n  mutate(\n    ANRIND = factor(\n      case_when(\n        ANRIND == \"LOW\" & AVAL &lt;= q1 ~ \"LOW LOW\",\n        ANRIND == \"HIGH\" & AVAL &gt;= q2 ~ \"HIGH HIGH\",\n        TRUE ~ as.character(ANRIND)\n      ),\n      levels = c(\"\", \"HIGH\", \"HIGH HIGH\", \"LOW\", \"LOW LOW\", \"NORMAL\")\n    ),\n    AVALCAT1 = factor(\n      case_when(\n        ANRIND %in% c(\"HIGH HIGH\", \"LOW LOW\") ~\n          sample(x = c(\"LAST\", \"REPLICATED\", \"SINGLE\"), size = n(), replace = TRUE, prob = c(0.3, 0.6, 0.1)),\n        TRUE ~ \"\"\n      ),\n      levels = c(\"\", c(\"LAST\", \"REPLICATED\", \"SINGLE\"))\n    ),\n    PARCAT2 = factor(ifelse(ANRIND %in% c(\"HIGH HIGH\", \"LOW LOW\"), \"LS\",\n      sample(c(\"SI\", \"CV\", \"LS\"), size = n(), replace = TRUE)\n    ))\n  ) %&gt;%\n  select(-q1, -q2)\n\n# Pre-processing steps\nadlb_f &lt;- adlb %&gt;%\n  filter(ONTRTFL == \"Y\" & PARCAT2 == \"LS\" & SAFFL == \"Y\" & !is.na(AVAL)) %&gt;%\n  mutate(abn_dir = factor(case_when(\n    ANRIND == \"LOW LOW\" ~ \"Low\",\n    ANRIND == \"HIGH HIGH\" ~ \"High\",\n    TRUE ~ \"\"\n  ), levels = c(\"Low\", \"High\", \"\"))) %&gt;%\n  df_explicit_na()\n\n# Construct analysis map\nmap &lt;- expand.grid(\n  PARAM = levels(adlb$PARAM),\n  abn_dir = c(\"Low\", \"High\"),\n  stringsAsFactors = FALSE\n) %&gt;%\n  arrange(PARAM, desc(abn_dir))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"Laboratory Test\"\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = \"unique_count\") %&gt;%\n  split_rows_by(\"abn_dir\", split_fun = trim_levels_to_map(map)) %&gt;%\n  count_abnormal_by_marked(\n    var = \"AVALCAT1\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\")\n  ) %&gt;%\n  append_topleft(\"  Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, df = adlb_f, alt_counts_df = adsl)\n\nhas_lbl &lt;- function(lbl) CombinationFunction(function(tr) obj_label(tr) == lbl || sum(unlist(row_values(tr))) != 0)\nresult &lt;- prune_table(result, keep_rows(has_lbl(\"Any Abnormality\")))\n\nresult\n\nLaboratory Test                            A: Drug X    B: Placebo   C: Combination\n  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      125          120            125      \n  Low                                                                              \n    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    \n    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  \n    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  \n  High                                                                             \n    Any Abnormality                            0            0              0       \nC-Reactive Protein Measurement (n)            129          130            121      \n  Low                                                                              \n    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    \n    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  \n    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  \n  High                                                                             \n    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   \n    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  \n    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  \nImmunoglobulin A Measurement (n)              129          122            121      \n  Low                                                                              \n    Any Abnormality                            0            0              0       \n  High                                                                             \n    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   \n    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  \n    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"Laboratory Test\"\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = \"unique_count\") %&gt;%\n  split_rows_by(\"abn_dir\", split_fun = trim_levels_to_map(map)) %&gt;%\n  count_abnormal_by_marked(\n    var = \"AVALCAT1\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\")\n  ) %&gt;%\n  append_topleft(\"  Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, df = adlb_f, alt_counts_df = adsl)\n\nresult\n\nLaboratory Test                            A: Drug X    B: Placebo   C: Combination\n  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      125          120            125      \n  Low                                                                              \n    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    \n    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  \n    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  \n  High                                                                             \n    Single, not last                           0            0              0       \n    Last or replicated                         0            0              0       \n    Any Abnormality                            0            0              0       \nC-Reactive Protein Measurement (n)            129          130            121      \n  Low                                                                              \n    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    \n    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  \n    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  \n  High                                                                             \n    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   \n    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  \n    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  \nImmunoglobulin A Measurement (n)              129          122            121      \n  Low                                                                              \n    Single, not last                           0            0              0       \n    Last or replicated                         0            0              0       \n    Any Abnormality                            0            0              0       \n  High                                                                             \n    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   \n    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  \n    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)  \n\n\n\n\n\nCode# This variant reflects user modifications made to the laboratory analysis data set related to\n# Safety Lab Standardization metadata.\n# There is no unique layout level variation.\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"Laboratory Test\",\n    split_fun = trim_levels_in_group(\"abn_dir\", drop_outlevs = TRUE)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = \"unique_count\") %&gt;%\n  split_rows_by(\"abn_dir\") %&gt;%\n  count_abnormal_by_marked(\n    var = \"AVALCAT1\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\")\n  ) %&gt;%\n  append_topleft(\"  Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, df = adlb_f, alt_counts_df = adsl)\n\nresult &lt;- result %&gt;% prune_table()\nresult\n\nLaboratory Test                            A: Drug X    B: Placebo   C: Combination\n  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      125          120            125      \n  Low                                                                              \n    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    \n    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  \n    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  \nC-Reactive Protein Measurement (n)            129          130            121      \n  Low                                                                              \n    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    \n    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  \n    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  \n  High                                                                             \n    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   \n    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  \n    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  \nImmunoglobulin A Measurement (n)              129          122            121      \n  High                                                                             \n    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   \n    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  \n    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)  \n\nCode# Another approach would be to create an empirical map by removing the ALT records\n# and use it in `trim_levels_to_map`.\n# this is an a posteriori approach, though.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:53:39 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Showing  All Categories\nTable with  Study-Specific  MLAs\nTable of Lab Abnormalities Showing  All Categories But Only for Parameter  Codes with At Least One Abnormality\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadlb &lt;- df_explicit_na(adlb)\n\nqntls &lt;- adlb %&gt;%\n  group_by(PARAMCD) %&gt;%\n  summarise(as_tibble(t(quantile(AVAL, probs = c(0.1, 0.9)))), .groups = \"drop_last\") %&gt;%\n  rename(q1 = 2, q2 = 3)\n\nadlb &lt;- adlb %&gt;%\n  left_join(qntls, by = \"PARAMCD\")\n\nset.seed(1)\n\n# Modify ANRIND and create AVALCAT1/PARCAT2\n# PARCAT2 is just used for filtering, but in order to be the\n# filtering as realistic as possible, will create the variable.\nadlb &lt;- adlb %&gt;%\n  mutate(\n    ANRIND = factor(\n      case_when(\n        ANRIND == \"LOW\" & AVAL &lt;= q1 ~ \"LOW LOW\",\n        ANRIND == \"HIGH\" & AVAL &gt;= q2 ~ \"HIGH HIGH\",\n        TRUE ~ as.character(ANRIND)\n      ),\n      levels = c(\"\", \"HIGH\", \"HIGH HIGH\", \"LOW\", \"LOW LOW\", \"NORMAL\")\n    ),\n    AVALCAT1 = factor(\n      case_when(\n        ANRIND %in% c(\"HIGH HIGH\", \"LOW LOW\") ~\n          sample(x = c(\"LAST\", \"REPLICATED\", \"SINGLE\"), size = n(), replace = TRUE, prob = c(0.3, 0.6, 0.1)),\n        TRUE ~ \"\"\n      ),\n      levels = c(\"\", c(\"LAST\", \"REPLICATED\", \"SINGLE\"))\n    ),\n    PARCAT2 = factor(ifelse(ANRIND %in% c(\"HIGH HIGH\", \"LOW LOW\"), \"LS\",\n      sample(c(\"SI\", \"CV\", \"LS\"), size = n(), replace = TRUE)\n    ))\n  ) %&gt;%\n  select(-q1, -q2)\n\n# Pre-processing steps\nadlb_f &lt;- adlb %&gt;%\n  filter(ONTRTFL == \"Y\" & PARCAT2 == \"LS\" & SAFFL == \"Y\" & !is.na(AVAL)) %&gt;%\n  mutate(abn_dir = factor(case_when(\n    ANRIND == \"LOW LOW\" ~ \"Low\",\n    ANRIND == \"HIGH HIGH\" ~ \"High\",\n    TRUE ~ \"\"\n  ), levels = c(\"Low\", \"High\", \"\"))) %&gt;%\n  df_explicit_na()\n\n# Construct analysis map\nmap &lt;- expand.grid(\n  PARAM = levels(adlb$PARAM),\n  abn_dir = c(\"Low\", \"High\"),\n  stringsAsFactors = FALSE\n) %&gt;%\n  arrange(PARAM, desc(abn_dir))\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"Laboratory Test\"\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = \"unique_count\") %&gt;%\n  split_rows_by(\"abn_dir\", split_fun = trim_levels_to_map(map)) %&gt;%\n  count_abnormal_by_marked(\n    var = \"AVALCAT1\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\")\n  ) %&gt;%\n  append_topleft(\"  Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, df = adlb_f, alt_counts_df = adsl)\n\nhas_lbl &lt;- function(lbl) CombinationFunction(function(tr) obj_label(tr) == lbl || sum(unlist(row_values(tr))) != 0)\nresult &lt;- prune_table(result, keep_rows(has_lbl(\"Any Abnormality\")))\n\nresult\n\nLaboratory Test                            A: Drug X    B: Placebo   C: Combination\n  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      125          120            125      \n  Low                                                                              \n    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    \n    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  \n    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  \n  High                                                                             \n    Any Abnormality                            0            0              0       \nC-Reactive Protein Measurement (n)            129          130            121      \n  Low                                                                              \n    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    \n    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  \n    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  \n  High                                                                             \n    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   \n    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  \n    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  \nImmunoglobulin A Measurement (n)              129          122            121      \n  Low                                                                              \n    Any Abnormality                            0            0              0       \n  High                                                                             \n    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   \n    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  \n    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)  \n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"Laboratory Test\"\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = \"unique_count\") %&gt;%\n  split_rows_by(\"abn_dir\", split_fun = trim_levels_to_map(map)) %&gt;%\n  count_abnormal_by_marked(\n    var = \"AVALCAT1\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\")\n  ) %&gt;%\n  append_topleft(\"  Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, df = adlb_f, alt_counts_df = adsl)\n\nresult\n\nLaboratory Test                            A: Drug X    B: Placebo   C: Combination\n  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      125          120            125      \n  Low                                                                              \n    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    \n    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  \n    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  \n  High                                                                             \n    Single, not last                           0            0              0       \n    Last or replicated                         0            0              0       \n    Any Abnormality                            0            0              0       \nC-Reactive Protein Measurement (n)            129          130            121      \n  Low                                                                              \n    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    \n    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  \n    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  \n  High                                                                             \n    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   \n    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  \n    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  \nImmunoglobulin A Measurement (n)              129          122            121      \n  Low                                                                              \n    Single, not last                           0            0              0       \n    Last or replicated                         0            0              0       \n    Any Abnormality                            0            0              0       \n  High                                                                             \n    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   \n    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  \n    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)  \n\n\n\n\n\nCode# This variant reflects user modifications made to the laboratory analysis data set related to\n# Safety Lab Standardization metadata.\n# There is no unique layout level variation.\n\n\n\n\n\nCodelyt &lt;- basic_table(show_colcounts = TRUE) %&gt;%\n  split_cols_by(\"ACTARM\") %&gt;%\n  split_rows_by(\n    \"PARAM\",\n    label_pos = \"topleft\",\n    split_label = \"Laboratory Test\",\n    split_fun = trim_levels_in_group(\"abn_dir\", drop_outlevs = TRUE)\n  ) %&gt;%\n  summarize_num_patients(var = \"USUBJID\", .stats = \"unique_count\") %&gt;%\n  split_rows_by(\"abn_dir\") %&gt;%\n  count_abnormal_by_marked(\n    var = \"AVALCAT1\",\n    variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\")\n  ) %&gt;%\n  append_topleft(\"  Direction of Abnormality\")\n\nresult &lt;- build_table(lyt, df = adlb_f, alt_counts_df = adsl)\n\nresult &lt;- result %&gt;% prune_table()\nresult\n\nLaboratory Test                            A: Drug X    B: Placebo   C: Combination\n  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    \n———————————————————————————————————————————————————————————————————————————————————\nAlanine Aminotransferase Measurement (n)      125          120            125      \n  Low                                                                              \n    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    \n    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  \n    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  \nC-Reactive Protein Measurement (n)            129          130            121      \n  Low                                                                              \n    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    \n    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  \n    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  \n  High                                                                             \n    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   \n    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  \n    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  \nImmunoglobulin A Measurement (n)              129          122            121      \n  High                                                                             \n    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   \n    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  \n    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)  \n\nCode# Another approach would be to create an empirical map by removing the ALT records\n# and use it in `trim_levels_to_map`.\n# this is an a posteriori approach, though.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:52:08 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         withr_3.0.0      \n[33] Rdpack_2.6        digest_0.6.34     grid_4.3.3        lifecycle_1.0.4  \n[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1439,7 +1439,7 @@
     "href": "tables/lab-results/lbt12_bl.html",
     "title": "LBT12_BL",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nanl &lt;- adhy\nanl$APERIODC &lt;- as.factor(anl$APERIODC) # to ensure the table is built even if there is no patients after filtering\nanl$ACTARM &lt;- as.factor(anl$ACTARM) # to ensure the table is built even if there is no patients after filtering\n\nanl &lt;- anl %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    PARAMCD %in% c(\"ASTPBASE\", \"ALTPBASE\", \"ALTASTPB\") & AVISIT == \"POST-BASELINE\"\n  ) %&gt;%\n  mutate(\n    ARM_AVALC = factor(\n      case_when(\n        AVALC == \"Y\" ~ as.character(ACTARM),\n        TRUE ~ \"Criteria not met\"\n      ),\n      levels = c(levels(anl$ACTARM), \"Criteria not met\")\n    ),\n    PARAM = factor(\n      case_when(\n        PARAMCD == \"ASTPBASE\" ~ \"AST&gt;3x Baseline\",\n        PARAMCD == \"ALTPBASE\" ~ \"ALT&gt;3x Baseline\",\n        PARAMCD == \"ALTASTPB\" ~ \"AST&gt;3x Baseline or ALT&gt;3x Baseline\"\n      ),\n      levels = c(\"AST&gt;3x Baseline\", \"ALT&gt;3x Baseline\", \"AST&gt;3x Baseline or ALT&gt;3x Baseline\")\n    ),\n    TITLE = factor(\"First Elevated Result Occurring During\")\n  )\n\nanl &lt;- df_explicit_na(anl)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"TITLE\") %&gt;%\n  split_cols_by(\"APERIODC\") %&gt;%\n  split_rows_by(\"PARAM\") %&gt;%\n  split_rows_by(\"ACTARM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\"ARM_AVALC\", .stats = \"fraction\", denom = \"n\", drop = TRUE) %&gt;%\n  build_table(anl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  dplyr::if_else(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                                      First Elevated Result Occurring During  \n                                          PERIOD 1              PERIOD 2      \n——————————————————————————————————————————————————————————————————————————————\nAST&gt;3x Baseline                                                               \n  A: Drug X                            14/134 (10.4%)         8/134 (6.0%)    \n  B: Placebo                            11/134 (8.2%)         11/134 (8.2%)   \n  C: Combination                       15/132 (11.4%)        14/132 (10.6%)   \nALT&gt;3x Baseline                                                               \n  A: Drug X                             7/134 (5.2%)         20/134 (14.9%)   \n  B: Placebo                            10/134 (7.5%)         9/134 (6.7%)    \n  C: Combination                        12/132 (9.1%)         8/132 (6.1%)    \nAST&gt;3x Baseline or ALT&gt;3x Baseline                                            \n  A: Drug X                             8/134 (6.0%)         22/134 (16.4%)   \n  B: Placebo                           19/134 (14.2%)        15/134 (11.2%)   \n  C: Combination                       14/132 (10.6%)         13/132 (9.8%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:54:00 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\n\nadhy &lt;- synthetic_cdisc_dataset(\"latest\", \"adhy\")\n\nanl &lt;- adhy\nanl$APERIODC &lt;- as.factor(anl$APERIODC) # to ensure the table is built even if there is no patients after filtering\nanl$ACTARM &lt;- as.factor(anl$ACTARM) # to ensure the table is built even if there is no patients after filtering\n\nanl &lt;- anl %&gt;%\n  filter(\n    SAFFL == \"Y\",\n    PARAMCD %in% c(\"ASTPBASE\", \"ALTPBASE\", \"ALTASTPB\") & AVISIT == \"POST-BASELINE\"\n  ) %&gt;%\n  mutate(\n    ARM_AVALC = factor(\n      case_when(\n        AVALC == \"Y\" ~ as.character(ACTARM),\n        TRUE ~ \"Criteria not met\"\n      ),\n      levels = c(levels(anl$ACTARM), \"Criteria not met\")\n    ),\n    PARAM = factor(\n      case_when(\n        PARAMCD == \"ASTPBASE\" ~ \"AST&gt;3x Baseline\",\n        PARAMCD == \"ALTPBASE\" ~ \"ALT&gt;3x Baseline\",\n        PARAMCD == \"ALTASTPB\" ~ \"AST&gt;3x Baseline or ALT&gt;3x Baseline\"\n      ),\n      levels = c(\"AST&gt;3x Baseline\", \"ALT&gt;3x Baseline\", \"AST&gt;3x Baseline or ALT&gt;3x Baseline\")\n    ),\n    TITLE = factor(\"First Elevated Result Occurring During\")\n  )\n\nanl &lt;- df_explicit_na(anl)\n\n\n\n\n\nCoderesult &lt;- basic_table() %&gt;%\n  split_cols_by(\"TITLE\") %&gt;%\n  split_cols_by(\"APERIODC\") %&gt;%\n  split_rows_by(\"PARAM\") %&gt;%\n  split_rows_by(\"ACTARM\", split_fun = drop_split_levels, child_labels = \"hidden\") %&gt;%\n  count_occurrences(\"ARM_AVALC\", .stats = \"fraction\", denom = \"n\", drop = TRUE) %&gt;%\n  build_table(anl)\n\ncriteria_fun &lt;- function(tr) {\n  row_label &lt;- obj_label(tr)\n  dplyr::if_else(row_label == \"Criteria not met\", TRUE, FALSE)\n}\n\nresult &lt;- result %&gt;% trim_rows(criteria = criteria_fun)\nresult\n\n                                      First Elevated Result Occurring During  \n                                          PERIOD 1              PERIOD 2      \n——————————————————————————————————————————————————————————————————————————————\nAST&gt;3x Baseline                                                               \n  A: Drug X                            14/134 (10.4%)         8/134 (6.0%)    \n  B: Placebo                            11/134 (8.2%)         11/134 (8.2%)   \n  C: Combination                       15/132 (11.4%)        14/132 (10.6%)   \nALT&gt;3x Baseline                                                               \n  A: Drug X                             7/134 (5.2%)         20/134 (14.9%)   \n  B: Placebo                            10/134 (7.5%)         9/134 (6.7%)    \n  C: Combination                        12/132 (9.1%)         8/132 (6.1%)    \nAST&gt;3x Baseline or ALT&gt;3x Baseline                                            \n  A: Drug X                             8/134 (6.0%)         22/134 (16.4%)   \n  B: Placebo                           19/134 (14.2%)        15/134 (11.2%)   \n  C: Combination                       14/132 (10.6%)         13/132 (9.8%)   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:52:29 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       \n[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] ggplot2_3.5.0     R6_2.5.1          generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    stringi_1.8.3    \n[29] broom_1.0.5       xfun_0.42         cli_3.6.2         Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Lab Results",
@@ -1451,7 +1451,7 @@
     "href": "tables/risk-management-plan/rmpt03.html",
     "title": "RMPT03",
     "section": "",
-    "text": "Data Setup\nStandard Table\nTable Excluding Empty Age Groups\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in sample scda dataset.\nset.seed(1, kind = \"Mersenne-Twister\")\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  ) %&gt;%\n  mutate(\n    AGEGR1 = factor(ifelse(AGE &lt; 65, \"&lt;65\", \"&gt;=65\")),\n    AGEGR2 = factor(case_when(\n      AGE &lt; 18 ~ \"&lt; 18\",\n      AGE &gt;= 18 & AGE &lt;= 40 ~ \"18 - 40\",\n      AGE &gt; 40 & AGE &lt;= 64 ~ \"41 - 64\",\n      TRUE ~ \"&gt;=65\"\n    ), levels = c(\"&lt; 18\", \"18 - 40\", \"41 - 64\", \"&gt;=65\")),\n    SEX = factor(case_when(\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"M\" ~ \"Male\"\n    ))\n  )\n\nadsl_f &lt;- adsl %&gt;%\n  filter(adsl$SAFFL == \"Y\") %&gt;%\n  mutate(SEX = factor(case_when(\n    SEX == \"F\" ~ \"Female\",\n    SEX == \"M\" ~ \"Male\"\n  )))\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Extent of Exposure by Age Group and Gender: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\"\n) %&gt;%\n  split_cols_by(\"SEX\", split_fun = add_overall_level(\"Total\", first = FALSE)) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Number of Patients\", sum_exposure = \"Patient Time*\"),\n    custom_label = \"Total Number of Patients and Patient Time\"\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"AGEGR1\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", \"Age group (years)\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nExtent of Exposure by Age Group and Gender: Safety-Evaluable Patients\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                          Female                                Male                                Total               \nAge group (years)                           Number of Patients   Patient Time*   Number of Patients   Patient Time*   Number of Patients   Patient Time*\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Number of Patients and Patient Time      134 (58.0%)           12591           83 (49.1%)           8050           217 (54.2%)           20641    \n  &lt;65                                          134 (58.0%)           12591           82 (48.5%)           7970           216 (54.0%)           20561    \n  &gt;=65                                           0 (0.0%)              0              1 (0.6%)             80              1 (0.2%)             80      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Duration of Exposure: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\"\n) %&gt;%\n  split_cols_by(\"SEX\", split_fun = add_overall_level(\"Total\", first = FALSE)) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Number of Patients\", sum_exposure = \"Patient Time*\"),\n    custom_label = \"Total Number of Patients and Patient Time\"\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"AGEGR2\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", \"Age group (years)\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\nresult\n\nDuration of Exposure: Safety-Evaluable Patients\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                          Female                                Male                                Total               \nAge group (years)                           Number of Patients   Patient Time*   Number of Patients   Patient Time*   Number of Patients   Patient Time*\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Number of Patients and Patient Time      134 (58.0%)           12591           83 (49.1%)           8050           217 (54.2%)           20641    \n  18 - 40                                      118 (51.1%)           11203           60 (35.5%)           5787           178 (44.5%)           16990    \n  41 - 64                                       16 (6.9%)            1388            22 (13.0%)           2183            38 (9.5%)            3571     \n  &gt;=65                                           0 (0.0%)              0              1 (0.6%)             80              1 (0.2%)             80      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n\n  labels &lt;- col_labels(ADEX)\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\",\n      SEX = factor(case_when(\n        SEX == \"F\" ~ \"Female\",\n        SEX == \"M\" ~ \"Male\"\n      ))\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  col_labels(ADEX) &lt;- labels\n\n  ADEX &lt;- ADEX %&gt;%\n    mutate(\n      AGEGR1 = factor(ifelse(AGE &lt; 65, \"&lt;65\", \"&gt;=65\")) %&gt;% with_label(\"Age group (years)\"),\n      AGEGR2 = factor(case_when(\n        AGE &lt; 18 ~ \"&lt; 18\",\n        AGE &gt;= 18 & AGE &lt;= 40 ~ \"18 - 40\",\n        AGE &gt; 40 & AGE &lt;= 64 ~ \"41 - 64\",\n        TRUE ~ \"&gt;=65\"\n      ), levels = c(\"&lt; 18\", \"18 - 40\", \"41 - 64\", \"&gt;=65\")) %&gt;% with_label(\"Age group (years)\")\n    )\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(SEX = factor(case_when(\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"M\" ~ \"Male\"\n    )) %&gt;% with_label(\"Sex\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"AGEGR1\", \"AGEGR2\")),\n        selected = \"AGEGR1\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:54:21 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nTable Excluding Empty Age Groups\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in sample scda dataset.\nset.seed(1, kind = \"Mersenne-Twister\")\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  ) %&gt;%\n  mutate(\n    AGEGR1 = factor(ifelse(AGE &lt; 65, \"&lt;65\", \"&gt;=65\")),\n    AGEGR2 = factor(case_when(\n      AGE &lt; 18 ~ \"&lt; 18\",\n      AGE &gt;= 18 & AGE &lt;= 40 ~ \"18 - 40\",\n      AGE &gt; 40 & AGE &lt;= 64 ~ \"41 - 64\",\n      TRUE ~ \"&gt;=65\"\n    ), levels = c(\"&lt; 18\", \"18 - 40\", \"41 - 64\", \"&gt;=65\")),\n    SEX = factor(case_when(\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"M\" ~ \"Male\"\n    ))\n  )\n\nadsl_f &lt;- adsl %&gt;%\n  filter(adsl$SAFFL == \"Y\") %&gt;%\n  mutate(SEX = factor(case_when(\n    SEX == \"F\" ~ \"Female\",\n    SEX == \"M\" ~ \"Male\"\n  )))\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Extent of Exposure by Age Group and Gender: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\"\n) %&gt;%\n  split_cols_by(\"SEX\", split_fun = add_overall_level(\"Total\", first = FALSE)) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Number of Patients\", sum_exposure = \"Patient Time*\"),\n    custom_label = \"Total Number of Patients and Patient Time\"\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"AGEGR1\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", \"Age group (years)\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nExtent of Exposure by Age Group and Gender: Safety-Evaluable Patients\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                          Female                                Male                                Total               \nAge group (years)                           Number of Patients   Patient Time*   Number of Patients   Patient Time*   Number of Patients   Patient Time*\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Number of Patients and Patient Time      134 (58.0%)           12591           83 (49.1%)           8050           217 (54.2%)           20641    \n  &lt;65                                          134 (58.0%)           12591           82 (48.5%)           7970           216 (54.0%)           20561    \n  &gt;=65                                           0 (0.0%)              0              1 (0.6%)             80              1 (0.2%)             80      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Duration of Exposure: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\"\n) %&gt;%\n  split_cols_by(\"SEX\", split_fun = add_overall_level(\"Total\", first = FALSE)) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Number of Patients\", sum_exposure = \"Patient Time*\"),\n    custom_label = \"Total Number of Patients and Patient Time\"\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"AGEGR2\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", \"Age group (years)\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f) %&gt;%\n  prune_table()\nresult\n\nDuration of Exposure: Safety-Evaluable Patients\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                          Female                                Male                                Total               \nAge group (years)                           Number of Patients   Patient Time*   Number of Patients   Patient Time*   Number of Patients   Patient Time*\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTotal Number of Patients and Patient Time      134 (58.0%)           12591           83 (49.1%)           8050           217 (54.2%)           20641    \n  18 - 40                                      118 (51.1%)           11203           60 (35.5%)           5787           178 (44.5%)           16990    \n  41 - 64                                       16 (6.9%)            1388            22 (13.0%)           2183            38 (9.5%)            3571     \n  &gt;=65                                           0 (0.0%)              0              1 (0.6%)             80              1 (0.2%)             80      \n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n\n  labels &lt;- col_labels(ADEX)\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\",\n      SEX = factor(case_when(\n        SEX == \"F\" ~ \"Female\",\n        SEX == \"M\" ~ \"Male\"\n      ))\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  col_labels(ADEX) &lt;- labels\n\n  ADEX &lt;- ADEX %&gt;%\n    mutate(\n      AGEGR1 = factor(ifelse(AGE &lt; 65, \"&lt;65\", \"&gt;=65\")) %&gt;% with_label(\"Age group (years)\"),\n      AGEGR2 = factor(case_when(\n        AGE &lt; 18 ~ \"&lt; 18\",\n        AGE &gt;= 18 & AGE &lt;= 40 ~ \"18 - 40\",\n        AGE &gt; 40 & AGE &lt;= 64 ~ \"41 - 64\",\n        TRUE ~ \"&gt;=65\"\n      ), levels = c(\"&lt; 18\", \"18 - 40\", \"41 - 64\", \"&gt;=65\")) %&gt;% with_label(\"Age group (years)\")\n    )\n\n  ADSL &lt;- ADSL %&gt;%\n    mutate(SEX = factor(case_when(\n      SEX == \"F\" ~ \"Female\",\n      SEX == \"M\" ~ \"Male\"\n    )) %&gt;% with_label(\"Sex\"))\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"AGEGR1\", \"AGEGR2\")),\n        selected = \"AGEGR1\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:52:50 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Risk Management Plan",
@@ -1463,7 +1463,7 @@
     "href": "tables/risk-management-plan/rmpt05.html",
     "title": "RMPT05",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in the sample dataset.\nset.seed(1)\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  ) %&gt;%\n  mutate()\n\nadsl_f &lt;- adsl %&gt;% filter(adsl$SAFFL == \"Y\")\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Extent of Exposure by Race: Safety-Evaluable Patients\",\n  main_footer = \"* Person time is the sum of exposure across all patients in unit: days.\",\n  show_colcounts = TRUE\n) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Patients\", sum_exposure = \"Person time*\")\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"RACE\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", obj_label(adex$RACE)))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nExtent of Exposure by Race: Safety-Evaluable Patients\n\n————————————————————————————————————————————————————————————————————————\n                                               Patients     Person time*\nRace                                            (N=400)       (N=400)   \n————————————————————————————————————————————————————————————————————————\nTotal patients numbers/person time            217 (54.2%)      20641    \n  ASIAN                                       113 (28.2%)      10239    \n  BLACK OR AFRICAN AMERICAN                   48 (12.0%)        4481    \n  WHITE                                       40 (10.0%)        4237    \n  AMERICAN INDIAN OR ALASKA NATIVE             14 (3.5%)        1566    \n  MULTIPLE                                     1 (0.2%)          58     \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER    1 (0.2%)          60     \n  OTHER                                        0 (0.0%)          0      \n  UNKNOWN                                      0 (0.0%)          0      \n————————————————————————————————————————————————————————————————————————\n\n* Person time is the sum of exposure across all patients in unit: days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n\n  labels &lt;- col_labels(ADEX)\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\"\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  col_labels(ADEX) &lt;- labels\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"RACE\", \"STRATA1\", \"SEX\")),\n        selected = \"RACE\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:54:52 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in the sample dataset.\nset.seed(1)\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  ) %&gt;%\n  mutate()\n\nadsl_f &lt;- adsl %&gt;% filter(adsl$SAFFL == \"Y\")\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Extent of Exposure by Race: Safety-Evaluable Patients\",\n  main_footer = \"* Person time is the sum of exposure across all patients in unit: days.\",\n  show_colcounts = TRUE\n) %&gt;%\n  summarize_patients_exposure_in_cols(\n    var = \"AVAL\", col_split = TRUE,\n    .labels = c(n_patients = \"Patients\", sum_exposure = \"Person time*\")\n  ) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"RACE\",\n    col_split = FALSE\n  ) %&gt;%\n  append_topleft(c(\"\", obj_label(adex$RACE)))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nExtent of Exposure by Race: Safety-Evaluable Patients\n\n————————————————————————————————————————————————————————————————————————\n                                               Patients     Person time*\nRace                                            (N=400)       (N=400)   \n————————————————————————————————————————————————————————————————————————\nTotal patients numbers/person time            217 (54.2%)      20641    \n  ASIAN                                       113 (28.2%)      10239    \n  BLACK OR AFRICAN AMERICAN                   48 (12.0%)        4481    \n  WHITE                                       40 (10.0%)        4237    \n  AMERICAN INDIAN OR ALASKA NATIVE             14 (3.5%)        1566    \n  MULTIPLE                                     1 (0.2%)          58     \n  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER    1 (0.2%)          60     \n  OTHER                                        0 (0.0%)          0      \n  UNKNOWN                                      0 (0.0%)          0      \n————————————————————————————————————————————————————————————————————————\n\n* Person time is the sum of exposure across all patients in unit: days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n\n  labels &lt;- col_labels(ADEX)\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\"\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  col_labels(ADEX) &lt;- labels\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"RACE\", \"STRATA1\", \"SEX\")),\n        selected = \"RACE\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:19 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Risk Management Plan",
@@ -1475,7 +1475,7 @@
     "href": "tables/risk-management-plan/rmpt04.html",
     "title": "RMPT04",
     "section": "",
-    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in sample scda dataset.\nset.seed(1, kind = \"Mersenne-Twister\")\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  )\n\nadsl_f &lt;- adsl %&gt;% filter(adsl$SAFFL == \"Y\")\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Extent of Exposure by Ethnic Origin: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\",\n  show_colcounts = TRUE\n) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"ETHNIC\",\n    col_split = TRUE,\n    add_total_level = TRUE,\n    custom_label = \"Total\"\n  ) %&gt;%\n  append_topleft(c(\"\", \"Ethnicity\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nExtent of Exposure by Ethnic Origin: Safety-Evaluable Patients\n\n——————————————————————————————————————————————————\n                          Patients     Person time\nEthnicity                  (N=400)       (N=400)  \n——————————————————————————————————————————————————\nHISPANIC OR LATINO        28 (7.0%)       2423    \nNOT HISPANIC OR LATINO   165 (41.2%)      15570   \nNOT REPORTED              18 (4.5%)       2085    \nUNKNOWN                   6 (1.5%)         563    \nTotal                    217 (54.2%)      20641   \n——————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n\n  labels &lt;- col_labels(ADEX)\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\"\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  col_labels(ADEX) &lt;- labels\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ETHNIC\", \"SEX\")),\n        selected = \"ETHNIC\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:15 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Table\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.\nadsl &lt;- df_explicit_na(adsl)\nadex &lt;- df_explicit_na(adex)\n\n# Simulate ADEX records with PARAMCD == \"TDURD\" as they are not in sample scda dataset.\nset.seed(1, kind = \"Mersenne-Twister\")\nadex2 &lt;- adex %&gt;%\n  distinct(USUBJID, .keep_all = TRUE) %&gt;%\n  mutate(\n    PARAMCD = \"TDURD\",\n    PARAM = \"Overall duration (days)\",\n    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)\n  ) %&gt;%\n  bind_rows(adex)\n\n\n# Now pre-processing steps are carried out.\nanl &lt;- adex2 %&gt;%\n  filter(\n    PARAMCD == \"TDURD\",\n    PARCAT2 == \"Drug A\",\n    SAFFL == \"Y\"\n  )\n\nadsl_f &lt;- adsl %&gt;% filter(adsl$SAFFL == \"Y\")\n\n\n\n\n\nCodelyt &lt;- basic_table(\n  title = \"Extent of Exposure by Ethnic Origin: Safety-Evaluable Patients\",\n  main_footer = \"* Patient Time is the sum of exposure across all patients in days.\",\n  show_colcounts = TRUE\n) %&gt;%\n  analyze_patients_exposure_in_cols(\n    var = \"ETHNIC\",\n    col_split = TRUE,\n    add_total_level = TRUE,\n    custom_label = \"Total\"\n  ) %&gt;%\n  append_topleft(c(\"\", \"Ethnicity\"))\n\nresult &lt;- build_table(lyt, df = anl, alt_counts_df = adsl_f)\nresult\n\nExtent of Exposure by Ethnic Origin: Safety-Evaluable Patients\n\n——————————————————————————————————————————————————\n                          Patients     Person time\nEthnicity                  (N=400)       (N=400)  \n——————————————————————————————————————————————————\nHISPANIC OR LATINO        28 (7.0%)       2423    \nNOT HISPANIC OR LATINO   165 (41.2%)      15570   \nNOT REPORTED              18 (4.5%)       2085    \nUNKNOWN                   6 (1.5%)         563    \nTotal                    217 (54.2%)      20641   \n——————————————————————————————————————————————————\n\n* Patient Time is the sum of exposure across all patients in days.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(dplyr)\n\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADEX &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\n  labels &lt;- col_labels(ADEX)\n  set.seed(1, kind = \"Mersenne-Twister\")\n\n  labels &lt;- col_labels(ADEX)\n  ADEX &lt;- ADEX %&gt;%\n    distinct(USUBJID, .keep_all = TRUE) %&gt;%\n    mutate(\n      PARAMCD = \"TDURD\",\n      PARAM = \"Overall duration (days)\",\n      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),\n      AVALU = \"Days\"\n    ) %&gt;%\n    bind_rows(ADEX)\n\n  col_labels(ADEX) &lt;- labels\n})\ndatanames &lt;- c(\"ADSL\", \"ADEX\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADEX &lt;- data[[\"ADEX\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_t_exposure(\n      label = \"Duration of Exposure Table\",\n      dataname = \"ADEX\",\n      paramcd = choices_selected(\n        choices = value_choices(ADEX, \"PARAMCD\", \"PARAM\"),\n        selected = \"TDURD\"\n      ),\n      col_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ARM\")),\n        selected = \"ARM\"\n      ),\n      row_by_var = choices_selected(\n        choices = variable_choices(ADEX, subset = c(\"ETHNIC\", \"SEX\")),\n        selected = \"ETHNIC\"\n      ),\n      parcat = choices_selected(\n        choices = value_choices(ADEX, \"PARCAT2\"),\n        selected = \"Drug A\"\n      ),\n      add_total = FALSE\n    )\n  ),\n  filter = teal_slices(teal_slice(\"ADSL\", \"SAFFL\", selected = \"Y\"))\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:40 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      dplyr_1.1.4                     \n [9] scda_0.1.6.9017                  tern_0.9.3.9024                 \n[11] rtables_0.6.6.9011               magrittr_2.0.3                  \n[13] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           \n [4] webshot2_0.1.1           promises_1.2.1           shinyjs_2.1.0           \n [7] digest_0.6.34            estimability_1.5         mime_0.12               \n[10] tern.gee_0.1.3.9004      lifecycle_1.0.4          ellipsis_0.3.2          \n[13] processx_3.8.3           survival_3.5-8           compiler_4.3.3          \n[16] sass_0.4.8               rlang_1.1.3              tools_4.3.3             \n[19] utf8_1.2.4               yaml_2.3.8               knitr_1.45              \n[22] htmlwidgets_1.6.4        websocket_1.4.1          multcomp_1.4-25         \n[25] withr_3.0.0              purrr_1.0.2              shinyWidgets_0.8.2      \n[28] geepack_1.3.10           grid_4.3.3               fansi_1.0.6             \n[31] teal.logger_0.1.3.9011   xtable_1.8-4             colorspace_2.1-0        \n[34] ggplot2_3.5.0            emmeans_1.10.0           scales_1.3.0            \n[37] MASS_7.3-60.0.1          cli_3.6.2                mvtnorm_1.2-4           \n[40] rmarkdown_2.26           generics_0.1.3           chromote_0.2.0          \n[43] cachem_1.0.8             splines_4.3.3            formatR_1.14            \n[46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            \n[49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             \n[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       \n[55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              \n[58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           \n[61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   \n[64] munsell_0.5.0            tibble_3.2.1             logger_0.3.0            \n[67] pillar_1.9.0             htmltools_0.5.7          brio_1.1.4              \n[70] R6_2.5.1                 Rdpack_2.6               evaluate_0.23           \n[73] lattice_0.22-5           rbibutils_2.2.16         backports_1.4.1         \n[76] memoise_2.0.1            broom_1.0.5              teal.reporter_0.3.0.9005\n[79] httpuv_1.6.14            bslib_0.6.1              Rcpp_1.0.12             \n[82] shinyvalidate_0.1.3      coda_0.19-4.1            nlme_3.1-164            \n[85] checkmate_2.3.1          xfun_0.42                zoo_1.8-12              \n[88] forcats_1.0.0            pkgconfig_2.0.3         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Tables",
       "Risk Management Plan",
@@ -1487,7 +1487,7 @@
     "href": "listings/pharmacokinetic/pkpl01.html",
     "title": "PKPL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\ndrug_a &lt;- \"Plasma Drug X\"\nspec &lt;- \"Plasma\"\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp_x &lt;- adpp %&gt;% filter(\n  PPCAT == drug_a,\n  PPSPEC == spec\n)\n\nout &lt;- adpp_x %&gt;%\n  mutate(PARAM = paste0(PARAM, \" (\", AVALU, \")\")) %&gt;%\n  select(TRT01A, USUBJID, AVISIT, PARAM, AVAL) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, USUBJID, AVISIT),\n    names_from = PARAM,\n    values_from = AVAL\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(\n  TRT01A = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  AVISIT = \"Visit\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"USUBJID\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = paste(\"Listing of\", drug_a, spec, \"PK Parameters, PK Population\\nProtocol: xxnnnnn\"),\n  subtitles = paste(\"Analyte:\", drug_a)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Plasma Drug X Plasma PK Parameters, PK Population\nProtocol: xxnnnnn\nAnalyte: Plasma Drug X\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group        Subject ID             Visit       AUC Infinity Obs (day*ug/mL)   Max Conc (ug/mL)   Total CL Obs (ml/day/kg)\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105      SCREENING           145.191684898102         35.5067335878638       5.1567601025515     \n                                          CYCLE 1 DAY 1         246.349321001289         31.2264481880236       4.41386730113941    \n                                          CYCLE 1 DAY 2         225.443483077171         43.4987268488453       4.81490455551286    \n                  AB12345-BRA-1-id-134      SCREENING           157.461703380688         32.1171292884992       6.0374125711965     \n                                          CYCLE 1 DAY 1         248.519038520728         21.7795763901966        4.561839813278     \n                                          CYCLE 1 DAY 2         230.794839837734         21.0627132276049       6.95200595313275    \n                   AB12345-BRA-1-id-42      SCREENING           194.224015921831         20.4565798423567       6.33709739029277    \n                                          CYCLE 1 DAY 1         210.528307366678         26.7845825535397       3.50776680376268    \n                                          CYCLE 1 DAY 2         167.592869235882         34.8276545020143       4.11969088323673    \n                   AB12345-BRA-1-id-93      SCREENING           186.75868084484          27.3983170052516       3.40495133575408    \n                                          CYCLE 1 DAY 1         160.406798051843         17.5296885232566       3.8508725098954     \n                                          CYCLE 1 DAY 2         252.04512291352          32.4903879259459       6.21586252565961    \n                  AB12345-BRA-11-id-217     SCREENING           215.367414313054         20.8879942219526       6.55883385955605    \n                                          CYCLE 1 DAY 1         272.094354596647         34.2812274648873       5.04441608634882    \n                                          CYCLE 1 DAY 2         174.183265394136          23.04243406023        4.07416508597243    \n                  AB12345-BRA-11-id-345     SCREENING           235.746948097021         26.5740342881269       5.52059967823211    \n                                          CYCLE 1 DAY 1         228.51025073098          39.7523164840323       6.32996327499752    \n                                          CYCLE 1 DAY 2         120.712762389698          24.37143486359        5.02620823564574    \n                  AB12345-BRA-11-id-397     SCREENING           165.925716319045         21.8770819414152       6.3713157196375     \n                                          CYCLE 1 DAY 1         201.536920713046         36.5841424116449       4.32440907316618    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:24 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9013 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\ndrug_a &lt;- \"Plasma Drug X\"\nspec &lt;- \"Plasma\"\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\nadpp_x &lt;- adpp %&gt;% filter(\n  PPCAT == drug_a,\n  PPSPEC == spec\n)\n\nout &lt;- adpp_x %&gt;%\n  mutate(PARAM = paste0(PARAM, \" (\", AVALU, \")\")) %&gt;%\n  select(TRT01A, USUBJID, AVISIT, PARAM, AVAL) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, USUBJID, AVISIT),\n    names_from = PARAM,\n    values_from = AVAL\n  )\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;% var_relabel(\n  TRT01A = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  AVISIT = \"Visit\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"USUBJID\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = paste(\"Listing of\", drug_a, spec, \"PK Parameters, PK Population\\nProtocol: xxnnnnn\"),\n  subtitles = paste(\"Analyte:\", drug_a)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Plasma Drug X Plasma PK Parameters, PK Population\nProtocol: xxnnnnn\nAnalyte: Plasma Drug X\n\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nTreatment Group        Subject ID             Visit       AUC Infinity Obs (day*ug/mL)   Max Conc (ug/mL)   Total CL Obs (ml/day/kg)\n————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105      SCREENING           145.191684898102         35.5067335878638       5.1567601025515     \n                                          CYCLE 1 DAY 1         246.349321001289         31.2264481880236       4.41386730113941    \n                                          CYCLE 1 DAY 2         225.443483077171         43.4987268488453       4.81490455551286    \n                  AB12345-BRA-1-id-134      SCREENING           157.461703380688         32.1171292884992       6.0374125711965     \n                                          CYCLE 1 DAY 1         248.519038520728         21.7795763901966        4.561839813278     \n                                          CYCLE 1 DAY 2         230.794839837734         21.0627132276049       6.95200595313275    \n                   AB12345-BRA-1-id-42      SCREENING           194.224015921831         20.4565798423567       6.33709739029277    \n                                          CYCLE 1 DAY 1         210.528307366678         26.7845825535397       3.50776680376268    \n                                          CYCLE 1 DAY 2         167.592869235882         34.8276545020143       4.11969088323673    \n                   AB12345-BRA-1-id-93      SCREENING           186.75868084484          27.3983170052516       3.40495133575408    \n                                          CYCLE 1 DAY 1         160.406798051843         17.5296885232566       3.8508725098954     \n                                          CYCLE 1 DAY 2         252.04512291352          32.4903879259459       6.21586252565961    \n                  AB12345-BRA-11-id-217     SCREENING           215.367414313054         20.8879942219526       6.55883385955605    \n                                          CYCLE 1 DAY 1         272.094354596647         34.2812274648873       5.04441608634882    \n                                          CYCLE 1 DAY 2         174.183265394136          23.04243406023        4.07416508597243    \n                  AB12345-BRA-11-id-345     SCREENING           235.746948097021         26.5740342881269       5.52059967823211    \n                                          CYCLE 1 DAY 1         228.51025073098          39.7523164840323       6.32996327499752    \n                                          CYCLE 1 DAY 2         120.712762389698          24.37143486359        5.02620823564574    \n                  AB12345-BRA-11-id-397     SCREENING           165.925716319045         21.8770819414152       6.3713157196375     \n                                          CYCLE 1 DAY 1         201.536920713046         36.5841424116449       4.32440907316618    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:49 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9015 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Pharmacokinetic",
@@ -1499,7 +1499,7 @@
     "href": "listings/pharmacokinetic/pkcl01.html",
     "title": "PKCL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(rlistings)\nlibrary(scda)\nlibrary(dplyr)\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\nanalyte &lt;- \"Plasma Drug X\"\n\nout &lt;- adpc %&gt;%\n  filter(PARAM == analyte) %&gt;%\n  select(ARM, USUBJID, VISIT, NFRLT, AFRLT, AVAL)\n\nvar_labels(out) &lt;- c(\n  ARM = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  VISIT = \"Visit\",\n  NFRLT = paste0(\"Nominal Sampling\\nTime (\", adpc$RELTMU[1], \")\"),\n  AFRLT = paste0(\"Actual Time\\nFrom First\\nDose (\", adpc$RELTMU[1], \")\"),\n  AVAL = paste0(\"Concentration\\n(\", adpc$AVALU[1], \")\")\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ARM\", \"USUBJID\", \"VISIT\"),\n  disp_cols = names(out),\n  main_title = paste(\n    \"Listing of\",\n    analyte,\n    \"Concentration by Treatment Group, Subject and Nominal Time, PK Population\\nProtocol: xxnnnnn\"\n  ),\n  subtitles = paste(\"Analyte:\", analyte)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Plasma Drug X Concentration by Treatment Group, Subject and Nominal Time, PK Population\nProtocol: xxnnnnn\nAnalyte: Plasma Drug X\n\n———————————————————————————————————————————————————————————————————————————————————————————————\n                                                                    Actual Time                \n                                                 Nominal Sampling   From First    Concentration\nTreatment Group        Subject ID        Visit      Time (hr)        Dose (hr)       (ug/mL)   \n———————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105   Day 1          0                0              0      \n                                                       0.5              0.5          10.404    \n                                                        1                1           13.987    \n                                                       1.5              1.5          14.142    \n                                                        2                2           12.744    \n                                                        3                3            8.803    \n                                                        4                4            5.461    \n                                                        8                8            0.562    \n                                                        12              12            0.049    \n                                         Day 2          24              24              0      \n                  AB12345-BRA-1-id-134   Day 1          0                0              0      \n                                                       0.5              0.5          14.405    \n                                                        1                1           17.863    \n                                                       1.5              1.5          16.613    \n                                                        2                2           13.735    \n                                                        3                3            7.923    \n                                                        4                4            4.064    \n                                                        8                8            0.178    \n                                                        12              12            0.006    \n                                         Day 2          24              24              0      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       rlistings_0.2.7.9010 \n[4] tibble_3.2.1          formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(rlistings)\nlibrary(scda)\nlibrary(dplyr)\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\nanalyte &lt;- \"Plasma Drug X\"\n\nout &lt;- adpc %&gt;%\n  filter(PARAM == analyte) %&gt;%\n  select(ARM, USUBJID, VISIT, NFRLT, AFRLT, AVAL)\n\nvar_labels(out) &lt;- c(\n  ARM = \"Treatment Group\",\n  USUBJID = \"Subject ID\",\n  VISIT = \"Visit\",\n  NFRLT = paste0(\"Nominal Sampling\\nTime (\", adpc$RELTMU[1], \")\"),\n  AFRLT = paste0(\"Actual Time\\nFrom First\\nDose (\", adpc$RELTMU[1], \")\"),\n  AVAL = paste0(\"Concentration\\n(\", adpc$AVALU[1], \")\")\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ARM\", \"USUBJID\", \"VISIT\"),\n  disp_cols = names(out),\n  main_title = paste(\n    \"Listing of\",\n    analyte,\n    \"Concentration by Treatment Group, Subject and Nominal Time, PK Population\\nProtocol: xxnnnnn\"\n  ),\n  subtitles = paste(\"Analyte:\", analyte)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Plasma Drug X Concentration by Treatment Group, Subject and Nominal Time, PK Population\nProtocol: xxnnnnn\nAnalyte: Plasma Drug X\n\n———————————————————————————————————————————————————————————————————————————————————————————————\n                                                                    Actual Time                \n                                                 Nominal Sampling   From First    Concentration\nTreatment Group        Subject ID        Visit      Time (hr)        Dose (hr)       (ug/mL)   \n———————————————————————————————————————————————————————————————————————————————————————————————\n   A: Drug X      AB12345-BRA-1-id-105   Day 1          0                0              0      \n                                                       0.5              0.5          10.404    \n                                                        1                1           13.987    \n                                                       1.5              1.5          14.142    \n                                                        2                2           12.744    \n                                                        3                3            8.803    \n                                                        4                4            5.461    \n                                                        8                8            0.562    \n                                                        12              12            0.049    \n                                         Day 2          24              24              0      \n                  AB12345-BRA-1-id-134   Day 1          0                0              0      \n                                                       0.5              0.5          14.405    \n                                                        1                1           17.863    \n                                                       1.5              1.5          16.613    \n                                                        2                2           13.735    \n                                                        3                3            7.923    \n                                                        4                4            4.064    \n                                                        8                8            0.178    \n                                                        12              12            0.006    \n                                         Day 2          24              24              0      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:53:56 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] dplyr_1.1.4           scda_0.1.6.9017       rlistings_0.2.7.9010 \n[4] tibble_3.2.1          formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Pharmacokinetic",
@@ -1511,7 +1511,7 @@
     "href": "listings/exposure/exl01.html",
     "title": "EXL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\nout &lt;- adex %&gt;%\n  filter(PARAMCD == \"DOSE\" & !is.na(AVAL) & SAFFL == \"Y\") %&gt;%\n  mutate(\n    CRTNPT = paste(SITEID, SUBJID, sep = \"/\")\n  ) %&gt;%\n  arrange(CRTNPT, AVISIT) %&gt;%\n  select(CRTNPT, AVISIT, EXSTDY, EXENDY, TRT01A, AVAL, AVALU, EXDOSFRQ, EXROUTE)\n\nvar_labels(out) &lt;- c(\n  CRTNPT = \"Center/Subject ID\",\n  AVISIT = \"Visit\",\n  EXSTDY = \"Study Day\\nFrom\",\n  EXENDY = \"Study Day\\nTo\",\n  TRT01A = \"Treatment\",\n  AVAL = \"Dose\",\n  AVALU = \"Unit\",\n  EXDOSFRQ = \"Frequency\",\n  EXROUTE = \"Route\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CRTNPT\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Exposure to Study Drug\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Exposure to Study Drug\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                Study Day   Study Day                                         \nTreatment   Center/Subject ID       Visit         From         To       Dose   Unit   Frequency      Route    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105        SCREENING        -1          -1       960     mg      ONCE      INTRAVENOUS \n                                  BASELINE          1           1       960     mg      ONCE      INTRAVENOUS \n                                WEEK 1 DAY 8        8           8       720     mg      ONCE      SUBCUTANEOUS\n                                WEEK 2 DAY 15      15          15       480     mg      ONCE      INTRAVENOUS \n                                WEEK 3 DAY 22      22          22       480     mg      ONCE      INTRAVENOUS \n                                WEEK 4 DAY 29      29          29       720     mg      ONCE      INTRAVENOUS \n                                WEEK 5 DAY 36      36          36       720     mg      ONCE      INTRAVENOUS \n              BRA-1/id-134        SCREENING        -1          -1       960     mg      ONCE      INTRAVENOUS \n                                  BASELINE          1           1       720     mg      ONCE      INTRAVENOUS \n                                WEEK 1 DAY 8        8           8       720     mg      ONCE      INTRAVENOUS \n                                WEEK 2 DAY 15      15          15       960     mg      ONCE      INTRAVENOUS \n                                WEEK 3 DAY 22      22          22       720     mg      ONCE      INTRAVENOUS \n                                WEEK 4 DAY 29      29          29       960     mg      ONCE      INTRAVENOUS \n                                WEEK 5 DAY 36      36          36       960     mg      ONCE      INTRAVENOUS \n               BRA-1/id-42        SCREENING        -1          -1       960     mg      ONCE      INTRAVENOUS \n                                  BASELINE          1           1       960     mg      ONCE      INTRAVENOUS \n                                WEEK 1 DAY 8        8           8       720     mg      ONCE      SUBCUTANEOUS\n                                WEEK 2 DAY 15      15          15       720     mg      ONCE      INTRAVENOUS \n                                WEEK 3 DAY 22      22          22       720     mg      ONCE      INTRAVENOUS \n                                WEEK 4 DAY 29      29          29       480     mg      ONCE      INTRAVENOUS \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:39 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadex &lt;- synthetic_cdisc_dataset(\"latest\", \"adex\")\n\nout &lt;- adex %&gt;%\n  filter(PARAMCD == \"DOSE\" & !is.na(AVAL) & SAFFL == \"Y\") %&gt;%\n  mutate(\n    CRTNPT = paste(SITEID, SUBJID, sep = \"/\")\n  ) %&gt;%\n  arrange(CRTNPT, AVISIT) %&gt;%\n  select(CRTNPT, AVISIT, EXSTDY, EXENDY, TRT01A, AVAL, AVALU, EXDOSFRQ, EXROUTE)\n\nvar_labels(out) &lt;- c(\n  CRTNPT = \"Center/Subject ID\",\n  AVISIT = \"Visit\",\n  EXSTDY = \"Study Day\\nFrom\",\n  EXENDY = \"Study Day\\nTo\",\n  TRT01A = \"Treatment\",\n  AVAL = \"Dose\",\n  AVALU = \"Unit\",\n  EXDOSFRQ = \"Frequency\",\n  EXROUTE = \"Route\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CRTNPT\", \"AVISIT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Exposure to Study Drug\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Exposure to Study Drug\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                Study Day   Study Day                                         \nTreatment   Center/Subject ID       Visit         From         To       Dose   Unit   Frequency      Route    \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105        SCREENING        -1          -1       960     mg      ONCE      INTRAVENOUS \n                                  BASELINE          1           1       960     mg      ONCE      INTRAVENOUS \n                                WEEK 1 DAY 8        8           8       720     mg      ONCE      SUBCUTANEOUS\n                                WEEK 2 DAY 15      15          15       480     mg      ONCE      INTRAVENOUS \n                                WEEK 3 DAY 22      22          22       480     mg      ONCE      INTRAVENOUS \n                                WEEK 4 DAY 29      29          29       720     mg      ONCE      INTRAVENOUS \n                                WEEK 5 DAY 36      36          36       720     mg      ONCE      INTRAVENOUS \n              BRA-1/id-134        SCREENING        -1          -1       960     mg      ONCE      INTRAVENOUS \n                                  BASELINE          1           1       720     mg      ONCE      INTRAVENOUS \n                                WEEK 1 DAY 8        8           8       720     mg      ONCE      INTRAVENOUS \n                                WEEK 2 DAY 15      15          15       960     mg      ONCE      INTRAVENOUS \n                                WEEK 3 DAY 22      22          22       720     mg      ONCE      INTRAVENOUS \n                                WEEK 4 DAY 29      29          29       960     mg      ONCE      INTRAVENOUS \n                                WEEK 5 DAY 36      36          36       960     mg      ONCE      INTRAVENOUS \n               BRA-1/id-42        SCREENING        -1          -1       960     mg      ONCE      INTRAVENOUS \n                                  BASELINE          1           1       960     mg      ONCE      INTRAVENOUS \n                                WEEK 1 DAY 8        8           8       720     mg      ONCE      SUBCUTANEOUS\n                                WEEK 2 DAY 15      15          15       720     mg      ONCE      INTRAVENOUS \n                                WEEK 3 DAY 22      22          22       720     mg      ONCE      INTRAVENOUS \n                                WEEK 4 DAY 29      29          29       480     mg      ONCE      INTRAVENOUS \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:03 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Exposure",
@@ -1523,7 +1523,7 @@
     "href": "listings/concomitant-medications/cml01.html",
     "title": "CML01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nout &lt;- adcm %&gt;%\n  filter(!is.na(CMDECOD)) %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    TRTSD = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    CMASTD = toupper(format(as.Date(ASTDTM), \"%d%b%Y\")),\n    CMSTRFL = ifelse(ASTDY &lt; 0, \"Yes\", \"No\"),\n    CMENRFL = ifelse(CMENRTPT == \"ONGOING\", \"Yes\", \"No\")\n  ) %&gt;%\n  select(\n    ID, AGSXRC, TRT01A, CMDECOD, TRTSD, CMASTD, ASTDY, ADURN,\n    CMSTRFL, CMENRFL, CMDOSE, CMDOSU, CMDOSFRQ, CMROUTE\n  )\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  AGSXRC = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  CMDECOD = \"Medication Name\",\n  TRTSD = \"Date of First\\nStudy Drug\\nAdministration\",\n  CMASTD = \"Medication\\nStart Date\",\n  ASTDY = \"Study Day\",\n  ADURN = \"Duration\\n(days)\",\n  CMSTRFL = \"Previous?\",\n  CMENRFL = \"Ongoing\\nat Final\\nContact?\",\n  CMDOSE = \"Dose\",\n  CMDOSU = \"Dose Unit\",\n  CMDOSFRQ = \"Frequency\",\n  CMROUTE = \"Route\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\", \"AGSXRC\", \"CMDECOD\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Previous and Concomitant Medications\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Previous and Concomitant Medications\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                   Date of First                                                    Ongoing                                                        \n                                                                                     Study Drug     Medication               Duration               at Final                                                       \nTreatment   Center/Patient ID            Age/Sex/Race            Medication Name   Administration   Start Date   Study Day    (days)    Previous?   Contact?   Dose   Dose Unit       Frequency           Route    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE              medname A_2/3      04NOV2020      21SEP2021       321        -148        No          No       30    mg/breath          Q4H          INTRAMUSCULAR\n                                                                                     04NOV2020      02DEC2021       393        -42         No          No       41     umol/L           TWICE            UNKNOWN   \n                                                                  medname B_1/4      04NOV2020      15APR2021       162        -172        No          No       25    ug/kg/day          Q4H           INTRAVENOUS \n                                                                                     04NOV2020      15APR2021       162        -172        No          No       25    ug/kg/day          Q4H           INTRAVENOUS \n                                                                                     04NOV2020      15APR2021       162        -172        No          No       25    ug/kg/day          Q4H           INTRAVENOUS \n                                                                  medname C_1/2      04NOV2020      19MAY2021       196        -165        No          No       22      ug/mL            TID             INHALED   \n                                                                                     04NOV2020      19MAY2021       196        -165        No          No       22      ug/mL            TID             INHALED   \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN    medname A_2/3      01JAN2020      09JUN2020       160        -258        No         Yes       48      DROP             QD            INTRAVENOUS \n                                                                                     01JAN2020      04APR2021       459        -194        No         Yes       57    ug/kg/day          Q4W              NASAL    \n                                                                                     01JAN2020      15APR2021       470        -130        No         Yes       83    ug/kg/day          Q4H             UNKNOWN   \n                                                                                     01JAN2020      20APR2021       475        -112        No         Yes       17    ug/kg/day          Q4H          SUBCUTANEOUS \n                                                                  medname A_3/3      01JAN2020      03OCT2021       641        -14         No         Yes       68        %       4 TIMES PER MONTH      UNKNOWN   \n                                                                                     01JAN2020      03OCT2021       641        -14         No         Yes       68        %       4 TIMES PER MONTH      UNKNOWN   \n                                                                  medname B_1/4      01JAN2020      19OCT2020       292        -49         No         Yes       5       ug/mL           TWICE            UNKNOWN   \n                                                                                     01JAN2020      19OCT2020       292        -49         No         Yes       5       ug/mL           TWICE            UNKNOWN   \n                                                                                     01JAN2020      19OCT2020       292        -49         No         Yes       5       ug/mL           TWICE            UNKNOWN   \n                                                                  medname B_2/4      01JAN2020      11MAY2021       496        -161        No         Yes       83      ug/mL           TWICE            RECTAL    \n                                                                                     01JAN2020      11MAY2021       496        -161        No         Yes       83      ug/mL           TWICE            RECTAL    \n                                                                                     01JAN2020      11MAY2021       496        -161        No         Yes       83      ug/mL           TWICE            RECTAL    \n                                                                  medname B_3/4      01JAN2020      09FEB2020       39         -505        No         Yes       85       ug              QD           SUBCUTANEOUS \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:46 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nout &lt;- adcm %&gt;%\n  filter(!is.na(CMDECOD)) %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    TRTSD = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    CMASTD = toupper(format(as.Date(ASTDTM), \"%d%b%Y\")),\n    CMSTRFL = ifelse(ASTDY &lt; 0, \"Yes\", \"No\"),\n    CMENRFL = ifelse(CMENRTPT == \"ONGOING\", \"Yes\", \"No\")\n  ) %&gt;%\n  select(\n    ID, AGSXRC, TRT01A, CMDECOD, TRTSD, CMASTD, ASTDY, ADURN,\n    CMSTRFL, CMENRFL, CMDOSE, CMDOSU, CMDOSFRQ, CMROUTE\n  )\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  AGSXRC = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  CMDECOD = \"Medication Name\",\n  TRTSD = \"Date of First\\nStudy Drug\\nAdministration\",\n  CMASTD = \"Medication\\nStart Date\",\n  ASTDY = \"Study Day\",\n  ADURN = \"Duration\\n(days)\",\n  CMSTRFL = \"Previous?\",\n  CMENRFL = \"Ongoing\\nat Final\\nContact?\",\n  CMDOSE = \"Dose\",\n  CMDOSU = \"Dose Unit\",\n  CMDOSFRQ = \"Frequency\",\n  CMROUTE = \"Route\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\", \"AGSXRC\", \"CMDECOD\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Previous and Concomitant Medications\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Previous and Concomitant Medications\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                   Date of First                                                    Ongoing                                                        \n                                                                                     Study Drug     Medication               Duration               at Final                                                       \nTreatment   Center/Patient ID            Age/Sex/Race            Medication Name   Administration   Start Date   Study Day    (days)    Previous?   Contact?   Dose   Dose Unit       Frequency           Route    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE              medname A_2/3      04NOV2020      21SEP2021       321        -148        No          No       30    mg/breath          Q4H          INTRAMUSCULAR\n                                                                                     04NOV2020      02DEC2021       393        -42         No          No       41     umol/L           TWICE            UNKNOWN   \n                                                                  medname B_1/4      04NOV2020      15APR2021       162        -172        No          No       25    ug/kg/day          Q4H           INTRAVENOUS \n                                                                                     04NOV2020      15APR2021       162        -172        No          No       25    ug/kg/day          Q4H           INTRAVENOUS \n                                                                                     04NOV2020      15APR2021       162        -172        No          No       25    ug/kg/day          Q4H           INTRAVENOUS \n                                                                  medname C_1/2      04NOV2020      19MAY2021       196        -165        No          No       22      ug/mL            TID             INHALED   \n                                                                                     04NOV2020      19MAY2021       196        -165        No          No       22      ug/mL            TID             INHALED   \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN    medname A_2/3      01JAN2020      09JUN2020       160        -258        No         Yes       48      DROP             QD            INTRAVENOUS \n                                                                                     01JAN2020      04APR2021       459        -194        No         Yes       57    ug/kg/day          Q4W              NASAL    \n                                                                                     01JAN2020      15APR2021       470        -130        No         Yes       83    ug/kg/day          Q4H             UNKNOWN   \n                                                                                     01JAN2020      20APR2021       475        -112        No         Yes       17    ug/kg/day          Q4H          SUBCUTANEOUS \n                                                                  medname A_3/3      01JAN2020      03OCT2021       641        -14         No         Yes       68        %       4 TIMES PER MONTH      UNKNOWN   \n                                                                                     01JAN2020      03OCT2021       641        -14         No         Yes       68        %       4 TIMES PER MONTH      UNKNOWN   \n                                                                  medname B_1/4      01JAN2020      19OCT2020       292        -49         No         Yes       5       ug/mL           TWICE            UNKNOWN   \n                                                                                     01JAN2020      19OCT2020       292        -49         No         Yes       5       ug/mL           TWICE            UNKNOWN   \n                                                                                     01JAN2020      19OCT2020       292        -49         No         Yes       5       ug/mL           TWICE            UNKNOWN   \n                                                                  medname B_2/4      01JAN2020      11MAY2021       496        -161        No         Yes       83      ug/mL           TWICE            RECTAL    \n                                                                                     01JAN2020      11MAY2021       496        -161        No         Yes       83      ug/mL           TWICE            RECTAL    \n                                                                                     01JAN2020      11MAY2021       496        -161        No         Yes       83      ug/mL           TWICE            RECTAL    \n                                                                  medname B_3/4      01JAN2020      09FEB2020       39         -505        No         Yes       85       ug              QD           SUBCUTANEOUS \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:11 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Concomitant Medications",
@@ -1535,7 +1535,7 @@
     "href": "listings/concomitant-medications/cml02b_gl.html",
     "title": "CML02B_GL",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nout &lt;- adcm %&gt;%\n  select(ATC1, ATC2, ATC3, ATC4, CMDECOD, CMTRT) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  ATC1 = \"ATC Class Level 1\",\n  ATC2 = \"ATC Class Level 2\",\n  ATC3 = \"ATC Class Level 3\",\n  ATC4 = \"ATC Class Level 4\",\n  CMDECOD = \"WHODrug Preferred Name\",\n  CMTRT = \"Investigator-Specified\\nTreatment Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                         Investigator-Specified\nATC Class Level 1   ATC Class Level 2   ATC Class Level 3   ATC Class Level 4   WHODrug Preferred Name       Treatment Term    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   ATCCLAS1 A          ATCCLAS2 A          ATCCLAS3 A          ATCCLAS4 A           medname A_2/3                A_2/3         \n                                                                                    medname A_3/3                A_3/3         \n                                                                                    medname A_1/3                A_1/3         \n  ATCCLAS1 A p2       ATCCLAS2 A p2       ATCCLAS3 A p2       ATCCLAS4 A p2         medname A_3/3                A_3/3         \n   ATCCLAS1 B          ATCCLAS2 B          ATCCLAS3 B          ATCCLAS4 B           medname B_1/4                B_1/4         \n                                                                                    medname B_4/4                B_4/4         \n                                                                                    medname B_2/4                B_2/4         \n                                                                                    medname B_3/4                B_3/4         \n  ATCCLAS1 B p2       ATCCLAS2 B p2       ATCCLAS3 B p2       ATCCLAS4 B p2         medname B_1/4                B_1/4         \n                                                                                    medname B_2/4                B_2/4         \n  ATCCLAS1 B p3       ATCCLAS2 B p3       ATCCLAS3 B p3       ATCCLAS4 B p3         medname B_1/4                B_1/4         \n                                                                                    medname B_2/4                B_2/4         \n   ATCCLAS1 C          ATCCLAS2 C          ATCCLAS3 C          ATCCLAS4 C           medname C_1/2                C_1/2         \n                                                                                    medname C_2/2                C_2/2         \n  ATCCLAS1 C p2       ATCCLAS2 C p2       ATCCLAS3 C p2       ATCCLAS4 C p2         medname C_1/2                C_1/2         \n                                                                                    medname C_2/2                C_2/2         \n  ATCCLAS1 C p3       ATCCLAS2 C p3       ATCCLAS3 C p3       ATCCLAS4 C p3         medname C_2/2                C_2/2         \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:55:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadcm &lt;- synthetic_cdisc_dataset(\"latest\", \"adcm\")\n\nout &lt;- adcm %&gt;%\n  select(ATC1, ATC2, ATC3, ATC4, CMDECOD, CMTRT) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  ATC1 = \"ATC Class Level 1\",\n  ATC2 = \"ATC Class Level 2\",\n  ATC3 = \"ATC Class Level 3\",\n  ATC4 = \"ATC Class Level 4\",\n  CMDECOD = \"WHODrug Preferred Name\",\n  CMTRT = \"Investigator-Specified\\nTreatment Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ATC1\", \"ATC2\", \"ATC3\", \"ATC4\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                         Investigator-Specified\nATC Class Level 1   ATC Class Level 2   ATC Class Level 3   ATC Class Level 4   WHODrug Preferred Name       Treatment Term    \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n   ATCCLAS1 A          ATCCLAS2 A          ATCCLAS3 A          ATCCLAS4 A           medname A_2/3                A_2/3         \n                                                                                    medname A_3/3                A_3/3         \n                                                                                    medname A_1/3                A_1/3         \n  ATCCLAS1 A p2       ATCCLAS2 A p2       ATCCLAS3 A p2       ATCCLAS4 A p2         medname A_3/3                A_3/3         \n   ATCCLAS1 B          ATCCLAS2 B          ATCCLAS3 B          ATCCLAS4 B           medname B_1/4                B_1/4         \n                                                                                    medname B_4/4                B_4/4         \n                                                                                    medname B_2/4                B_2/4         \n                                                                                    medname B_3/4                B_3/4         \n  ATCCLAS1 B p2       ATCCLAS2 B p2       ATCCLAS3 B p2       ATCCLAS4 B p2         medname B_1/4                B_1/4         \n                                                                                    medname B_2/4                B_2/4         \n  ATCCLAS1 B p3       ATCCLAS2 B p3       ATCCLAS3 B p3       ATCCLAS4 B p3         medname B_1/4                B_1/4         \n                                                                                    medname B_2/4                B_2/4         \n   ATCCLAS1 C          ATCCLAS2 C          ATCCLAS3 C          ATCCLAS4 C           medname C_1/2                C_1/2         \n                                                                                    medname C_2/2                C_2/2         \n  ATCCLAS1 C p2       ATCCLAS2 C p2       ATCCLAS3 C p2       ATCCLAS4 C p2         medname C_1/2                C_1/2         \n                                                                                    medname C_2/2                C_2/2         \n  ATCCLAS1 C p3       ATCCLAS2 C p3       ATCCLAS3 C p3       ATCCLAS4 C p3         medname C_2/2                C_2/2         \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:17 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Concomitant Medications",
@@ -1547,7 +1547,7 @@
     "href": "listings/ADA/adal02.html",
     "title": "ADAL02",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\") %&gt;%\n  filter(NFRLT %% 1 == 0 & NFRLT &gt; 0)\n\ntrt &lt;- \"A: Drug X\"\ndrug_a &lt;- \"A: Drug X Antibody\"\ndrugcd &lt;- unique(adab$PARAMCD[adab$PARAM == \"Antibody titer units\"])[1]\nmin_titer &lt;- 1.10\n\nadab_x &lt;- adab %&gt;%\n  filter(\n    ARM == trt,\n    PARCAT1 == drug_a,\n    ADPBLPFL == \"Y\"\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  filter(if_any(matches(\"Treatment Emergent - Positive\"), ~ .x == 1)) %&gt;%\n  # filter(`Treatment Emergent - Positive` == 1) %&gt;%\n  mutate(\n    VISN = factor(paste0(\n      VISIT, \"\\n(Day \",\n      ifelse(\n        NFRLT %% 1 == 0,\n        NFRLT,\n        as.character(format(round(NFRLT, 2), nsmall = 2))\n      ),\n      \")\"\n    )),\n    PTES = ifelse(\n      ifelse(\"Treatment induced ADA\" %in% names(.), `Treatment induced ADA` == 1, FALSE),\n      ifelse(\n        \"Transient ADA\" %in% names(.) & `Transient ADA` == 1,\n        \"Induced (Transient)\",\n        \"Induced (Persistent)\"\n      ),\n      \"Enhanced\"\n    )\n  ) %&gt;%\n  mutate(\n    AVAL = paste0(\n      ifelse(\n        ifelse(\"ADA interpreted per sample result\" %in% names(.), `ADA interpreted per sample result` == 0, FALSE),\n        \"NEGATIVE\",\n        ifelse(\n          ifelse(\"Antibody titer units\" %in% names(.), !is.na(`Antibody titer units`), FALSE),\n          ifelse(\n            `Antibody titer units` &lt; min_titer,\n            paste0(\"&lt;\", format(min_titer, nsmall = 2)),\n            as.character(format(round(`Antibody titer units`, 2), nsmall = 2))\n          ),\n          \"---\"\n        )\n      ),\n      ifelse(\n        ifelse(\"NAB interpreted per sample result\" %in% names(.), `NAB interpreted per sample result` == 1, FALSE),\n        \"*\",\n        \"\"\n      )\n    )\n  )\n\nout &lt;- adab_x %&gt;%\n  select(USUBJID, VISN, AVAL, PTES) %&gt;%\n  tidyr::pivot_wider(\n    names_from = VISN,\n    values_from = AVAL\n  ) %&gt;%\n  select(USUBJID, unique(adab_x$VISN[order(adab_x$NFRLT)]), PTES)\n\nvar_labels(out) &lt;- names(out)\n\nout &lt;- out %&gt;%\n  var_relabel(\n    USUBJID = \"Subject ID\",\n    PTES = \"Patient Treatment\\nEmergent ADA Status\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"USUBJID\",\n  disp_cols = names(out),\n  main_title = paste0(\n    \"Listing of Anti-\", drugcd, \" Antibody Data for Treatment Emergent ADA Positive Patients, PK Population\",\n    \"\\nProtocol: \", unique(adab$PARCAT1)[1]\n  ),\n  subtitles = paste(\"\\nTreatment Group:\", trt),\n  \n  main_footer = \"Minimum reportable titer = 1.10 (example only)\n--- = No sample evaluated\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)\nNumber of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\nTreatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\nTreatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nTransient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of &lt; 16 weeks, irrespective of any negative samples in between.\nPersistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.\nAsterisk denotes sample that tested positive for Neutralizing Antibodies.\"\n  \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Anti-R1800000 Antibody Data for Treatment Emergent ADA Positive Patients, PK Population\nProtocol: A: Drug X Antibody\n\nTreatment Group: A: Drug X\n\n—————————————————————————————————————————————————————\n                         Day 2     Patient Treatment \n     Subject ID         (Day 1)   Emergent ADA Status\n—————————————————————————————————————————————————————\nAB12345-BRA-1-id-105      ---          Enhanced      \nAB12345-BRA-1-id-134      ---          Enhanced      \n AB12345-BRA-1-id-42      ---          Enhanced      \n AB12345-BRA-1-id-93      ---          Enhanced      \nAB12345-BRA-11-id-217     ---          Enhanced      \nAB12345-BRA-11-id-345     ---          Enhanced      \nAB12345-BRA-11-id-397     ---          Enhanced      \nAB12345-BRA-11-id-50      ---          Enhanced      \nAB12345-BRA-13-id-177     ---          Enhanced      \nAB12345-BRA-14-id-23      ---          Enhanced      \nAB12345-BRA-15-id-36      ---          Enhanced      \nAB12345-BRA-2-id-296      ---          Enhanced      \nAB12345-BRA-6-id-369      ---          Enhanced      \n AB12345-CAN-1-id-18      ---          Enhanced      \nAB12345-CAN-11-id-139     ---          Enhanced      \nAB12345-CAN-14-id-104     ---          Enhanced      \nAB12345-CHN-1-id-119      ---          Enhanced      \nAB12345-CHN-1-id-123      ---          Enhanced      \nAB12345-CHN-1-id-133      ---          Enhanced      \nAB12345-CHN-1-id-199      ---          Enhanced      \n—————————————————————————————————————————————————————\n\nMinimum reportable titer = 1.10 (example only)\n--- = No sample evaluated\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)\nNumber of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\nTreatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\nTreatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nTransient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of &lt; 16 weeks, irrespective of any negative samples in between.\nPersistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.\nAsterisk denotes sample that tested positive for Neutralizing Antibodies.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:01 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9013 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(rlistings)\nlibrary(scda)\n\nadab &lt;- synthetic_cdisc_dataset(\"latest\", \"adab\") %&gt;%\n  filter(NFRLT %% 1 == 0 & NFRLT &gt; 0)\n\ntrt &lt;- \"A: Drug X\"\ndrug_a &lt;- \"A: Drug X Antibody\"\ndrugcd &lt;- unique(adab$PARAMCD[adab$PARAM == \"Antibody titer units\"])[1]\nmin_titer &lt;- 1.10\n\nadab_x &lt;- adab %&gt;%\n  filter(\n    ARM == trt,\n    PARCAT1 == drug_a,\n    ADPBLPFL == \"Y\"\n  ) %&gt;%\n  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %&gt;%\n  unique() %&gt;%\n  tidyr::pivot_wider(\n    names_from = PARAM,\n    values_from = AVAL\n  ) %&gt;%\n  filter(if_any(matches(\"Treatment Emergent - Positive\"), ~ .x == 1)) %&gt;%\n  # filter(`Treatment Emergent - Positive` == 1) %&gt;%\n  mutate(\n    VISN = factor(paste0(\n      VISIT, \"\\n(Day \",\n      ifelse(\n        NFRLT %% 1 == 0,\n        NFRLT,\n        as.character(format(round(NFRLT, 2), nsmall = 2))\n      ),\n      \")\"\n    )),\n    PTES = ifelse(\n      ifelse(\"Treatment induced ADA\" %in% names(.), `Treatment induced ADA` == 1, FALSE),\n      ifelse(\n        \"Transient ADA\" %in% names(.) & `Transient ADA` == 1,\n        \"Induced (Transient)\",\n        \"Induced (Persistent)\"\n      ),\n      \"Enhanced\"\n    )\n  ) %&gt;%\n  mutate(\n    AVAL = paste0(\n      ifelse(\n        ifelse(\"ADA interpreted per sample result\" %in% names(.), `ADA interpreted per sample result` == 0, FALSE),\n        \"NEGATIVE\",\n        ifelse(\n          ifelse(\"Antibody titer units\" %in% names(.), !is.na(`Antibody titer units`), FALSE),\n          ifelse(\n            `Antibody titer units` &lt; min_titer,\n            paste0(\"&lt;\", format(min_titer, nsmall = 2)),\n            as.character(format(round(`Antibody titer units`, 2), nsmall = 2))\n          ),\n          \"---\"\n        )\n      ),\n      ifelse(\n        ifelse(\"NAB interpreted per sample result\" %in% names(.), `NAB interpreted per sample result` == 1, FALSE),\n        \"*\",\n        \"\"\n      )\n    )\n  )\n\nout &lt;- adab_x %&gt;%\n  select(USUBJID, VISN, AVAL, PTES) %&gt;%\n  tidyr::pivot_wider(\n    names_from = VISN,\n    values_from = AVAL\n  ) %&gt;%\n  select(USUBJID, unique(adab_x$VISN[order(adab_x$NFRLT)]), PTES)\n\nvar_labels(out) &lt;- names(out)\n\nout &lt;- out %&gt;%\n  var_relabel(\n    USUBJID = \"Subject ID\",\n    PTES = \"Patient Treatment\\nEmergent ADA Status\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"USUBJID\",\n  disp_cols = names(out),\n  main_title = paste0(\n    \"Listing of Anti-\", drugcd, \" Antibody Data for Treatment Emergent ADA Positive Patients, PK Population\",\n    \"\\nProtocol: \", unique(adab$PARCAT1)[1]\n  ),\n  subtitles = paste(\"\\nTreatment Group:\", trt),\n  \n  main_footer = \"Minimum reportable titer = 1.10 (example only)\n--- = No sample evaluated\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)\nNumber of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\nTreatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\nTreatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nTransient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of &lt; 16 weeks, irrespective of any negative samples in between.\nPersistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.\nAsterisk denotes sample that tested positive for Neutralizing Antibodies.\"\n  \n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Anti-R1800000 Antibody Data for Treatment Emergent ADA Positive Patients, PK Population\nProtocol: A: Drug X Antibody\n\nTreatment Group: A: Drug X\n\n—————————————————————————————————————————————————————\n                         Day 2     Patient Treatment \n     Subject ID         (Day 1)   Emergent ADA Status\n—————————————————————————————————————————————————————\nAB12345-BRA-1-id-105      ---          Enhanced      \nAB12345-BRA-1-id-134      ---          Enhanced      \n AB12345-BRA-1-id-42      ---          Enhanced      \n AB12345-BRA-1-id-93      ---          Enhanced      \nAB12345-BRA-11-id-217     ---          Enhanced      \nAB12345-BRA-11-id-345     ---          Enhanced      \nAB12345-BRA-11-id-397     ---          Enhanced      \nAB12345-BRA-11-id-50      ---          Enhanced      \nAB12345-BRA-13-id-177     ---          Enhanced      \nAB12345-BRA-14-id-23      ---          Enhanced      \nAB12345-BRA-15-id-36      ---          Enhanced      \nAB12345-BRA-2-id-296      ---          Enhanced      \nAB12345-BRA-6-id-369      ---          Enhanced      \n AB12345-CAN-1-id-18      ---          Enhanced      \nAB12345-CAN-11-id-139     ---          Enhanced      \nAB12345-CAN-14-id-104     ---          Enhanced      \nAB12345-CHN-1-id-119      ---          Enhanced      \nAB12345-CHN-1-id-123      ---          Enhanced      \nAB12345-CHN-1-id-133      ---          Enhanced      \nAB12345-CHN-1-id-199      ---          Enhanced      \n—————————————————————————————————————————————————————\n\nMinimum reportable titer = 1.10 (example only)\n--- = No sample evaluated\nADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)\nNumber of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.\nTreatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.\nTreatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.\nTransient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of &lt; 16 weeks, irrespective of any negative samples in between.\nPersistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.\nAsterisk denotes sample that tested positive for Neutralizing Antibodies.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:24 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] scda_0.1.6.9017       rlistings_0.2.7.9010  tibble_3.2.1         \n[4] formatters_0.5.5.9015 dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "ADA",
@@ -1559,7 +1559,7 @@
     "href": "listings/disposition/dsl02.html",
     "title": "DSL02",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(AEWITHFL == \"Y\") %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    DISCONT = ifelse(!is.na(DCSREAS) & EOSSTT != \"COMPLETED\", \"Yes\", \"No\"),\n    SSADTM = as.POSIXct(\n      strftime(TRTSDTM, format = \"%Y-%m-%d %H:%M:%S\"),\n      format = \"%Y-%m-%d\",\n      tz = \"UTC\"\n    ),\n    SSAEDY = as.numeric(ceiling(difftime(EOSDT, SSADTM, units = \"days\"))),\n    RANDEDY = as.numeric(ceiling(difftime(EOSDT, RANDDT, units = \"days\"))),\n  ) %&gt;%\n  filter(DISCONT == \"Yes\") %&gt;%\n  select(ID, ASR, TRT01A, SSADTM, EOSDY, SSAEDY, RANDEDY, DCSREAS)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  SSADTM = \"Date of First\\nStudy Drug\\nAdministration\",\n  EOSDY = \"Day of Last\\nStudy Drug\\nAdministration\",\n  SSAEDY = \"Day of Study\\nDiscontinuation\\nRelative to First\\nStudy Drug\\nAdministration\",\n  RANDEDY = \"Day of Study\\nDiscontinuation\\nRelative to\\nRandomization\",\n  DCSREAS = \"Reason for\\nDiscontinuation\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"TRT01A\",\n  disp_cols = names(out),\n  main_title = \"Listing of Patients Who Discontinued Early from Study\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patients Who Discontinued Early from Study\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                                          Day of Study                                        \n                                                                                                                         Discontinuation     Day of Study                     \n                                                                                      Date of First     Day of Last     Relative to First   Discontinuation                   \n                                                                                        Study Drug       Study Drug        Study Drug         Relative to        Reason for   \n  Treatment      Center/Patient ID                    Age/Sex/Race                    Administration   Administration    Administration      Randomization    Discontinuation \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n  A: Drug X         CHN-1/id-62                        36/F/WHITE                       2020-11-22          455                454                455              DEATH      \n  B: Placebo        CHN-9/id-11      28/F/NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER     2021-01-27          388                387                388              DEATH      \nC: Combination     USA-11/id-136                       38/F/ASIAN                       2019-10-02          865                864                868              DEATH      \n                   USA-11/id-100                       40/F/ASIAN                       2020-03-10          705                705                707         LACK OF EFFICACY\n                   CHN-11/id-91              44/M/BLACK OR AFRICAN AMERICAN             2020-05-29          625                625                626              DEATH      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:08 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(AEWITHFL == \"Y\") %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    DISCONT = ifelse(!is.na(DCSREAS) & EOSSTT != \"COMPLETED\", \"Yes\", \"No\"),\n    SSADTM = as.POSIXct(\n      strftime(TRTSDTM, format = \"%Y-%m-%d %H:%M:%S\"),\n      format = \"%Y-%m-%d\",\n      tz = \"UTC\"\n    ),\n    SSAEDY = as.numeric(ceiling(difftime(EOSDT, SSADTM, units = \"days\"))),\n    RANDEDY = as.numeric(ceiling(difftime(EOSDT, RANDDT, units = \"days\"))),\n  ) %&gt;%\n  filter(DISCONT == \"Yes\") %&gt;%\n  select(ID, ASR, TRT01A, SSADTM, EOSDY, SSAEDY, RANDEDY, DCSREAS)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  SSADTM = \"Date of First\\nStudy Drug\\nAdministration\",\n  EOSDY = \"Day of Last\\nStudy Drug\\nAdministration\",\n  SSAEDY = \"Day of Study\\nDiscontinuation\\nRelative to First\\nStudy Drug\\nAdministration\",\n  RANDEDY = \"Day of Study\\nDiscontinuation\\nRelative to\\nRandomization\",\n  DCSREAS = \"Reason for\\nDiscontinuation\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = \"TRT01A\",\n  disp_cols = names(out),\n  main_title = \"Listing of Patients Who Discontinued Early from Study\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patients Who Discontinued Early from Study\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                                          Day of Study                                        \n                                                                                                                         Discontinuation     Day of Study                     \n                                                                                      Date of First     Day of Last     Relative to First   Discontinuation                   \n                                                                                        Study Drug       Study Drug        Study Drug         Relative to        Reason for   \n  Treatment      Center/Patient ID                    Age/Sex/Race                    Administration   Administration    Administration      Randomization    Discontinuation \n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n  A: Drug X         CHN-1/id-62                        36/F/WHITE                       2020-11-22          455                454                455              DEATH      \n  B: Placebo        CHN-9/id-11      28/F/NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER     2021-01-27          388                387                388              DEATH      \nC: Combination     USA-11/id-136                       38/F/ASIAN                       2019-10-02          865                864                868              DEATH      \n                   USA-11/id-100                       40/F/ASIAN                       2020-03-10          705                705                707         LACK OF EFFICACY\n                   CHN-11/id-91              44/M/BLACK OR AFRICAN AMERICAN             2020-05-29          625                625                626              DEATH      \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Disposition",
@@ -1571,7 +1571,7 @@
     "href": "listings/adverse-events/ael04.html",
     "title": "AEL04",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(!is.na(DTHADY)) %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    TRTSD = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\"))\n  ) %&gt;%\n  arrange(SUBJID) %&gt;%\n  select(ID, AGSXRC, TRT01A, TRTSD, EOSDY, DTHADY, DTHCAUS, ADTHAUT)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  AGSXRC = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  TRTSD = \"Date of First\\nStudy Drug\\nAdministration\",\n  EOSDY = \"Day of Last\\nStudy Drug\\nAdministration\",\n  DTHADY = \"Day of\\nDeath\",\n  DTHCAUS = \"Cause of Death\",\n  ADTHAUT = \"Autopsy\\nPerformed?\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Patient Deaths\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patient Deaths\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                 Date of First     Day of Last                                                        \n                                                                   Study Drug       Study Drug     Day of                                    Autopsy  \nTreatment   Center/Patient ID            Age/Sex/Race            Administration   Administration   Death           Cause of Death           Performed?\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE               04NOV2020           473          496             ADVERSE EVENT              Yes    \n               BRA-1/id-93                34/F/ASIAN               20JUN2020           610          657             ADVERSE EVENT              Yes    \n              BRA-11/id-217               43/M/ASIAN               28SEP2019           871          893             ADVERSE EVENT              Yes    \n              BRA-15/id-36                38/F/ASIAN               08JAN2020           767          812          DISEASE PROGRESSION           Yes    \n              CAN-11/id-139               31/M/ASIAN               15SEP2020           519          563             ADVERSE EVENT              Yes    \n              CHN-1/id-123                27/F/ASIAN               28JAN2020           750          750          DISEASE PROGRESSION           Yes    \n              CHN-1/id-199      27/M/BLACK OR AFRICAN AMERICAN     08JAN2020           773          779          DISEASE PROGRESSION           Yes    \n              CHN-1/id-235                39/M/ASIAN               13JUL2019           950          968             ADVERSE EVENT              Yes    \n               CHN-1/id-62                36/F/WHITE               22NOV2020           455          497             ADVERSE EVENT              Yes    \n              CHN-12/id-258               45/M/WHITE               01MAR2020           721          756                SUICIDE                  No    \n              CHN-15/id-14      38/F/BLACK OR AFRICAN AMERICAN     29SEP2019           871          919          DISEASE PROGRESSION           Yes    \n              CHN-17/id-182     37/M/BLACK OR AFRICAN AMERICAN     18SEP2019           879          902           LOST TO FOLLOW UP             NA    \n              CHN-17/id-92                29/M/ASIAN               01MAR2020           720          721           LOST TO FOLLOW UP             NA    \n               CHN-2/id-22                29/M/ASIAN               15JAN2021           393          409                MISSING                  NA    \n              CHN-2/id-223                29/F/ASIAN               18NOV2020           453          468          DISEASE PROGRESSION           Yes    \n              CHN-2/id-272                41/F/ASIAN               11JAN2020           768          773          DISEASE PROGRESSION           Yes    \n              CHN-2/id-274                40/F/ASIAN               14OCT2019           860          890                SUICIDE                  No    \n              CHN-3/id-128                32/M/ASIAN               24FEB2019           1084         1105            ADVERSE EVENT              Yes    \n              CHN-5/id-108      28/F/BLACK OR AFRICAN AMERICAN     11SEP2019           886          935                MISSING                  NA    \n               CHN-6/id-30                29/M/ASIAN               24AUG2020           539          578     Post-study reporting of death       No    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:15 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(!is.na(DTHADY)) %&gt;%\n  mutate(\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    AGSXRC = paste(AGE, SEX, RACE, sep = \"/\"),\n    TRTSD = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\"))\n  ) %&gt;%\n  arrange(SUBJID) %&gt;%\n  select(ID, AGSXRC, TRT01A, TRTSD, EOSDY, DTHADY, DTHCAUS, ADTHAUT)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  AGSXRC = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  TRTSD = \"Date of First\\nStudy Drug\\nAdministration\",\n  EOSDY = \"Day of Last\\nStudy Drug\\nAdministration\",\n  DTHADY = \"Day of\\nDeath\",\n  DTHCAUS = \"Cause of Death\",\n  ADTHAUT = \"Autopsy\\nPerformed?\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Patient Deaths\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patient Deaths\n\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                 Date of First     Day of Last                                                        \n                                                                   Study Drug       Study Drug     Day of                                    Autopsy  \nTreatment   Center/Patient ID            Age/Sex/Race            Administration   Administration   Death           Cause of Death           Performed?\n——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE               04NOV2020           473          496             ADVERSE EVENT              Yes    \n               BRA-1/id-93                34/F/ASIAN               20JUN2020           610          657             ADVERSE EVENT              Yes    \n              BRA-11/id-217               43/M/ASIAN               28SEP2019           871          893             ADVERSE EVENT              Yes    \n              BRA-15/id-36                38/F/ASIAN               08JAN2020           767          812          DISEASE PROGRESSION           Yes    \n              CAN-11/id-139               31/M/ASIAN               15SEP2020           519          563             ADVERSE EVENT              Yes    \n              CHN-1/id-123                27/F/ASIAN               28JAN2020           750          750          DISEASE PROGRESSION           Yes    \n              CHN-1/id-199      27/M/BLACK OR AFRICAN AMERICAN     08JAN2020           773          779          DISEASE PROGRESSION           Yes    \n              CHN-1/id-235                39/M/ASIAN               13JUL2019           950          968             ADVERSE EVENT              Yes    \n               CHN-1/id-62                36/F/WHITE               22NOV2020           455          497             ADVERSE EVENT              Yes    \n              CHN-12/id-258               45/M/WHITE               01MAR2020           721          756                SUICIDE                  No    \n              CHN-15/id-14      38/F/BLACK OR AFRICAN AMERICAN     29SEP2019           871          919          DISEASE PROGRESSION           Yes    \n              CHN-17/id-182     37/M/BLACK OR AFRICAN AMERICAN     18SEP2019           879          902           LOST TO FOLLOW UP             NA    \n              CHN-17/id-92                29/M/ASIAN               01MAR2020           720          721           LOST TO FOLLOW UP             NA    \n               CHN-2/id-22                29/M/ASIAN               15JAN2021           393          409                MISSING                  NA    \n              CHN-2/id-223                29/F/ASIAN               18NOV2020           453          468          DISEASE PROGRESSION           Yes    \n              CHN-2/id-272                41/F/ASIAN               11JAN2020           768          773          DISEASE PROGRESSION           Yes    \n              CHN-2/id-274                40/F/ASIAN               14OCT2019           860          890                SUICIDE                  No    \n              CHN-3/id-128                32/M/ASIAN               24FEB2019           1084         1105            ADVERSE EVENT              Yes    \n              CHN-5/id-108      28/F/BLACK OR AFRICAN AMERICAN     11SEP2019           886          935                MISSING                  NA    \n               CHN-6/id-30                29/M/ASIAN               24AUG2020           539          578     Post-study reporting of death       No    \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:38 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Adverse Events",
@@ -1583,7 +1583,7 @@
     "href": "listings/adverse-events/ael02.html",
     "title": "AEL02",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    Date_First = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    Duration = AENDY - ASTDY + 1,\n    Serious = ifelse(AESER == \"Y\", \"Yes\", ifelse(AESER == \"N\", \"No\", \"\")),\n    Related = ifelse(AEREL == \"Y\", \"Yes\", ifelse(AEREL == \"N\", \"No\", \"\")),\n    Outcome = case_when(\n      AEOUT == \"FATAL\" ~ 1,\n      AEOUT == \"NOT RECOVERED/NOT RESOLVED\" ~ 2,\n      AEOUT == \"RECOVERED/RESOLVED\" ~ 3,\n      AEOUT == \"RECOVERED/RESOLVED WITH SEQUELAE\" ~ 4,\n      AEOUT == \"RECOVERING/RESOLVING\" ~ 5,\n      AEOUT == \"UNKNOWN\" ~ 6\n    ),\n    Treated = ifelse(AECONTRT == \"Y\", \"Yes\", ifelse(AECONTRT == \"N\", \"No\", \"\")),\n    Action = case_when(\n      AEACN == \"DOSE INCREASED\" ~ 1,\n      AEACN == \"DOSE NOT CHANGED\" ~ 2,\n      AEACN == \"DOSE REDUCED\" | AEACN == \"DOSE RATE REDUCED\" ~ 3,\n      AEACN == \"DRUG INTERRUPTED\" ~ 4,\n      AEACN == \"DRUG WITHDRAWN\" ~ 5,\n      AEACN == \"NOT APPLICABLE\" | AEACN == \"NOT EVALUABLE\" ~ 6,\n      AEACN == \"UNKNOWN\" ~ 7\n    )\n  ) %&gt;%\n  select(CPID, ASR, TRT01A, AEDECOD, Date_First, ASTDY, Duration, Serious, AESEV, Related, Outcome, Treated, Action)\n\nvar_labels(out) &lt;- c(\n  CPID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  AEDECOD = \"Adverse\\nEvent MedDRA\\nPreferred Term\",\n  Date_First = \"Date of\\nFirst Study\\nDrug\\nAdministration\",\n  ASTDY = \"Study\\nDay of\\nOnset\",\n  Duration = \"AE\\nDuration\\nin Days\",\n  Serious = \"Serious\",\n  AESEV = \"Most\\nExtreme\\nIntensity\",\n  Related = \"Caused by\\nStudy\\nDrug\",\n  Outcome = \"Outcome\\n(1)\",\n  Treated = \"Treatment\\nfor AE\",\n  Action = \"Action\\nTaken\\n(2)\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\", \"ASR\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Adverse Events\",\n  main_footer = \"\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Adverse Events\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                     Date of                                                                                         \n                                                                    Adverse        First Study     Study       AE                  Most      Caused by                         Action\n                                                                  Event MedDRA         Drug        Day of   Duration              Extreme      Study     Outcome   Treatment   Taken \nTreatment   Center/Patient ID            Age/Sex/Race            Preferred Term   Administration   Onset    in Days    Serious   Intensity     Drug        (1)      for AE      (2)  \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             dcd B.2.1.2.1      04NOV2020       162       173        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.1.1.4.2      04NOV2020       196       166        No      MODERATE       No          5         No         2   \n                                                                 dcd A.1.1.1.2      04NOV2020       321       149        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd A.1.1.1.2      04NOV2020       393        43        Yes     MODERATE       No          5         No         5   \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   dcd C.2.1.2.1      01JAN2020        39       506        No      MODERATE       Yes         2         No         6   \n                                                                 dcd D.1.1.1.1      01JAN2020        64       630        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd C.2.1.2.1      01JAN2020        95       306        No      MODERATE       Yes         5         No         2   \n                                                                 dcd A.1.1.1.2      01JAN2020       160       259        Yes     MODERATE       No          2         Yes        2   \n                                                                 dcd B.2.2.3.1      01JAN2020       292        50        Yes       MILD         No          3         No         2   \n                                                                 dcd A.1.1.1.2      01JAN2020       459       195        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd B.1.1.1.1      01JAN2020       470       131        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd A.1.1.1.2      01JAN2020       475       113        Yes     MODERATE       No          4         Yes        2   \n                                                                 dcd C.1.1.1.3      01JAN2020       496       162        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd B.1.1.1.1      01JAN2020       641        15        Yes      SEVERE        Yes         1         Yes        6   \n               BRA-1/id-93                34/F/ASIAN             dcd D.1.1.4.2      20JUN2020        44        47        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.2.1.5.3      20JUN2020        79       300        No        MILD         Yes         5         No         3   \n                                                                 dcd A.1.1.1.1      20JUN2020       136        78        No        MILD         No          3         Yes        2   \n                                                                 dcd D.2.1.5.3      20JUN2020       260       305        No        MILD         Yes         5         No         2   \n                                                                 dcd C.1.1.1.3      20JUN2020       281       106        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd D.1.1.1.1      20JUN2020       316       250        Yes      SEVERE        Yes         1         No         6   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:22 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    Date_First = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    Duration = AENDY - ASTDY + 1,\n    Serious = ifelse(AESER == \"Y\", \"Yes\", ifelse(AESER == \"N\", \"No\", \"\")),\n    Related = ifelse(AEREL == \"Y\", \"Yes\", ifelse(AEREL == \"N\", \"No\", \"\")),\n    Outcome = case_when(\n      AEOUT == \"FATAL\" ~ 1,\n      AEOUT == \"NOT RECOVERED/NOT RESOLVED\" ~ 2,\n      AEOUT == \"RECOVERED/RESOLVED\" ~ 3,\n      AEOUT == \"RECOVERED/RESOLVED WITH SEQUELAE\" ~ 4,\n      AEOUT == \"RECOVERING/RESOLVING\" ~ 5,\n      AEOUT == \"UNKNOWN\" ~ 6\n    ),\n    Treated = ifelse(AECONTRT == \"Y\", \"Yes\", ifelse(AECONTRT == \"N\", \"No\", \"\")),\n    Action = case_when(\n      AEACN == \"DOSE INCREASED\" ~ 1,\n      AEACN == \"DOSE NOT CHANGED\" ~ 2,\n      AEACN == \"DOSE REDUCED\" | AEACN == \"DOSE RATE REDUCED\" ~ 3,\n      AEACN == \"DRUG INTERRUPTED\" ~ 4,\n      AEACN == \"DRUG WITHDRAWN\" ~ 5,\n      AEACN == \"NOT APPLICABLE\" | AEACN == \"NOT EVALUABLE\" ~ 6,\n      AEACN == \"UNKNOWN\" ~ 7\n    )\n  ) %&gt;%\n  select(CPID, ASR, TRT01A, AEDECOD, Date_First, ASTDY, Duration, Serious, AESEV, Related, Outcome, Treated, Action)\n\nvar_labels(out) &lt;- c(\n  CPID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  AEDECOD = \"Adverse\\nEvent MedDRA\\nPreferred Term\",\n  Date_First = \"Date of\\nFirst Study\\nDrug\\nAdministration\",\n  ASTDY = \"Study\\nDay of\\nOnset\",\n  Duration = \"AE\\nDuration\\nin Days\",\n  Serious = \"Serious\",\n  AESEV = \"Most\\nExtreme\\nIntensity\",\n  Related = \"Caused by\\nStudy\\nDrug\",\n  Outcome = \"Outcome\\n(1)\",\n  Treated = \"Treatment\\nfor AE\",\n  Action = \"Action\\nTaken\\n(2)\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\", \"ASR\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Adverse Events\",\n  main_footer = \"\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Adverse Events\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                     Date of                                                                                         \n                                                                    Adverse        First Study     Study       AE                  Most      Caused by                         Action\n                                                                  Event MedDRA         Drug        Day of   Duration              Extreme      Study     Outcome   Treatment   Taken \nTreatment   Center/Patient ID            Age/Sex/Race            Preferred Term   Administration   Onset    in Days    Serious   Intensity     Drug        (1)      for AE      (2)  \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             dcd B.2.1.2.1      04NOV2020       162       173        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.1.1.4.2      04NOV2020       196       166        No      MODERATE       No          5         No         2   \n                                                                 dcd A.1.1.1.2      04NOV2020       321       149        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd A.1.1.1.2      04NOV2020       393        43        Yes     MODERATE       No          5         No         5   \n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   dcd C.2.1.2.1      01JAN2020        39       506        No      MODERATE       Yes         2         No         6   \n                                                                 dcd D.1.1.1.1      01JAN2020        64       630        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd C.2.1.2.1      01JAN2020        95       306        No      MODERATE       Yes         5         No         2   \n                                                                 dcd A.1.1.1.2      01JAN2020       160       259        Yes     MODERATE       No          2         Yes        2   \n                                                                 dcd B.2.2.3.1      01JAN2020       292        50        Yes       MILD         No          3         No         2   \n                                                                 dcd A.1.1.1.2      01JAN2020       459       195        Yes     MODERATE       No          5         Yes        2   \n                                                                 dcd B.1.1.1.1      01JAN2020       470       131        Yes      SEVERE        Yes         1         No         6   \n                                                                 dcd A.1.1.1.2      01JAN2020       475       113        Yes     MODERATE       No          4         Yes        2   \n                                                                 dcd C.1.1.1.3      01JAN2020       496       162        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd B.1.1.1.1      01JAN2020       641        15        Yes      SEVERE        Yes         1         Yes        6   \n               BRA-1/id-93                34/F/ASIAN             dcd D.1.1.4.2      20JUN2020        44        47        No      MODERATE       No          5         Yes        2   \n                                                                 dcd D.2.1.5.3      20JUN2020        79       300        No        MILD         Yes         5         No         3   \n                                                                 dcd A.1.1.1.1      20JUN2020       136        78        No        MILD         No          3         Yes        2   \n                                                                 dcd D.2.1.5.3      20JUN2020       260       305        No        MILD         Yes         5         No         2   \n                                                                 dcd C.1.1.1.3      20JUN2020       281       106        No       SEVERE        Yes         5         Yes        2   \n                                                                 dcd D.1.1.1.1      20JUN2020       316       250        Yes      SEVERE        Yes         1         No         6   \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\n\n(1) Outcome: 1 = fatal; 2 = not recovered/not resolved; 3 = recovered/resolved;\n    4 = recovered/resolved with sequelae; 5 = recovering/resolving; 6 = unknown.\n(2) Action taken with study drug: 1 = dose increased; 2 = dose not changed;\n    3 = dose reduced; 4 = drug interrupted; 5 = drug withdrawn; 6 = not applicable;\n    7 = unknown.\n*  Study day derived from imputed onset date.\n** Duration derived from imputed onset date and/or end date.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:45 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Adverse Events",
@@ -1595,7 +1595,7 @@
     "href": "listings/adverse-events/ael01.html",
     "title": "AEL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  select(AESOC, AEDECOD, AELLT, AETERM) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  AESOC = \"MedDRA System Organ Class\",\n  AEDECOD = \"MedDRA Preferred Term\",\n  AELLT = \"MedDRA Lowest Level Term\",\n  AETERM = \"Investigator-Specified\\nAdverse Event Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"AESOC\", \"AEDECOD\", \"AELLT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                               Investigator-Specified\nMedDRA System Organ Class   MedDRA Preferred Term   MedDRA Lowest Level Term     Adverse Event Term  \n—————————————————————————————————————————————————————————————————————————————————————————————————————\n          cl A                  dcd A.1.1.1.1            llt A.1.1.1.1             trm A.1.1.1.1     \n                                dcd A.1.1.1.2            llt A.1.1.1.2             trm A.1.1.1.2     \n          cl B                  dcd B.1.1.1.1            llt B.1.1.1.1             trm B.1.1.1.1     \n                                dcd B.2.1.2.1            llt B.2.1.2.1             trm B.2.1.2.1     \n                                dcd B.2.2.3.1            llt B.2.2.3.1             trm B.2.2.3.1     \n          cl C                  dcd C.1.1.1.3            llt C.1.1.1.3             trm C.1.1.1.3     \n                                dcd C.2.1.2.1            llt C.2.1.2.1             trm C.2.1.2.1     \n          cl D                  dcd D.1.1.1.1            llt D.1.1.1.1             trm D.1.1.1.1     \n                                dcd D.1.1.4.2            llt D.1.1.4.2             trm D.1.1.4.2     \n                                dcd D.2.1.5.3            llt D.2.1.5.3             trm D.2.1.5.3     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:30 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\")\n\nout &lt;- adae %&gt;%\n  select(AESOC, AEDECOD, AELLT, AETERM) %&gt;%\n  unique()\n\nvar_labels(out) &lt;- c(\n  AESOC = \"MedDRA System Organ Class\",\n  AEDECOD = \"MedDRA Preferred Term\",\n  AELLT = \"MedDRA Lowest Level Term\",\n  AETERM = \"Investigator-Specified\\nAdverse Event Term\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"AESOC\", \"AEDECOD\", \"AELLT\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                               Investigator-Specified\nMedDRA System Organ Class   MedDRA Preferred Term   MedDRA Lowest Level Term     Adverse Event Term  \n—————————————————————————————————————————————————————————————————————————————————————————————————————\n          cl A                  dcd A.1.1.1.1            llt A.1.1.1.1             trm A.1.1.1.1     \n                                dcd A.1.1.1.2            llt A.1.1.1.2             trm A.1.1.1.2     \n          cl B                  dcd B.1.1.1.1            llt B.1.1.1.1             trm B.1.1.1.1     \n                                dcd B.2.1.2.1            llt B.2.1.2.1             trm B.2.1.2.1     \n                                dcd B.2.2.3.1            llt B.2.2.3.1             trm B.2.2.3.1     \n          cl C                  dcd C.1.1.1.3            llt C.1.1.1.3             trm C.1.1.1.3     \n                                dcd C.2.1.2.1            llt C.2.1.2.1             trm C.2.1.2.1     \n          cl D                  dcd D.1.1.1.1            llt D.1.1.1.1             trm D.1.1.1.1     \n                                dcd D.1.1.4.2            llt D.1.1.4.2             trm D.1.1.4.2     \n                                dcd D.2.1.5.3            llt D.2.1.5.3             trm D.2.1.5.3     \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:52 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Adverse Events",
@@ -1607,7 +1607,7 @@
     "href": "listings/medical-history/mhl01.html",
     "title": "MHL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadmh &lt;- synthetic_cdisc_dataset(\"latest\", \"admh\")\n\nout &lt;- admh %&gt;%\n  mutate(\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    TRTSDTM = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    ASTDTM = toupper(format(as.Date(ASTDTM), \"%d%b%Y\")),\n    AENDTM = toupper(format(as.Date(AENDTM), \"%d%b%Y\"))\n  ) %&gt;%\n  select(ID, ASR, TRT01A, MHBODSYS, MHDECOD, TRTSDTM, ASTDTM, ASTDY, AENDTM, AENDY, ATIREL)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  MHBODSYS = \"SOC\",\n  MHDECOD = \"Disease Term\",\n  TRTSDTM = \"Date of First\\nStudy Drug\\nAdministration\",\n  ASTDTM = \"Start Date\\nof Disease\",\n  ASTDY = \"Start Day\\nof Disease\",\n  AENDTM = \"End Date\\nof Disease\",\n  AENDY = \"End Day\\nof Disease\",\n  ATIREL = \"Time Relation\\nof Disease\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\", \"ASR\", \"MHBODSYS\", \"MHDECOD\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Medical History and Concurrent Diseases\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Medical History and Concurrent Diseases\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                       Date of First                                                                         \n                                                                                         Study Drug     Start Date   Start Day     End Date     End Day       Time Relation  \nTreatment   Center/Patient ID            Age/Sex/Race            SOC    Disease Term   Administration   of Disease   of Disease   of Disease   of Disease      of Disease    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             cl A    trm A_2/2       04NOV2020      21SEP2021       321       16FEB2022       469       PRIOR_CONCOMITANT\n                                                                                         04NOV2020      02DEC2021       393       13JAN2022       435       PRIOR_CONCOMITANT\n                                                                 cl B    trm B_2/3       04NOV2020      15APR2021       162       04OCT2021       334       PRIOR_CONCOMITANT\n                                                                 cl D    trm D_2/3       04NOV2020      19MAY2021       196       31OCT2021       361       PRIOR_CONCOMITANT\n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   cl A    trm A_2/2       01JAN2020      09JUN2020       160       22FEB2021       418       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      04APR2021       459       15OCT2021       653       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      20APR2021       475       10AUG2021       587       PRIOR_CONCOMITANT\n                                                                 cl B    trm B_1/3       01JAN2020      15APR2021       470       23AUG2021       600       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      03OCT2021       641       17OCT2021       655       PRIOR_CONCOMITANT\n                                                                         trm B_3/3       01JAN2020      19OCT2020       292       07DEC2020       341       PRIOR_CONCOMITANT\n                                                                 cl C    trm C_1/2       01JAN2020      11MAY2021       496       19OCT2021       657       PRIOR_CONCOMITANT\n                                                                         trm C_2/2       01JAN2020      09FEB2020        39       28JUN2021       544       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      05APR2020        95       04FEB2021       400       PRIOR_CONCOMITANT\n                                                                 cl D    trm D_1/3       01JAN2020      05MAR2020        64       24NOV2021       693       PRIOR_CONCOMITANT\n               BRA-1/id-93                34/F/ASIAN             cl A    trm A_1/2       20JUN2020      03NOV2020       136       19JAN2021       213       PRIOR_CONCOMITANT\n                                                                 cl B    trm B_1/3       20JUN2020      25NOV2021       523       18DEC2021       546       PRIOR_CONCOMITANT\n                                                                         trm B_3/3       20JUN2020      06FEB2022       596       15FEB2022       605       PRIOR_CONCOMITANT\n                                                                 cl C    trm C_1/2       20JUN2020      28MAR2021       281       11JUL2021       386       PRIOR_CONCOMITANT\n                                                                 cl D    trm D_1/3       20JUN2020      02MAY2021       316       06JAN2022       565       PRIOR_CONCOMITANT\n                                                                         trm D_2/3       20JUN2020      03AUG2020        44       18SEP2020        90       PRIOR_CONCOMITANT\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:37 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadmh &lt;- synthetic_cdisc_dataset(\"latest\", \"admh\")\n\nout &lt;- admh %&gt;%\n  mutate(\n    ASR = paste(AGE, SEX, RACE, sep = \"/\"),\n    ID = paste(SITEID, SUBJID, sep = \"/\"),\n    TRTSDTM = toupper(format(as.Date(TRTSDTM), \"%d%b%Y\")),\n    ASTDTM = toupper(format(as.Date(ASTDTM), \"%d%b%Y\")),\n    AENDTM = toupper(format(as.Date(AENDTM), \"%d%b%Y\"))\n  ) %&gt;%\n  select(ID, ASR, TRT01A, MHBODSYS, MHDECOD, TRTSDTM, ASTDTM, ASTDY, AENDTM, AENDY, ATIREL)\n\nvar_labels(out) &lt;- c(\n  ID = \"Center/Patient ID\",\n  ASR = \"Age/Sex/Race\",\n  TRT01A = \"Treatment\",\n  MHBODSYS = \"SOC\",\n  MHDECOD = \"Disease Term\",\n  TRTSDTM = \"Date of First\\nStudy Drug\\nAdministration\",\n  ASTDTM = \"Start Date\\nof Disease\",\n  ASTDY = \"Start Day\\nof Disease\",\n  AENDTM = \"End Date\\nof Disease\",\n  AENDY = \"End Day\\nof Disease\",\n  ATIREL = \"Time Relation\\nof Disease\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"ID\", \"ASR\", \"MHBODSYS\", \"MHDECOD\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Medical History and Concurrent Diseases\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Medical History and Concurrent Diseases\n\n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                       Date of First                                                                         \n                                                                                         Study Drug     Start Date   Start Day     End Date     End Day       Time Relation  \nTreatment   Center/Patient ID            Age/Sex/Race            SOC    Disease Term   Administration   of Disease   of Disease   of Disease   of Disease      of Disease    \n—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-134                47/M/WHITE             cl A    trm A_2/2       04NOV2020      21SEP2021       321       16FEB2022       469       PRIOR_CONCOMITANT\n                                                                                         04NOV2020      02DEC2021       393       13JAN2022       435       PRIOR_CONCOMITANT\n                                                                 cl B    trm B_2/3       04NOV2020      15APR2021       162       04OCT2021       334       PRIOR_CONCOMITANT\n                                                                 cl D    trm D_2/3       04NOV2020      19MAY2021       196       31OCT2021       361       PRIOR_CONCOMITANT\n               BRA-1/id-42      36/M/BLACK OR AFRICAN AMERICAN   cl A    trm A_2/2       01JAN2020      09JUN2020       160       22FEB2021       418       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      04APR2021       459       15OCT2021       653       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      20APR2021       475       10AUG2021       587       PRIOR_CONCOMITANT\n                                                                 cl B    trm B_1/3       01JAN2020      15APR2021       470       23AUG2021       600       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      03OCT2021       641       17OCT2021       655       PRIOR_CONCOMITANT\n                                                                         trm B_3/3       01JAN2020      19OCT2020       292       07DEC2020       341       PRIOR_CONCOMITANT\n                                                                 cl C    trm C_1/2       01JAN2020      11MAY2021       496       19OCT2021       657       PRIOR_CONCOMITANT\n                                                                         trm C_2/2       01JAN2020      09FEB2020        39       28JUN2021       544       PRIOR_CONCOMITANT\n                                                                                         01JAN2020      05APR2020        95       04FEB2021       400       PRIOR_CONCOMITANT\n                                                                 cl D    trm D_1/3       01JAN2020      05MAR2020        64       24NOV2021       693       PRIOR_CONCOMITANT\n               BRA-1/id-93                34/F/ASIAN             cl A    trm A_1/2       20JUN2020      03NOV2020       136       19JAN2021       213       PRIOR_CONCOMITANT\n                                                                 cl B    trm B_1/3       20JUN2020      25NOV2021       523       18DEC2021       546       PRIOR_CONCOMITANT\n                                                                         trm B_3/3       20JUN2020      06FEB2022       596       15FEB2022       605       PRIOR_CONCOMITANT\n                                                                 cl C    trm C_1/2       20JUN2020      28MAR2021       281       11JUL2021       386       PRIOR_CONCOMITANT\n                                                                 cl D    trm D_1/3       20JUN2020      02MAY2021       316       06JAN2022       565       PRIOR_CONCOMITANT\n                                                                         trm D_2/3       20JUN2020      03AUG2020        44       18SEP2020        90       PRIOR_CONCOMITANT\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:54:59 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Medical History",
@@ -1619,7 +1619,7 @@
     "href": "listings/lab-results/lbl02a.html",
     "title": "LBL02A",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(\n    ANRIND %in% c(\"LOW\", \"HIGH\"),\n    !ATOXGR %in% c(\"0\", \"\"),\n    LBTEST != \"\"\n  ) %&gt;%\n  mutate(\n    LBTEST_U = paste0(LBTEST, \" (\", AVALU, \")\"),\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ADTM = toupper(format(as.Date(ADTM), \"%d%b%Y\")),\n    AVAL = format(round(AVAL, 1), nsmall = 1),\n    LBNRNG = paste(format(round(ANRLO, 1), nsmall = 1), format(round(ANRHI, 1), nsmall = 1), sep = \" - \"),\n    ANRIND_GR = factor(\n      case_when(\n        ANRIND == \"LOW\" ~ paste0(\"L\", sub(\"-\", \"\", ATOXGR)),\n        ANRIND == \"HIGH\" ~ paste0(\"H\", ATOXGR)\n      )\n    )\n  ) %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, ADTM, AVAL, ADY, LBNRNG, ANRIND_GR) %&gt;%\n  unique() %&gt;%\n  arrange(CPID, ADY) %&gt;%\n  group_by(LBTEST_U, CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  mutate(DLD = ifelse(is.na(DLD), 0, DLD))\n\nout &lt;- adlb_x %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, ADTM, DLD, AVAL, LBNRNG, ANRIND_GR)\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    LBTEST_U = \"Lab Test (Unit)\",\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    ADTM = \"Date\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\",\n    AVAL = \"Result\",\n    LBNRNG = \"Lab Normal\\nRange\",\n    ANRIND_GR = \"NCI\\nCTCAE\\nGrade\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"LBTEST_U\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 1\",\n  main_footer = \"NCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 1\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                  Days Since                           NCI \n                                                                             Study               Last Dose of            Lab Normal   CTCAE\nTreatment                Lab Test (Unit)                 Center/Patient ID    Day      Date       Study Drug    Result     Range      Grade\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X   Alanine Aminotransferase Measurement (U/L)     BRA-1/id-105       80     27MAY2020        0           4.3    7.0 - 55.0    L4  \n                                                                              398    10APR2021       318          3.7    7.0 - 55.0    L3  \n                                                            BRA-1/id-93       207    13JAN2021        0           6.1    7.0 - 55.0    L1  \n                                                           BRA-11/id-397      326    19MAY2021        0           1.5    7.0 - 55.0    L1  \n                                                           BRA-14/id-23       594    22JUN2021        0           0.6    7.0 - 55.0    L1  \n                                                           BRA-2/id-296       281    30OCT2021        0           2.6    7.0 - 55.0    L1  \n                                                                              734    26JAN2023       453          3.9    7.0 - 55.0    L1  \n                                                            CAN-1/id-18       465    12APR2022        0           1.8    7.0 - 55.0    L2  \n                                                           CHN-1/id-133       127    13DEC2020        0           5.6    7.0 - 55.0    L2  \n                                                                              197    21FEB2021        70          1.2    7.0 - 55.0    L4  \n                                                           CHN-1/id-199       277    11OCT2020        0           5.3    7.0 - 55.0    L4  \n                                                                              672    10NOV2021       395          6.8    7.0 - 55.0    L2  \n                                                           CHN-1/id-235       348    25JUN2020        0           3.0    7.0 - 55.0    L3  \n                                                                              381    28JUL2020        33          4.7    7.0 - 55.0    L1  \n                                                            CHN-1/id-26       905    07JAN2023        0           1.1    7.0 - 55.0    L1  \n                                                           CHN-1/id-275       364    01JUN2021        0           5.1    7.0 - 55.0    L2  \n                                                           CHN-1/id-277       483    12MAY2022        0           1.1    7.0 - 55.0    L4  \n                                                           CHN-1/id-287       182    23MAY2020        0           5.9    7.0 - 55.0    L2  \n                                                                              191    01JUN2020        9           5.3    7.0 - 55.0    L3  \n                                                                              515    21APR2021       324          2.9    7.0 - 55.0    L4  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:45 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(\n    ANRIND %in% c(\"LOW\", \"HIGH\"),\n    !ATOXGR %in% c(\"0\", \"\"),\n    LBTEST != \"\"\n  ) %&gt;%\n  mutate(\n    LBTEST_U = paste0(LBTEST, \" (\", AVALU, \")\"),\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    ADTM = toupper(format(as.Date(ADTM), \"%d%b%Y\")),\n    AVAL = format(round(AVAL, 1), nsmall = 1),\n    LBNRNG = paste(format(round(ANRLO, 1), nsmall = 1), format(round(ANRHI, 1), nsmall = 1), sep = \" - \"),\n    ANRIND_GR = factor(\n      case_when(\n        ANRIND == \"LOW\" ~ paste0(\"L\", sub(\"-\", \"\", ATOXGR)),\n        ANRIND == \"HIGH\" ~ paste0(\"H\", ATOXGR)\n      )\n    )\n  ) %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, ADTM, AVAL, ADY, LBNRNG, ANRIND_GR) %&gt;%\n  unique() %&gt;%\n  arrange(CPID, ADY) %&gt;%\n  group_by(LBTEST_U, CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  mutate(DLD = ifelse(is.na(DLD), 0, DLD))\n\nout &lt;- adlb_x %&gt;%\n  select(LBTEST_U, TRT01A, CPID, ADY, ADTM, DLD, AVAL, LBNRNG, ANRIND_GR)\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    LBTEST_U = \"Lab Test (Unit)\",\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    ADTM = \"Date\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\",\n    AVAL = \"Result\",\n    LBNRNG = \"Lab Normal\\nRange\",\n    ANRIND_GR = \"NCI\\nCTCAE\\nGrade\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"LBTEST_U\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 1\",\n  main_footer = \"NCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Abnormalities Defined by NCI CTCAE Grade &gt;= 1\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                                                                                  Days Since                           NCI \n                                                                             Study               Last Dose of            Lab Normal   CTCAE\nTreatment                Lab Test (Unit)                 Center/Patient ID    Day      Date       Study Drug    Result     Range      Grade\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X   Alanine Aminotransferase Measurement (U/L)     BRA-1/id-105       80     27MAY2020        0           4.3    7.0 - 55.0    L4  \n                                                                              398    10APR2021       318          3.7    7.0 - 55.0    L3  \n                                                            BRA-1/id-93       207    13JAN2021        0           6.1    7.0 - 55.0    L1  \n                                                           BRA-11/id-397      326    19MAY2021        0           1.5    7.0 - 55.0    L1  \n                                                           BRA-14/id-23       594    22JUN2021        0           0.6    7.0 - 55.0    L1  \n                                                           BRA-2/id-296       281    30OCT2021        0           2.6    7.0 - 55.0    L1  \n                                                                              734    26JAN2023       453          3.9    7.0 - 55.0    L1  \n                                                            CAN-1/id-18       465    12APR2022        0           1.8    7.0 - 55.0    L2  \n                                                           CHN-1/id-133       127    13DEC2020        0           5.6    7.0 - 55.0    L2  \n                                                                              197    21FEB2021        70          1.2    7.0 - 55.0    L4  \n                                                           CHN-1/id-199       277    11OCT2020        0           5.3    7.0 - 55.0    L4  \n                                                                              672    10NOV2021       395          6.8    7.0 - 55.0    L2  \n                                                           CHN-1/id-235       348    25JUN2020        0           3.0    7.0 - 55.0    L3  \n                                                                              381    28JUL2020        33          4.7    7.0 - 55.0    L1  \n                                                            CHN-1/id-26       905    07JAN2023        0           1.1    7.0 - 55.0    L1  \n                                                           CHN-1/id-275       364    01JUN2021        0           5.1    7.0 - 55.0    L2  \n                                                           CHN-1/id-277       483    12MAY2022        0           1.1    7.0 - 55.0    L4  \n                                                           CHN-1/id-287       182    23MAY2020        0           5.9    7.0 - 55.0    L2  \n                                                                              191    01JUN2020        9           5.3    7.0 - 55.0    L3  \n                                                                              515    21APR2021       324          2.9    7.0 - 55.0    L4  \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:06 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1    pkgconfig_2.0.3  \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Lab Results",
@@ -1631,7 +1631,7 @@
     "href": "listings/lab-results/lbl01.html",
     "title": "LBL01",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(!is.na(AVAL) & !is.na(LBSEQ) & !is.na(ADY) & ANRIND != \"\") %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    PARAM_U = paste0(PARAMCD, \"\\n(\", AVALU, \" / NCI CTCAE grade)\"),\n    ANRIND = factor(\n      case_when(\n        ANRIND == \"LOW\" ~ \"L\",\n        ANRIND == \"HIGH\" ~ \"H\",\n        TRUE ~ as.character(ANRIND)\n      ),\n      levels = c(\"\", \"H\", \"L\", \"NORMAL\")\n    ),\n    AVALC = as.character(format(round(AVAL, 2), nsmall = 2))\n  ) %&gt;%\n  mutate(AVAL_GR = ifelse(\n    !ANRIND %in% c(\"H\", \"L\"),\n    AVALC,\n    paste(AVALC, paste0(ANRIND, sub(\"-\", \"\", ATOXGR)), sep = \" / \")\n  )) %&gt;%\n  select(CPID, TRT01A, ADY, LBSEQ, PARAM_U, AVAL_GR) %&gt;%\n  unique()\n\nout &lt;- adlb_x %&gt;%\n  arrange(CPID, ADY, PARAM_U, LBSEQ) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, CPID, ADY),\n    names_from = PARAM_U,\n    values_from = AVAL_GR\n  ) %&gt;%\n  group_by(CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  select(CPID, TRT01A, ADY, DLD, unique(adlb_x$PARAM_U))\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Test Results\",\n  main_footer = \"NCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Test Results\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                         Days Since                                                                                \n                                Study   Last Dose of             ALT                       CRP                        IGA          \nTreatment   Center/Patient ID    Day     Study Drug    (U/L / NCI CTCAE grade)   (mg/L / NCI CTCAE grade)   (g/L / NCI CTCAE grade)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105       80          NA               4.30 / L4                    9.09                       2.84         \n                                 149         69                 24.70                      9.15                       2.93         \n                                 314        165                 24.87                   10.89 / H2                    2.66         \n                                 398         84               3.67 / L3                    8.14                       2.86         \n                                 470         72                 18.55                   10.59 / H3                    2.88         \n                                 528         58                  7.73                   10.09 / H3                    2.93         \n                                 535         7                  23.51                      9.20                       2.88         \n              BRA-1/id-134       225         NA                  7.37                   10.46 / H1                    2.86         \n                                 227         2                  16.42                    7.43 / L1                    2.76         \n                                 255         28                 11.16                      9.41                       2.75         \n                                 269         14                 15.89                   10.02 / H4                    2.84         \n                                 357         88                 24.35                      8.01                       2.86         \n                                 436         79                 16.03                      8.51                       2.98         \n                                 440         4                  17.21                    7.63 / L3                    2.78         \n               BRA-1/id-42       176         NA                 16.56                      8.88                       2.75         \n                                 477        301                 16.49                      8.11                       2.95         \n                                 534         57                 18.71                      8.78                       2.95         \n                                 550         16                 11.44                      9.53                       2.99         \n                                 569         19                 21.66                   10.32 / H2                 3.00 / H1       \n                                 608         39                 24.61                    7.95 / L2                    2.78         \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:56:53 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(rlistings)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_x &lt;- adlb %&gt;%\n  filter(!is.na(AVAL) & !is.na(LBSEQ) & !is.na(ADY) & ANRIND != \"\") %&gt;%\n  mutate(\n    CPID = paste(SITEID, SUBJID, sep = \"/\"),\n    PARAM_U = paste0(PARAMCD, \"\\n(\", AVALU, \" / NCI CTCAE grade)\"),\n    ANRIND = factor(\n      case_when(\n        ANRIND == \"LOW\" ~ \"L\",\n        ANRIND == \"HIGH\" ~ \"H\",\n        TRUE ~ as.character(ANRIND)\n      ),\n      levels = c(\"\", \"H\", \"L\", \"NORMAL\")\n    ),\n    AVALC = as.character(format(round(AVAL, 2), nsmall = 2))\n  ) %&gt;%\n  mutate(AVAL_GR = ifelse(\n    !ANRIND %in% c(\"H\", \"L\"),\n    AVALC,\n    paste(AVALC, paste0(ANRIND, sub(\"-\", \"\", ATOXGR)), sep = \" / \")\n  )) %&gt;%\n  select(CPID, TRT01A, ADY, LBSEQ, PARAM_U, AVAL_GR) %&gt;%\n  unique()\n\nout &lt;- adlb_x %&gt;%\n  arrange(CPID, ADY, PARAM_U, LBSEQ) %&gt;%\n  tidyr::pivot_wider(\n    id_cols = c(TRT01A, CPID, ADY),\n    names_from = PARAM_U,\n    values_from = AVAL_GR\n  ) %&gt;%\n  group_by(CPID) %&gt;%\n  mutate(DLD = ADY - lag(ADY)) %&gt;%\n  ungroup() %&gt;%\n  select(CPID, TRT01A, ADY, DLD, unique(adlb_x$PARAM_U))\n\nvar_labels(out) &lt;- names(out)\nout &lt;- out %&gt;%\n  var_relabel(\n    TRT01A = \"Treatment\",\n    CPID = \"Center/Patient ID\",\n    ADY = \"Study\\nDay\",\n    DLD = \"Days Since\\nLast Dose of\\nStudy Drug\"\n  )\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"TRT01A\", \"CPID\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Laboratory Test Results\",\n  main_footer = \"NCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\"\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Laboratory Test Results\n\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n                                         Days Since                                                                                \n                                Study   Last Dose of             ALT                       CRP                        IGA          \nTreatment   Center/Patient ID    Day     Study Drug    (U/L / NCI CTCAE grade)   (mg/L / NCI CTCAE grade)   (g/L / NCI CTCAE grade)\n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\nA: Drug X     BRA-1/id-105       80          NA               4.30 / L4                    9.09                       2.84         \n                                 149         69                 24.70                      9.15                       2.93         \n                                 314        165                 24.87                   10.89 / H2                    2.66         \n                                 398         84               3.67 / L3                    8.14                       2.86         \n                                 470         72                 18.55                   10.59 / H3                    2.88         \n                                 528         58                  7.73                   10.09 / H3                    2.93         \n                                 535         7                  23.51                      9.20                       2.88         \n              BRA-1/id-134       225         NA                  7.37                   10.46 / H1                    2.86         \n                                 227         2                  16.42                    7.43 / L1                    2.76         \n                                 255         28                 11.16                      9.41                       2.75         \n                                 269         14                 15.89                   10.02 / H4                    2.84         \n                                 357         88                 24.35                      8.01                       2.86         \n                                 436         79                 16.03                      8.51                       2.98         \n                                 440         4                  17.21                    7.63 / L3                    2.78         \n               BRA-1/id-42       176         NA                 16.56                      8.88                       2.75         \n                                 477        301                 16.49                      8.11                       2.95         \n                                 534         57                 18.71                      8.78                       2.95         \n                                 550         16                 11.44                      9.53                       2.99         \n                                 569         19                 21.66                   10.32 / H2                 3.00 / H1       \n                                 608         39                 24.61                    7.95 / L2                    2.78         \n———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————\n\nNCI CTCAE grade is displayed as abnormal high (H) or low (L) followed by the grade.\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:14 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     purrr_1.0.2       generics_0.1.3   \n [9] jsonlite_1.8.8    glue_1.7.0        backports_1.4.1   htmltools_0.5.7  \n[13] brio_1.1.4        fansi_1.0.6       rmarkdown_2.26    grid_4.3.3       \n[17] evaluate_0.23     fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  \n[21] compiler_4.3.3    codetools_0.2-19  htmlwidgets_1.6.4 testthat_3.2.1   \n[25] pkgconfig_2.0.3   tidyr_1.3.1       digest_0.6.34     R6_2.5.1         \n[29] tidyselect_1.2.0  utf8_1.2.4        pillar_1.9.0      magrittr_2.0.3   \n[33] checkmate_2.3.1   withr_3.0.0       tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Lab Results",
@@ -1643,7 +1643,7 @@
     "href": "listings/development-safety-update-report/dsur4.html",
     "title": "DSUR4",
     "section": "",
-    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(DTHFL == \"Y\") %&gt;%\n  mutate(ID = paste(SITEID, SUBJID, sep = \"/\")) %&gt;%\n  select(ARM, ID, DTHCAUS)\ndeath_num &lt;- length(unique(out$ID))\nvar_labels(out) &lt;- c(\n  ARM = \"Treatment Group\",\n  ID = \"Center/Patient ID\",\n  DTHCAUS = \"Cause of Death\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ARM\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Patients Who Died During Reporting Period\",\n  subtitles = paste(\"Number of patient deaths during reporting period =\", death_num)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patients Who Died During Reporting Period\nNumber of patient deaths during reporting period = 70\n\n———————————————————————————————————————————————————————————————————\nTreatment Group   Center/Patient ID          Cause of Death        \n———————————————————————————————————————————————————————————————————\n   A: Drug X        CHN-3/id-128              ADVERSE EVENT        \n                     RUS-1/id-52           DISEASE PROGRESSION     \n                    CHN-1/id-235              ADVERSE EVENT        \n                    CHN-5/id-108                 MISSING           \n                    CHN-17/id-182           LOST TO FOLLOW UP      \n                    BRA-11/id-217             ADVERSE EVENT        \n                    CHN-15/id-14           DISEASE PROGRESSION     \n                    CHN-2/id-274                 SUICIDE           \n                    USA-11/id-339                UNKNOWN           \n                    BRA-15/id-36           DISEASE PROGRESSION     \n                    CHN-1/id-199           DISEASE PROGRESSION     \n                    CHN-2/id-272           DISEASE PROGRESSION     \n                    CHN-1/id-123           DISEASE PROGRESSION     \n                    CHN-12/id-258                SUICIDE           \n                    CHN-17/id-92            LOST TO FOLLOW UP      \n                     NGA-1/id-46           DISEASE PROGRESSION     \n                     BRA-1/id-93              ADVERSE EVENT        \n                     CHN-6/id-30      Post-study reporting of death\n                    CAN-11/id-139             ADVERSE EVENT        \n                    GBR-1/id-319              ADVERSE EVENT        \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:00 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9013\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Listing\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(rlistings)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\nout &lt;- adsl %&gt;%\n  filter(DTHFL == \"Y\") %&gt;%\n  mutate(ID = paste(SITEID, SUBJID, sep = \"/\")) %&gt;%\n  select(ARM, ID, DTHCAUS)\ndeath_num &lt;- length(unique(out$ID))\nvar_labels(out) &lt;- c(\n  ARM = \"Treatment Group\",\n  ID = \"Center/Patient ID\",\n  DTHCAUS = \"Cause of Death\"\n)\n\n\n\n\n\nCodelsting &lt;- as_listing(\n  out,\n  key_cols = c(\"ARM\"),\n  disp_cols = names(out),\n  main_title = \"Listing of Patients Who Died During Reporting Period\",\n  subtitles = paste(\"Number of patient deaths during reporting period =\", death_num)\n)\n\nsorting incoming data by key columns\n\nCodehead(lsting, 20)\n\nListing of Patients Who Died During Reporting Period\nNumber of patient deaths during reporting period = 70\n\n———————————————————————————————————————————————————————————————————\nTreatment Group   Center/Patient ID          Cause of Death        \n———————————————————————————————————————————————————————————————————\n   A: Drug X        CHN-3/id-128              ADVERSE EVENT        \n                     RUS-1/id-52           DISEASE PROGRESSION     \n                    CHN-1/id-235              ADVERSE EVENT        \n                    CHN-5/id-108                 MISSING           \n                    CHN-17/id-182           LOST TO FOLLOW UP      \n                    BRA-11/id-217             ADVERSE EVENT        \n                    CHN-15/id-14           DISEASE PROGRESSION     \n                    CHN-2/id-274                 SUICIDE           \n                    USA-11/id-339                UNKNOWN           \n                    BRA-15/id-36           DISEASE PROGRESSION     \n                    CHN-1/id-199           DISEASE PROGRESSION     \n                    CHN-2/id-272           DISEASE PROGRESSION     \n                    CHN-1/id-123           DISEASE PROGRESSION     \n                    CHN-12/id-258                SUICIDE           \n                    CHN-17/id-92            LOST TO FOLLOW UP      \n                     NGA-1/id-46           DISEASE PROGRESSION     \n                     BRA-1/id-93              ADVERSE EVENT        \n                     CHN-6/id-30      Post-study reporting of death\n                    CAN-11/id-139             ADVERSE EVENT        \n                    GBR-1/id-319              ADVERSE EVENT        \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:21 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] rlistings_0.2.7.9010  tibble_3.2.1          formatters_0.5.5.9015\n[4] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] vctrs_0.6.5       cli_3.6.2         knitr_1.45        rlang_1.1.3      \n [5] xfun_0.42         stringi_1.8.3     generics_0.1.3    jsonlite_1.8.8   \n [9] glue_1.7.0        backports_1.4.1   htmltools_0.5.7   brio_1.1.4       \n[13] fansi_1.0.6       rmarkdown_2.26    grid_4.3.3        evaluate_0.23    \n[17] fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4   compiler_4.3.3   \n[21] codetools_0.2-19  pkgconfig_2.0.3   htmlwidgets_1.6.4 testthat_3.2.1   \n[25] digest_0.6.34     R6_2.5.1          tidyselect_1.2.0  utf8_1.2.4       \n[29] pillar_1.9.0      magrittr_2.0.3    checkmate_2.3.1   withr_3.0.0      \n[33] tools_4.3.3      \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Listings",
       "Development Safety Update Report",
@@ -1655,7 +1655,7 @@
     "href": "graphs/pharmacokinetic/pkpg04.html",
     "title": "PKPG04",
     "section": "",
-    "text": "Data Setup\nPlot of CMAX by Visit\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(ggrepel)\nlibrary(nestcolor)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n\n\n\n\n\nCode# filtered parameter\nadpp &lt;- adpp %&gt;% filter(toupper(PARAMCD) == \"CMAX\" & toupper(PPSPEC) == \"PLASMA\")\n\nuse_title &lt;- paste0(\"Boxplot of \", as.character(unique(adpp$PARAM)), \" by Visit\")\nuse_subtitle &lt;- paste0(\n  \"Analyte:\\nPK Parameter: \",\n  as.character(unique(adpp$PARAM)),\n  \" (\",\n  as.character(unique(adpp$AVALU)),\n  \")\"\n)\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\np &lt;- ggplot(adpp, aes(x = AVISIT, y = AVAL, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(geom = \"point\", fun = \"mean\", col = \"black\", size = 5, shape = 8, position = position_dodge(0.75)) +\n  geom_text_repel(\n    data = . %&gt;% dplyr::group_by(AVISIT, ACTARM) %&gt;% dplyr::filter(AVAL %in% boxplot.stats(AVAL)$out),\n    aes(x = AVISIT, y = AVAL, label = SUBJID, color = ACTARM),\n    size = 3, hjust = -0.2, position = position_dodge(0.75)\n  ) +\n  labs(\n    title = use_title,\n    subtitle = use_subtitle,\n    caption = use_footnote,\n    x = \"Visit\",\n    y = paste0(as.character(unique(adpp$PARAM)), \" (\", as.character(unique(adpp$AVALU)), \")\")\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\nplot &lt;- p + geom_point(aes(fill = ACTARM), size = 2, shape = 21, position = position_dodge(0.75))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:12 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggrepel_0.9.5         ggplot2_3.5.0        \n[4] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[7] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] utf8_1.2.4        generics_0.1.3    tidyr_1.3.1       lattice_0.22-5   \n [5] digest_0.6.34     evaluate_0.23     grid_4.3.3        fastmap_1.1.1    \n [9] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[13] survival_3.5-8    purrr_1.0.2       fansi_1.0.6       scales_1.3.0     \n[17] codetools_0.2-19  Rdpack_2.6        cli_3.6.2         rlang_1.1.3      \n[21] rbibutils_2.2.16  munsell_0.5.0     splines_4.3.3     withr_3.0.0      \n[25] yaml_2.3.8        tools_4.3.3       checkmate_2.3.1   colorspace_2.1-0 \n[29] broom_1.0.5       vctrs_0.6.5       R6_2.5.1          lifecycle_1.0.4  \n[33] htmlwidgets_1.6.4 pkgconfig_2.0.3   pillar_1.9.0      gtable_0.3.4     \n[37] glue_1.7.0        Rcpp_1.0.12       xfun_0.42         tibble_3.2.1     \n[41] tidyselect_1.2.0  knitr_1.45        farver_2.1.1      htmltools_0.5.7  \n[45] rmarkdown_2.26    labeling_0.4.3    testthat_3.2.1    compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot of CMAX by Visit\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(ggrepel)\nlibrary(nestcolor)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n\n\n\n\n\nCode# filtered parameter\nadpp &lt;- adpp %&gt;% filter(toupper(PARAMCD) == \"CMAX\" & toupper(PPSPEC) == \"PLASMA\")\n\nuse_title &lt;- paste0(\"Boxplot of \", as.character(unique(adpp$PARAM)), \" by Visit\")\nuse_subtitle &lt;- paste0(\n  \"Analyte:\\nPK Parameter: \",\n  as.character(unique(adpp$PARAM)),\n  \" (\",\n  as.character(unique(adpp$AVALU)),\n  \")\"\n)\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\np &lt;- ggplot(adpp, aes(x = AVISIT, y = AVAL, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(geom = \"point\", fun = \"mean\", col = \"black\", size = 5, shape = 8, position = position_dodge(0.75)) +\n  geom_text_repel(\n    data = . %&gt;% dplyr::group_by(AVISIT, ACTARM) %&gt;% dplyr::filter(AVAL %in% boxplot.stats(AVAL)$out),\n    aes(x = AVISIT, y = AVAL, label = SUBJID, color = ACTARM),\n    size = 3, hjust = -0.2, position = position_dodge(0.75)\n  ) +\n  labs(\n    title = use_title,\n    subtitle = use_subtitle,\n    caption = use_footnote,\n    x = \"Visit\",\n    y = paste0(as.character(unique(adpp$PARAM)), \" (\", as.character(unique(adpp$AVALU)), \")\")\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\nplot &lt;- p + geom_point(aes(fill = ACTARM), size = 2, shape = 21, position = position_dodge(0.75))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:32 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggrepel_0.9.5         ggplot2_3.5.0        \n[4] dplyr_1.1.4           scda_0.1.6.9017       tern_0.9.3.9024      \n[7] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] utf8_1.2.4        generics_0.1.3    tidyr_1.3.1       lattice_0.22-5   \n [5] digest_0.6.34     evaluate_0.23     grid_4.3.3        fastmap_1.1.1    \n [9] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[13] survival_3.5-8    purrr_1.0.2       fansi_1.0.6       scales_1.3.0     \n[17] codetools_0.2-19  Rdpack_2.6        cli_3.6.2         rlang_1.1.3      \n[21] rbibutils_2.2.16  munsell_0.5.0     splines_4.3.3     withr_3.0.0      \n[25] yaml_2.3.8        tools_4.3.3       checkmate_2.3.1   colorspace_2.1-0 \n[29] broom_1.0.5       vctrs_0.6.5       R6_2.5.1          lifecycle_1.0.4  \n[33] htmlwidgets_1.6.4 pkgconfig_2.0.3   pillar_1.9.0      gtable_0.3.4     \n[37] glue_1.7.0        Rcpp_1.0.12       xfun_0.42         tibble_3.2.1     \n[41] tidyselect_1.2.0  knitr_1.45        farver_2.1.1      htmltools_0.5.7  \n[45] rmarkdown_2.26    labeling_0.4.3    testthat_3.2.1    compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -1667,7 +1667,7 @@
     "href": "graphs/pharmacokinetic/pkcg02.html",
     "title": "PKCG02",
     "section": "",
-    "text": "Data Setup\nPlot in Linear Scale\nPlot in Log Scale\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID)) %&gt;%\n  filter(Patient_ID %in% unique(Patient_ID)[1:10])\n\nuse_title1 &lt;- \"Plot of X Concentration (ug/mL) Over Time by Treatment: \\nPK Evaluable Patients\"\nuse_subtitle1 &lt;- \"Analyte: Plasma X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote1 &lt;- \"Program: \\nOutput:\"\n\nplot &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"AVAL\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title1,\n  subtitle = use_subtitle1,\n  caption = use_footnote1,\n  plotting_choices = \"all_in_one\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID)) %&gt;%\n  mutate(avalog = ifelse(AVAL != 0, AVAL, 0.001)) %&gt;%\n  filter(Patient_ID %in% unique(Patient_ID)[1:10])\n\nuse_title3 &lt;- \"Log-scale Plot of X Concentration (ug/mL) Over Time by Treatment: \\nPK Evaluable Patients\"\nuse_subtitle3 &lt;- \"Analyte: Plasma X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote3 &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"avalog\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title3,\n  caption = use_footnote3,\n  subtitle = use_subtitle3,\n  plotting_choices = \"all_in_one\"\n)\nplot &lt;- result + ggplot2::scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10), labels = c(0.001, 0.01, 0.1, 1, 10))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:25 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    broom_1.0.5       xfun_0.42        \n[29] cli_3.6.2         withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        farver_2.1.1      codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot in Linear Scale\nPlot in Log Scale\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\")\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID)) %&gt;%\n  filter(Patient_ID %in% unique(Patient_ID)[1:10])\n\nuse_title1 &lt;- \"Plot of X Concentration (ug/mL) Over Time by Treatment: \\nPK Evaluable Patients\"\nuse_subtitle1 &lt;- \"Analyte: Plasma X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote1 &lt;- \"Program: \\nOutput:\"\n\nplot &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"AVAL\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title1,\n  subtitle = use_subtitle1,\n  caption = use_footnote1,\n  plotting_choices = \"all_in_one\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeadpc_a &lt;- adpc %&gt;%\n  filter(\n    PARAM == \"Plasma Drug X\",\n    ARMCD == \"ARM A\"\n  ) %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID)) %&gt;%\n  mutate(avalog = ifelse(AVAL != 0, AVAL, 0.001)) %&gt;%\n  filter(Patient_ID %in% unique(Patient_ID)[1:10])\n\nuse_title3 &lt;- \"Log-scale Plot of X Concentration (ug/mL) Over Time by Treatment: \\nPK Evaluable Patients\"\nuse_subtitle3 &lt;- \"Analyte: Plasma X (ug/mL) \\nTreatment Group: ARM A\"\nuse_footnote3 &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_ipp(\n  df = adpc_a,\n  xvar = \"NFRLT\",\n  yvar = \"avalog\",\n  xlab = \"Norminal Time from First Dose (hr)\",\n  ylab = \"Concentration (ug/mL)\",\n  id_var = \"Patient_ID\",\n  add_baseline_hline = FALSE,\n  yvar_baseline = \"AVAL\", #  yvar_baseline cannot be NA or ignore\n  title = use_title3,\n  caption = use_footnote3,\n  subtitle = use_subtitle3,\n  plotting_choices = \"all_in_one\"\n)\nplot &lt;- result + ggplot2::scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10), labels = c(0.001, 0.01, 0.1, 1, 10))\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:45 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1  \n[21] tibble_3.2.1      munsell_0.5.0     pillar_1.9.0      rlang_1.1.3      \n[25] utf8_1.2.4        testthat_3.2.1    broom_1.0.5       xfun_0.42        \n[29] cli_3.6.2         withr_3.0.0       Rdpack_2.6        digest_0.6.34    \n[33] grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23    \n[37] glue_1.7.0        farver_2.1.1      codetools_0.2-19  survival_3.5-8   \n[41] fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2       rmarkdown_2.26   \n[45] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -1679,7 +1679,7 @@
     "href": "graphs/pharmacokinetic/pkcg03.html",
     "title": "PKCG03",
     "section": "",
-    "text": "Data Setup\nPlot in Linear Scale\nPlot in Log Scale\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadsl_f &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n  filter(SAFFL == \"Y\" & ARM != \"B: Placebo\") %&gt;%\n  df_explicit_na()\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\") %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID)) %&gt;%\n  filter(PARAM == \"Plasma Drug X\") %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\n\n\n\n\n\nCodeuse_title &lt;- \"Plot of Mean (+/- SD) Plasma Concentrations Over Time by Treatment, \\nPK Evaluable Patients\"\nuse_subtitle &lt;- \"Analyte:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_lineplot(\n  df = adpc,\n  variables = control_lineplot_vars(\n    x = \"NFRLT\",\n    y = \"AVAL\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM\",\n    y_unit = \"AVALU\",\n    subject_var = \"USUBJID\"\n  ),\n  alt_counts_df = adsl_f,\n  y_lab = \"Concentration\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest()\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0))\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeuse_title &lt;- \"Plot of Mean (+/- SD) Log-Scale Plasma Concentrations Over Time by Treatment, \\nPK Evaluable Patients\"\nuse_subtitle &lt;- \"Analyte:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nadpc &lt;- adpc %&gt;%\n  mutate(avalog = ifelse(AVAL != 0, AVAL, 0.001))\n\nresult &lt;- g_lineplot(\n  df = adpc,\n  variables = control_lineplot_vars(\n    x = \"NFRLT\",\n    y = \"avalog\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM\",\n    y_unit = \"AVALU\",\n    subject_var = \"USUBJID\"\n  ),\n  alt_counts_df = adsl_f,\n  y_lab = \"Concentration\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest()\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0)) +\n  ggplot2::scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10), labels = c(0.001, 0.01, 0.1, 1, 10))\n\nScale for y is already present.\nAdding another scale for y, which will replace the existing scale.\n\nCodeplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:39 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9013\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        farver_2.1.1      codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot in Linear Scale\nPlot in Log Scale\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadsl_f &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n  filter(SAFFL == \"Y\" & ARM != \"B: Placebo\") %&gt;%\n  df_explicit_na()\n\nadpc &lt;- synthetic_cdisc_dataset(\"latest\", \"adpc\") %&gt;%\n  mutate(Patient_ID = sub(\".*id-\", \"\", USUBJID)) %&gt;%\n  filter(PARAM == \"Plasma Drug X\") %&gt;%\n  droplevels() %&gt;%\n  df_explicit_na()\n\n\n\n\n\n\nCodeuse_title &lt;- \"Plot of Mean (+/- SD) Plasma Concentrations Over Time by Treatment, \\nPK Evaluable Patients\"\nuse_subtitle &lt;- \"Analyte:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nresult &lt;- g_lineplot(\n  df = adpc,\n  variables = control_lineplot_vars(\n    x = \"NFRLT\",\n    y = \"AVAL\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM\",\n    y_unit = \"AVALU\",\n    subject_var = \"USUBJID\"\n  ),\n  alt_counts_df = adsl_f,\n  y_lab = \"Concentration\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest()\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0))\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeuse_title &lt;- \"Plot of Mean (+/- SD) Log-Scale Plasma Concentrations Over Time by Treatment, \\nPK Evaluable Patients\"\nuse_subtitle &lt;- \"Analyte:\"\nuse_footnote &lt;- \"Program: \\nOutput:\"\n\nadpc &lt;- adpc %&gt;%\n  mutate(avalog = ifelse(AVAL != 0, AVAL, 0.001))\n\nresult &lt;- g_lineplot(\n  df = adpc,\n  variables = control_lineplot_vars(\n    x = \"NFRLT\",\n    y = \"avalog\",\n    group_var = \"ARM\",\n    paramcd = \"PARAM\",\n    y_unit = \"AVALU\",\n    subject_var = \"USUBJID\"\n  ),\n  alt_counts_df = adsl_f,\n  y_lab = \"Concentration\",\n  y_lab_add_paramcd = FALSE,\n  y_lab_add_unit = TRUE,\n  interval = \"mean_sdi\",\n  whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),\n  title = use_title,\n  subtitle = use_subtitle,\n  caption = use_footnote,\n  ggtheme = theme_nest()\n)\n\nplot &lt;- result + theme(plot.caption = element_text(hjust = 0)) +\n  ggplot2::scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10), labels = c(0.001, 0.01, 0.1, 1, 10))\n\nScale for y is already present.\nAdding another scale for y, which will replace the existing scale.\n\nCodeplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:55:58 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  ggplot2_3.5.0         dplyr_1.1.4          \n[4] scda_0.1.6.9017       tern_0.9.3.9024       rtables_0.6.6.9011   \n[7] magrittr_2.0.3        formatters_0.5.5.9015\n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  tidyr_1.3.1       splines_4.3.3    \n [9] scales_1.3.0      yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5   \n[13] R6_2.5.1          labeling_0.4.3    generics_0.1.3    knitr_1.45       \n[17] forcats_1.0.0     rbibutils_2.2.16  htmlwidgets_1.6.4 backports_1.4.1  \n[21] checkmate_2.3.1   tibble_3.2.1      munsell_0.5.0     pillar_1.9.0     \n[25] rlang_1.1.3       utf8_1.2.4        testthat_3.2.1    broom_1.0.5      \n[29] xfun_0.42         cli_3.6.2         withr_3.0.0       Rdpack_2.6       \n[33] digest_0.6.34     grid_4.3.3        lifecycle_1.0.4   vctrs_0.6.5      \n[37] evaluate_0.23     glue_1.7.0        farver_2.1.1      codetools_0.2-19 \n[41] survival_3.5-8    fansi_1.0.6       colorspace_2.1-0  purrr_1.0.2      \n[45] rmarkdown_2.26    tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -1691,7 +1691,7 @@
     "href": "graphs/pharmacokinetic/pkpg06.html",
     "title": "PKPG06",
     "section": "",
-    "text": "Data Setup\nPlot with Whiskers at ±1.5  Times Inter-Quartile Range\nPlot with Whiskers at  Minimum and Maximum Values\nPlot with Whiskers at  5th and 95th Percentiles\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(tern)\nlibrary(nestcolor)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n# Filter NAs\nadpp &lt;- adpp %&gt;%\n  filter(PPSPEC != \"NA\" & PARAM != \"NA\" & PPCAT != \"NA\") %&gt;%\n  filter(PARAMCD == \"CMAX\" | PARAMCD == \"AUCIFO\", AVISITN == 1)\n\n# filter data by PPCAT and calculate ratio\nanl_x &lt;- adpp %&gt;%\n  filter(PPCAT %in% c(\"Metabolite Drug X\", \"Plasma Drug X\")) %&gt;%\n  pivot_wider(\n    id_cols = c(USUBJID, ACTARM, PARAMCD, AVISIT),\n    names_from = PPCAT,\n    values_from = AVAL\n  ) %&gt;%\n  dplyr::mutate(ratio = `Metabolite Drug X` / `Plasma Drug X`) %&gt;%\n  filter(!is.na(ratio), ratio != Inf)\n\nanl_y &lt;- adpp %&gt;%\n  filter(PPCAT %in% c(\"Metabolite Drug Y\", \"Plasma Drug Y\")) %&gt;%\n  pivot_wider(\n    id_cols = c(USUBJID, ACTARM, PARAMCD, AVISIT),\n    names_from = PPCAT,\n    values_from = AVAL\n  ) %&gt;%\n  dplyr::mutate(ratio = `Metabolite Drug Y` / `Plasma Drug Y`) %&gt;%\n  filter(!is.na(ratio), ratio != Inf)\n\n# functions to calculate custom quantiles and outliers\nquantiles &lt;- function(x) {\n  quant &lt;- quantile(x, probs = c(0.05, 0.25, 0.5, 0.75, 0.95))\n  names(quant) &lt;- c(\"ymin\", \"lower\", \"middle\", \"upper\", \"ymax\")\n  quant\n}\n\noutliers &lt;- function(x) {\n  return(x &lt; quantile(x, 0.05) | x &gt; quantile(x, 0.95))\n}\n\n\n\n\nDrug X Boxplot\n\n\nCodeplot &lt;- ggplot(anl_x, aes(x = PARAMCD, y = ratio, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(\n    geom = \"point\",\n    fun = \"mean\",\n    col = \"black\",\n    size = 5,\n    shape = 8,\n    position = position_dodge(0.75)\n  ) +\n  geom_text(\n    data = . %&gt;% dplyr::group_by(PARAMCD, ACTARM) %&gt;% dplyr::filter(ratio %in% boxplot.stats(ratio)$out),\n    aes(x = PARAMCD, y = ratio, label = USUBJID, col = ACTARM),\n    size = 3,\n    hjust = -0.2,\n    position = position_dodge(0.75),\n    show.legend = FALSE\n  ) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug X, Metabolite Drug X \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_x$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_x$AVISIT)))\n    ),\n    caption = \"Program: \\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# PKPG06\nplot\n\n\n\n\n\n\n\nDrug Y Boxplot\n\n\nCodeplot &lt;- ggplot(anl_y, aes(x = PARAMCD, y = ratio, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(\n    geom = \"point\",\n    fun = \"mean\",\n    col = \"black\",\n    size = 5,\n    shape = 8,\n    position = position_dodge(0.75)\n  ) +\n  geom_text(\n    data = . %&gt;% dplyr::group_by(PARAMCD, ACTARM) %&gt;% dplyr::filter(ratio %in% boxplot.stats(ratio)$out),\n    aes(x = PARAMCD, y = ratio, label = USUBJID, color = ACTARM),\n    size = 3,\n    hjust = -0.2,\n    position = position_dodge(0.75),\n    show.legend = FALSE\n  ) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug Y, Metabolite Drug Y \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_y$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_y$AVISIT)))\n    ),\n    caption = \"Program: \\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# result\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCode# whiskers are formed with the minimum and maximum values\nplot &lt;- ggplot(anl_x, aes(x = PARAMCD, y = ratio, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 0) +\n  stat_boxplot(geom = \"errorbar\", coef = NULL) +\n  stat_summary(geom = \"point\", fun = \"mean\", col = \"black\", size = 5, shape = 8, position = position_dodge(0.75)) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug X, Metabolite Drug X \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_x$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_x$AVISIT)))\n    ),\n    caption = \"Program:\\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# result\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl_x_without_outliers &lt;- anl_x %&gt;%\n  dplyr::group_by(PARAMCD, ACTARM) %&gt;%\n  dplyr::mutate(outlier = ifelse(outliers(ratio), ratio, as.numeric(NA)))\nplot &lt;- ggplot(anl_x, aes(PARAMCD, ratio, fill = ACTARM, label = USUBJID)) +\n  stat_summary(\n    fun.data = quantiles, geom = \"boxplot\",\n    position = position_dodge(1)\n  ) +\n  stat_summary(\n    geom = \"point\",\n    fun = \"mean\",\n    col = \"black\",\n    size = 5,\n    shape = 8,\n    position = position_dodge(1)\n  ) +\n  stat_summary(\n    fun.data = quantiles, geom = \"errorbar\",\n    position = position_dodge(1)\n  ) +\n  geom_point(\n    data = anl_x_without_outliers,\n    aes(x = PARAMCD, y = outlier),\n    na.rm = TRUE,\n    size = 2,\n    position = position_dodge(1),\n    show.legend = FALSE\n  ) +\n  geom_text(\n    data = anl_x_without_outliers,\n    aes(x = PARAMCD, y = outlier, label = USUBJID, color = ACTARM),\n    na.rm = TRUE,\n    size = 3,\n    hjust = -0.2,\n    vjust = 1,\n    position = position_dodge(1),\n    show.legend = FALSE\n  ) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug X, Metabolite Drug X \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_x$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_x$AVISIT)))\n    ),\n    caption = \"Program: \\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# result\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:57:54 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9013 tidyr_1.3.1          \n[7] ggplot2_3.5.0         scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  splines_4.3.3     scales_1.3.0     \n [9] yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5    R6_2.5.1         \n[13] labeling_0.4.3    generics_0.1.3    knitr_1.45        rbibutils_2.2.16 \n[17] htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1   tibble_3.2.1     \n[21] munsell_0.5.0     pillar_1.9.0      rlang_1.1.3       utf8_1.2.4       \n[25] testthat_3.2.1    broom_1.0.5       xfun_0.42         cli_3.6.2        \n[29] withr_3.0.0       Rdpack_2.6        digest_0.6.34     grid_4.3.3       \n[33] lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23     glue_1.7.0       \n[37] farver_2.1.1      codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  rmarkdown_2.26    purrr_1.0.2       tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot with Whiskers at ±1.5  Times Inter-Quartile Range\nPlot with Whiskers at  Minimum and Maximum Values\nPlot with Whiskers at  5th and 95th Percentiles\nteal App\nReproducibility\n\n\n\n\nCodelibrary(dplyr)\nlibrary(scda)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(tern)\nlibrary(nestcolor)\n\nadpp &lt;- synthetic_cdisc_dataset(\"latest\", \"adpp\")\n\n# Filter NAs\nadpp &lt;- adpp %&gt;%\n  filter(PPSPEC != \"NA\" & PARAM != \"NA\" & PPCAT != \"NA\") %&gt;%\n  filter(PARAMCD == \"CMAX\" | PARAMCD == \"AUCIFO\", AVISITN == 1)\n\n# filter data by PPCAT and calculate ratio\nanl_x &lt;- adpp %&gt;%\n  filter(PPCAT %in% c(\"Metabolite Drug X\", \"Plasma Drug X\")) %&gt;%\n  pivot_wider(\n    id_cols = c(USUBJID, ACTARM, PARAMCD, AVISIT),\n    names_from = PPCAT,\n    values_from = AVAL\n  ) %&gt;%\n  dplyr::mutate(ratio = `Metabolite Drug X` / `Plasma Drug X`) %&gt;%\n  filter(!is.na(ratio), ratio != Inf)\n\nanl_y &lt;- adpp %&gt;%\n  filter(PPCAT %in% c(\"Metabolite Drug Y\", \"Plasma Drug Y\")) %&gt;%\n  pivot_wider(\n    id_cols = c(USUBJID, ACTARM, PARAMCD, AVISIT),\n    names_from = PPCAT,\n    values_from = AVAL\n  ) %&gt;%\n  dplyr::mutate(ratio = `Metabolite Drug Y` / `Plasma Drug Y`) %&gt;%\n  filter(!is.na(ratio), ratio != Inf)\n\n# functions to calculate custom quantiles and outliers\nquantiles &lt;- function(x) {\n  quant &lt;- quantile(x, probs = c(0.05, 0.25, 0.5, 0.75, 0.95))\n  names(quant) &lt;- c(\"ymin\", \"lower\", \"middle\", \"upper\", \"ymax\")\n  quant\n}\n\noutliers &lt;- function(x) {\n  return(x &lt; quantile(x, 0.05) | x &gt; quantile(x, 0.95))\n}\n\n\n\n\nDrug X Boxplot\n\n\nCodeplot &lt;- ggplot(anl_x, aes(x = PARAMCD, y = ratio, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(\n    geom = \"point\",\n    fun = \"mean\",\n    col = \"black\",\n    size = 5,\n    shape = 8,\n    position = position_dodge(0.75)\n  ) +\n  geom_text(\n    data = . %&gt;% dplyr::group_by(PARAMCD, ACTARM) %&gt;% dplyr::filter(ratio %in% boxplot.stats(ratio)$out),\n    aes(x = PARAMCD, y = ratio, label = USUBJID, col = ACTARM),\n    size = 3,\n    hjust = -0.2,\n    position = position_dodge(0.75),\n    show.legend = FALSE\n  ) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug X, Metabolite Drug X \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_x$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_x$AVISIT)))\n    ),\n    caption = \"Program: \\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# PKPG06\nplot\n\n\n\n\n\n\n\nDrug Y Boxplot\n\n\nCodeplot &lt;- ggplot(anl_y, aes(x = PARAMCD, y = ratio, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 2) +\n  stat_boxplot(geom = \"errorbar\") +\n  stat_summary(\n    geom = \"point\",\n    fun = \"mean\",\n    col = \"black\",\n    size = 5,\n    shape = 8,\n    position = position_dodge(0.75)\n  ) +\n  geom_text(\n    data = . %&gt;% dplyr::group_by(PARAMCD, ACTARM) %&gt;% dplyr::filter(ratio %in% boxplot.stats(ratio)$out),\n    aes(x = PARAMCD, y = ratio, label = USUBJID, color = ACTARM),\n    size = 3,\n    hjust = -0.2,\n    position = position_dodge(0.75),\n    show.legend = FALSE\n  ) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug Y, Metabolite Drug Y \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_y$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_y$AVISIT)))\n    ),\n    caption = \"Program: \\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# result\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCode# whiskers are formed with the minimum and maximum values\nplot &lt;- ggplot(anl_x, aes(x = PARAMCD, y = ratio, fill = ACTARM)) +\n  geom_boxplot(outlier.size = 0) +\n  stat_boxplot(geom = \"errorbar\", coef = NULL) +\n  stat_summary(geom = \"point\", fun = \"mean\", col = \"black\", size = 5, shape = 8, position = position_dodge(0.75)) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug X, Metabolite Drug X \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_x$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_x$AVISIT)))\n    ),\n    caption = \"Program:\\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# result\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl_x_without_outliers &lt;- anl_x %&gt;%\n  dplyr::group_by(PARAMCD, ACTARM) %&gt;%\n  dplyr::mutate(outlier = ifelse(outliers(ratio), ratio, as.numeric(NA)))\nplot &lt;- ggplot(anl_x, aes(PARAMCD, ratio, fill = ACTARM, label = USUBJID)) +\n  stat_summary(\n    fun.data = quantiles, geom = \"boxplot\",\n    position = position_dodge(1)\n  ) +\n  stat_summary(\n    geom = \"point\",\n    fun = \"mean\",\n    col = \"black\",\n    size = 5,\n    shape = 8,\n    position = position_dodge(1)\n  ) +\n  stat_summary(\n    fun.data = quantiles, geom = \"errorbar\",\n    position = position_dodge(1)\n  ) +\n  geom_point(\n    data = anl_x_without_outliers,\n    aes(x = PARAMCD, y = outlier),\n    na.rm = TRUE,\n    size = 2,\n    position = position_dodge(1),\n    show.legend = FALSE\n  ) +\n  geom_text(\n    data = anl_x_without_outliers,\n    aes(x = PARAMCD, y = outlier, label = USUBJID, color = ACTARM),\n    na.rm = TRUE,\n    size = 3,\n    hjust = -0.2,\n    vjust = 1,\n    position = position_dodge(1),\n    show.legend = FALSE\n  ) +\n  labs(\n    title = \"Boxplot of Metabolite to Parent Ratios by Treatment\",\n    subtitle = paste0(\n      \"Analyte: Plasma Drug X, Metabolite Drug X \",\n      \"\\nPK Parameter: \",\n      as.character(paste(unique(anl_x$PARAMCD), collapse = \", \")),\n      \"\\nVisit: \",\n      as.character((unique(anl_x$AVISIT)))\n    ),\n    caption = \"Program: \\nOutput:\",\n    x = \"Parameter\",\n    y = \"Metabolite to Parent Ratio\"\n  ) +\n  theme(plot.caption = element_text(hjust = 0)) +\n  theme_nest()\n\n# result\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:56:14 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  tern_0.9.3.9024       rtables_0.6.6.9011   \n[4] magrittr_2.0.3        formatters_0.5.5.9015 tidyr_1.3.1          \n[7] ggplot2_3.5.0         scda_0.1.6.9017       dplyr_1.1.4          \n\nloaded via a namespace (and not attached):\n [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   \n [5] brio_1.1.4        tidyselect_1.2.0  splines_4.3.3     scales_1.3.0     \n [9] yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5    R6_2.5.1         \n[13] labeling_0.4.3    generics_0.1.3    knitr_1.45        rbibutils_2.2.16 \n[17] htmlwidgets_1.6.4 backports_1.4.1   checkmate_2.3.1   tibble_3.2.1     \n[21] munsell_0.5.0     pillar_1.9.0      rlang_1.1.3       utf8_1.2.4       \n[25] testthat_3.2.1    broom_1.0.5       xfun_0.42         cli_3.6.2        \n[29] withr_3.0.0       Rdpack_2.6        digest_0.6.34     grid_4.3.3       \n[33] lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23     glue_1.7.0       \n[37] farver_2.1.1      codetools_0.2-19  survival_3.5-8    fansi_1.0.6      \n[41] colorspace_2.1-0  rmarkdown_2.26    purrr_1.0.2       tools_4.3.3      \n[45] pkgconfig_2.0.3   htmltools_0.5.7  \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Pharmacokinetic",
@@ -1703,7 +1703,7 @@
     "href": "graphs/efficacy/fstg01.html",
     "title": "FSTG01",
     "section": "",
-    "text": "Data Setup\nStandard Plot\nPlot Specifying Class Variables and  Options for the Treatment Variable\nPlot Selecting Columns and  Changing the Alpha Level\nPlot with Fixed  Symbol Size\nPlot of CR Only, Setting  Values Indicating Response\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(nestcolor)\n\nadsl_f &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n  select(STUDYID, USUBJID, ARMCD, ARM, SEX, BMRKR2, STRATA1, STRATA2)\n\nadrs_f &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\") %&gt;%\n  filter(PARAMCD == \"INVET\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVISIT, AVALC)\n\nanl &lt;- inner_join(adsl_f, adrs_f, by = c(\"STUDYID\", \"USUBJID\"))\nanl &lt;- df_explicit_na(anl)\nanl_labels &lt;- var_labels(anl)\n\nanl_rsp_arms_ab &lt;- anl %&gt;%\n  mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) %&gt;%\n  filter(ARMCD %in% c(\"ARM B\", \"ARM A\")) %&gt;%\n  mutate(\n    ARMCD = relevel(ARMCD, ref = \"ARM B\") %&gt;%\n      droplevels(),\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels()\n  ) %&gt;%\n  droplevels()\nvar_labels(anl_rsp_arms_ab) &lt;- c(anl_labels, is_rsp = \"Is Responder\")\n\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_arms_ab,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodeanl_rsp_comb_arms_ac &lt;- anl %&gt;%\n  mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) %&gt;%\n  filter(ARMCD %in% c(\"ARM B\", \"ARM A\", \"ARM C\")) %&gt;%\n  mutate(\n    ARMCD = relevel(ARMCD, ref = \"ARM B\") %&gt;%\n      droplevels() %&gt;%\n      combine_levels(levels = c(\"ARM A\", \"ARM C\")),\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels() %&gt;%\n      combine_levels(levels = c(\"A: Drug X\", \"C: Combination\")),\n    # reorder levels of SEX\n    SEX = forcats::fct_relevel(SEX, \"M\", \"F\"),\n    # reorder levels of STRATA1 by frequency\n    STRATA1 = forcats::fct_infreq(STRATA1)\n  ) %&gt;%\n  droplevels()\nvar_labels(anl_rsp_comb_arms_ac) &lt;- c(anl_labels, is_rsp = \"Is Responder\")\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARMCD\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_comb_arms_ac,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_arms_ab,\n  conf_level = 0.90\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_arms_ab,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(\n  tbl = result,\n  col_symbol_size = NULL\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodeanl_cr_arms_ab &lt;- anl %&gt;%\n  mutate(is_rsp = AVALC == \"CR\") %&gt;%\n  filter(ARMCD %in% c(\"ARM B\", \"ARM A\")) %&gt;%\n  mutate(\n    ARMCD = relevel(ARMCD, ref = \"ARM B\") %&gt;%\n      droplevels(),\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels()\n  ) %&gt;%\n  droplevels()\nvar_labels(anl_cr_arms_ab) &lt;- c(anl_labels, is_rsp = \"Is CR\")\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_cr_arms_ab,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADRS &lt;- data[[\"ADRS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_forest_rsp(\n      label = \"Forest Response\",\n      dataname = \"ADRS\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        \"ARMCD\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        \"BESRSPI\"\n      ),\n      subgroup_var = choices_selected(\n        variable_choices(ADSL, names(ADSL)),\n        c(\"BMRKR2\", \"SEX\")\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"STRATA1\", \"STRATA2\")),\n        \"STRATA2\"\n      ),\n      plot_height = c(600L, 200L, 2000L),\n      plot_width = c(1100L, 200L, 2000L)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:58:34 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9013           \n[13] dplyr_1.1.4                      scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            cowplot_1.1.3            grid_4.3.3              \n[76] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[79] nlme_3.1-164             cli_3.6.2                textshaping_0.3.7       \n[82] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[85] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[88] htmlwidgets_1.6.4        farver_2.1.1             memoise_2.0.1           \n[91] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[94] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Plot\nPlot Specifying Class Variables and  Options for the Treatment Variable\nPlot Selecting Columns and  Changing the Alpha Level\nPlot with Fixed  Symbol Size\nPlot of CR Only, Setting  Values Indicating Response\nteal App\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern)\nlibrary(nestcolor)\n\nadsl_f &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\") %&gt;%\n  select(STUDYID, USUBJID, ARMCD, ARM, SEX, BMRKR2, STRATA1, STRATA2)\n\nadrs_f &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\") %&gt;%\n  filter(PARAMCD == \"INVET\") %&gt;%\n  select(STUDYID, USUBJID, PARAMCD, AVISIT, AVALC)\n\nanl &lt;- inner_join(adsl_f, adrs_f, by = c(\"STUDYID\", \"USUBJID\"))\nanl &lt;- df_explicit_na(anl)\nanl_labels &lt;- var_labels(anl)\n\nanl_rsp_arms_ab &lt;- anl %&gt;%\n  mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) %&gt;%\n  filter(ARMCD %in% c(\"ARM B\", \"ARM A\")) %&gt;%\n  mutate(\n    ARMCD = relevel(ARMCD, ref = \"ARM B\") %&gt;%\n      droplevels(),\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels()\n  ) %&gt;%\n  droplevels()\nvar_labels(anl_rsp_arms_ab) &lt;- c(anl_labels, is_rsp = \"Is Responder\")\n\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_arms_ab,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodeanl_rsp_comb_arms_ac &lt;- anl %&gt;%\n  mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) %&gt;%\n  filter(ARMCD %in% c(\"ARM B\", \"ARM A\", \"ARM C\")) %&gt;%\n  mutate(\n    ARMCD = relevel(ARMCD, ref = \"ARM B\") %&gt;%\n      droplevels() %&gt;%\n      combine_levels(levels = c(\"ARM A\", \"ARM C\")),\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels() %&gt;%\n      combine_levels(levels = c(\"A: Drug X\", \"C: Combination\")),\n    # reorder levels of SEX\n    SEX = forcats::fct_relevel(SEX, \"M\", \"F\"),\n    # reorder levels of STRATA1 by frequency\n    STRATA1 = forcats::fct_infreq(STRATA1)\n  ) %&gt;%\n  droplevels()\nvar_labels(anl_rsp_comb_arms_ac) &lt;- c(anl_labels, is_rsp = \"Is Responder\")\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARMCD\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_comb_arms_ac,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_arms_ab,\n  conf_level = 0.90\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodedf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_rsp_arms_ab,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(\n  tbl = result,\n  col_symbol_size = NULL\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\n\nCodeanl_cr_arms_ab &lt;- anl %&gt;%\n  mutate(is_rsp = AVALC == \"CR\") %&gt;%\n  filter(ARMCD %in% c(\"ARM B\", \"ARM A\")) %&gt;%\n  mutate(\n    ARMCD = relevel(ARMCD, ref = \"ARM B\") %&gt;%\n      droplevels(),\n    ARM = relevel(ARM, ref = \"B: Placebo\") %&gt;%\n      droplevels()\n  ) %&gt;%\n  droplevels()\nvar_labels(anl_cr_arms_ab) &lt;- c(anl_labels, is_rsp = \"Is CR\")\n\ndf &lt;- extract_rsp_subgroups(\n  variables = list(\n    rsp = \"is_rsp\",\n    arm = \"ARM\",\n    subgroups = c(\"SEX\", \"BMRKR2\"),\n    strata = \"STRATA2\"\n  ),\n  data = anl_cr_arms_ab,\n  conf_level = 0.95\n)\n\nresult &lt;- basic_table() %&gt;%\n  tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\"))\n\nplot &lt;- g_forest(tbl = result)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADRS &lt;- synthetic_cdisc_dataset(\"latest\", \"adrs\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADRS\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\narm_ref_comp &lt;- list(\n  ARM = list(\n    ref = \"B: Placebo\",\n    comp = c(\"A: Drug X\", \"C: Combination\")\n  ),\n  ARMCD = list(\n    ref = \"ARM B\",\n    comp = c(\"ARM A\", \"ARM C\")\n  )\n)\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADRS &lt;- data[[\"ADRS\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_forest_rsp(\n      label = \"Forest Response\",\n      dataname = \"ADRS\",\n      arm_var = choices_selected(\n        variable_choices(ADSL, c(\"ARM\", \"ARMCD\")),\n        \"ARMCD\"\n      ),\n      arm_ref_comp = arm_ref_comp,\n      paramcd = choices_selected(\n        value_choices(ADRS, \"PARAMCD\", \"PARAM\"),\n        \"BESRSPI\"\n      ),\n      subgroup_var = choices_selected(\n        variable_choices(ADSL, names(ADSL)),\n        c(\"BMRKR2\", \"SEX\")\n      ),\n      strata_var = choices_selected(\n        variable_choices(ADSL, c(\"STRATA1\", \"STRATA2\")),\n        \"STRATA2\"\n      ),\n      plot_height = c(600L, 200L, 2000L),\n      plot_width = c(1100L, 200L, 2000L)\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:56:54 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] tern_0.9.3.9024                  rtables_0.6.6.9011              \n[11] magrittr_2.0.3                   formatters_0.5.5.9015           \n[13] dplyr_1.1.4                      scda_0.1.6.9017                 \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           ggplot2_3.5.0           \n[64] munsell_0.5.0            scales_1.3.0             xtable_1.8-4            \n[67] glue_1.7.0               emmeans_1.10.0           tools_4.3.3             \n[70] webshot2_0.1.1           webshot_0.5.5            forcats_1.0.0           \n[73] mvtnorm_1.2-4            cowplot_1.1.3            grid_4.3.3              \n[76] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[79] nlme_3.1-164             cli_3.6.2                textshaping_0.3.7       \n[82] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[85] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[88] htmlwidgets_1.6.4        farver_2.1.1             memoise_2.0.1           \n[91] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[94] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Efficacy",
@@ -1715,7 +1715,7 @@
     "href": "graphs/efficacy/mmrmg02.html",
     "title": "MMRMG02",
     "section": "",
-    "text": "Data Setup\nComparing Multiple Populations\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern.mmrm)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\nadqs_f &lt;- adqs %&gt;%\n  dplyr::filter(PARAMCD == \"FKSI-FWB\" & !AVISIT %in% c(\"BASELINE\")) %&gt;%\n  droplevels() %&gt;%\n  dplyr::mutate(ARMCD = factor(ARMCD, levels = c(\"ARM B\", \"ARM A\", \"ARM C\"))) %&gt;%\n  dplyr::mutate(\n    AVISITN = rank(AVISITN) %&gt;%\n      as.factor() %&gt;%\n      as.numeric() %&gt;%\n      as.factor()\n  )\n\n# Simulation of groups.\nset.seed(2)\nadqs_f_with_groups &lt;- rbind(\n  within(\n    adqs_f[sample(seq_len(nrow(adqs_f)), size = 1 / 2 * nrow(adqs_f)), ],\n    group &lt;- \"subpopulation 1\"\n  ),\n  within(\n    adqs_f,\n    {\n      group &lt;- \"subpopulation 2\"\n      AVAL &lt;- AVAL + rnorm(length(AVAL), mean = 10, sd = 2)\n      USUBJID &lt;- paste0(USUBJID, \"-S2\")\n    }\n  )\n)\nadqs_f_with_groups$group &lt;- factor(adqs_f_with_groups$group)\n\n\n\n\nFor the following part, an MMRM model is fitted for the dataset and from this result forest plots are constructed for each set of subgroups, treatment arm, and visit that is of interest.\nFirst an MMRM model is fitted for the whole dataset.\n\nCodemmrm_results &lt;- fit_mmrm(\n  data = adqs_f_with_groups,\n  vars = list(\n    response = \"AVAL\",\n    covariates = c(),\n    id = \"USUBJID\",\n    arm = \"ARMCD\",\n    visit = \"AVISIT\"\n  ),\n  cor_struct = \"unstructured\",\n  weights_emmeans = \"equal\",\n  parallel = TRUE\n)\n\nmmrm() registered as emmeans extension\n\n\nApplying the tern.mmrm::extract_mmrm_subgroups function prepares the obtained mmrm_results for a specific visit - in this case we use the SCREENING visit - and treatment arm relative to the reference arm. From these results a table is obtained using the tern.mmrm::tabulate_mmrm_subgroups function from which the graphic can be directly obtained with tern::g_forest.\nHere we compare ARM A with the reference arm, ARM B.\n\nCodedf_a &lt;- extract_mmrm_subgroups(\n  fit = mmrm_results,\n  visit = \"SCREENING\",\n  subgroups = c(\"group\", \"SEX\"),\n  treatment_arm = \"ARM A\"\n)\n\ntab_a &lt;- basic_table() %&gt;%\n  tabulate_mmrm_subgroups(\n    df = df_a,\n    vars = c(\"n_tot\", \"diff\", \"ci\", \"pval\")\n  )\n\nplot &lt;- g_forest(\n  tab_a,\n  logx = FALSE,\n  xlim = c(-5, 2.5),\n  x_at = c(-5, -2.5, 0, 2.5),\n  vline = 0\n)\nplot\n\n\n\n\n\n\n\nThen we compare ARM C with ARM B.\n\nCodedf_c &lt;- extract_mmrm_subgroups(\n  fit = mmrm_results,\n  visit = \"SCREENING\",\n  subgroups = c(\"group\", \"SEX\"),\n  treatment_arm = \"ARM C\"\n)\n\ntab_c &lt;- basic_table() %&gt;%\n  tabulate_mmrm_subgroups(\n    df = df_c,\n    vars = c(\"n_tot\", \"diff\", \"ci\", \"pval\")\n  )\n\nplot &lt;- g_forest(\n  tab_c,\n  logx = FALSE,\n  xlim = c(-5, 2.5),\n  x_at = c(-5, -2.5, 0, 2.5),\n  vline = 0\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 17:59:28 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  tern.mmrm_0.3.0.9005  tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013\n[7] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] gtable_0.3.4      TMB_1.9.10        xfun_0.42         ggplot2_3.5.0    \n [5] htmlwidgets_1.6.4 lattice_0.22-5    vctrs_0.6.5       tools_4.3.3      \n [9] Rdpack_2.6        generics_0.1.3    parallel_4.3.3    sandwich_3.1-0   \n[13] tibble_3.2.1      mmrm_0.3.11       fansi_1.0.6       pkgconfig_2.0.3  \n[17] Matrix_1.6-5      checkmate_2.3.1   lifecycle_1.0.4   farver_2.1.1     \n[21] compiler_4.3.3    stringr_1.5.1     brio_1.1.4        munsell_0.5.0    \n[25] codetools_0.2-19  htmltools_0.5.7   yaml_2.3.8        pillar_1.9.0     \n[29] tidyr_1.3.1       MASS_7.3-60.0.1   multcomp_1.4-25   nlme_3.1-164     \n[33] parallelly_1.37.1 tidyselect_1.2.0  digest_0.6.34     mvtnorm_1.2-4    \n[37] stringi_1.8.3     purrr_1.0.2       labeling_0.4.3    splines_4.3.3    \n[41] cowplot_1.1.3     fastmap_1.1.1     grid_4.3.3        colorspace_2.1-0 \n[45] cli_3.6.2         survival_3.5-8    utf8_1.2.4        broom_1.0.5      \n[49] TH.data_1.1-2     withr_3.0.0       scales_1.3.0      backports_1.4.1  \n[53] estimability_1.5  rmarkdown_2.26    emmeans_1.10.0    zoo_1.8-12       \n[57] coda_0.19-4.1     evaluate_0.23     knitr_1.45        rbibutils_2.2.16 \n[61] testthat_3.2.1    rlang_1.1.3       Rcpp_1.0.12       xtable_1.8-4     \n[65] glue_1.7.0        jsonlite_1.8.8    R6_2.5.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nComparing Multiple Populations\nReproducibility\n\n\n\n\nCodelibrary(scda)\nlibrary(dplyr)\nlibrary(tern.mmrm)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadqs &lt;- synthetic_cdisc_dataset(\"latest\", \"adqs\")\n\nadqs_f &lt;- adqs %&gt;%\n  dplyr::filter(PARAMCD == \"FKSI-FWB\" & !AVISIT %in% c(\"BASELINE\")) %&gt;%\n  droplevels() %&gt;%\n  dplyr::mutate(ARMCD = factor(ARMCD, levels = c(\"ARM B\", \"ARM A\", \"ARM C\"))) %&gt;%\n  dplyr::mutate(\n    AVISITN = rank(AVISITN) %&gt;%\n      as.factor() %&gt;%\n      as.numeric() %&gt;%\n      as.factor()\n  )\n\n# Simulation of groups.\nset.seed(2)\nadqs_f_with_groups &lt;- rbind(\n  within(\n    adqs_f[sample(seq_len(nrow(adqs_f)), size = 1 / 2 * nrow(adqs_f)), ],\n    group &lt;- \"subpopulation 1\"\n  ),\n  within(\n    adqs_f,\n    {\n      group &lt;- \"subpopulation 2\"\n      AVAL &lt;- AVAL + rnorm(length(AVAL), mean = 10, sd = 2)\n      USUBJID &lt;- paste0(USUBJID, \"-S2\")\n    }\n  )\n)\nadqs_f_with_groups$group &lt;- factor(adqs_f_with_groups$group)\n\n\n\n\nFor the following part, an MMRM model is fitted for the dataset and from this result forest plots are constructed for each set of subgroups, treatment arm, and visit that is of interest.\nFirst an MMRM model is fitted for the whole dataset.\n\nCodemmrm_results &lt;- fit_mmrm(\n  data = adqs_f_with_groups,\n  vars = list(\n    response = \"AVAL\",\n    covariates = c(),\n    id = \"USUBJID\",\n    arm = \"ARMCD\",\n    visit = \"AVISIT\"\n  ),\n  cor_struct = \"unstructured\",\n  weights_emmeans = \"equal\",\n  parallel = TRUE\n)\n\nmmrm() registered as emmeans extension\n\n\nApplying the tern.mmrm::extract_mmrm_subgroups function prepares the obtained mmrm_results for a specific visit - in this case we use the SCREENING visit - and treatment arm relative to the reference arm. From these results a table is obtained using the tern.mmrm::tabulate_mmrm_subgroups function from which the graphic can be directly obtained with tern::g_forest.\nHere we compare ARM A with the reference arm, ARM B.\n\nCodedf_a &lt;- extract_mmrm_subgroups(\n  fit = mmrm_results,\n  visit = \"SCREENING\",\n  subgroups = c(\"group\", \"SEX\"),\n  treatment_arm = \"ARM A\"\n)\n\ntab_a &lt;- basic_table() %&gt;%\n  tabulate_mmrm_subgroups(\n    df = df_a,\n    vars = c(\"n_tot\", \"diff\", \"ci\", \"pval\")\n  )\n\nplot &lt;- g_forest(\n  tab_a,\n  logx = FALSE,\n  xlim = c(-5, 2.5),\n  x_at = c(-5, -2.5, 0, 2.5),\n  vline = 0\n)\nplot\n\n\n\n\n\n\n\nThen we compare ARM C with ARM B.\n\nCodedf_c &lt;- extract_mmrm_subgroups(\n  fit = mmrm_results,\n  visit = \"SCREENING\",\n  subgroups = c(\"group\", \"SEX\"),\n  treatment_arm = \"ARM C\"\n)\n\ntab_c &lt;- basic_table() %&gt;%\n  tabulate_mmrm_subgroups(\n    df = df_c,\n    vars = c(\"n_tot\", \"diff\", \"ci\", \"pval\")\n  )\n\nplot &lt;- g_forest(\n  tab_c,\n  logx = FALSE,\n  xlim = c(-5, 2.5),\n  x_at = c(-5, -2.5, 0, 2.5),\n  vline = 0\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:57:46 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n[1] nestcolor_0.1.2.9009  tern.mmrm_0.3.0.9005  tern_0.9.3.9024      \n[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015\n[7] dplyr_1.1.4           scda_0.1.6.9017      \n\nloaded via a namespace (and not attached):\n [1] gtable_0.3.4      TMB_1.9.10        xfun_0.42         ggplot2_3.5.0    \n [5] htmlwidgets_1.6.4 lattice_0.22-5    vctrs_0.6.5       tools_4.3.3      \n [9] Rdpack_2.6        generics_0.1.3    parallel_4.3.3    sandwich_3.1-0   \n[13] tibble_3.2.1      mmrm_0.3.11       fansi_1.0.6       pkgconfig_2.0.3  \n[17] Matrix_1.6-5      checkmate_2.3.1   lifecycle_1.0.4   farver_2.1.1     \n[21] compiler_4.3.3    stringr_1.5.1     brio_1.1.4        munsell_0.5.0    \n[25] codetools_0.2-19  htmltools_0.5.7   yaml_2.3.8        pillar_1.9.0     \n[29] tidyr_1.3.1       MASS_7.3-60.0.1   multcomp_1.4-25   nlme_3.1-164     \n[33] parallelly_1.37.1 tidyselect_1.2.0  digest_0.6.34     mvtnorm_1.2-4    \n[37] stringi_1.8.3     purrr_1.0.2       labeling_0.4.3    splines_4.3.3    \n[41] cowplot_1.1.3     fastmap_1.1.1     grid_4.3.3        colorspace_2.1-0 \n[45] cli_3.6.2         survival_3.5-8    utf8_1.2.4        broom_1.0.5      \n[49] TH.data_1.1-2     withr_3.0.0       scales_1.3.0      backports_1.4.1  \n[53] estimability_1.5  rmarkdown_2.26    emmeans_1.10.0    zoo_1.8-12       \n[57] coda_0.19-4.1     evaluate_0.23     knitr_1.45        rbibutils_2.2.16 \n[61] testthat_3.2.1    rlang_1.1.3       Rcpp_1.0.12       xtable_1.8-4     \n[65] glue_1.7.0        jsonlite_1.8.8    R6_2.5.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Efficacy",
@@ -1727,7 +1727,7 @@
     "href": "graphs/other/bwg01.html",
     "title": "BWG01",
     "section": "",
-    "text": "Data Setup\nStandard Plot\nPlot Changing  Whiskers\nPlot Adding  Outliers\nPlot Specifying Marker  for Outliers and  Adding Patient ID\nPlot Specifying  Marker for Mean\nPlot by Treatment  and Timepoint\nPlot by Timepoint  and Treatment\nPlot with  Table Section\nPlot with Number of Patients  only in Table Section\nteal App\nReproducibility\n\n\n\n\nCode# generic code for all plots\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\nadlb &lt;- adlb %&gt;% filter(PARAMCD == \"ALT\" & AVISIT == \"WEEK 2 DAY 15\")\n\n# Definition of boxplot boundaries and whiskers\nfive_num &lt;- function(x, probs = c(0, 0.25, 0.5, 0.75, 1)) {\n  r &lt;- quantile(x, probs)\n  names(r) &lt;- c(\"ymin\", \"lower\", \"middle\", \"upper\", \"ymax\")\n  r\n}\n\n# get outliers based on quantile\n# for outliers based on IQR see coef in geom_boxplot\nget_outliers &lt;- function(x, probs = c(0.05, 0.95)) {\n  r &lt;- subset(x, x &lt; quantile(x, probs[1]) | quantile(x, probs[2]) &lt; x)\n  if (!is.null(x)) {\n    x_names &lt;- subset(names(x), x &lt; quantile(x, probs[1]) | quantile(x, probs[2]) &lt; x)\n    names(r) &lt;- x_names\n  }\n  r\n}\n\n# create theme used for all plots\ntheme_bp &lt;- theme(\n  plot.title = element_text(hjust = 0),\n  plot.subtitle = element_text(hjust = 0),\n  plot.caption = element_text(hjust = 0),\n  panel.background = element_rect(fill = \"white\", color = \"grey50\")\n)\n\n# assign fill color and outline color\nfc &lt;- \"#eaeef5\"\noc &lt;- getOption(\"ggplot2.discrete.fill\")[1]\n\n# get plot metadata data to derive coordinates for adding annotations\nbp_annos &lt;- function(bp, color, annos = 1) {\n  bp_mdat &lt;- ggplot_build(bp)$data[[1]]\n  if (annos == 1) {\n    bp &lt;- bp +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymax, yend = ymax\n      ), linewidth = .5, color = color) +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymin, yend = ymin\n      ), linewidth = .5, color = color)\n  } else {\n    bp &lt;- bp +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymax, yend = ymax\n      ), linewidth = .5, color = color) +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymin, yend = ymin\n      ), linewidth = .5, color = color) +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin, xend = xmax,\n        y = middle, yend = middle\n      ), colour = color, linewidth = .5)\n  }\n  return(bp)\n}\n\n\n\n\n\nCodebp_1 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(geom = \"boxplot\", fun.data = five_num, fill = fc, color = oc) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_1, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodebp_3 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\", fun.data = five_num,\n    fun.args = list(probs = c(0.05, 0.25, 0.5, 0.75, 0.95)), fill = fc, color = oc\n  ) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the 5th and 95th percentile. Values outside the whiskers have not been plotted.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_3, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodebp_4 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\", fun.data = five_num,\n    fun.args = list(probs = c(0.05, 0.25, 0.5, 0.75, 0.95)), fill = fc, color = oc\n  ) +\n  stat_summary(geom = \"point\", fun = get_outliers, shape = 1) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the 5th and 95th percentile.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_4, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodeadlb_o &lt;- adlb %&gt;%\n  group_by(ARMCD) %&gt;%\n  mutate(OUTLIER = AVAL &lt; quantile(AVAL, 0.01) | AVAL &gt; quantile(AVAL, 0.99)) %&gt;%\n  filter(OUTLIER == TRUE) %&gt;%\n  select(ARMCD, AVAL, USUBJID)\n# Next step may be study-specific: shorten USUBJID to make annotation labels\n# next 2 lines of code split USUBJID by \"-\" and take the last syllable as ID\nn_split &lt;- max(vapply(strsplit(adlb_o$USUBJID, \"-\"), length, numeric(1)))\nadlb_o$ID &lt;- vapply(strsplit(adlb_o$USUBJID, \"-\"), `[[`, n_split, FUN.VALUE = \"a\")\n\nbp_5 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\", fun.data = five_num,\n    fun.args = list(probs = c(0.01, 0.25, 0.5, 0.75, 0.99)), fill = fc, color = oc\n  ) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  geom_point(data = adlb_o, aes(x = ARMCD, y = AVAL), shape = 1) +\n  geom_text(data = adlb_o, aes(x = ARMCD, y = AVAL, label = ID), size = 3, hjust = -0.2) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the largest and smallest observed value within 1.5*IQR.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_5, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodebp_6 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  geom_boxplot(fill = fc, color = oc) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 5) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_6, oc)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_v &lt;- adlb %&gt;%\n  filter(PARAMCD == \"ALT\" & AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\", \"WEEK 4 DAY 29\"))\n\nbp_7 &lt;- ggplot(adlb_v, aes(x = AVISIT, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\",\n    fun.data = five_num,\n    position = position_dodge2(.5),\n    aes(fill = ARMCD, color = ARMCD)\n  ) +\n  stat_summary(\n    geom = \"point\",\n    fun = mean,\n    aes(group = ARMCD),\n    size = 3,\n    shape = 8,\n    position = position_dodge2(1)\n  ) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Visit\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_7, oc, 2)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodebp_8 &lt;- ggplot(adlb_v, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\",\n    fun.data = five_num,\n    position = position_dodge2(width = .5),\n    aes(fill = AVISIT, color = AVISIT)\n  ) +\n  stat_summary(\n    geom = \"point\",\n    fun = mean,\n    aes(group = AVISIT),\n    size = 3,\n    shape = 8,\n    position = position_dodge2(1)\n  ) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_8, oc, 2)\nplot\n\n\n\n\n\n\n\n\n\n\nCode# Make wide dataset with summary statistics\nadlb_wide &lt;- adlb %&gt;%\n  group_by(ARMCD) %&gt;%\n  summarise(\n    n = n(),\n    mean = round(mean(AVAL), 1),\n    median = round(median(AVAL), 1),\n    median_ci = paste(round(DescTools::MedianCI(AVAL)[1:2], 1), collapse = \" - \"),\n    q1_q3 = paste(round(quantile(AVAL, c(0.25, 0.75)), 1), collapse = \" - \"),\n    min_max = paste0(round(min(AVAL), 1), \"-\", max(round(AVAL, 2)))\n  )\n\n# Make long dataset\nadlb_long &lt;- tidyr::gather(adlb_wide, key = type, value = stat, n:min_max)\nadlb_long &lt;- adlb_long %&gt;% mutate(\n  type_lbl = case_when(\n    type == \"n\" ~ \"n\",\n    type == \"mean\" ~ \"Mean\",\n    type == \"median\" ~ \"Median\",\n    type == \"median_ci\" ~ \"95% CI for Median\",\n    type == \"q1_q3\" ~ \"25% and 75%-ile\",\n    type == \"min_max\" ~ \"Min - Max\"\n  )\n)\nadlb_long$type_lbl &lt;- factor(adlb_long$type_lbl,\n  levels = c(\"Min - Max\", \"25% and 75%-ile\", \"95% CI for Median\", \"Median\", \"Mean\", \"n\")\n)\n\nbp_9 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(geom = \"boxplot\", fun.data = five_num, fill = fc, color = oc) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\ntbl_theme &lt;- theme(\n  panel.border = element_blank(),\n  panel.grid.major = element_blank(),\n  panel.grid.minor = element_blank(),\n  axis.ticks = element_blank(),\n  axis.title = element_blank(),\n  axis.text.y = element_text(face = \"plain\"),\n  axis.text.x = element_blank()\n)\n\ntbl_1 &lt;- ggplot(adlb_long, aes(x = ARMCD, y = type_lbl, label = stat), vjust = 10) +\n  geom_text(size = 3) +\n  scale_y_discrete(labels = levels(adlb_long$type_lbl)) +\n  theme_bw() +\n  tbl_theme +\n  labs(caption = \"The whiskers extend to the minimum and maximum values.\") +\n  theme_bp\n\nplot &lt;- ggpubr::ggarrange(bp_annos(bp_9, oc), tbl_1,\n  heights = c(4, 2),\n  ncol = 1, nrow = 2, align = \"v\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\nCodetbl_2 &lt;- adlb_long %&gt;%\n  filter(type == \"n\") %&gt;%\n  ggplot(aes(x = ARMCD, y = type_lbl, label = stat)) +\n  geom_text(size = 3) +\n  scale_y_discrete(labels = \"n\") +\n  theme_bw() +\n  tbl_theme +\n  labs(caption = \"The whiskers extend to the minimum and maximum values.\") +\n  theme_bp\n\nplot &lt;- ggpubr::ggarrange(bp_annos(bp_9, oc), tbl_2,\n  heights = c(6, 1),\n  ncol = 1, nrow = 2, align = \"v\"\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.general)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(tern)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n  # If PARAMCD and AVISIT are not factors, convert to factors\n  # Also fill in missing values with \"&lt;Missing&gt;\"\n  ADLB &lt;- ADLB %&gt;%\n    df_explicit_na(\n      omit_columns = setdiff(names(ADLB), c(\"PARAMCD\", \"AVISIT\")),\n      char_as_factor = TRUE\n    )\n\n  # If statment below fails, pre-process ADLB to be one record per\n  # study, subject, param and visit eg. filter with ANLFL = 'Y'\n  stopifnot(nrow(ADLB) == nrow(unique(ADLB[, c(\"STUDYID\", \"USUBJID\", \"PARAMCD\", \"AVISIT\")])))\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_bivariate(\n      x = data_extract_spec(\n        dataname = \"ADSL\",\n        select = select_spec(\n          label = \"Select variable:\",\n          choices = names(ADSL),\n          selected = \"ARMCD\",\n          fixed = FALSE\n        )\n      ),\n      y = data_extract_spec(\n        dataname = \"ADLB\",\n        filter = list(\n          filter_spec(\n            vars = c(\"PARAMCD\"),\n            choices = levels(ADLB$PARAMCD),\n            selected = levels(ADLB$PARAMCD)[1],\n            multiple = FALSE,\n            label = \"Choose lab parameter\"\n          ),\n          filter_spec(\n            vars = c(\"AVISIT\"),\n            choices = levels(ADLB$AVISIT),\n            selected = levels(ADLB$AVISIT)[1],\n            multiple = FALSE,\n            label = \"Choose visit\"\n          )\n        ),\n        select = select_spec(\n          label = \"Select variable:\",\n          choices = names(ADLB),\n          selected = \"AVAL\",\n          multiple = FALSE,\n          fixed = TRUE\n        )\n      ),\n      row_facet = data_extract_spec(\n        dataname = \"ADLB\",\n        select = select_spec(\n          label = \"Select variables:\",\n          choices = names(ADLB),\n          selected = NULL,\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      ),\n      col_facet = data_extract_spec(\n        dataname = \"ADLB\",\n        select = select_spec(\n          label = \"Select variables:\",\n          choices = names(ADLB),\n          selected = NULL,\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: Removed 8000 rows containing non-finite outside the scale range\n(`stat_boxplot()`).\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 18:00:15 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] tern_0.9.3.9024                 rtables_0.6.6.9011             \n [3] magrittr_2.0.3                  formatters_0.5.5.9013          \n [5] teal.modules.general_0.3.0.9000 teal.transform_0.5.0.9001      \n [7] teal_0.15.2.9003                teal.slice_0.5.0.9004          \n [9] teal.data_0.5.0.9002            teal.code_0.5.0.9003           \n[11] shiny_1.8.0                     ggmosaic_0.3.3                 \n[13] nestcolor_0.1.2.9009            ggplot2_3.5.0                  \n[15] dplyr_1.1.4                     scda_0.1.6.9017                \n\nloaded via a namespace (and not attached):\n  [1] rstudioapi_0.15.0        jsonlite_1.8.8           farver_2.1.1            \n  [4] rmarkdown_2.26           ragg_1.2.7               vctrs_0.6.5             \n  [7] memoise_2.0.1            rstatix_0.7.2            webshot_0.5.5           \n [10] htmltools_0.5.7          forcats_1.0.0            broom_1.0.5             \n [13] cellranger_1.1.0         sass_0.4.8               bslib_0.6.1             \n [16] htmlwidgets_1.6.4        fontawesome_0.5.2        testthat_3.2.1          \n [19] rootSolve_1.8.2.4        plotly_4.10.4            cachem_1.0.8            \n [22] mime_0.12                lifecycle_1.0.4          teal.widgets_0.4.2.9006 \n [25] pkgconfig_2.0.3          webshot2_0.1.1           Matrix_1.6-5            \n [28] R6_2.5.1                 fastmap_1.1.1            rbibutils_2.2.16        \n [31] digest_0.6.34            Exact_3.2                colorspace_2.1-0        \n [34] shinycssloaders_1.0.0    ps_1.7.6                 textshaping_0.3.7       \n [37] ggpubr_0.6.0             labeling_0.4.3           fansi_1.0.6             \n [40] httr_1.4.7               abind_1.4-5              compiler_4.3.3          \n [43] proxy_0.4-27             withr_3.0.0              backports_1.4.1         \n [46] carData_3.0-5            logger_0.3.0             ggsignif_0.6.4          \n [49] MASS_7.3-60.0.1          gld_2.6.6                tools_4.3.3             \n [52] chromote_0.2.0           httpuv_1.6.14            shinyvalidate_0.1.3     \n [55] glue_1.7.0               callr_3.7.5              promises_1.2.1          \n [58] grid_4.3.3               checkmate_2.3.1          generics_0.1.3          \n [61] gtable_0.3.4             class_7.3-22             tidyr_1.3.1             \n [64] websocket_1.4.1          data.table_1.15.2        lmom_3.0                \n [67] car_3.1-2                utf8_1.2.4               ggrepel_0.9.5           \n [70] pillar_1.9.0             stringr_1.5.1            later_1.3.2             \n [73] splines_4.3.3            lattice_0.22-5           survival_3.5-8          \n [76] tidyselect_1.2.0         knitr_1.45               teal.logger_0.1.3.9011  \n [79] xfun_0.42                expm_0.999-9             brio_1.1.4              \n [82] stringi_1.8.3            lazyeval_0.2.2           yaml_2.3.8              \n [85] boot_1.3-30              shinyWidgets_0.8.2       evaluate_0.23           \n [88] codetools_0.2-19         tibble_3.2.1             cli_3.6.2               \n [91] xtable_1.8-4             DescTools_0.99.54        systemfonts_1.0.6       \n [94] Rdpack_2.6               munsell_0.5.0            processx_3.8.3          \n [97] jquerylib_0.1.4          Rcpp_1.0.12              teal.reporter_0.3.0.9005\n[100] readxl_1.4.3             ellipsis_0.3.2           viridisLite_0.4.2       \n[103] mvtnorm_1.2-4            scales_1.3.0             e1071_1.7-14            \n[106] purrr_1.0.2              rlang_1.1.3              cowplot_1.1.3           \n[109] formatR_1.14             shinyjs_2.1.0           \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nStandard Plot\nPlot Changing  Whiskers\nPlot Adding  Outliers\nPlot Specifying Marker  for Outliers and  Adding Patient ID\nPlot Specifying  Marker for Mean\nPlot by Treatment  and Timepoint\nPlot by Timepoint  and Treatment\nPlot with  Table Section\nPlot with Number of Patients  only in Table Section\nteal App\nReproducibility\n\n\n\n\nCode# generic code for all plots\nlibrary(scda)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(nestcolor)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\nadlb &lt;- adlb %&gt;% filter(PARAMCD == \"ALT\" & AVISIT == \"WEEK 2 DAY 15\")\n\n# Definition of boxplot boundaries and whiskers\nfive_num &lt;- function(x, probs = c(0, 0.25, 0.5, 0.75, 1)) {\n  r &lt;- quantile(x, probs)\n  names(r) &lt;- c(\"ymin\", \"lower\", \"middle\", \"upper\", \"ymax\")\n  r\n}\n\n# get outliers based on quantile\n# for outliers based on IQR see coef in geom_boxplot\nget_outliers &lt;- function(x, probs = c(0.05, 0.95)) {\n  r &lt;- subset(x, x &lt; quantile(x, probs[1]) | quantile(x, probs[2]) &lt; x)\n  if (!is.null(x)) {\n    x_names &lt;- subset(names(x), x &lt; quantile(x, probs[1]) | quantile(x, probs[2]) &lt; x)\n    names(r) &lt;- x_names\n  }\n  r\n}\n\n# create theme used for all plots\ntheme_bp &lt;- theme(\n  plot.title = element_text(hjust = 0),\n  plot.subtitle = element_text(hjust = 0),\n  plot.caption = element_text(hjust = 0),\n  panel.background = element_rect(fill = \"white\", color = \"grey50\")\n)\n\n# assign fill color and outline color\nfc &lt;- \"#eaeef5\"\noc &lt;- getOption(\"ggplot2.discrete.fill\")[1]\n\n# get plot metadata data to derive coordinates for adding annotations\nbp_annos &lt;- function(bp, color, annos = 1) {\n  bp_mdat &lt;- ggplot_build(bp)$data[[1]]\n  if (annos == 1) {\n    bp &lt;- bp +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymax, yend = ymax\n      ), linewidth = .5, color = color) +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymin, yend = ymin\n      ), linewidth = .5, color = color)\n  } else {\n    bp &lt;- bp +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymax, yend = ymax\n      ), linewidth = .5, color = color) +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin + (xmax - xmin) / 4, xend = xmax - (xmax - xmin) / 4,\n        y = ymin, yend = ymin\n      ), linewidth = .5, color = color) +\n      geom_segment(data = bp_mdat, aes(\n        x = xmin, xend = xmax,\n        y = middle, yend = middle\n      ), colour = color, linewidth = .5)\n  }\n  return(bp)\n}\n\n\n\n\n\nCodebp_1 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(geom = \"boxplot\", fun.data = five_num, fill = fc, color = oc) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_1, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodebp_3 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\", fun.data = five_num,\n    fun.args = list(probs = c(0.05, 0.25, 0.5, 0.75, 0.95)), fill = fc, color = oc\n  ) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the 5th and 95th percentile. Values outside the whiskers have not been plotted.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_3, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodebp_4 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\", fun.data = five_num,\n    fun.args = list(probs = c(0.05, 0.25, 0.5, 0.75, 0.95)), fill = fc, color = oc\n  ) +\n  stat_summary(geom = \"point\", fun = get_outliers, shape = 1) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the 5th and 95th percentile.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_4, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodeadlb_o &lt;- adlb %&gt;%\n  group_by(ARMCD) %&gt;%\n  mutate(OUTLIER = AVAL &lt; quantile(AVAL, 0.01) | AVAL &gt; quantile(AVAL, 0.99)) %&gt;%\n  filter(OUTLIER == TRUE) %&gt;%\n  select(ARMCD, AVAL, USUBJID)\n# Next step may be study-specific: shorten USUBJID to make annotation labels\n# next 2 lines of code split USUBJID by \"-\" and take the last syllable as ID\nn_split &lt;- max(vapply(strsplit(adlb_o$USUBJID, \"-\"), length, numeric(1)))\nadlb_o$ID &lt;- vapply(strsplit(adlb_o$USUBJID, \"-\"), `[[`, n_split, FUN.VALUE = \"a\")\n\nbp_5 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\", fun.data = five_num,\n    fun.args = list(probs = c(0.01, 0.25, 0.5, 0.75, 0.99)), fill = fc, color = oc\n  ) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  geom_point(data = adlb_o, aes(x = ARMCD, y = AVAL), shape = 1) +\n  geom_text(data = adlb_o, aes(x = ARMCD, y = AVAL, label = ID), size = 3, hjust = -0.2) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the largest and smallest observed value within 1.5*IQR.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_5, oc)\nplot\n\n\n\n\n\n\n\n\n\n\nCodebp_6 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  geom_boxplot(fill = fc, color = oc) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 5) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_6, oc)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\nadlb_v &lt;- adlb %&gt;%\n  filter(PARAMCD == \"ALT\" & AVISIT %in% c(\"WEEK 1 DAY 8\", \"WEEK 2 DAY 15\", \"WEEK 3 DAY 22\", \"WEEK 4 DAY 29\"))\n\nbp_7 &lt;- ggplot(adlb_v, aes(x = AVISIT, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\",\n    fun.data = five_num,\n    position = position_dodge2(.5),\n    aes(fill = ARMCD, color = ARMCD)\n  ) +\n  stat_summary(\n    geom = \"point\",\n    fun = mean,\n    aes(group = ARMCD),\n    size = 3,\n    shape = 8,\n    position = position_dodge2(1)\n  ) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Visit\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_7, oc, 2)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodebp_8 &lt;- ggplot(adlb_v, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(\n    geom = \"boxplot\",\n    fun.data = five_num,\n    position = position_dodge2(width = .5),\n    aes(fill = AVISIT, color = AVISIT)\n  ) +\n  stat_summary(\n    geom = \"point\",\n    fun = mean,\n    aes(group = AVISIT),\n    size = 3,\n    shape = 8,\n    position = position_dodge2(1)\n  ) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    caption = \"The whiskers extend to the minimum and maximum values.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\nplot &lt;- bp_annos(bp_8, oc, 2)\nplot\n\n\n\n\n\n\n\n\n\n\nCode# Make wide dataset with summary statistics\nadlb_wide &lt;- adlb %&gt;%\n  group_by(ARMCD) %&gt;%\n  summarise(\n    n = n(),\n    mean = round(mean(AVAL), 1),\n    median = round(median(AVAL), 1),\n    median_ci = paste(round(DescTools::MedianCI(AVAL)[1:2], 1), collapse = \" - \"),\n    q1_q3 = paste(round(quantile(AVAL, c(0.25, 0.75)), 1), collapse = \" - \"),\n    min_max = paste0(round(min(AVAL), 1), \"-\", max(round(AVAL, 2)))\n  )\n\n# Make long dataset\nadlb_long &lt;- tidyr::gather(adlb_wide, key = type, value = stat, n:min_max)\nadlb_long &lt;- adlb_long %&gt;% mutate(\n  type_lbl = case_when(\n    type == \"n\" ~ \"n\",\n    type == \"mean\" ~ \"Mean\",\n    type == \"median\" ~ \"Median\",\n    type == \"median_ci\" ~ \"95% CI for Median\",\n    type == \"q1_q3\" ~ \"25% and 75%-ile\",\n    type == \"min_max\" ~ \"Min - Max\"\n  )\n)\nadlb_long$type_lbl &lt;- factor(adlb_long$type_lbl,\n  levels = c(\"Min - Max\", \"25% and 75%-ile\", \"95% CI for Median\", \"Median\", \"Mean\", \"n\")\n)\n\nbp_9 &lt;- ggplot(adlb, aes(x = ARMCD, y = AVAL)) +\n  stat_summary(geom = \"boxplot\", fun.data = five_num, fill = fc, color = oc) +\n  stat_summary(geom = \"point\", fun = mean, size = 3, shape = 8) +\n  labs(\n    title = \"Box Plot of Laboratory Test Results\",\n    subtitle = paste(\"Visit:\", adlb$AVISIT[1]),\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  ) +\n  theme_bp\n\ntbl_theme &lt;- theme(\n  panel.border = element_blank(),\n  panel.grid.major = element_blank(),\n  panel.grid.minor = element_blank(),\n  axis.ticks = element_blank(),\n  axis.title = element_blank(),\n  axis.text.y = element_text(face = \"plain\"),\n  axis.text.x = element_blank()\n)\n\ntbl_1 &lt;- ggplot(adlb_long, aes(x = ARMCD, y = type_lbl, label = stat), vjust = 10) +\n  geom_text(size = 3) +\n  scale_y_discrete(labels = levels(adlb_long$type_lbl)) +\n  theme_bw() +\n  tbl_theme +\n  labs(caption = \"The whiskers extend to the minimum and maximum values.\") +\n  theme_bp\n\nplot &lt;- ggpubr::ggarrange(bp_annos(bp_9, oc), tbl_1,\n  heights = c(4, 2),\n  ncol = 1, nrow = 2, align = \"v\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\nCodetbl_2 &lt;- adlb_long %&gt;%\n  filter(type == \"n\") %&gt;%\n  ggplot(aes(x = ARMCD, y = type_lbl, label = stat)) +\n  geom_text(size = 3) +\n  scale_y_discrete(labels = \"n\") +\n  theme_bw() +\n  tbl_theme +\n  labs(caption = \"The whiskers extend to the minimum and maximum values.\") +\n  theme_bp\n\nplot &lt;- ggpubr::ggarrange(bp_annos(bp_9, oc), tbl_2,\n  heights = c(6, 1),\n  ncol = 1, nrow = 2, align = \"v\"\n)\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.general)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  library(tern)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n\n  # If PARAMCD and AVISIT are not factors, convert to factors\n  # Also fill in missing values with \"&lt;Missing&gt;\"\n  ADLB &lt;- ADLB %&gt;%\n    df_explicit_na(\n      omit_columns = setdiff(names(ADLB), c(\"PARAMCD\", \"AVISIT\")),\n      char_as_factor = TRUE\n    )\n\n  # If statment below fails, pre-process ADLB to be one record per\n  # study, subject, param and visit eg. filter with ANLFL = 'Y'\n  stopifnot(nrow(ADLB) == nrow(unique(ADLB[, c(\"STUDYID\", \"USUBJID\", \"PARAMCD\", \"AVISIT\")])))\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADSL &lt;- data[[\"ADSL\"]]\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_bivariate(\n      x = data_extract_spec(\n        dataname = \"ADSL\",\n        select = select_spec(\n          label = \"Select variable:\",\n          choices = names(ADSL),\n          selected = \"ARMCD\",\n          fixed = FALSE\n        )\n      ),\n      y = data_extract_spec(\n        dataname = \"ADLB\",\n        filter = list(\n          filter_spec(\n            vars = c(\"PARAMCD\"),\n            choices = levels(ADLB$PARAMCD),\n            selected = levels(ADLB$PARAMCD)[1],\n            multiple = FALSE,\n            label = \"Choose lab parameter\"\n          ),\n          filter_spec(\n            vars = c(\"AVISIT\"),\n            choices = levels(ADLB$AVISIT),\n            selected = levels(ADLB$AVISIT)[1],\n            multiple = FALSE,\n            label = \"Choose visit\"\n          )\n        ),\n        select = select_spec(\n          label = \"Select variable:\",\n          choices = names(ADLB),\n          selected = \"AVAL\",\n          multiple = FALSE,\n          fixed = TRUE\n        )\n      ),\n      row_facet = data_extract_spec(\n        dataname = \"ADLB\",\n        select = select_spec(\n          label = \"Select variables:\",\n          choices = names(ADLB),\n          selected = NULL,\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      ),\n      col_facet = data_extract_spec(\n        dataname = \"ADLB\",\n        select = select_spec(\n          label = \"Select variables:\",\n          choices = names(ADLB),\n          selected = NULL,\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      )\n    )\n  )\n)\n\nshinyApp(app$ui, app$server)\n\nWarning: Removed 8000 rows containing non-finite outside the scale range\n(`stat_boxplot()`).\n\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:58:31 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] tern_0.9.3.9024                 rtables_0.6.6.9011             \n [3] magrittr_2.0.3                  formatters_0.5.5.9015          \n [5] teal.modules.general_0.3.0.9001 teal.transform_0.5.0.9002      \n [7] teal_0.15.2.9004                teal.slice_0.5.0.9005          \n [9] teal.data_0.5.0.9003            teal.code_0.5.0.9003           \n[11] shiny_1.8.0                     ggmosaic_0.3.3                 \n[13] nestcolor_0.1.2.9009            ggplot2_3.5.0                  \n[15] dplyr_1.1.4                     scda_0.1.6.9017                \n\nloaded via a namespace (and not attached):\n  [1] rstudioapi_0.15.0        jsonlite_1.8.8           farver_2.1.1            \n  [4] rmarkdown_2.26           ragg_1.2.7               vctrs_0.6.5             \n  [7] memoise_2.0.1            rstatix_0.7.2            webshot_0.5.5           \n [10] htmltools_0.5.7          forcats_1.0.0            broom_1.0.5             \n [13] cellranger_1.1.0         sass_0.4.8               bslib_0.6.1             \n [16] htmlwidgets_1.6.4        fontawesome_0.5.2        testthat_3.2.1          \n [19] rootSolve_1.8.2.4        plotly_4.10.4            cachem_1.0.8            \n [22] mime_0.12                lifecycle_1.0.4          teal.widgets_0.4.2.9008 \n [25] pkgconfig_2.0.3          webshot2_0.1.1           Matrix_1.6-5            \n [28] R6_2.5.1                 fastmap_1.1.1            rbibutils_2.2.16        \n [31] digest_0.6.34            Exact_3.2                colorspace_2.1-0        \n [34] shinycssloaders_1.0.0    ps_1.7.6                 textshaping_0.3.7       \n [37] ggpubr_0.6.0             labeling_0.4.3           fansi_1.0.6             \n [40] httr_1.4.7               abind_1.4-5              compiler_4.3.3          \n [43] proxy_0.4-27             withr_3.0.0              backports_1.4.1         \n [46] carData_3.0-5            logger_0.3.0             ggsignif_0.6.4          \n [49] MASS_7.3-60.0.1          gld_2.6.6                tools_4.3.3             \n [52] chromote_0.2.0           httpuv_1.6.14            shinyvalidate_0.1.3     \n [55] glue_1.7.0               callr_3.7.5              promises_1.2.1          \n [58] grid_4.3.3               checkmate_2.3.1          generics_0.1.3          \n [61] gtable_0.3.4             class_7.3-22             tidyr_1.3.1             \n [64] websocket_1.4.1          data.table_1.15.2        lmom_3.0                \n [67] car_3.1-2                utf8_1.2.4               ggrepel_0.9.5           \n [70] pillar_1.9.0             stringr_1.5.1            later_1.3.2             \n [73] splines_4.3.3            lattice_0.22-5           survival_3.5-8          \n [76] tidyselect_1.2.0         knitr_1.45               teal.logger_0.1.3.9011  \n [79] xfun_0.42                expm_0.999-9             brio_1.1.4              \n [82] stringi_1.8.3            lazyeval_0.2.2           yaml_2.3.8              \n [85] boot_1.3-30              shinyWidgets_0.8.2       evaluate_0.23           \n [88] codetools_0.2-19         tibble_3.2.1             cli_3.6.2               \n [91] xtable_1.8-4             DescTools_0.99.54        systemfonts_1.0.6       \n [94] Rdpack_2.6               munsell_0.5.0            processx_3.8.3          \n [97] jquerylib_0.1.4          Rcpp_1.0.12              teal.reporter_0.3.0.9005\n[100] readxl_1.4.3             ellipsis_0.3.2           viridisLite_0.4.2       \n[103] mvtnorm_1.2-4            scales_1.3.0             e1071_1.7-14            \n[106] purrr_1.0.2              rlang_1.1.3              cowplot_1.1.3           \n[109] formatR_1.14             shinyjs_2.1.0           \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Other",
@@ -1739,7 +1739,7 @@
     "href": "graphs/other/brg01.html",
     "title": "BRG01",
     "section": "",
-    "text": "Data Setup\nPlot of Frequency\nPlot of Percentage\nPlot of Frequency with  Total Number of Subjects\nPlot of Frequency  with Horizontal Bars\nPlot of Percentage  and Confidence Intervals\nPlot of Percentage by  Treatment and Covariate\nPlot of Percentage by  Covariate and Treatment\nPlot of Percentage  with Mean and Median\nPlot of Categorical  Variable\nteal App\nReproducibility\n\n\n\n\nCodelibrary(binom)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(scda)\nlibrary(tidyr)\nlibrary(tern)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# filtered population\npatpop_df &lt;- adsl %&gt;%\n  group_by(STUDYID) %&gt;%\n  summarise(patpop = n())\npatpop &lt;- setNames(patpop_df$patpop, patpop_df$STUDYID)\ndenom &lt;- patpop_df$patpop\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\") %&gt;%\n  mutate(AETOXGRC = as.character(AETOXGR))\nattributes(adae$AETOXGRC)$label &lt;- \"Analysis Toxicity Grade (C)\"\nvl_ae &lt;- var_labels(adae)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\nvl_lb &lt;- var_labels(adlb)\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_bar(fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  geom_text(stat = \"count\", aes(label = ggplot2::after_stat(count)), vjust = -.5) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Frequency of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Frequency\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESOC == \"cl A\")\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(ACTARM) %&gt;%\n  summarise(n = n()) %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_col(aes(x = ACTARM, y = pct), fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  scale_y_continuous(labels = scales::comma) +\n  geom_text(aes(y = pct, label = pct), stat = \"identity\", vjust = -0.5) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With cl A Disorders: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESOC == \"cl A\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\nanl &lt;- anl %&gt;%\n  group_by(ACTARM) %&gt;%\n  mutate(n = n()) %&gt;%\n  mutate(xvarlabel = paste0(ACTARM, \"\\n\\nN = \", n))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"xvarlabel\"]])) +\n  geom_bar(fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Frequency of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Frequency\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_bar(fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Frequency of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Frequency\") +\n  coord_flip()\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adlb %&gt;%\n  filter(PARAMCD == \"ALT\" & ANRIND == \"HIGH\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\npatpop5 &lt;- as.data.frame(table(adsl$ACTARM)) %&gt;%\n  mutate(ACTARM = as.vector(Var1)) %&gt;%\n  mutate(TRTPOP = Freq) %&gt;%\n  select(ACTARM, TRTPOP)\n\nanlpop &lt;- as.data.frame(table(anl$ANRIND, anl$ACTARM)) %&gt;%\n  filter(Var1 == \"HIGH\") %&gt;%\n  mutate(ACTARM = as.vector(Var2)) %&gt;%\n  mutate(EVENTCOUNT = Freq) %&gt;%\n  select(ACTARM, EVENTCOUNT)\n\nanl &lt;- left_join(patpop5, anlpop, by = \"ACTARM\")\nCIs &lt;- binom.confint(x = anl$EVENTCOUNT, n = anl$TRTPOP, methods = \"exact\") \nanl &lt;- cbind(anl, CIs[, 4:6])\n\nplot &lt;- ggplot(anl) +\n  geom_bar(\n    aes(x = ACTARM, y = mean * 100),\n    stat = \"identity\",\n    fill = getOption(\"ggplot2.discrete.fill\")[1],\n    alpha = 0.7\n  ) +\n  geom_errorbar(\n    aes(x = ACTARM, ymin = lower * 100, ymax = upper * 100),\n    width = 0.5,\n    colour = \"#20b4ff\",\n    alpha = 0.9,\n    linewidth = 1.0\n  ) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects with Abnormaly High ALT: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percentage\")\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\n\npatpop6 &lt;- dplyr::count(adsl, ACTARM, SEX, name = \"N_arm_sex\")\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(ACTARM, SEX) %&gt;%\n  summarise(n = n(), .groups = \"drop\") %&gt;%\n  left_join(patpop6, by = c(\"ACTARM\", \"SEX\")) %&gt;%\n  mutate(pct = round((n / N_arm_sex) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]], fill = .data[[\"SEX\"]])) +\n  geom_col(aes(y = pct), position = position_dodge()) +\n  guides(fill = guide_legend(title = getElement(vl_ae, \"SEX\"))) +\n  scale_y_continuous(labels = scales::comma) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(SEX, ACTARM) %&gt;%\n  summarise(n = n(), .groups = \"drop_last\") %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"SEX\"]], fill = .data[[\"ACTARM\"]])) +\n  geom_col(aes(y = pct), position = position_dodge()) +\n  guides(fill = guide_legend(title = getElement(vl_ae, \"ACTARM\"))) +\n  scale_y_continuous(labels = scales::comma) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"SEX\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl1 &lt;- adlb %&gt;%\n  filter(AVISIT == \"BASELINE\", PARAMCD == \"CRP\" & ANRIND == \"HIGH\")\n\nanl &lt;- anl1[!duplicated(anl1$USUBJID), ] %&gt;%\n  group_by(ACTARM) %&gt;%\n  summarise(n = n()) %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\ngraph &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_col(aes(x = ACTARM, y = pct), fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  scale_y_continuous(labels = scales::comma, expand = expansion(c(0, .25))) +\n  geom_text(aes(y = pct, label = pct), stat = \"identity\", vjust = -0.5) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects with Abnormaly High CRP at Baseline \\n Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_lb, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\n\nsd_w &lt;- anl1 %&gt;%\n  group_by(ACTARM) %&gt;%\n  summarise(\n    mean = round(mean(AVAL, na.rm = TRUE), 2),\n    median = round(median(AVAL, na.rm = TRUE), 2)\n  ) %&gt;%\n  arrange(ACTARM)\n\nsd_l &lt;- gather(sd_w, key = stat, value = value, mean:median)\nsd_l &lt;- sd_l %&gt;% mutate(\n  stat_lbl = case_when(\n    stat == \"mean\" ~ \"Mean\",\n    stat == \"median\" ~ \"Median\"\n  )\n)\n\nsd_l$stat_lbl &lt;- factor(sd_l$stat_lbl, levels = c(\"Median\", \"Mean\"))\n\ntbl_theme &lt;- theme(\n  panel.border = element_blank(),\n  panel.grid.major = element_blank(),\n  panel.grid.minor = element_blank(),\n  axis.ticks = element_blank(),\n  axis.title = element_blank(),\n  axis.text.y = element_text(face = \"plain\"),\n  axis.text.x = element_blank()\n)\n\ntbl &lt;- ggplot(sd_l, aes(x = ACTARM, y = stat_lbl, label = value)) +\n  geom_text(size = 3) +\n  scale_y_discrete(labels = levels(sd_l$stat_lbl)) +\n  theme_bw() +\n  tbl_theme\n\nplot &lt;- ggpubr::ggarrange(graph, tbl,\n  heights = c(4, 1),\n  ncol = 1, nrow = 2, align = \"v\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae\n\nanl &lt;- anl %&gt;%\n  arrange(USUBJID, AETOXGRC) %&gt;%\n  group_by(USUBJID) %&gt;%\n  slice(n()) %&gt;%\n  ungroup()\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(ACTARM, AETOXGRC) %&gt;%\n  summarise(n = n(), .groups = \"drop_last\") %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]], fill = .data[[\"AETOXGRC\"]])) +\n  geom_col(aes(y = pct)) +\n  guides(fill = guide_legend(title = getElement(vl_ae, \"AETOXGRC\"))) +\n  scale_y_continuous(labels = scales::comma) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With AE by Highest \\n NCI CTCAE Grade: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 18:00:42 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] nestcolor_0.1.2.9009  tern_0.9.3.9024       rtables_0.6.6.9011   \n [4] magrittr_2.0.3        formatters_0.5.5.9013 tidyr_1.3.1          \n [7] scda_0.1.6.9017       ggplot2_3.5.0         dplyr_1.1.4          \n[10] binom_1.1-1.1        \n\nloaded via a namespace (and not attached):\n [1] utf8_1.2.4        generics_0.1.3    rstatix_0.7.2     lattice_0.22-5   \n [5] digest_0.6.34     evaluate_0.23     grid_4.3.3        fastmap_1.1.1    \n [9] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[13] survival_3.5-8    purrr_1.0.2       fansi_1.0.6       scales_1.3.0     \n[17] codetools_0.2-19  abind_1.4-5       Rdpack_2.6        cli_3.6.2        \n[21] rlang_1.1.3       rbibutils_2.2.16  cowplot_1.1.3     munsell_0.5.0    \n[25] splines_4.3.3     withr_3.0.0       yaml_2.3.8        tools_4.3.3      \n[29] ggsignif_0.6.4    checkmate_2.3.1   colorspace_2.1-0  ggpubr_0.6.0     \n[33] broom_1.0.5       vctrs_0.6.5       R6_2.5.1          lifecycle_1.0.4  \n[37] car_3.1-2         htmlwidgets_1.6.4 pkgconfig_2.0.3   pillar_1.9.0     \n[41] gtable_0.3.4      glue_1.7.0        xfun_0.42         tibble_3.2.1     \n[45] tidyselect_1.2.0  knitr_1.45        farver_2.1.1      htmltools_0.5.7  \n[49] carData_3.0-5     rmarkdown_2.26    labeling_0.4.3    testthat_3.2.1   \n[53] compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot of Frequency\nPlot of Percentage\nPlot of Frequency with  Total Number of Subjects\nPlot of Frequency  with Horizontal Bars\nPlot of Percentage  and Confidence Intervals\nPlot of Percentage by  Treatment and Covariate\nPlot of Percentage by  Covariate and Treatment\nPlot of Percentage  with Mean and Median\nPlot of Categorical  Variable\nteal App\nReproducibility\n\n\n\n\nCodelibrary(binom)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(scda)\nlibrary(tidyr)\nlibrary(tern)\nlibrary(nestcolor)\n\nadsl &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n\n# filtered population\npatpop_df &lt;- adsl %&gt;%\n  group_by(STUDYID) %&gt;%\n  summarise(patpop = n())\npatpop &lt;- setNames(patpop_df$patpop, patpop_df$STUDYID)\ndenom &lt;- patpop_df$patpop\n\nadae &lt;- synthetic_cdisc_dataset(\"latest\", \"adae\") %&gt;%\n  mutate(AETOXGRC = as.character(AETOXGR))\nattributes(adae$AETOXGRC)$label &lt;- \"Analysis Toxicity Grade (C)\"\nvl_ae &lt;- var_labels(adae)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\nvl_lb &lt;- var_labels(adlb)\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_bar(fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  geom_text(stat = \"count\", aes(label = ggplot2::after_stat(count)), vjust = -.5) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Frequency of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Frequency\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESOC == \"cl A\")\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(ACTARM) %&gt;%\n  summarise(n = n()) %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_col(aes(x = ACTARM, y = pct), fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  scale_y_continuous(labels = scales::comma) +\n  geom_text(aes(y = pct, label = pct), stat = \"identity\", vjust = -0.5) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With cl A Disorders: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESOC == \"cl A\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\nanl &lt;- anl %&gt;%\n  group_by(ACTARM) %&gt;%\n  mutate(n = n()) %&gt;%\n  mutate(xvarlabel = paste0(ACTARM, \"\\n\\nN = \", n))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"xvarlabel\"]])) +\n  geom_bar(fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Frequency of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Frequency\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_bar(fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Frequency of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Frequency\") +\n  coord_flip()\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adlb %&gt;%\n  filter(PARAMCD == \"ALT\" & ANRIND == \"HIGH\")\nanl &lt;- anl[!duplicated(anl$USUBJID), ]\n\npatpop5 &lt;- as.data.frame(table(adsl$ACTARM)) %&gt;%\n  mutate(ACTARM = as.vector(Var1)) %&gt;%\n  mutate(TRTPOP = Freq) %&gt;%\n  select(ACTARM, TRTPOP)\n\nanlpop &lt;- as.data.frame(table(anl$ANRIND, anl$ACTARM)) %&gt;%\n  filter(Var1 == \"HIGH\") %&gt;%\n  mutate(ACTARM = as.vector(Var2)) %&gt;%\n  mutate(EVENTCOUNT = Freq) %&gt;%\n  select(ACTARM, EVENTCOUNT)\n\nanl &lt;- left_join(patpop5, anlpop, by = \"ACTARM\")\nCIs &lt;- binom.confint(x = anl$EVENTCOUNT, n = anl$TRTPOP, methods = \"exact\") \nanl &lt;- cbind(anl, CIs[, 4:6])\n\nplot &lt;- ggplot(anl) +\n  geom_bar(\n    aes(x = ACTARM, y = mean * 100),\n    stat = \"identity\",\n    fill = getOption(\"ggplot2.discrete.fill\")[1],\n    alpha = 0.7\n  ) +\n  geom_errorbar(\n    aes(x = ACTARM, ymin = lower * 100, ymax = upper * 100),\n    width = 0.5,\n    colour = \"#20b4ff\",\n    alpha = 0.9,\n    linewidth = 1.0\n  ) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects with Abnormaly High ALT: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percentage\")\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\n\npatpop6 &lt;- dplyr::count(adsl, ACTARM, SEX, name = \"N_arm_sex\")\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(ACTARM, SEX) %&gt;%\n  summarise(n = n(), .groups = \"drop\") %&gt;%\n  left_join(patpop6, by = c(\"ACTARM\", \"SEX\")) %&gt;%\n  mutate(pct = round((n / N_arm_sex) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]], fill = .data[[\"SEX\"]])) +\n  geom_col(aes(y = pct), position = position_dodge()) +\n  guides(fill = guide_legend(title = getElement(vl_ae, \"SEX\"))) +\n  scale_y_continuous(labels = scales::comma) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae %&gt;%\n  filter(AESER == \"Y\")\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(SEX, ACTARM) %&gt;%\n  summarise(n = n(), .groups = \"drop_last\") %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"SEX\"]], fill = .data[[\"ACTARM\"]])) +\n  geom_col(aes(y = pct), position = position_dodge()) +\n  guides(fill = guide_legend(title = getElement(vl_ae, \"ACTARM\"))) +\n  scale_y_continuous(labels = scales::comma) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With Serious AEs: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"SEX\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\n\n\nCodeanl1 &lt;- adlb %&gt;%\n  filter(AVISIT == \"BASELINE\", PARAMCD == \"CRP\" & ANRIND == \"HIGH\")\n\nanl &lt;- anl1[!duplicated(anl1$USUBJID), ] %&gt;%\n  group_by(ACTARM) %&gt;%\n  summarise(n = n()) %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\ngraph &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]])) +\n  geom_col(aes(x = ACTARM, y = pct), fill = getOption(\"ggplot2.discrete.fill\")[1]) +\n  scale_y_continuous(labels = scales::comma, expand = expansion(c(0, .25))) +\n  geom_text(aes(y = pct, label = pct), stat = \"identity\", vjust = -0.5) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects with Abnormaly High CRP at Baseline \\n Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_lb, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\n\nsd_w &lt;- anl1 %&gt;%\n  group_by(ACTARM) %&gt;%\n  summarise(\n    mean = round(mean(AVAL, na.rm = TRUE), 2),\n    median = round(median(AVAL, na.rm = TRUE), 2)\n  ) %&gt;%\n  arrange(ACTARM)\n\nsd_l &lt;- gather(sd_w, key = stat, value = value, mean:median)\nsd_l &lt;- sd_l %&gt;% mutate(\n  stat_lbl = case_when(\n    stat == \"mean\" ~ \"Mean\",\n    stat == \"median\" ~ \"Median\"\n  )\n)\n\nsd_l$stat_lbl &lt;- factor(sd_l$stat_lbl, levels = c(\"Median\", \"Mean\"))\n\ntbl_theme &lt;- theme(\n  panel.border = element_blank(),\n  panel.grid.major = element_blank(),\n  panel.grid.minor = element_blank(),\n  axis.ticks = element_blank(),\n  axis.title = element_blank(),\n  axis.text.y = element_text(face = \"plain\"),\n  axis.text.x = element_blank()\n)\n\ntbl &lt;- ggplot(sd_l, aes(x = ACTARM, y = stat_lbl, label = value)) +\n  geom_text(size = 3) +\n  scale_y_discrete(labels = levels(sd_l$stat_lbl)) +\n  theme_bw() +\n  tbl_theme\n\nplot &lt;- ggpubr::ggarrange(graph, tbl,\n  heights = c(4, 1),\n  ncol = 1, nrow = 2, align = \"v\"\n)\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeanl &lt;- adae\n\nanl &lt;- anl %&gt;%\n  arrange(USUBJID, AETOXGRC) %&gt;%\n  group_by(USUBJID) %&gt;%\n  slice(n()) %&gt;%\n  ungroup()\n\nanl &lt;- anl[!duplicated(anl$USUBJID), ] %&gt;%\n  group_by(ACTARM, AETOXGRC) %&gt;%\n  summarise(n = n(), .groups = \"drop_last\") %&gt;%\n  mutate(N = patpop) %&gt;%\n  mutate(N = as.numeric(N)) %&gt;%\n  mutate(pct = round((n / N) * 100, 2))\n\nplot &lt;- ggplot(anl, aes(x = .data[[\"ACTARM\"]], fill = .data[[\"AETOXGRC\"]])) +\n  geom_col(aes(y = pct)) +\n  guides(fill = guide_legend(title = getElement(vl_ae, \"AETOXGRC\"))) +\n  scale_y_continuous(labels = scales::comma) +\n  theme(plot.title = element_text(hjust = 0.5)) +\n  ggtitle(\"Percent of Subjects With AE by Highest \\n NCI CTCAE Grade: Safety-Evaluable Subjects\") +\n  xlab(paste0(getElement(vl_ae, \"ACTARM\"), \" (N = \", denom, \")\")) +\n  ylab(\"Percent\")\nplot\n\n\n\n\n\n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCode# In progress\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:58:57 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] nestcolor_0.1.2.9009  tern_0.9.3.9024       rtables_0.6.6.9011   \n [4] magrittr_2.0.3        formatters_0.5.5.9015 tidyr_1.3.1          \n [7] scda_0.1.6.9017       ggplot2_3.5.0         dplyr_1.1.4          \n[10] binom_1.1-1.1        \n\nloaded via a namespace (and not attached):\n [1] utf8_1.2.4        generics_0.1.3    rstatix_0.7.2     lattice_0.22-5   \n [5] digest_0.6.34     evaluate_0.23     grid_4.3.3        fastmap_1.1.1    \n [9] jsonlite_1.8.8    Matrix_1.6-5      backports_1.4.1   brio_1.1.4       \n[13] survival_3.5-8    purrr_1.0.2       fansi_1.0.6       scales_1.3.0     \n[17] codetools_0.2-19  abind_1.4-5       Rdpack_2.6        cli_3.6.2        \n[21] rlang_1.1.3       rbibutils_2.2.16  cowplot_1.1.3     munsell_0.5.0    \n[25] splines_4.3.3     withr_3.0.0       yaml_2.3.8        tools_4.3.3      \n[29] ggsignif_0.6.4    checkmate_2.3.1   colorspace_2.1-0  ggpubr_0.6.0     \n[33] broom_1.0.5       vctrs_0.6.5       R6_2.5.1          lifecycle_1.0.4  \n[37] car_3.1-2         htmlwidgets_1.6.4 pkgconfig_2.0.3   pillar_1.9.0     \n[41] gtable_0.3.4      glue_1.7.0        xfun_0.42         tibble_3.2.1     \n[45] tidyselect_1.2.0  knitr_1.45        farver_2.1.1      htmltools_0.5.7  \n[49] carData_3.0-5     rmarkdown_2.26    labeling_0.4.3    testthat_3.2.1   \n[53] compiler_4.3.3   \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Other",
@@ -1751,7 +1751,7 @@
     "href": "graphs/other/cig01.html",
     "title": "CIG01",
     "section": "",
-    "text": "Data Setup\nPlot of Mean and  95% CIs for Mean\nPlot of Confidence Interval Using  a Different Stratification Variable\nPlot of Median and  95% CIs for Median\nPlot of Median and 95% CIs for  Median Using Different Alpha Level\nTable of Mean  and Median\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(nestcolor)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n  filter(PARAMCD == \"ALT\", AVISIT == \"BASELINE\")\n\n\n\n\nThe function stat_mean_ci from the tern package can be used with default values to draw the 95% confidence interval around the mean.\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = SEX,\n    lty = SEX, shape = SEX\n  )\n) +\n  stat_summary(\n    fun.data = tern::stat_mean_ci,\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = mean,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Mean and 95% CIs for mean are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = STRATA2,\n    lty = STRATA2, shape = STRATA2\n  )\n) +\n  stat_summary(\n    fun.data = tern::stat_mean_ci,\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = mean,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Mean and 95% CIs for mean are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\nThe function stat_median_ci from the tern package works similarly to stat_mean_ci.\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = STRATA1,\n    lty = STRATA1, shape = STRATA1\n  )\n) +\n  stat_summary(\n    fun.data = stat_median_ci,\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = median,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Median and 95% CIs for median are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\nTo modify the confidence level for the estimation of the confidence interval, the call to stat_mean_ci (or stat_median_ci) can be slightly modified.\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = SEX,\n    lty = SEX, shape = SEX\n  )\n) +\n  stat_summary(\n    fun.data = function(x) tern::stat_mean_ci(x, conf_level = 0.9),\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = mean,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Mean and 90% CIs for mean are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\nThe corresponding table is simply obtained using the rtables framework:\n\nCodelyt &lt;- basic_table() %&gt;%\n  split_cols_by(var = \"ARMCD\") %&gt;%\n  analyze_vars(vars = \"AVAL\", .stats = c(\"mean_sd\", \"median\"))\ntable &lt;- build_table(lyt = lyt, df = adlb)\ntable\n\n              ARM A        ARM B        ARM C   \n————————————————————————————————————————————————\nMean (SD)   17.7 (9.9)   18.7 (9.8)   19.5 (9.1)\nMedian         17.5         18.2         19.0   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_ci(\n      label = \"Confidence Interval Plot\",\n      x_var = data_extract_spec(\n        dataname = \"ADSL\",\n        select = select_spec(\n          choices = c(\"ARMCD\", \"BMRKR2\"),\n          selected = c(\"ARMCD\"),\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      ),\n      y_var = data_extract_spec(\n        dataname = \"ADLB\",\n        filter = list(\n          filter_spec(\n            vars = \"PARAMCD\",\n            choices = levels(ADLB$PARAMCD),\n            selected = levels(ADLB$PARAMCD)[1],\n            multiple = FALSE,\n            label = \"Select lab:\"\n          ),\n          filter_spec(\n            vars = \"AVISIT\",\n            choices = levels(ADLB$AVISIT),\n            selected = levels(ADLB$AVISIT)[1],\n            multiple = FALSE,\n            label = \"Select visit:\"\n          )\n        ),\n        select = select_spec(\n          label = \"Analyzed Value\",\n          choices = c(\"AVAL\", \"CHG\"),\n          selected = \"AVAL\",\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      ),\n      color = data_extract_spec(\n        dataname = \"ADSL\",\n        select = select_spec(\n          label = \"Color by variable\",\n          choices = c(\"SEX\", \"STRATA1\", \"STRATA2\"),\n          selected = c(\"STRATA1\"),\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      )\n    )\n  ),\n  header = \"Example of Confidence Interval Plot\",\n  footer = tags$p(\n    class = \"text-muted\", \"Source: `teal.modules.clinical::tm_g_ci`\"\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 18:01:11 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       \n [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           \n [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] scda_0.1.6.9017                  dplyr_1.1.4                     \n[11] ggplot2_3.5.0                    tern_0.9.3.9024                 \n[13] rtables_0.6.6.9011               magrittr_2.0.3                  \n[15] formatters_0.5.5.9013           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           munsell_0.5.0           \n[64] scales_1.3.0             xtable_1.8-4             glue_1.7.0              \n[67] emmeans_1.10.0           tools_4.3.3              webshot2_0.1.1          \n[70] webshot_0.5.5            mvtnorm_1.2-4            grid_4.3.3              \n[73] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[76] nlme_3.1-164             cli_3.6.2                textshaping_0.3.7       \n[79] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[82] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[85] htmlwidgets_1.6.4        farver_2.1.1             memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[91] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
+    "text": "Data Setup\nPlot of Mean and  95% CIs for Mean\nPlot of Confidence Interval Using  a Different Stratification Variable\nPlot of Median and  95% CIs for Median\nPlot of Median and 95% CIs for  Median Using Different Alpha Level\nTable of Mean  and Median\nteal App\nReproducibility\n\n\n\n\nCodelibrary(tern)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(scda)\nlibrary(nestcolor)\n\nadlb &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\") %&gt;%\n  filter(PARAMCD == \"ALT\", AVISIT == \"BASELINE\")\n\n\n\n\nThe function stat_mean_ci from the tern package can be used with default values to draw the 95% confidence interval around the mean.\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = SEX,\n    lty = SEX, shape = SEX\n  )\n) +\n  stat_summary(\n    fun.data = tern::stat_mean_ci,\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = mean,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Mean and 95% CIs for mean are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = STRATA2,\n    lty = STRATA2, shape = STRATA2\n  )\n) +\n  stat_summary(\n    fun.data = tern::stat_mean_ci,\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = mean,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Mean and 95% CIs for mean are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\nThe function stat_median_ci from the tern package works similarly to stat_mean_ci.\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = STRATA1,\n    lty = STRATA1, shape = STRATA1\n  )\n) +\n  stat_summary(\n    fun.data = stat_median_ci,\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = median,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Median and 95% CIs for median are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\nTo modify the confidence level for the estimation of the confidence interval, the call to stat_mean_ci (or stat_median_ci) can be slightly modified.\n\n\nCodeplot &lt;- ggplot(\n  data = adlb,\n  mapping = aes(\n    x = ARMCD, y = AVAL, color = SEX,\n    lty = SEX, shape = SEX\n  )\n) +\n  stat_summary(\n    fun.data = function(x) tern::stat_mean_ci(x, conf_level = 0.9),\n    geom = \"errorbar\",\n    width = 0.1,\n    position = position_dodge(width = 0.5)\n  ) +\n  stat_summary(\n    fun = mean,\n    geom = \"point\",\n    position = position_dodge(width = 0.5)\n  ) +\n  labs(\n    title = \"Confidence Interval Plot by Treatment Group\",\n    caption = \"Mean and 90% CIs for mean are displayed.\",\n    x = \"Treatment Group\",\n    y = paste0(adlb$PARAMCD[1], \" (\", adlb$AVALU[1], \")\")\n  )\nplot\n\n\n\n\n\n\n\n\n\nThe corresponding table is simply obtained using the rtables framework:\n\nCodelyt &lt;- basic_table() %&gt;%\n  split_cols_by(var = \"ARMCD\") %&gt;%\n  analyze_vars(vars = \"AVAL\", .stats = c(\"mean_sd\", \"median\"))\ntable &lt;- build_table(lyt = lyt, df = adlb)\ntable\n\n              ARM A        ARM B        ARM C   \n————————————————————————————————————————————————\nMean (SD)   17.7 (9.9)   18.7 (9.8)   19.5 (9.1)\nMedian         17.5         18.2         19.0   \n\n\n\nCodeif (exists(\"tenv\")) {\n  filename &lt;- paste0(gsub(\"\\\\.rmarkdown$\", \"\", knitr::current_input(dir = TRUE)), \".rds\")\n  filepath &lt;- gsub(normalizePath(Sys.getenv(\"QUARTO_PROJECT_DIR\")), file.path(Sys.getenv(\"QUARTO_TESTTHAT_DATA_PATH\"), Sys.getenv(\"QUARTO_PROFILE\")), filename)\n  if (!dir.exists(dirname(filepath))) {\n    dir.create(dirname(filepath), recursive = TRUE)\n  }\n  saveRDS(tenv, filepath)\n}\n\n\n\n\n\nCodelibrary(teal.modules.clinical)\n\n## Data reproducible code\ndata &lt;- teal_data()\ndata &lt;- within(data, {\n  library(scda)\n  ADSL &lt;- synthetic_cdisc_dataset(\"latest\", \"adsl\")\n  ADLB &lt;- synthetic_cdisc_dataset(\"latest\", \"adlb\")\n})\ndatanames &lt;- c(\"ADSL\", \"ADLB\")\ndatanames(data) &lt;- datanames\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames]\n\n## Reusable Configuration For Modules\nADLB &lt;- data[[\"ADLB\"]]\n\n## Setup App\napp &lt;- init(\n  data = data,\n  modules = modules(\n    tm_g_ci(\n      label = \"Confidence Interval Plot\",\n      x_var = data_extract_spec(\n        dataname = \"ADSL\",\n        select = select_spec(\n          choices = c(\"ARMCD\", \"BMRKR2\"),\n          selected = c(\"ARMCD\"),\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      ),\n      y_var = data_extract_spec(\n        dataname = \"ADLB\",\n        filter = list(\n          filter_spec(\n            vars = \"PARAMCD\",\n            choices = levels(ADLB$PARAMCD),\n            selected = levels(ADLB$PARAMCD)[1],\n            multiple = FALSE,\n            label = \"Select lab:\"\n          ),\n          filter_spec(\n            vars = \"AVISIT\",\n            choices = levels(ADLB$AVISIT),\n            selected = levels(ADLB$AVISIT)[1],\n            multiple = FALSE,\n            label = \"Select visit:\"\n          )\n        ),\n        select = select_spec(\n          label = \"Analyzed Value\",\n          choices = c(\"AVAL\", \"CHG\"),\n          selected = \"AVAL\",\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      ),\n      color = data_extract_spec(\n        dataname = \"ADSL\",\n        select = select_spec(\n          label = \"Color by variable\",\n          choices = c(\"SEX\", \"STRATA1\", \"STRATA2\"),\n          selected = c(\"STRATA1\"),\n          multiple = FALSE,\n          fixed = FALSE\n        )\n      )\n    )\n  ),\n  header = \"Example of Confidence Interval Plot\",\n  footer = tags$p(\n    class = \"text-muted\", \"Source: `teal.modules.clinical::tm_g_ci`\"\n  )\n)\n\nshinyApp(app$ui, app$server)\n\n\n\n\n\n\n\n\n\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:59:26 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       \n [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           \n [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            \n [7] shiny_1.8.0                      nestcolor_0.1.2.9009            \n [9] scda_0.1.6.9017                  dplyr_1.1.4                     \n[11] ggplot2_3.5.0                    tern_0.9.3.9024                 \n[13] rtables_0.6.6.9011               magrittr_2.0.3                  \n[15] formatters_0.5.5.9015           \n\nloaded via a namespace (and not attached):\n [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     \n [4] logger_0.3.0             testthat_3.2.1           sandwich_3.1-0          \n [7] rlang_1.1.3              multcomp_1.4-25          compiler_4.3.3          \n[10] systemfonts_1.0.6        callr_3.7.5              vctrs_0.6.5             \n[13] pkgconfig_2.0.3          fastmap_1.1.1            backports_1.4.1         \n[16] ellipsis_0.3.2           fontawesome_0.5.2        labeling_0.4.3          \n[19] utf8_1.2.4               promises_1.2.1           rmarkdown_2.26          \n[22] ps_1.7.6                 ragg_1.2.7               purrr_1.0.2             \n[25] xfun_0.42                shinyvalidate_0.1.3      cachem_1.0.8            \n[28] teal.reporter_0.3.0.9005 jsonlite_1.8.8           later_1.3.2             \n[31] broom_1.0.5              R6_2.5.1                 bslib_0.6.1             \n[34] stringi_1.8.3            brio_1.1.4               jquerylib_0.1.4         \n[37] estimability_1.5         Rcpp_1.0.12              knitr_1.45              \n[40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           \n[43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        \n[46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         \n[49] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          \n[52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           \n[55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            \n[58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           \n[61] generics_0.1.3           chromote_0.2.0           munsell_0.5.0           \n[64] scales_1.3.0             xtable_1.8-4             glue_1.7.0              \n[67] emmeans_1.10.0           tools_4.3.3              webshot2_0.1.1          \n[70] webshot_0.5.5            mvtnorm_1.2-4            grid_4.3.3              \n[73] tidyr_1.3.1              rbibutils_2.2.16         colorspace_2.1-0        \n[76] nlme_3.1-164             cli_3.6.2                textshaping_0.3.7       \n[79] fansi_1.0.6              geepack_1.3.10           gtable_0.3.4            \n[82] sass_0.4.8               digest_0.6.34            TH.data_1.1-2           \n[85] htmlwidgets_1.6.4        farver_2.1.1             memoise_2.0.1           \n[88] htmltools_0.5.7          lifecycle_1.0.4          shinyWidgets_0.8.2      \n[91] mime_0.12                MASS_7.3-60.0.1         \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload",
     "crumbs": [
       "Graphs",
       "Other",
@@ -1763,7 +1763,7 @@
     "href": "repro.html#reproducibility",
     "title": "",
     "section": "Reproducibility",
-    "text": "Reproducibility\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-13 18:01:35 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nloaded via a namespace (and not attached):\n [1] htmlwidgets_1.6.4 compiler_4.3.3    fastmap_1.1.1     cli_3.6.2        \n [5] tools_4.3.3       htmltools_0.5.7   yaml_2.3.8        codetools_0.2-19 \n [9] rmarkdown_2.26    knitr_1.45        jsonlite_1.8.8    xfun_0.42        \n[13] digest_0.6.34     rlang_1.1.3       evaluate_0.23    \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload"
+    "text": "Reproducibility\nTimestamp\n\nCodeSys.time()\n\n[1] \"2024-03-16 17:59:50 UTC\"\n\n\nSession Info\n\nCodesessionInfo()\n\nR version 4.3.3 (2024-02-29)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.3 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 \nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\ntime zone: Etc/UTC\ntzcode source: system (glibc)\n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nloaded via a namespace (and not attached):\n [1] htmlwidgets_1.6.4 compiler_4.3.3    fastmap_1.1.1     cli_3.6.2        \n [5] tools_4.3.3       htmltools_0.5.7   yaml_2.3.8        codetools_0.2-19 \n [9] rmarkdown_2.26    knitr_1.45        jsonlite_1.8.8    xfun_0.42        \n[13] digest_0.6.34     rlang_1.1.3       evaluate_0.23    \n\n\n\n.lock file\nDownload the .lock file and use renv::restore() on it to recreate environment used to generate this website.\n\nCodeparent_file_path &lt;- knitr::current_input(dir = TRUE)\n\nbook_root_dir &lt;- parent_file_path\nwhile (!file.exists(file.path(book_root_dir, \"_quarto.yml\"))) book_root_dir &lt;- dirname(book_root_dir)\n\nlock_path &lt;- file.path(\n  \"../../assets/www/lock\",\n  paste0(\n    gsub(\n      file.path(book_root_dir, \"\"),\n      \"\",\n      gsub(\n        \"\\\\.rmarkdown$\",\n        \"\",\n        knitr::current_input(dir = TRUE)\n      )\n    ),\n    \".lock\"\n  )\n)\n\nwithr::with_options(\n  list(renv.verbose = FALSE),\n  renv::snapshot(parent_file_path, lockfile = lock_path, prompt = FALSE, force = TRUE)\n)\n\n\nDownload"
   },
   {
     "objectID": "CONTRIBUTING.html#table-of-contents",
diff --git a/development/sitemap.xml b/development/sitemap.xml
index 6af5aa6c91..87efc7e7f4 100644
--- a/development/sitemap.xml
+++ b/development/sitemap.xml
@@ -2,578 +2,578 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/test-utils/save_results.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/index.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/appendix/reproducibility.html</loc>
-    <lastmod>2024-03-15T16:10:03.140Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.700Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/other/mng01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/other/ltg01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/other/ippg01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/efficacy/mmrmg01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/efficacy/fstg02.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/efficacy/kmg01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/pharmacokinetic/pkcg01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/pharmacokinetic/pkpg01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/pharmacokinetic/pkpg02.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/graphs/pharmacokinetic/pkpg03.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/lab-results/lbl02b.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/lab-results/lbl02a_rls.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/lab-results/lbl01_rls.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/vital-signs/vsl01.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/adverse-events/ael02_ed.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/adverse-events/ael01_nollt.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/adverse-events/ael03.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/disposition/dsl01.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/ECG/egl01.html</loc>
-    <lastmod>2024-03-15T16:10:03.152Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/concomitant-medications/cml02a_gl.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/efficacy/oncl01.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.712Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/pharmacokinetic/pkpl04.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/pharmacokinetic/pkcl02.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/listings/pharmacokinetic/pkpl02.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/risk-management-plan/rmpt06.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/risk-management-plan/rmpt01.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt10.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt15.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt09.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt01.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt03.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt10_bl.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt02.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt04.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/lab-results/lbt07.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/medical-history/mht01.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/vital-signs/vst02.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet01_aesi.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet02_smq.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet05.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet09.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet06.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet09_smq.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet02.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/adverse-events/aet10.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/disposition/pdt02.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/ADA/adat02.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/ADA/adat01.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/ADA/adat04b.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/ECG/egt01.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/ECG/egt05_qtcat.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/concomitant-medications/cmt01a.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/concomitant-medications/cmt01b.html</loc>
-    <lastmod>2024-03-15T16:10:03.156Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/efficacy/dort01.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/efficacy/rspt01.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/efficacy/rbmit01.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/efficacy/aovt02.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/efficacy/coxt02.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
   </url>
   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/tables/efficacy/aovt03.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.716Z</lastmod>
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   <url>
     <loc>https://insightsengineering.github.io/tlg-catalog/test-utils/envir_hook.html</loc>
-    <lastmod>2024-03-15T16:10:03.160Z</lastmod>
+    <lastmod>2024-03-16T17:25:10.720Z</lastmod>
   </url>
 </urlset>
diff --git a/development/tables/ADA/adat01.html b/development/tables/ADA/adat01.html
index 44cca99091..81d5c851ed 100644
--- a/development/tables/ADA/adat01.html
+++ b/development/tables/ADA/adat01.html
@@ -1720,7 +1720,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:42:19 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1752,7 +1752,7 @@
 other attached packages:
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 [4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[7] formatters_0.5.5.9013
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/ADA/adat02.html b/development/tables/ADA/adat02.html
index de33d797e8..3c722e9223 100644
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+++ b/development/tables/ADA/adat02.html
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:43:50 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:42:31 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1653,7 +1653,7 @@
 
 other attached packages:
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-[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/ADA/adat03.html b/development/tables/ADA/adat03.html
index 783092a79e..fde7dc721f 100644
--- a/development/tables/ADA/adat03.html
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:43:31 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:42:13 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1626,7 +1626,7 @@
 
 other attached packages:
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-[4] formatters_0.5.5.9013 scda_0.1.6.9017       dplyr_1.1.4          
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/ADA/adat04a.html b/development/tables/ADA/adat04a.html
index a8bd5bd285..7fa7989229 100644
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@@ -1751,7 +1751,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:42:25 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1783,7 +1783,7 @@
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/ADA/adat04b.html b/development/tables/ADA/adat04b.html
index f7587b98e0..2f03f1ca7e 100644
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:42:07 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/ECG/egt01.html b/development/tables/ECG/egt01.html
index 530035486f..0cc07519a0 100644
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:41:40 UTC"</code></pre>
 </div>
 </div>
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@@ -1729,12 +1729,12 @@
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-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
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 loaded via a namespace (and not attached):
@@ -1753,7 +1753,7 @@
 [37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
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-[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
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diff --git a/development/tables/ECG/egt02.html b/development/tables/ECG/egt02.html
index 9cb1ad0ca6..b3df1ae93e 100644
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:42:40 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:41:23 UTC"</code></pre>
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 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1637,12 +1637,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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 [13] dplyr_1.1.4                     
 
 loaded via a namespace (and not attached):
@@ -1663,7 +1663,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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diff --git a/development/tables/ECG/egt02_files/figure-html/teal-1.png b/development/tables/ECG/egt02_files/figure-html/teal-1.png
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diff --git a/development/tables/ECG/egt03.html b/development/tables/ECG/egt03.html
index 84ba2fc615..23adaf748e 100644
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+++ b/development/tables/ECG/egt03.html
@@ -1693,7 +1693,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:43:18 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:42:00 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1723,12 +1723,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
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  [7] shiny_1.8.0                      dplyr_1.1.4                     
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-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
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 [13] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
@@ -1749,7 +1749,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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diff --git a/development/tables/ECG/egt03_files/figure-html/teal-1.png b/development/tables/ECG/egt03_files/figure-html/teal-1.png
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diff --git a/development/tables/ECG/egt04.html b/development/tables/ECG/egt04.html
index 1a16c721ae..d6014dff3a 100644
--- a/development/tables/ECG/egt04.html
+++ b/development/tables/ECG/egt04.html
@@ -1553,7 +1553,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:42:09 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:40:53 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1584,7 +1584,7 @@
 
 other attached packages:
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-[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/ECG/egt05_qtcat.html b/development/tables/ECG/egt05_qtcat.html
index e8e044a4b9..3c0c13365e 100644
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:42:25 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:41:08 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1765,13 +1765,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
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+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
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  [7] shiny_1.8.0                      dplyr_1.1.4                     
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-[13] formatters_0.5.5.9013           
+[13] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1791,7 +1791,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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diff --git a/development/tables/ECG/egt05_qtcat_files/figure-html/teal-1.png b/development/tables/ECG/egt05_qtcat_files/figure-html/teal-1.png
index ad98d235b6..a2ad090f66 100644
Binary files a/development/tables/ECG/egt05_qtcat_files/figure-html/teal-1.png and b/development/tables/ECG/egt05_qtcat_files/figure-html/teal-1.png differ
diff --git a/development/tables/adverse-events/aet01.html b/development/tables/adverse-events/aet01.html
index a81068553e..2375775349 100644
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 loaded via a namespace (and not attached):
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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@@ -2428,13 +2428,13 @@
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 loaded via a namespace (and not attached):
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
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 [13] dplyr_1.1.4                     
 
 loaded via a namespace (and not attached):
@@ -2565,7 +2565,7 @@
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index f298f45467..1283404744 100644
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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index f20ee16181..1a1f68e2c0 100644
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 </details><div class="cell-output cell-output-stdout">
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
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 loaded via a namespace (and not attached):
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 </details><div class="cell-output cell-output-stdout">
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
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-[11] formatters_0.5.5.9013            dplyr_1.1.4                     
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 [13] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
@@ -2054,7 +2054,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 loaded via a namespace (and not attached):
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 loaded via a namespace (and not attached):
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 loaded via a namespace (and not attached):
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
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 loaded via a namespace (and not attached):
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 loaded via a namespace (and not attached):
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 loaded via a namespace (and not attached):
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/efficacy/aovt01.html b/development/tables/efficacy/aovt01.html
index 76be052d72..37217cfc5b 100644
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 </details><div class="cell-output cell-output-stdout">
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
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 loaded via a namespace (and not attached):
@@ -1661,7 +1661,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 </details><div class="cell-output cell-output-stdout">
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 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:38:13 UTC"</code></pre>
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 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
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 loaded via a namespace (and not attached):
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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 [15] dplyr_1.1.4                     
 
 loaded via a namespace (and not attached):
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-[49] websocket_1.4.1          processx_3.8.3           teal.widgets_0.4.2.9006 
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 </details><div class="cell-output cell-output-stdout">
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 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
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-[13] magrittr_2.0.3                   formatters_0.5.5.9013           
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 loaded via a namespace (and not attached):
  [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     
@@ -1760,7 +1760,7 @@
 [40] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [43] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
 [46] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         
-[49] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
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 [52] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           
 [55] evaluate_0.23            survival_3.5-8           pillar_1.9.0            
 [58] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           
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diff --git a/development/tables/efficacy/ratet01.html b/development/tables/efficacy/ratet01.html
index f6447470b4..f9cd40f6e0 100644
--- a/development/tables/efficacy/ratet01.html
+++ b/development/tables/efficacy/ratet01.html
@@ -1570,7 +1570,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:38:16 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:37:09 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1601,7 +1601,7 @@
 
 other attached packages:
 [1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   
-[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      
+[4] magrittr_2.0.3        formatters_0.5.5.9015 scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
  [1] sandwich_3.1-0    utf8_1.2.4        generics_0.1.3    tidyr_1.3.1      
diff --git a/development/tables/efficacy/rbmit01.html b/development/tables/efficacy/rbmit01.html
index 9f1d45ac80..aa8e427488 100644
--- a/development/tables/efficacy/rbmit01.html
+++ b/development/tables/efficacy/rbmit01.html
@@ -1555,8 +1555,8 @@
 <pre><code>
 SAMPLING FOR MODEL 'MMRM' NOW (CHAIN 1).
 Chain 1: 
-Chain 1: Gradient evaluation took 0.000367 seconds
-Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.67 seconds.
+Chain 1: Gradient evaluation took 0.000349 seconds
+Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.49 seconds.
 Chain 1: Adjust your expectations accordingly!
 Chain 1: 
 Chain 1: 
@@ -1573,9 +1573,9 @@
 Chain 1: Iteration: 1160 / 1200 [ 96%]  (Sampling)
 Chain 1: Iteration: 1200 / 1200 [100%]  (Sampling)
 Chain 1: 
-Chain 1:  Elapsed Time: 0.559 seconds (Warm-up)
-Chain 1:                1.791 seconds (Sampling)
-Chain 1:                2.35 seconds (Total)
+Chain 1:  Elapsed Time: 0.557 seconds (Warm-up)
+Chain 1:                1.78 seconds (Sampling)
+Chain 1:                2.337 seconds (Total)
 Chain 1: </code></pre>
 </div>
 </div>
@@ -1713,7 +1713,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:39:33 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:38:24 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1745,7 +1745,7 @@
 other attached packages:
 [1] broom_1.0.5           dplyr_1.1.4           tern.rbmi_0.1.2.9012 
 [4] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[7] formatters_0.5.5.9013 rbmi_1.2.6           
+[7] formatters_0.5.5.9015 rbmi_1.2.6           
 
 loaded via a namespace (and not attached):
  [1] gtable_0.3.4       TMB_1.9.10         xfun_0.42          ggplot2_3.5.0     
diff --git a/development/tables/efficacy/rspt01.html b/development/tables/efficacy/rspt01.html
index e05861e5d3..514946c9ac 100644
--- a/development/tables/efficacy/rspt01.html
+++ b/development/tables/efficacy/rspt01.html
@@ -1883,7 +1883,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:40:10 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:38:59 UTC"</code></pre>
 </div>
 </div>
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@@ -1913,12 +1913,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
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+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
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  [7] shiny_1.8.0                      scda_0.1.6.9017                 
  [9] tern_0.9.3.9024                  rtables_0.6.6.9011              
-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
+[11] magrittr_2.0.3                   formatters_0.5.5.9015           
 [13] dplyr_1.1.4                     
 
 loaded via a namespace (and not attached):
@@ -1939,7 +1939,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/efficacy/rspt01_files/figure-html/teal-1.png b/development/tables/efficacy/rspt01_files/figure-html/teal-1.png
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diff --git a/development/tables/efficacy/ttet01.html b/development/tables/efficacy/ttet01.html
index 56d15d7635..bb3a16dac7 100644
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@@ -2098,7 +2098,7 @@
 <div class="cell" data-renv.ignore="true">
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:40:33 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:39:21 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -2128,13 +2128,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
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+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
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  [7] shiny_1.8.0                      dplyr_1.1.4                     
  [9] scda_0.1.6.9017                  tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013           
+[13] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -2154,7 +2154,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/efficacy/ttet01_files/figure-html/teal-1.png b/development/tables/efficacy/ttet01_files/figure-html/teal-1.png
index f75be52df0..16de8ee2e8 100644
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diff --git a/development/tables/exposure/ext01.html b/development/tables/exposure/ext01.html
index 21065de732..a94c30c2a2 100644
--- a/development/tables/exposure/ext01.html
+++ b/development/tables/exposure/ext01.html
@@ -1884,7 +1884,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:36:39 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:35:35 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1914,13 +1914,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
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  [7] shiny_1.8.0                      tidyr_1.3.1                     
  [9] dplyr_1.1.4                      tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013            scda_0.1.6.9017                 
+[13] formatters_0.5.5.9015            scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
  [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     
@@ -1938,7 +1938,7 @@
 [37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
 [43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         
-[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
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 [49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           
 [52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            
 [55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           
diff --git a/development/tables/exposure/ext01_files/figure-html/teal-1.png b/development/tables/exposure/ext01_files/figure-html/teal-1.png
index 505447ed5e..da014fa34a 100644
Binary files a/development/tables/exposure/ext01_files/figure-html/teal-1.png and b/development/tables/exposure/ext01_files/figure-html/teal-1.png differ
diff --git a/development/tables/lab-results/lbt01.html b/development/tables/lab-results/lbt01.html
index 288dc26bd4..759796cf61 100644
--- a/development/tables/lab-results/lbt01.html
+++ b/development/tables/lab-results/lbt01.html
@@ -1641,7 +1641,7 @@
 <div class="cell" data-renv.ignore="true">
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:53:01 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:51:31 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1671,12 +1671,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      dplyr_1.1.4                     
  [9] tern_0.9.3.9024                  rtables_0.6.6.9011              
-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
+[11] magrittr_2.0.3                   formatters_0.5.5.9015           
 [13] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
@@ -1695,7 +1695,7 @@
 [37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
 [43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         
-[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
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 [49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           
 [52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            
 [55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           
diff --git a/development/tables/lab-results/lbt01_files/figure-html/teal-1.png b/development/tables/lab-results/lbt01_files/figure-html/teal-1.png
index 1e7505ba89..aa9040d14a 100644
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diff --git a/development/tables/lab-results/lbt02.html b/development/tables/lab-results/lbt02.html
index c1eab1b22b..7cd7cfabc0 100644
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+++ b/development/tables/lab-results/lbt02.html
@@ -1612,7 +1612,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:51:53 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:50:24 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1642,12 +1642,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] dplyr_1.1.4                      teal.modules.clinical_0.9.0.9009
- [3] teal.transform_0.5.0.9001        teal_0.15.2.9003                
- [5] teal.slice_0.5.0.9004            teal.data_0.5.0.9002            
+ [1] dplyr_1.1.4                      teal.modules.clinical_0.9.0.9010
+ [3] teal.transform_0.5.0.9002        teal_0.15.2.9004                
+ [5] teal.slice_0.5.0.9005            teal.data_0.5.0.9003            
  [7] teal.code_0.5.0.9003             shiny_1.8.0                     
  [9] tern_0.9.3.9024                  rtables_0.6.6.9011              
-[11] magrittr_2.0.3                   formatters_0.5.5.9013           
+[11] magrittr_2.0.3                   formatters_0.5.5.9015           
 [13] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
@@ -1666,7 +1666,7 @@
 [37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
 [43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         
-[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
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 [49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           
 [52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            
 [55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           
diff --git a/development/tables/lab-results/lbt02_files/figure-html/teal-1.png b/development/tables/lab-results/lbt02_files/figure-html/teal-1.png
index 20b189b00d..2543b81ff6 100644
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diff --git a/development/tables/lab-results/lbt03.html b/development/tables/lab-results/lbt03.html
index 11aa11e5d1..315f4d7387 100644
--- a/development/tables/lab-results/lbt03.html
+++ b/development/tables/lab-results/lbt03.html
@@ -1713,7 +1713,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:52:39 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:51:10 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1743,12 +1743,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      tern_0.9.3.9024                 
  [9] rtables_0.6.6.9011               magrittr_2.0.3                  
-[11] formatters_0.5.5.9013            dplyr_1.1.4                     
+[11] formatters_0.5.5.9015            dplyr_1.1.4                     
 [13] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
@@ -1767,7 +1767,7 @@
 [37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
 [43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         
-[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
+[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          
 [49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           
 [52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            
 [55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           
diff --git a/development/tables/lab-results/lbt03_files/figure-html/teal-1.png b/development/tables/lab-results/lbt03_files/figure-html/teal-1.png
index b389f44152..256565f538 100644
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diff --git a/development/tables/lab-results/lbt04.html b/development/tables/lab-results/lbt04.html
index dca9738db9..8510857740 100644
--- a/development/tables/lab-results/lbt04.html
+++ b/development/tables/lab-results/lbt04.html
@@ -1575,7 +1575,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:51:21 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:49:53 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1605,12 +1605,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      tern_0.9.3.9024                 
  [9] rtables_0.6.6.9011               magrittr_2.0.3                  
-[11] formatters_0.5.5.9013            dplyr_1.1.4                     
+[11] formatters_0.5.5.9015            dplyr_1.1.4                     
 [13] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
@@ -1631,7 +1631,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
+[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       
 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/lab-results/lbt04_files/figure-html/teal-1.png b/development/tables/lab-results/lbt04_files/figure-html/teal-1.png
index 085d46370b..1c4f74a78b 100644
Binary files a/development/tables/lab-results/lbt04_files/figure-html/teal-1.png and b/development/tables/lab-results/lbt04_files/figure-html/teal-1.png differ
diff --git a/development/tables/lab-results/lbt05.html b/development/tables/lab-results/lbt05.html
index de5450ddf7..7d9665fc68 100644
--- a/development/tables/lab-results/lbt05.html
+++ b/development/tables/lab-results/lbt05.html
@@ -1699,7 +1699,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:53:39 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:52:08 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1730,7 +1730,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
+[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/lab-results/lbt06.html b/development/tables/lab-results/lbt06.html
index 61d4d80b27..0dad4fa528 100644
--- a/development/tables/lab-results/lbt06.html
+++ b/development/tables/lab-results/lbt06.html
@@ -1731,7 +1731,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:52:24 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:50:55 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1762,7 +1762,7 @@
 
 other attached packages:
 [1] dplyr_1.1.4           tern_0.9.3.9024       rtables_0.6.6.9011   
-[4] magrittr_2.0.3        formatters_0.5.5.9013 scda_0.1.6.9017      
+[4] magrittr_2.0.3        formatters_0.5.5.9015 scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/lab-results/lbt07.html b/development/tables/lab-results/lbt07.html
index 84274e535a..ee5a09cb39 100644
--- a/development/tables/lab-results/lbt07.html
+++ b/development/tables/lab-results/lbt07.html
@@ -1642,7 +1642,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:51:01 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:49:33 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1672,13 +1672,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      forcats_1.0.0                   
  [9] dplyr_1.1.4                      tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013            scda_0.1.6.9017                 
+[13] formatters_0.5.5.9015            scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1698,7 +1698,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
+[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       
 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/lab-results/lbt07_files/figure-html/teal-1.png b/development/tables/lab-results/lbt07_files/figure-html/teal-1.png
index b481e34a02..609cf5e1a4 100644
Binary files a/development/tables/lab-results/lbt07_files/figure-html/teal-1.png and b/development/tables/lab-results/lbt07_files/figure-html/teal-1.png differ
diff --git a/development/tables/lab-results/lbt08.html b/development/tables/lab-results/lbt08.html
index 4e105a714d..0986b2ebc5 100644
--- a/development/tables/lab-results/lbt08.html
+++ b/development/tables/lab-results/lbt08.html
@@ -1553,7 +1553,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:52:46 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:51:17 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1584,7 +1584,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
+[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/lab-results/lbt09.html b/development/tables/lab-results/lbt09.html
index 2372ec900d..43dc0246ea 100644
--- a/development/tables/lab-results/lbt09.html
+++ b/development/tables/lab-results/lbt09.html
@@ -1692,7 +1692,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:53:32 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:52:02 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1723,7 +1723,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
+[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/lab-results/lbt10.html b/development/tables/lab-results/lbt10.html
index 87a36f5971..b90b5a7c8e 100644
--- a/development/tables/lab-results/lbt10.html
+++ b/development/tables/lab-results/lbt10.html
@@ -1563,7 +1563,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:54:06 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:52:35 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1594,7 +1594,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
+[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/lab-results/lbt10_bl.html b/development/tables/lab-results/lbt10_bl.html
index 9948c554d1..afbdf0cbda 100644
--- a/development/tables/lab-results/lbt10_bl.html
+++ b/development/tables/lab-results/lbt10_bl.html
@@ -1563,7 +1563,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:52:16 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:50:47 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1594,7 +1594,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
+[4] formatters_0.5.5.9015 dplyr_1.1.4           scda_0.1.6.9017      
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/lab-results/lbt11.html b/development/tables/lab-results/lbt11.html
index 8d32be6dee..2e401ef364 100644
--- a/development/tables/lab-results/lbt11.html
+++ b/development/tables/lab-results/lbt11.html
@@ -1714,7 +1714,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:52:09 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:50:40 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1744,13 +1744,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      dplyr_1.1.4                     
  [9] scda_0.1.6.9017                  tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013           
+[13] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1770,7 +1770,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
+[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       
 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/lab-results/lbt11_bl.html b/development/tables/lab-results/lbt11_bl.html
index 2bbdf670fb..8a6e6522f9 100644
--- a/development/tables/lab-results/lbt11_bl.html
+++ b/development/tables/lab-results/lbt11_bl.html
@@ -1712,7 +1712,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:51:37 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:50:09 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1742,13 +1742,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      dplyr_1.1.4                     
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 loaded via a namespace (and not attached):
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index bd210f356c..4f89831e2e 100644
--- a/development/tables/lab-results/lbt12.html
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/lab-results/lbt12_bl.html b/development/tables/lab-results/lbt12_bl.html
index 5835764461..158701242a 100644
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:52:29 UTC"</code></pre>
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 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1573,7 +1573,7 @@
 
 other attached packages:
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-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/lab-results/lbt13.html b/development/tables/lab-results/lbt13.html
index 9ef9dbacc7..868a2f6d45 100644
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 </details><div class="cell-output cell-output-stdout">
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 other attached packages:
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 loaded via a namespace (and not attached):
@@ -2664,7 +2664,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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diff --git a/development/tables/lab-results/lbt13_files/figure-html/teal-1.png b/development/tables/lab-results/lbt13_files/figure-html/teal-1.png
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diff --git a/development/tables/lab-results/lbt14.html b/development/tables/lab-results/lbt14.html
index 437f6697b8..b5093f7fe7 100644
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 </details><div class="cell-output cell-output-stdout">
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 loaded via a namespace (and not attached):
@@ -2268,7 +2268,7 @@
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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diff --git a/development/tables/lab-results/lbt14_files/figure-html/teal-1.png b/development/tables/lab-results/lbt14_files/figure-html/teal-1.png
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diff --git a/development/tables/lab-results/lbt15.html b/development/tables/lab-results/lbt15.html
index 588768b059..c9de64ef69 100644
--- a/development/tables/lab-results/lbt15.html
+++ b/development/tables/lab-results/lbt15.html
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:53:54 UTC"</code></pre>
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 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1695,13 +1695,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
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 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1721,7 +1721,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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diff --git a/development/tables/lab-results/lbt15_files/figure-html/teal-1.png b/development/tables/lab-results/lbt15_files/figure-html/teal-1.png
index c068e3f0d3..1216454564 100644
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diff --git a/development/tables/medical-history/mht01.html b/development/tables/medical-history/mht01.html
index 9498a76f09..6b30b8ae52 100644
--- a/development/tables/medical-history/mht01.html
+++ b/development/tables/medical-history/mht01.html
@@ -1752,7 +1752,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:50:18 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:48:51 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1782,13 +1782,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
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  [7] shiny_1.8.0                      scda_0.1.6.9017                 
  [9] dplyr_1.1.4                      tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013           
+[13] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1808,7 +1808,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
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diff --git a/development/tables/medical-history/mht01_files/figure-html/teal-1.png b/development/tables/medical-history/mht01_files/figure-html/teal-1.png
index a43278c6cf..4e1ae48b1d 100644
Binary files a/development/tables/medical-history/mht01_files/figure-html/teal-1.png and b/development/tables/medical-history/mht01_files/figure-html/teal-1.png differ
diff --git a/development/tables/pharmacokinetic/pkct01.html b/development/tables/pharmacokinetic/pkct01.html
index 5c556bd5b3..dec1c118ae 100644
--- a/development/tables/pharmacokinetic/pkct01.html
+++ b/development/tables/pharmacokinetic/pkct01.html
@@ -1699,7 +1699,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:35:12 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:34:10 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1730,7 +1730,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/pharmacokinetic/pkpt02.html b/development/tables/pharmacokinetic/pkpt02.html
index eaba2edf0c..acd345e095 100644
--- a/development/tables/pharmacokinetic/pkpt02.html
+++ b/development/tables/pharmacokinetic/pkpt02.html
@@ -1602,7 +1602,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:35:29 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:34:27 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1633,7 +1633,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/pharmacokinetic/pkpt03.html b/development/tables/pharmacokinetic/pkpt03.html
index b176d83014..a879183e5c 100644
--- a/development/tables/pharmacokinetic/pkpt03.html
+++ b/development/tables/pharmacokinetic/pkpt03.html
@@ -1714,7 +1714,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:35:04 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:34:02 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1745,7 +1745,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/pharmacokinetic/pkpt04.html b/development/tables/pharmacokinetic/pkpt04.html
index 3de5d9dba1..34fa94363d 100644
--- a/development/tables/pharmacokinetic/pkpt04.html
+++ b/development/tables/pharmacokinetic/pkpt04.html
@@ -1618,7 +1618,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:34:50 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:33:49 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1649,7 +1649,7 @@
 
 other attached packages:
 [1] tern_0.9.3.9024       rtables_0.6.6.9011    magrittr_2.0.3       
-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/pharmacokinetic/pkpt05.html b/development/tables/pharmacokinetic/pkpt05.html
index a58f82e4d3..a5e0eddaa0 100644
--- a/development/tables/pharmacokinetic/pkpt05.html
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@@ -1590,7 +1590,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:34:44 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:33:44 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1621,7 +1621,7 @@
 
 other attached packages:
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-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/pharmacokinetic/pkpt06.html b/development/tables/pharmacokinetic/pkpt06.html
index 4731942848..1d7ef1fb2c 100644
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:35:41 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:34:38 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1603,7 +1603,7 @@
 
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-[4] magrittr_2.0.3        formatters_0.5.5.9013 dplyr_1.1.4          
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 loaded via a namespace (and not attached):
diff --git a/development/tables/pharmacokinetic/pkpt07.html b/development/tables/pharmacokinetic/pkpt07.html
index 34e95eda4d..57b0b4ad77 100644
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:34:33 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1614,7 +1614,7 @@
 
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-[4] magrittr_2.0.3        formatters_0.5.5.9013 dplyr_1.1.4          
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 loaded via a namespace (and not attached):
diff --git a/development/tables/pharmacokinetic/pkpt08.html b/development/tables/pharmacokinetic/pkpt08.html
index 9e1e6cab15..66d6f34395 100644
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:34:16 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1600,7 +1600,7 @@
 
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-[4] formatters_0.5.5.9013 scda_0.1.6.9017       dplyr_1.1.4          
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/pharmacokinetic/pkpt11.html b/development/tables/pharmacokinetic/pkpt11.html
index af8857f17b..fb3dfe94b8 100644
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:34:21 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1659,7 +1659,7 @@
 
 other attached packages:
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-[4] formatters_0.5.5.9013 dplyr_1.1.4           scda_0.1.6.9017      
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 loaded via a namespace (and not attached):
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diff --git a/development/tables/risk-management-plan/rmpt01.html b/development/tables/risk-management-plan/rmpt01.html
index e8841a8318..ec6fdee128 100644
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@@ -1625,7 +1625,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:54:36 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:05 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1655,13 +1655,13 @@
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 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
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 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1681,7 +1681,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
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-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
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 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/risk-management-plan/rmpt01_files/figure-html/teal-1.png b/development/tables/risk-management-plan/rmpt01_files/figure-html/teal-1.png
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diff --git a/development/tables/risk-management-plan/rmpt03.html b/development/tables/risk-management-plan/rmpt03.html
index 314d833c18..15aeb84861 100644
--- a/development/tables/risk-management-plan/rmpt03.html
+++ b/development/tables/risk-management-plan/rmpt03.html
@@ -1684,7 +1684,7 @@
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 </details><div class="cell-output cell-output-stdout">
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+<pre><code>[1] "2024-03-16 17:52:50 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1714,13 +1714,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
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  [7] shiny_1.8.0                      dplyr_1.1.4                     
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 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013           
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 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1740,7 +1740,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
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 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/risk-management-plan/rmpt03_files/figure-html/teal-1.png b/development/tables/risk-management-plan/rmpt03_files/figure-html/teal-1.png
index 580a329c70..17a477f1b4 100644
Binary files a/development/tables/risk-management-plan/rmpt03_files/figure-html/teal-1.png and b/development/tables/risk-management-plan/rmpt03_files/figure-html/teal-1.png differ
diff --git a/development/tables/risk-management-plan/rmpt04.html b/development/tables/risk-management-plan/rmpt04.html
index b560253f7b..dee1f047d8 100644
--- a/development/tables/risk-management-plan/rmpt04.html
+++ b/development/tables/risk-management-plan/rmpt04.html
@@ -1606,7 +1606,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:15 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:40 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1636,13 +1636,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
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- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
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+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
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  [7] shiny_1.8.0                      dplyr_1.1.4                     
  [9] scda_0.1.6.9017                  tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013           
+[13] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1662,7 +1662,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
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 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/risk-management-plan/rmpt04_files/figure-html/teal-1.png b/development/tables/risk-management-plan/rmpt04_files/figure-html/teal-1.png
index b756856009..35aa75991c 100644
Binary files a/development/tables/risk-management-plan/rmpt04_files/figure-html/teal-1.png and b/development/tables/risk-management-plan/rmpt04_files/figure-html/teal-1.png differ
diff --git a/development/tables/risk-management-plan/rmpt05.html b/development/tables/risk-management-plan/rmpt05.html
index d4bbe8c8e0..af97dd283c 100644
--- a/development/tables/risk-management-plan/rmpt05.html
+++ b/development/tables/risk-management-plan/rmpt05.html
@@ -1612,7 +1612,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:54:52 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:19 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1642,13 +1642,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
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  [7] shiny_1.8.0                      dplyr_1.1.4                     
  [9] scda_0.1.6.9017                  tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013           
+[13] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] tidyselect_1.2.0         fastmap_1.1.1            TH.data_1.1-2           
@@ -1668,7 +1668,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
+[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       
 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/risk-management-plan/rmpt05_files/figure-html/teal-1.png b/development/tables/risk-management-plan/rmpt05_files/figure-html/teal-1.png
index 311abbaaa2..632c06f1df 100644
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diff --git a/development/tables/risk-management-plan/rmpt06.html b/development/tables/risk-management-plan/rmpt06.html
index fb9e6c88c5..18d06252f9 100644
--- a/development/tables/risk-management-plan/rmpt06.html
+++ b/development/tables/risk-management-plan/rmpt06.html
@@ -1853,7 +1853,7 @@
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 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb13"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:55:00 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:53:26 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1884,7 +1884,7 @@
 
 other attached packages:
 [1] scda_0.1.6.9017       dplyr_1.1.4           tern_0.9.3.9024      
-[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9013
+[4] rtables_0.6.6.9011    magrittr_2.0.3        formatters_0.5.5.9015
 
 loaded via a namespace (and not attached):
  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
diff --git a/development/tables/safety/enrollment01.html b/development/tables/safety/enrollment01.html
index b7145218d7..d1edd3020c 100644
--- a/development/tables/safety/enrollment01.html
+++ b/development/tables/safety/enrollment01.html
@@ -2099,7 +2099,7 @@
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 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:50:02 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:48:35 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -2129,12 +2129,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      tern_0.9.3.9024                 
  [9] rtables_0.6.6.9011               magrittr_2.0.3                  
-[11] formatters_0.5.5.9013            scda_0.1.6.9017                 
+[11] formatters_0.5.5.9015            scda_0.1.6.9017                 
 [13] forcats_1.0.0                    dplyr_1.1.4                     
 
 loaded via a namespace (and not attached):
@@ -2155,7 +2155,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
+[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       
 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/safety/enrollment01_files/figure-html/teal-1.png b/development/tables/safety/enrollment01_files/figure-html/teal-1.png
index 1ec1f67d4c..ba1ec64229 100644
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diff --git a/development/tables/vital-signs/vst01.html b/development/tables/vital-signs/vst01.html
index a3a5f2fad8..82fe85e7b9 100644
--- a/development/tables/vital-signs/vst01.html
+++ b/development/tables/vital-signs/vst01.html
@@ -1733,7 +1733,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:49:16 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:47:50 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1763,13 +1763,13 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      dplyr_1.1.4                     
  [9] scda_0.1.6.9017                  tern_0.9.3.9024                 
 [11] rtables_0.6.6.9011               magrittr_2.0.3                  
-[13] formatters_0.5.5.9013           
+[13] formatters_0.5.5.9015           
 
 loaded via a namespace (and not attached):
  [1] Rdpack_2.6               formatR_1.14             tern.gee_0.1.3.9004     
@@ -1787,7 +1787,7 @@
 [37] zoo_1.8-12               teal.logger_0.1.3.9011   httpuv_1.6.14           
 [40] Matrix_1.6-5             splines_4.3.3            tidyselect_1.2.0        
 [43] yaml_2.3.8               codetools_0.2-19         websocket_1.4.1         
-[46] processx_3.8.3           teal.widgets_0.4.2.9006  lattice_0.22-5          
+[46] processx_3.8.3           teal.widgets_0.4.2.9008  lattice_0.22-5          
 [49] tibble_3.2.1             withr_3.0.0              coda_0.19-4.1           
 [52] evaluate_0.23            survival_3.5-8           pillar_1.9.0            
 [55] shinycssloaders_1.0.0    checkmate_2.3.1          shinyjs_2.1.0           
diff --git a/development/tables/vital-signs/vst01_files/figure-html/teal-1.png b/development/tables/vital-signs/vst01_files/figure-html/teal-1.png
index c279f96d19..2cdc5d1b19 100644
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diff --git a/development/tables/vital-signs/vst02.html b/development/tables/vital-signs/vst02.html
index 8741e99712..a16bb88492 100644
--- a/development/tables/vital-signs/vst02.html
+++ b/development/tables/vital-signs/vst02.html
@@ -1628,7 +1628,7 @@
 <div class="cell" data-renv.ignore="true">
 <details open="" class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">Sys.time</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details><div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-03-13 17:49:32 UTC"</code></pre>
+<pre><code>[1] "2024-03-16 17:48:06 UTC"</code></pre>
 </div>
 </div>
 </section><section id="session-info" class="level3"><h3 class="anchored" data-anchor-id="session-info">Session Info</h3>
@@ -1658,12 +1658,12 @@
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
- [1] teal.modules.clinical_0.9.0.9009 teal.transform_0.5.0.9001       
- [3] teal_0.15.2.9003                 teal.slice_0.5.0.9004           
- [5] teal.data_0.5.0.9002             teal.code_0.5.0.9003            
+ [1] teal.modules.clinical_0.9.0.9010 teal.transform_0.5.0.9002       
+ [3] teal_0.15.2.9004                 teal.slice_0.5.0.9005           
+ [5] teal.data_0.5.0.9003             teal.code_0.5.0.9003            
  [7] shiny_1.8.0                      tern_0.9.3.9024                 
  [9] rtables_0.6.6.9011               magrittr_2.0.3                  
-[11] formatters_0.5.5.9013            dplyr_1.1.4                     
+[11] formatters_0.5.5.9015            dplyr_1.1.4                     
 [13] scda_0.1.6.9017                 
 
 loaded via a namespace (and not attached):
@@ -1684,7 +1684,7 @@
 [43] cachem_1.0.8             splines_4.3.3            formatR_1.14            
 [46] vctrs_0.6.5              webshot_0.5.5            Matrix_1.6-5            
 [49] sandwich_3.1-0           jsonlite_1.8.8           callr_3.7.5             
-[52] teal.widgets_0.4.2.9006  testthat_3.2.1           fontawesome_0.5.2       
+[52] teal.widgets_0.4.2.9008  testthat_3.2.1           fontawesome_0.5.2       
 [55] jquerylib_0.1.4          tidyr_1.3.1              glue_1.7.0              
 [58] ps_1.7.6                 codetools_0.2-19         stringi_1.8.3           
 [61] gtable_0.3.4             later_1.3.2              shinycssloaders_1.0.0   
diff --git a/development/tables/vital-signs/vst02_files/figure-html/teal-1.png b/development/tables/vital-signs/vst02_files/figure-html/teal-1.png
index 8f0cd66246..1eee82f82d 100644
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