diff --git a/main/404.html b/main/404.html
index a9959f17b9..7bf761eeaf 100644
--- a/main/404.html
+++ b/main/404.html
@@ -25,7 +25,7 @@
     
     <a class="navbar-brand me-2" href="https://insightsengineering.github.io/tern/index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -51,32 +51,6 @@
 <li class="nav-item">
   <a class="nav-link" href="https://insightsengineering.github.io/tern/news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="https://insightsengineering.github.io/tern/#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/CODE_OF_CONDUCT.html b/main/CODE_OF_CONDUCT.html
index d05cb43d3f..3261fecffe 100644
--- a/main/CODE_OF_CONDUCT.html
+++ b/main/CODE_OF_CONDUCT.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/CONTRIBUTING.html b/main/CONTRIBUTING.html
index b5121eebf2..48ddc2b7cf 100644
--- a/main/CONTRIBUTING.html
+++ b/main/CONTRIBUTING.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/LICENSE-text.html b/main/LICENSE-text.html
index ee1652edfc..a27f76dd0d 100644
--- a/main/LICENSE-text.html
+++ b/main/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/SECURITY.html b/main/SECURITY.html
index 231153b72d..1f9b9f7904 100644
--- a/main/SECURITY.html
+++ b/main/SECURITY.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/articles/index.html b/main/articles/index.html
index 2c74cf7054..9c9522f4c8 100644
--- a/main/articles/index.html
+++ b/main/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/articles/missing_values.html b/main/articles/missing_values.html
index 037b511bad..fb95cef572 100644
--- a/main/articles/missing_values.html
+++ b/main/articles/missing_values.html
@@ -27,7 +27,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -53,32 +53,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/articles/tables.html b/main/articles/tables.html
index e4f02526de..15046f1450 100644
--- a/main/articles/tables.html
+++ b/main/articles/tables.html
@@ -27,7 +27,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -53,32 +53,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/articles/tern.html b/main/articles/tern.html
index 6e633ba282..af25388e64 100644
--- a/main/articles/tern.html
+++ b/main/articles/tern.html
@@ -27,7 +27,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -53,32 +53,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html
index 2463df9384..c6b6f02080 100644
--- a/main/articles/tern_formats.html
+++ b/main/articles/tern_formats.html
@@ -27,7 +27,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -53,32 +53,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/authors.html b/main/authors.html
index fde7773071..ede626553c 100644
--- a/main/authors.html
+++ b/main/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
@@ -138,13 +112,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Zhu J, Sabanés Bové D, Stoilova J, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2023).
 <em>tern: Create Common TLGs Used in Clinical Trials</em>.
-R package version 0.9.1.9013, <a href="https://github.com/insightsengineering/tern" class="external-link">https://github.com/insightsengineering/tern</a>. 
+R package version 0.9.2, <a href="https://github.com/insightsengineering/tern" class="external-link">https://github.com/insightsengineering/tern</a>. 
 </p>
       <pre>@Manual{,
   title = {tern: Create Common TLGs Used in Clinical Trials},
   author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li},
   year = {2023},
-  note = {R package version 0.9.1.9013},
+  note = {R package version 0.9.2},
   url = {https://github.com/insightsengineering/tern},
 }</pre>
     </div>
diff --git a/main/index.html b/main/index.html
index f107cbf7e6..de39fc9586 100644
--- a/main/index.html
+++ b/main/index.html
@@ -29,7 +29,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -55,32 +55,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/news/index.html b/main/news/index.html
index 1c6765a787..ebee43c0c3 100644
--- a/main/news/index.html
+++ b/main/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="active nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
@@ -89,23 +63,23 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="0.9.1.9013" id="tern-0919013">tern 0.9.1.9013<a class="anchor" aria-label="anchor" href="#tern-0919013"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.2" id="tern-092">tern 0.9.2<a class="anchor" aria-label="anchor" href="#tern-092"></a></h2>
 <div class="section level4">
-<h4 id="new-features-0-9-1-9013">New Features<a class="anchor" aria-label="anchor" href="#new-features-0-9-1-9013"></a></h4>
+<h4 id="new-features-0-9-2">New Features<a class="anchor" aria-label="anchor" href="#new-features-0-9-2"></a></h4>
 <ul><li>Added the <code>na_str</code> argument to <code>analyze</code> &amp; <code>summarize_row_groups</code> wrapper functions <code>count_abnormal</code>, <code>count_abnormal_by_baseline</code>, <code>count_abnormal_by_marked</code>, <code>count_abnormal_by_worst_grade</code>, <code>count_abnormal_lab_worsen_by_baseline</code>, <code>count_cumulative</code>, <code>count_missed_doses</code>, <code>count_occurrences</code>, <code>count_occurrences_by_grade</code>, <code>summarize_occurrences_by_grade</code>, <code>summarize_patients_events_in_cols</code>, <code>count_patients_with_event</code>, <code>count_patients_with_flags</code>, <code>count_values</code>, <code>estimate_multinomial_response</code>, <code>estimate_proportion</code>, <code>h_tab_one_biomarker</code>, <code>estimate_incidence_rate</code>, <code>logistic_summary_by_flag</code>, <code>estimate_odds_ratio</code>, <code>estimate_proportion_diff</code>, <code>test_proportion_diff</code>, <code>summarize_ancova</code>, <code>summarize_change</code>, <code>summarize_glm_count</code>, <code>summarize_num_patients</code>, <code>analyze_num_patients</code>, <code>summarize_patients_exposure_in_cols</code>, <code>coxph_pairwise</code>, <code>tabulate_survival_subgroups</code>, <code>surv_time</code>, and <code>surv_timepoint</code>.</li>
 </ul></div>
 <div class="section level4">
-<h4 id="enhancements-0-9-1-9013">Enhancements<a class="anchor" aria-label="anchor" href="#enhancements-0-9-1-9013"></a></h4>
+<h4 id="enhancements-0-9-2">Enhancements<a class="anchor" aria-label="anchor" href="#enhancements-0-9-2"></a></h4>
 <ul><li>Added formatting function <code>format_count_fraction_lt10</code> for formatting <code>count_fraction</code> with special consideration when count is less than 10.</li>
 <li>Updated <code>s_summary.logical</code> output for <code>count_fraction</code> when denominator is zero to display as <code>NA</code> instead of <code>0</code> in tables.</li>
 <li>Updated <code>analyze_vars_in_cols</code> to allow character input to indicate whether nominal time point is post-dose or pre-dose when applying the 1/3 imputation rule.</li>
 </ul></div>
 <div class="section level4">
-<h4 id="bug-fixes-0-9-1-9013">Bug Fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-1-9013"></a></h4>
+<h4 id="bug-fixes-0-9-2">Bug Fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-2"></a></h4>
 <ul><li>Fixed bug in <code>g_km</code> causing an error when converting certain annotation width units.</li>
 </ul></div>
 <div class="section level4">
-<h4 id="miscellaneous-0-9-1-9013">Miscellaneous<a class="anchor" aria-label="anchor" href="#miscellaneous-0-9-1-9013"></a></h4>
+<h4 id="miscellaneous-0-9-2">Miscellaneous<a class="anchor" aria-label="anchor" href="#miscellaneous-0-9-2"></a></h4>
 <ul><li>Began deprecation of <code>na_level</code> argument in <code>s_count_abnormal_by_baseline</code>, <code>a_summary</code>, <code>analyze_vars</code>, <code>analyze_vars_in_cols</code>, <code>compare_vars</code>, <code>h_map_for_count_abnormal</code>, <code>h_stack_by_baskets</code>, <code>summarize_colvars</code>, <code>a_coxreg</code>, and <code>summarize_coxreg</code> and replaced it with the <code>na_str</code> argument.</li>
 </ul></div>
 </div>
diff --git a/main/pkgdown.yml b/main/pkgdown.yml
index a0fab1675f..2e29faa38e 100644
--- a/main/pkgdown.yml
+++ b/main/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
   tables: tables.html
   tern: tern.html
   tern_formats: tern_formats.html
-last_built: 2023-10-19T13:13Z
+last_built: 2023-10-20T10:37Z
 urls:
   reference: https://insightsengineering.github.io/tern/reference
   article: https://insightsengineering.github.io/tern/articles
diff --git a/main/pull_request_template.html b/main/pull_request_template.html
index 6d935e4ffc..b406cd8b16 100644
--- a/main/pull_request_template.html
+++ b/main/pull_request_template.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html
index 3941da7ae5..5a7ffbca79 100644
--- a/main/reference/abnormal.html
+++ b/main/reference/abnormal.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -51,32 +51,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html
index 76070ab4e8..af88ca3d41 100644
--- a/main/reference/abnormal_by_baseline.html
+++ b/main/reference/abnormal_by_baseline.html
@@ -46,7 +46,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,32 +71,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html
index 9eb8d281d1..db1f192a9f 100644
--- a/main/reference/abnormal_by_marked.html
+++ b/main/reference/abnormal_by_marked.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -55,32 +55,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html
index 8077a8ff01..5347d30541 100644
--- a/main/reference/abnormal_by_worst_grade.html
+++ b/main/reference/abnormal_by_worst_grade.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -55,32 +55,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html
index fd228e93b7..e167514ac2 100644
--- a/main/reference/abnormal_by_worst_grade_worsen.html
+++ b/main/reference/abnormal_by_worst_grade_worsen.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html
index bee3d22ed6..d88f2862db 100644
--- a/main/reference/add_riskdiff.html
+++ b/main/reference/add_riskdiff.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -43,32 +43,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html
index c3ecd360fc..7b7b025584 100644
--- a/main/reference/add_rowcounts.html
+++ b/main/reference/add_rowcounts.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html
index e5083b12ae..0819a1570e 100644
--- a/main/reference/aesi_label.html
+++ b/main/reference/aesi_label.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html
index 3d35b4796d..8cf4c8570a 100644
--- a/main/reference/afun_riskdiff.html
+++ b/main/reference/afun_riskdiff.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -2098,26 +2072,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> ----------------------------</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                       row_name    formatted_cell indent_mod</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1                            n                10          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2                          Sum               0.4          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3                         Mean               0.0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4                           SD               1.1          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5                           SE               0.4          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6                    Mean (SD)         0.0 (1.1)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7                    Mean (SE)         0.0 (0.4)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8                  Mean 95% CI     (-0.78, 0.85)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9                Mean -/+ 1xSE     (-0.33, 0.40)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10               Mean -/+ 1xSD     (-1.11, 1.18)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11 Mean p-value (H0: mean = 0)              0.92          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2                          Sum              -4.5          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3                         Mean              -0.4          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4                           SD               1.0          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5                           SE               0.3          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6                    Mean (SD)        -0.4 (1.0)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7                    Mean (SE)        -0.4 (0.3)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8                  Mean 95% CI     (-1.14, 0.25)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9                Mean -/+ 1xSE    (-0.75, -0.14)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10               Mean -/+ 1xSD     (-1.42, 0.53)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11 Mean p-value (H0: mean = 0)              0.18          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 12                      Median              -0.2          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   Median Absolute Deviation               0.0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14               Median 95% CI     (-0.85, 1.32)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15             25% and 75%-ile        -0.8 - 1.1          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16                         IQR               1.9          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17                   Min - Max        -1.6 - 1.9          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18                     Minimum              -1.6          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19                     Maximum               1.9          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20          Median (Min - Max) -0.2 (-1.6 - 1.9)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21                      CV (%)            3196.6          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13   Median Absolute Deviation              -0.0          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14               Median 95% CI     (-1.50, 0.49)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15             25% and 75%-ile        -1.2 - 0.3          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16                         IQR               1.5          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17                   Min - Max        -2.1 - 1.0          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18                     Minimum              -2.1          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19                     Maximum               1.0          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20          Median (Min - Max) -0.2 (-2.1 - 1.0)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21                      CV (%)            -219.8          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 22              Geometric Mean                NA          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 23       Geometric Mean 95% CI                NA          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 24         CV % Geometric Mean                NA          0</span>
@@ -2151,29 +2125,29 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> ----------------------------</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                       row_name  formatted_cell indent_mod</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1                            n              10          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2                          Sum            53.0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3                         Mean             5.3          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4                           SD             1.0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5                           SE             0.3          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6                    Mean (SD)       5.3 (1.0)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7                    Mean (SE)       5.3 (0.3)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8                  Mean 95% CI    (4.62, 5.99)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9                Mean -/+ 1xSE    (5.00, 5.61)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10               Mean -/+ 1xSD    (4.34, 6.26)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2                          Sum            50.1          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3                         Mean             5.0          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4                           SD             1.2          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5                           SE             0.4          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6                    Mean (SD)       5.0 (1.2)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7                    Mean (SE)       5.0 (0.4)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8                  Mean 95% CI    (4.13, 5.88)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9                Mean -/+ 1xSE    (4.62, 5.39)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10               Mean -/+ 1xSD    (3.78, 6.23)          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 11 Mean p-value (H0: mean = 0)            0.00          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12                      Median             5.5          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12                      Median             5.3          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 13   Median Absolute Deviation             0.0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14               Median 95% CI    (4.11, 6.12)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15             25% and 75%-ile       4.7 - 6.0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16                         IQR             1.3          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17                   Min - Max       3.5 - 6.5          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18                     Minimum             3.5          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19                     Maximum             6.5          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20          Median (Min - Max) 5.5 (3.5 - 6.5)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21                      CV (%)            18.1          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22              Geometric Mean             5.2          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23       Geometric Mean 95% CI    (4.53, 6.01)          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24         CV % Geometric Mean            19.9          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14               Median 95% CI    (3.48, 6.48)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15             25% and 75%-ile       3.8 - 6.1          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16                         IQR             2.3          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17                   Min - Max       3.3 - 6.6          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18                     Minimum             3.3          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19                     Maximum             6.6          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20          Median (Min - Max) 5.3 (3.3 - 6.6)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21                      CV (%)            24.5          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22              Geometric Mean             4.9          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23       Geometric Mean 95% CI    (4.05, 5.84)          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24         CV % Geometric Mean            26.1          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 25            p-value (t-test)         &lt;0.0001          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      row_label</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1                            n</span>
diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html
index 2513190bd0..ee153ee025 100644
--- a/main/reference/analyze_vars_in_cols.html
+++ b/main/reference/analyze_vars_in_cols.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -41,32 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html
index e9dbb16a3d..5f1664b962 100644
--- a/main/reference/append_varlabels.html
+++ b/main/reference/append_varlabels.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -41,32 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html
index 2ec0e4cdb5..7ea58c0b71 100644
--- a/main/reference/argument_convention.html
+++ b/main/reference/argument_convention.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html
index b92e9ae164..b20b39e688 100644
--- a/main/reference/arrange_grobs.html
+++ b/main/reference/arrange_grobs.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html
index 4a41b4559f..62fe17ac58 100644
--- a/main/reference/as.rtable.html
+++ b/main/reference/as.rtable.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
@@ -139,16 +113,16 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">as.rtable</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        a       b  </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ——————————————————</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1    1.00    -1.77</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2    2.00    0.29 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3    3.00    -0.50</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4    4.00    0.65 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5    5.00    0.66 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6    6.00    -2.82</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7    7.00    1.35 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8    8.00    0.50 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    9.00    0.32 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   10.00   1.76 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1    1.00    -0.55</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2    2.00    0.97 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3    3.00    0.45 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4    4.00    0.83 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5    5.00    -0.63</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6    6.00    -0.12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7    7.00    -0.70</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8    8.00    -0.06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9    9.00    0.20 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10   10.00   0.42 </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html
index 6cda46b634..8cad036c29 100644
--- a/main/reference/as_factor_keep_attributes.html
+++ b/main/reference/as_factor_keep_attributes.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/assertions.html b/main/reference/assertions.html
index 9ecfaa17cc..0baf3f7c64 100644
--- a/main/reference/assertions.html
+++ b/main/reference/assertions.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html
index 1eb5eda89b..db1626ebae 100644
--- a/main/reference/bins_percent_labels.html
+++ b/main/reference/bins_percent_labels.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html
index 13a3a05ade..5efe688b8f 100644
--- a/main/reference/c_label_n.html
+++ b/main/reference/c_label_n.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html
index 7fd8727239..8934e6df11 100644
--- a/main/reference/c_label_n_alt.html
+++ b/main/reference/c_label_n_alt.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html
index a513e6c57b..3a801a0623 100644
--- a/main/reference/cfun_by_flag.html
+++ b/main/reference/cfun_by_flag.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
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index f3ff890802..632cd60d06 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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index db0b5885f9..f8a847a14b 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html
index 9a1f8c8830..2a891210dd 100644
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -39,32 +39,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html
index 10f8620bd9..e5cae3f639 100644
--- a/main/reference/combine_groups.html
+++ b/main/reference/combine_groups.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html
index 2c95e41781..37ae7b352e 100644
--- a/main/reference/combine_levels.html
+++ b/main/reference/combine_levels.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html
index 82df4a9af2..e178b2b0e3 100644
--- a/main/reference/combine_vectors.html
+++ b/main/reference/combine_vectors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html
index d364823802..c715705966 100644
--- a/main/reference/compare_variables.html
+++ b/main/reference/compare_variables.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
@@ -325,55 +299,55 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $sum</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      sum </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51.15441 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 55.16893 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     mean </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5.115441 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5.516893 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $sd</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        sd </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.9698247 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.8702667 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $se</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        se </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.3066855 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.2752025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_sd</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      mean        sd </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5.1154408 0.9698247 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5.5168927 0.8702667 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_se</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      mean        se </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5.1154408 0.3066855 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5.5168927 0.2752025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_ci</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_ci_lwr mean_ci_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    4.421670    5.809212 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.894341    6.139444 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean 95% CI"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_sei</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_sei_lwr mean_sei_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     4.808755     5.422126 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     5.241690     5.792095 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean -/+ 1xSE"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_sdi</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_sdi_lwr mean_sdi_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     4.145616     6.085266 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     4.646626     6.387159 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean -/+ 1xSD"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_pval</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      p_value </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.472872e-08 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8.894777e-09 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean p-value (H0: mean = 0)"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $median</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   median </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.997242 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5.570577 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mad</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          mad </span>
@@ -381,7 +355,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $median_ci</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> median_ci_lwr median_ci_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       4.09203       5.99734 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      4.652630      6.115083 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"conf_level")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.9785156</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
@@ -389,52 +363,52 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $quantiles</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> quantile_0.25 quantile_0.75 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      4.276008      5.861620 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      4.788385      6.102656 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "25% and 75%-ile"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $iqr</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      iqr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.585612 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.314271 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $range</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      min      max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.792829 6.848199 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3.940419 6.973096 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $min</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      min </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.792829 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3.940419 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $max</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6.848199 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6.973096 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $median_range</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   median      min      max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.997242 3.792829 6.848199 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5.570577 3.940419 6.973096 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Median (Min - Max)"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $cv</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>       cv </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18.95877 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15.77458 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $geom_mean</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> geom_mean </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.033803 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5.452097 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $geom_mean_ci</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_ci_lwr mean_ci_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    4.397113    5.762683 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.846722    6.133086 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Geometric Mean 95% CI"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $geom_cv</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  geom_cv </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19.07365 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16.56498 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $pval</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 3.561901e-05</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.084023e-09</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## If one group has not more than 1 value, then p-value is not calculated.</span></span></span>
@@ -445,55 +419,55 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $sum</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      sum </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48.71622 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 49.73612 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     mean </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.871622 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4.973612 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $sd</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        sd </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.9660467 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      sd </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.08076 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $se</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        se </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.3054908 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.3417662 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_sd</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      mean        sd </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.8716224 0.9660467 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     mean       sd </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4.973612 1.080760 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_se</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      mean        se </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.8716224 0.3054908 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4.9736118 0.3417662 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_ci</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_ci_lwr mean_ci_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    4.180554    5.562691 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.200483    5.746741 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean 95% CI"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_sei</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_sei_lwr mean_sei_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     4.566132     5.177113 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     4.631846     5.315378 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean -/+ 1xSE"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_sdi</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_sdi_lwr mean_sdi_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3.905576     5.837669 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     3.892852     6.054372 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean -/+ 1xSD"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mean_pval</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      p_value </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6.621947e-08 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.466974e-07 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Mean p-value (H0: mean = 0)"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $median</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   median </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.925955 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4.932608 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $mad</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           mad </span>
@@ -501,7 +475,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $median_ci</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> median_ci_lwr median_ci_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      4.214380      5.713326 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      3.994767      5.825929 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"conf_level")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.9785156</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
@@ -509,49 +483,49 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $quantiles</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> quantile_0.25 quantile_0.75 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      4.219359      5.446682 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      4.664539      5.807297 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "25% and 75%-ile"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $iqr</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      iqr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.227323 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.142758 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $range</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      min      max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.166494 6.564464 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.786148 6.663672 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $min</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      min </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.166494 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.786148 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $max</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6.564464 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6.663672 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $median_range</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   median      min      max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.925955 3.166494 6.564464 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4.932608 2.786148 6.663672 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Median (Min - Max)"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $cv</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>       cv </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19.83008 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21.72988 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $geom_mean</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> geom_mean </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  4.781985 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4.853244 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $geom_mean_ci</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> mean_ci_lwr mean_ci_upr </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    4.125787    5.542552 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.078462    5.775211 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"label")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Geometric Mean 95% CI"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $geom_cv</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  geom_cv </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20.85449 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24.67715 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $pval</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> character(0)</span>
diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html
index b95266f989..c83b398c83 100644
--- a/main/reference/control_analyze_vars.html
+++ b/main/reference/control_analyze_vars.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
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diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html
index 0427033efc..b77d1c7b70 100644
--- a/main/reference/control_coxph.html
+++ b/main/reference/control_coxph.html
@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html
index e944fa4011..3c84598115 100644
--- a/main/reference/control_coxreg.html
+++ b/main/reference/control_coxreg.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
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diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html
index ca0e85db1a..8c6481bd33 100644
--- a/main/reference/control_incidence_rate.html
+++ b/main/reference/control_incidence_rate.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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 </li>
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
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diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html
index 0c79122508..ba7e209ce0 100644
--- a/main/reference/control_lineplot_vars.html
+++ b/main/reference/control_lineplot_vars.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html
index 71b760d444..f500078903 100644
--- a/main/reference/control_logistic.html
+++ b/main/reference/control_logistic.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
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-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/control_step.html b/main/reference/control_step.html
index 87d0a06d9d..06a927a52c 100644
--- a/main/reference/control_step.html
+++ b/main/reference/control_step.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html
index 6708f3601d..b937fff4c2 100644
--- a/main/reference/control_surv_time.html
+++ b/main/reference/control_surv_time.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html
index bbd8de5b45..14dc4da27c 100644
--- a/main/reference/control_surv_timepoint.html
+++ b/main/reference/control_surv_timepoint.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
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diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html
index fabb06ce44..98ce8ace3a 100644
--- a/main/reference/count_cumulative.html
+++ b/main/reference/count_cumulative.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html
index c51803354b..526fffda89 100644
--- a/main/reference/count_missed_doses.html
+++ b/main/reference/count_missed_doses.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html
index fc556bcde7..f22ffbf711 100644
--- a/main/reference/count_occurrences.html
+++ b/main/reference/count_occurrences.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -43,32 +43,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html
index 6e3bc07bd4..c2896390a9 100644
--- a/main/reference/count_occurrences_by_grade.html
+++ b/main/reference/count_occurrences_by_grade.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -41,32 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html
index 274da5712b..91a2f57516 100644
--- a/main/reference/count_patients_events_in_cols.html
+++ b/main/reference/count_patients_events_in_cols.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-reports">
diff --git a/main/reference/count_patients_with_event.html b/main/reference/count_patients_with_event.html
index 256a9c91e0..2439e31672 100644
--- a/main/reference/count_patients_with_event.html
+++ b/main/reference/count_patients_with_event.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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diff --git a/main/reference/count_patients_with_flags.html b/main/reference/count_patients_with_flags.html
index 5776386e01..701b263d77 100644
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@@ -12,7 +12,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/count_values_funs.html b/main/reference/count_values_funs.html
index c0e71ec466..8318f61dd2 100644
--- a/main/reference/count_values_funs.html
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
 <li class="nav-item">
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 </li>
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-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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diff --git a/main/reference/cox_regression.html b/main/reference/cox_regression.html
index 8ffe9d216f..37e20295b2 100644
--- a/main/reference/cox_regression.html
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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 </li>
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/cox_regression_inter.html b/main/reference/cox_regression_inter.html
index a2d398c0b4..f4d38c0720 100644
--- a/main/reference/cox_regression_inter.html
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@@ -16,7 +16,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -41,32 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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index e77b66187a..c9b6f49d52 100644
--- a/main/reference/create_afun_compare.html
+++ b/main/reference/create_afun_compare.html
@@ -12,7 +12,7 @@
     
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diff --git a/main/reference/create_afun_summary.html b/main/reference/create_afun_summary.html
index c4b9e1b122..a7f92d129f 100644
--- a/main/reference/create_afun_summary.html
+++ b/main/reference/create_afun_summary.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/cut_quantile_bins.html b/main/reference/cut_quantile_bins.html
index 6301c83f96..f12ce5aec3 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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diff --git a/main/reference/d_count_abnormal_by_baseline.html b/main/reference/d_count_abnormal_by_baseline.html
index 0bde63a962..89fdaf4740 100644
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+++ b/main/reference/d_count_abnormal_by_baseline.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
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-          data-bs-toggle="dropdown" role="button"
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
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diff --git a/main/reference/d_count_cumulative.html b/main/reference/d_count_cumulative.html
index eaf45a21d2..a19a77ba71 100644
--- a/main/reference/d_count_cumulative.html
+++ b/main/reference/d_count_cumulative.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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index cad2822063..f43ce1e56a 100644
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index ead8b9b0d3..d487ff66c8 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
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diff --git a/main/reference/d_pkparam.html b/main/reference/d_pkparam.html
index 6a6d45667e..8efa9e7435 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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diff --git a/main/reference/d_proportion.html b/main/reference/d_proportion.html
index 3296a45eee..dfe79f0606 100644
--- a/main/reference/d_proportion.html
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/d_proportion_diff.html b/main/reference/d_proportion_diff.html
index bc6b15425b..2f6f929bc1 100644
--- a/main/reference/d_proportion_diff.html
+++ b/main/reference/d_proportion_diff.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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 </li>
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/d_rsp_subgroups_colvars.html b/main/reference/d_rsp_subgroups_colvars.html
index 3af2607b73..6c6aaa59cb 100644
--- a/main/reference/d_rsp_subgroups_colvars.html
+++ b/main/reference/d_rsp_subgroups_colvars.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/d_survival_subgroups_colvars.html b/main/reference/d_survival_subgroups_colvars.html
index 404901cbd4..7dbe488df1 100644
--- a/main/reference/d_survival_subgroups_colvars.html
+++ b/main/reference/d_survival_subgroups_colvars.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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diff --git a/main/reference/d_test_proportion_diff.html b/main/reference/d_test_proportion_diff.html
index f0fab97dee..20c4936d38 100644
--- a/main/reference/d_test_proportion_diff.html
+++ b/main/reference/d_test_proportion_diff.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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 <li class="nav-item dropdown">
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diff --git a/main/reference/day2month.html b/main/reference/day2month.html
index e6799fb981..f86240e87c 100644
--- a/main/reference/day2month.html
+++ b/main/reference/day2month.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -12,7 +12,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -14,7 +14,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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index 4b8e539b95..f9e60af1a6 100644
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@@ -12,7 +12,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/estimate_proportions.html b/main/reference/estimate_proportions.html
index 09c7cfc115..ca212e6781 100644
--- a/main/reference/estimate_proportions.html
+++ b/main/reference/estimate_proportions.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/ex_data.html b/main/reference/ex_data.html
index b3edc7fd41..65a86ce194 100644
--- a/main/reference/ex_data.html
+++ b/main/reference/ex_data.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/explicit_na.html b/main/reference/explicit_na.html
index 3ad7815826..566c1a062e 100644
--- a/main/reference/explicit_na.html
+++ b/main/reference/explicit_na.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
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diff --git a/main/reference/extract_by_name.html b/main/reference/extract_by_name.html
index c36aea10f3..8f06eac129 100644
--- a/main/reference/extract_by_name.html
+++ b/main/reference/extract_by_name.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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@@ -20,7 +20,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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diff --git a/main/reference/f_conf_level.html b/main/reference/f_conf_level.html
index 7573be6369..4144c9dd25 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
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diff --git a/main/reference/f_pval.html b/main/reference/f_pval.html
index c27434fc40..31ec24e24c 100644
--- a/main/reference/f_pval.html
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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diff --git a/main/reference/fct_collapse_only.html b/main/reference/fct_collapse_only.html
index 703861dc0d..fefd69a80e 100644
--- a/main/reference/fct_collapse_only.html
+++ b/main/reference/fct_collapse_only.html
@@ -16,7 +16,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -41,32 +41,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
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-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/fct_discard.html b/main/reference/fct_discard.html
index 2fa9583944..44589eee26 100644
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@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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 </li>
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-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
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@@ -14,7 +14,7 @@
     
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index 903df71859..130484f215 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
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diff --git a/main/reference/fit_logistic.html b/main/reference/fit_logistic.html
index e0dd65641f..e459678176 100644
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@@ -12,7 +12,7 @@
     
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/fit_rsp_step.html b/main/reference/fit_rsp_step.html
index 02bc9b15cd..001e5dd8ed 100644
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@@ -24,7 +24,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -49,32 +49,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/fit_survival_step.html b/main/reference/fit_survival_step.html
index 7df1d75783..0c2b977935 100644
--- a/main/reference/fit_survival_step.html
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@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -47,32 +47,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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diff --git a/main/reference/forest_viewport.html b/main/reference/forest_viewport.html
index 1564817289..2593c50ffc 100644
--- a/main/reference/forest_viewport.html
+++ b/main/reference/forest_viewport.html
@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/format_auto.html b/main/reference/format_auto.html
index b02e0d55a0..f599e5c719 100644
--- a/main/reference/format_auto.html
+++ b/main/reference/format_auto.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -45,32 +45,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/format_count_fraction.html b/main/reference/format_count_fraction.html
index b69cd99bb9..b8313355b6 100644
--- a/main/reference/format_count_fraction.html
+++ b/main/reference/format_count_fraction.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
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diff --git a/main/reference/format_count_fraction_fixed_dp.html b/main/reference/format_count_fraction_fixed_dp.html
index 892e770bcb..e713c0c848 100644
--- a/main/reference/format_count_fraction_fixed_dp.html
+++ b/main/reference/format_count_fraction_fixed_dp.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/format_count_fraction_lt10.html b/main/reference/format_count_fraction_lt10.html
index 030fd1a56a..2afa61cd2d 100644
--- a/main/reference/format_count_fraction_lt10.html
+++ b/main/reference/format_count_fraction_lt10.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/format_extreme_values.html b/main/reference/format_extreme_values.html
index 0e49ff1f60..1fba09b715 100644
--- a/main/reference/format_extreme_values.html
+++ b/main/reference/format_extreme_values.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/format_extreme_values_ci.html b/main/reference/format_extreme_values_ci.html
index e6364d17c8..4c8ef0dd18 100644
--- a/main/reference/format_extreme_values_ci.html
+++ b/main/reference/format_extreme_values_ci.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/format_fraction.html b/main/reference/format_fraction.html
index b6edd52fdc..359be5d5f0 100644
--- a/main/reference/format_fraction.html
+++ b/main/reference/format_fraction.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/format_fraction_fixed_dp.html b/main/reference/format_fraction_fixed_dp.html
index a8e0bf9c33..5190e94d11 100644
--- a/main/reference/format_fraction_fixed_dp.html
+++ b/main/reference/format_fraction_fixed_dp.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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diff --git a/main/reference/formatting_functions.html b/main/reference/formatting_functions.html
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index cb1648a9a7..593cd9785c 100644
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diff --git a/main/reference/g_km.html b/main/reference/g_km.html
index 5fab9f5794..101876b85a 100644
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@@ -16,7 +16,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/g_lineplot.html b/main/reference/g_lineplot.html
index 86dd4e9e20..17570e19c0 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -37,32 +37,6 @@
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diff --git a/main/reference/g_step.html b/main/reference/g_step.html
index 12219250d5..be2fb5af27 100644
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/g_waterfall.html b/main/reference/g_waterfall.html
index 3e33c329f0..d326b1d749 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
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index 246d171475..5e709e9441 100644
--- a/main/reference/get_covariates.html
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@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/get_smooths.html b/main/reference/get_smooths.html
index 77eac2f1a0..63a19ffafa 100644
--- a/main/reference/get_smooths.html
+++ b/main/reference/get_smooths.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/groups_list_to_df.html b/main/reference/groups_list_to_df.html
index a7a0b90e22..c1410ed0b3 100644
--- a/main/reference/groups_list_to_df.html
+++ b/main/reference/groups_list_to_df.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/h_adlb_worsen.html b/main/reference/h_adlb_worsen.html
index f1667cc5b4..e9452f25cd 100644
--- a/main/reference/h_adlb_worsen.html
+++ b/main/reference/h_adlb_worsen.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/h_adsl_adlb_merge_using_worst_flag.html b/main/reference/h_adsl_adlb_merge_using_worst_flag.html
index 857dcca876..5a47fb8f7f 100644
--- a/main/reference/h_adsl_adlb_merge_using_worst_flag.html
+++ b/main/reference/h_adsl_adlb_merge_using_worst_flag.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -41,32 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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index 3018469e8e..1458ec7ec4 100644
--- a/main/reference/h_ancova.html
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -35,32 +35,6 @@
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index 5d7d687dbb..a8c74af98b 100644
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@@ -18,7 +18,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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diff --git a/main/reference/h_col_indices.html b/main/reference/h_col_indices.html
index a3cf422d92..08df05e351 100644
--- a/main/reference/h_col_indices.html
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/h_count_cumulative.html b/main/reference/h_count_cumulative.html
index a7003d4393..10aca8226e 100644
--- a/main/reference/h_count_cumulative.html
+++ b/main/reference/h_count_cumulative.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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diff --git a/main/reference/h_cox_regression.html b/main/reference/h_cox_regression.html
index 6370f59311..51718e0b1a 100644
--- a/main/reference/h_cox_regression.html
+++ b/main/reference/h_cox_regression.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
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-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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diff --git a/main/reference/h_data_plot.html b/main/reference/h_data_plot.html
index d53b695b31..c3be00f499 100644
--- a/main/reference/h_data_plot.html
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@@ -30,7 +30,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -55,32 +55,6 @@
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diff --git a/main/reference/h_decompose_gg.html b/main/reference/h_decompose_gg.html
index eb00b0dd36..368383f8bb 100644
--- a/main/reference/h_decompose_gg.html
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/h_format_row.html b/main/reference/h_format_row.html
index 42c4c80e64..1e9640b7d8 100644
--- a/main/reference/h_format_row.html
+++ b/main/reference/h_format_row.html
@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<!-- start dropdown for versions --> <li class="nav-item dropdown">
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/h_g_ipp.html b/main/reference/h_g_ipp.html
index 9493462973..3f5b4db4ee 100644
--- a/main/reference/h_g_ipp.html
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@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
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index 29c0452f6d..314fcace72 100644
--- a/main/reference/h_ggkm.html
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@@ -12,7 +12,7 @@
     
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index 63b0349677..e773cd60e0 100644
--- a/main/reference/h_glm_count.html
+++ b/main/reference/h_glm_count.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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diff --git a/main/reference/h_grob_coxph.html b/main/reference/h_grob_coxph.html
index 0a2b40d81f..b56873ea79 100644
--- a/main/reference/h_grob_coxph.html
+++ b/main/reference/h_grob_coxph.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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diff --git a/main/reference/h_grob_median_surv.html b/main/reference/h_grob_median_surv.html
index 9a52c343f9..4f615e50e0 100644
--- a/main/reference/h_grob_median_surv.html
+++ b/main/reference/h_grob_median_surv.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/h_grob_tbl_at_risk.html b/main/reference/h_grob_tbl_at_risk.html
index a56dd3e2bb..fdeb040a2d 100644
--- a/main/reference/h_grob_tbl_at_risk.html
+++ b/main/reference/h_grob_tbl_at_risk.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -41,32 +41,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
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-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
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diff --git a/main/reference/h_grob_y_annot.html b/main/reference/h_grob_y_annot.html
index 79c37101ce..e48929775a 100644
--- a/main/reference/h_grob_y_annot.html
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@@ -12,7 +12,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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diff --git a/main/reference/h_incidence_rate.html b/main/reference/h_incidence_rate.html
index 24bbfa1d3d..92aedabe34 100644
--- a/main/reference/h_incidence_rate.html
+++ b/main/reference/h_incidence_rate.html
@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/h_km_layout.html b/main/reference/h_km_layout.html
index dcc9af8f47..5345b8a94d 100644
--- a/main/reference/h_km_layout.html
+++ b/main/reference/h_km_layout.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
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-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
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diff --git a/main/reference/h_logistic_regression.html b/main/reference/h_logistic_regression.html
index 5e1a167fa5..4e33d943de 100644
--- a/main/reference/h_logistic_regression.html
+++ b/main/reference/h_logistic_regression.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/h_map_for_count_abnormal.html b/main/reference/h_map_for_count_abnormal.html
index 12d1f16038..1127f2fcec 100644
--- a/main/reference/h_map_for_count_abnormal.html
+++ b/main/reference/h_map_for_count_abnormal.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/h_odds_ratio.html b/main/reference/h_odds_ratio.html
index c71021b67e..7ab7c5e200 100644
--- a/main/reference/h_odds_ratio.html
+++ b/main/reference/h_odds_ratio.html
@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/h_pkparam_sort.html b/main/reference/h_pkparam_sort.html
index cc6ee5a2fb..3593fdcd44 100644
--- a/main/reference/h_pkparam_sort.html
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@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/h_prop_diff.html b/main/reference/h_prop_diff.html
index 2cdda8df47..5cc62b07b8 100644
--- a/main/reference/h_prop_diff.html
+++ b/main/reference/h_prop_diff.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/h_prop_diff_test.html b/main/reference/h_prop_diff_test.html
index a52e2b096d..294fc24f7f 100644
--- a/main/reference/h_prop_diff_test.html
+++ b/main/reference/h_prop_diff_test.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
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@@ -12,7 +12,7 @@
     
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index ec64078f78..83f466f0dc 100644
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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index a3f268f818..e7fc15da09 100644
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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index f905f0a421..4dbdad22c1 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/h_split_param.html b/main/reference/h_split_param.html
index 863019c698..49cbe4ce7c 100644
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@@ -16,7 +16,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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-        <a href="#" class="nav-link dropdown-toggle"
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index 6365e068c2..e09fb96549 100644
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@@ -18,7 +18,7 @@
     
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diff --git a/main/reference/h_step.html b/main/reference/h_step.html
index cb7f4c1510..55874ce131 100644
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@@ -12,7 +12,7 @@
     
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diff --git a/main/reference/h_survival_biomarkers_subgroups.html b/main/reference/h_survival_biomarkers_subgroups.html
index 31366bb90f..2d620c0589 100644
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@@ -14,7 +14,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -39,32 +39,6 @@
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diff --git a/main/reference/h_survival_duration_subgroups.html b/main/reference/h_survival_duration_subgroups.html
index 6634ea4fb5..26c34ad9d7 100644
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@@ -14,7 +14,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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diff --git a/main/reference/h_tab_one_biomarker.html b/main/reference/h_tab_one_biomarker.html
index 9ede7eddce..892d054045 100644
--- a/main/reference/h_tab_one_biomarker.html
+++ b/main/reference/h_tab_one_biomarker.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/h_tbl_coxph_pairwise.html b/main/reference/h_tbl_coxph_pairwise.html
index 2fc1a19357..552116a1f2 100644
--- a/main/reference/h_tbl_coxph_pairwise.html
+++ b/main/reference/h_tbl_coxph_pairwise.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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diff --git a/main/reference/h_tbl_median_surv.html b/main/reference/h_tbl_median_surv.html
index 86340be96e..f0442b163e 100644
--- a/main/reference/h_tbl_median_surv.html
+++ b/main/reference/h_tbl_median_surv.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/h_worsen_counter.html b/main/reference/h_worsen_counter.html
index 426262ea43..3d9054ba18 100644
--- a/main/reference/h_worsen_counter.html
+++ b/main/reference/h_worsen_counter.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -41,32 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/h_xticks.html b/main/reference/h_xticks.html
index abfca03f0c..3ef2335ec2 100644
--- a/main/reference/h_xticks.html
+++ b/main/reference/h_xticks.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -41,32 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
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diff --git a/main/reference/imputation_rule.html b/main/reference/imputation_rule.html
index b312a85d36..ae80770bde 100644
--- a/main/reference/imputation_rule.html
+++ b/main/reference/imputation_rule.html
@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -35,32 +35,6 @@
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/incidence_rate.html b/main/reference/incidence_rate.html
index 1199defefa..d3c41ffdd8 100644
--- a/main/reference/incidence_rate.html
+++ b/main/reference/incidence_rate.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/index.html b/main/reference/index.html
index ecbc501194..2932fa9e88 100644
--- a/main/reference/index.html
+++ b/main/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -35,32 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
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   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/individual_patient_plot.html b/main/reference/individual_patient_plot.html
index 88a9db7985..2c3b26336d 100644
--- a/main/reference/individual_patient_plot.html
+++ b/main/reference/individual_patient_plot.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/labels_or_names.html b/main/reference/labels_or_names.html
index 6f330181e8..43bd9a2cf9 100644
--- a/main/reference/labels_or_names.html
+++ b/main/reference/labels_or_names.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
 <li class="nav-item">
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diff --git a/main/reference/logistic_regression_cols.html b/main/reference/logistic_regression_cols.html
index 9571ef7e17..687b15f226 100644
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@@ -14,7 +14,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -39,32 +39,6 @@
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/logistic_summary_by_flag.html b/main/reference/logistic_summary_by_flag.html
index a554743bfe..3b2c86f9e5 100644
--- a/main/reference/logistic_summary_by_flag.html
+++ b/main/reference/logistic_summary_by_flag.html
@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/make_names.html b/main/reference/make_names.html
index b9285b7c97..414bd670f2 100644
--- a/main/reference/make_names.html
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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 </li>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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diff --git a/main/reference/month2day.html b/main/reference/month2day.html
index de8d25e4e4..5a22d06b03 100644
--- a/main/reference/month2day.html
+++ b/main/reference/month2day.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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index 731e8686b9..f05eae948a 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
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diff --git a/main/reference/n_available.html b/main/reference/n_available.html
index e7ec030da2..bb7937c9a0 100644
--- a/main/reference/n_available.html
+++ b/main/reference/n_available.html
@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/odds_ratio.html b/main/reference/odds_ratio.html
index e2eaad4b5f..74536e350c 100644
--- a/main/reference/odds_ratio.html
+++ b/main/reference/odds_ratio.html
@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/prop_diff.html b/main/reference/prop_diff.html
index 2708e1405d..00cdbd3428 100644
--- a/main/reference/prop_diff.html
+++ b/main/reference/prop_diff.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/prop_diff_test.html b/main/reference/prop_diff_test.html
index d36d2138c7..45c8bf1196 100644
--- a/main/reference/prop_diff_test.html
+++ b/main/reference/prop_diff_test.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
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index 7bde50a194..174b2b5dc7 100644
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@@ -18,7 +18,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
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diff --git a/main/reference/range_noinf.html b/main/reference/range_noinf.html
index 11e3baca42..af8730c26f 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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diff --git a/main/reference/reapply_varlabels.html b/main/reference/reapply_varlabels.html
index 3bb2f79490..4ae4f3e279 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/response_biomarkers_subgroups.html b/main/reference/response_biomarkers_subgroups.html
index df72c3ecb2..620f7cd698 100644
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/response_subgroups.html b/main/reference/response_subgroups.html
index bfc9808417..4d54e9e39f 100644
--- a/main/reference/response_subgroups.html
+++ b/main/reference/response_subgroups.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/rtables_access.html b/main/reference/rtables_access.html
index a652b026e0..339b3d9255 100644
--- a/main/reference/rtables_access.html
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@@ -16,7 +16,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -41,32 +41,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/s_cox_multivariate.html b/main/reference/s_cox_multivariate.html
index beea3c73e4..91d09bb2ba 100644
--- a/main/reference/s_cox_multivariate.html
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@@ -24,7 +24,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -49,32 +49,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
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diff --git a/main/reference/sas_na.html b/main/reference/sas_na.html
index 5527586546..1178322ac4 100644
--- a/main/reference/sas_na.html
+++ b/main/reference/sas_na.html
@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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 </li>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
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diff --git a/main/reference/score_occurrences.html b/main/reference/score_occurrences.html
index 1445e3bf1f..51c07ac97b 100644
--- a/main/reference/score_occurrences.html
+++ b/main/reference/score_occurrences.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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 </li>
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-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
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index 2da9ffb26e..7bd2b509ff 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -35,32 +35,6 @@
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index 553a8cb6f0..8393edcad8 100644
--- a/main/reference/split_text_grob.html
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/stack_grobs.html b/main/reference/stack_grobs.html
index c78d34668f..dcf594e89b 100644
--- a/main/reference/stack_grobs.html
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
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diff --git a/main/reference/stat_mean_ci.html b/main/reference/stat_mean_ci.html
index 186666c173..894573a827 100644
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@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/stat_mean_pval.html b/main/reference/stat_mean_pval.html
index dbad4c8f6f..eee88b2b6a 100644
--- a/main/reference/stat_mean_pval.html
+++ b/main/reference/stat_mean_pval.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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index fcb1bffbd4..88807b51ac 100644
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@@ -14,7 +14,7 @@
     
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index 910a928c3a..828d579bbe 100644
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@@ -16,7 +16,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -41,32 +41,6 @@
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index dc4817739f..1f101e18aa 100644
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@@ -16,7 +16,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -39,32 +39,6 @@
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-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
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diff --git a/main/reference/summarize_change.html b/main/reference/summarize_change.html
index 3cf42351cd..e37d4d88f4 100644
--- a/main/reference/summarize_change.html
+++ b/main/reference/summarize_change.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -43,32 +43,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
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diff --git a/main/reference/summarize_colvars.html b/main/reference/summarize_colvars.html
index bee8d67695..e0b0ceb079 100644
--- a/main/reference/summarize_colvars.html
+++ b/main/reference/summarize_colvars.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -45,32 +45,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/summarize_functions.html b/main/reference/summarize_functions.html
index 53a2d4e219..a8141154c5 100644
--- a/main/reference/summarize_functions.html
+++ b/main/reference/summarize_functions.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -65,32 +65,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.9">v0.7.9</a> </div></li>
-<!-- end dropdown for versions -->
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-reports">Reports</a>
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diff --git a/main/reference/summarize_glm_count.html b/main/reference/summarize_glm_count.html
index 54086a2071..380adee720 100644
--- a/main/reference/summarize_glm_count.html
+++ b/main/reference/summarize_glm_count.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/latest-tag">latest-tag</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc2">v0.8.5-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5-rc1">v0.8.5-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4-rc1">v0.8.4-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc2">v0.8.3-rc2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc2">v0.8.2-rc2</a>
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@@ -28,7 +28,7 @@
     
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diff --git a/main/reference/summarize_num_patients.html b/main/reference/summarize_num_patients.html
index a005c2064f..13c9410521 100644
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@@ -12,7 +12,7 @@
     
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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--- a/main/reference/summarize_patients_exposure_in_cols.html
+++ b/main/reference/summarize_patients_exposure_in_cols.html
@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -39,32 +39,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/survival_biomarkers_subgroups.html b/main/reference/survival_biomarkers_subgroups.html
index 08311ef1c6..e06a6166cc 100644
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+++ b/main/reference/survival_biomarkers_subgroups.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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 </li>
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-          data-bs-toggle="dropdown" role="button"
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-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/main">main</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.3-rc1">v0.8.3-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.7.10">v0.7.10</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.0">v0.9.0</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.4">v0.8.4</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2">v0.8.2</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.1">v0.8.1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.0">v0.8.0</a>
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diff --git a/main/reference/survival_coxph_pairwise.html b/main/reference/survival_coxph_pairwise.html
index 1fd12c69a7..f04f72046f 100644
--- a/main/reference/survival_coxph_pairwise.html
+++ b/main/reference/survival_coxph_pairwise.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-        <a href="#" class="nav-link dropdown-toggle"
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index 968f4abe17..9dc50252ff 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -37,32 +37,6 @@
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diff --git a/main/reference/survival_time.html b/main/reference/survival_time.html
index 5247999e5d..ac307ad9f3 100644
--- a/main/reference/survival_time.html
+++ b/main/reference/survival_time.html
@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -39,32 +39,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/survival_timepoint.html b/main/reference/survival_timepoint.html
index 96036c17f7..6f927c25a1 100644
--- a/main/reference/survival_timepoint.html
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.2-rc1">v0.8.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/tern-package.html b/main/reference/tern-package.html
index 19266d8556..dc0c455df7 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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index 1e3a6d5658..5f95514055 100644
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
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index a246f87313..4f38884656 100644
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1-rc3">v0.9.1-rc3</a>
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@@ -12,7 +12,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -37,32 +37,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.2-rc1">v0.9.2-rc1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.8.5">v0.8.5</a>
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index 2925671f44..ac7ff7bfd0 100644
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@@ -18,7 +18,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -43,32 +43,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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@@ -10,7 +10,7 @@
     
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index 5249e25d6c..9d4a383410 100644
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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diff --git a/main/reference/univariate.html b/main/reference/univariate.html
index fbe1e48130..a0bc871f9f 100644
--- a/main/reference/univariate.html
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@@ -14,7 +14,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
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@@ -39,32 +39,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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diff --git a/main/reference/unlist_and_blank_na.html b/main/reference/unlist_and_blank_na.html
index 8da39cd03a..7dfbedec4f 100644
--- a/main/reference/unlist_and_blank_na.html
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@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -35,32 +35,6 @@
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/release-candidate">release-candidate</a>
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-<a class="dropdown-item" data-toggle="tooltip" title="" href="https://insightsengineering.github.io/tern/v0.9.1">v0.9.1</a>
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diff --git a/main/reference/update_weights_strat_wilson.html b/main/reference/update_weights_strat_wilson.html
index 11ac677379..07913549fb 100644
--- a/main/reference/update_weights_strat_wilson.html
+++ b/main/reference/update_weights_strat_wilson.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tern</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.1.9013</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.2</small>
 
     <div style="width: 140px; margin-left: 0.5rem;"> <small class="nav-text text-muted me-auto">part of</small> <img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/nest.png?raw=true" alt="NEST" style="height: 30px;"><a href="https://pharmaverse.org/" class="external-link"><img src="https://github.com/insightsengineering/hex-stickers/blob/main/PNG/pharmaverse.png?raw=true" alt="pharmaverse" style="height: 28px;"></a> </div>
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@@ -41,32 +41,6 @@
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 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
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index aa79b264d6..0449110572 100644
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-[{"path":[]},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [INSERT CONTACT METHOD]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. 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Please also see articles within project documentation additional information.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contribution Guidelines","text":"Code Conduct governs project. Participants contributors expected follow rules outlined therein.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Contribution Guidelines","text":"contributions covered project’s license.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"issues","dir":"","previous_headings":"","what":"Issues","title":"Contribution Guidelines","text":"use GitHub track issues, feature requests, bugs. submitting new issue, please check issue already reported. issue already exists, please upvote existing issue 👍. new feature requests, please elaborate context benefit feature users, developers, relevant personas.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"github-flow","dir":"","previous_headings":"Pull requests","what":"GitHub Flow","title":"Contribution Guidelines","text":"repository uses GitHub Flow model collaboration. submit pull request: Create branch Please see branch naming convention . don’t write access repository, please fork . Make changes Make sure code passes checks imposed GitHub Actions well documented well tested unit tests sufficiently covering changes introduced Create pull request (PR) pull request description, please link relevant issue (), provide detailed description change, include assumptions. Address review comments, Post approval Merge PR write access. Otherwise, reviewer merge PR behalf. Pat back Congratulations! 🎉 now official contributor project! grateful contribution.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"branch-naming-convention","dir":"","previous_headings":"Pull requests","what":"Branch naming convention","title":"Contribution Guidelines","text":"Suppose changes related current issue current project; please name branch follows: <issue_id>_<short_description>. Please use underscore (_) delimiter word separation. example, 420_fix_ui_bug suitable branch name change resolving UI-related bug reported issue number 420 current project. change affects multiple repositories, please name branches follows: <issue_id>_<issue_repo>_<short description>. example, 69_awesomeproject_fix_spelling_error reference issue 69 reported project awesomeproject aims resolve one spelling errors multiple (likely related) repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"monorepo-and-stageddependencies","dir":"","previous_headings":"Pull requests","what":"monorepo and staged.dependencies","title":"Contribution Guidelines","text":"Sometimes might need change upstream dependent package(s) able submit meaningful change. using staged.dependencies functionality simulate monorepo behavior. dependency configuration already specified project’s staged_dependencies.yaml file. need name feature branches appropriately. exception branch naming convention described . Please refer staged.dependencies package documentation details.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"coding-guidelines","dir":"","previous_headings":"","what":"Coding guidelines","title":"Contribution Guidelines","text":"repository follows unified processes standards adopted maintainers ensure software development carried consistently within teams cohesively across repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"style-guide","dir":"","previous_headings":"Coding guidelines","what":"Style guide","title":"Contribution Guidelines","text":"repository follows standard tidyverse style guide uses lintr lint checks. Customized lint configurations available repository’s .lintr file.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"dependency-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency management","title":"Contribution Guidelines","text":"Lightweight right weight. repository follows tinyverse recommedations limiting dependencies minimum.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"dependency-version-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency version management","title":"Contribution Guidelines","text":"code compatible (!) historical versions given dependenct package, required specify minimal version DESCRIPTION file. particular: development version requires (imports) development version another package - required put abc (>= 1.2.3.9000).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"r--package-versions","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"R & package versions","title":"Contribution Guidelines","text":"continuously test packages newest R version along recent dependencies CRAN BioConductor. recommend working environment also set way. can find details R version packages used R CMD check GitHub Action execution log - step prints R sessionInfo(). discover bugs older R versions older set dependencies, please create relevant bug reports.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"pre-commit","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"pre-commit","title":"Contribution Guidelines","text":"highly recommend use pre-commit tool combined R hooks pre-commit execute checks committing pushing changes. Pre-commit hooks already available repository’s .pre-commit-config.yaml file.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"recognition-model","dir":"","previous_headings":"","what":"Recognition model","title":"Contribution Guidelines","text":"mentioned previously, contributions deeply valued appreciated. contribution data available part repository insights, recognize significant contribution hence add contributor package authors list, following rules enforced: Minimum 5% lines code authored* (determined git blame query) top 5 contributors terms number commits lines added lines removed* *Excluding auto-generated code, including limited roxygen comments renv.lock files. package maintainer also reserves right adjust criteria recognize contributions.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contribution Guidelines","text":"questions regarding contribution guidelines, please contact package/repository maintainer.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/SECURITY.html","id":"reporting-security-issues","dir":"","previous_headings":"","what":"Reporting Security Issues","title":"Security Policy","text":"believe found security vulnerability repositories organization, please report us coordinated disclosure. Please report security vulnerabilities public GitHub issues, discussions, pull requests. Instead, please send email vulnerability.management[@]roche.com. Please include much information listed can help us better understand resolve issue: type issue (e.g., buffer overflow, SQL injection, cross-site scripting) Full paths source file(s) related manifestation issue location affected source code (tag/branch/commit direct URL) special configuration required reproduce issue Step--step instructions reproduce issue Proof--concept exploit code (possible) Impact issue, including attacker might exploit issue information help us triage report quickly.","code":""},{"path":"https://insightsengineering.github.io/tern/SECURITY.html","id":"data-security-standards-dss","dir":"","previous_headings":"","what":"Data Security Standards (DSS)","title":"Security Policy","text":"Please make sure reporting issues form bug, feature, pull request, sensitive information PII, PHI, PCI completely removed text attachments, including pictures videos.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/missing_values.html","id":"variable-class-conversion","dir":"Articles","previous_headings":"","what":"Variable Class Conversion","title":"Missing Values in Tern","text":"rtables requires split variables factors. try split variable isn’t, warning message appear. purposefully convert SEX variable character demonstrate happens try splitting rows variable. fix , df_explict_na convert factor resulting table generated.","code":"adsl <- tern_ex_adsl adsl$SEX <- as.character(adsl$SEX)  vars <- c(\"AGE\", \"SEX\", \"RACE\", \"BMRKR1\") var_labels <- c(   \"Age (yr)\",   \"Sex\",   \"Race\",   \"Continous Level Biomarker 1\" )  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures  #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures  #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures  #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures result #>                                                A: Drug X    B: Placebo    C: Combination   All Patients #>                                                 (N=69)        (N=73)          (N=58)         (N=200)    #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                                                #>   n                                               69            73              58             200      #>   Mean (SD)                                   34.1 (6.8)    35.8 (7.1)      36.1 (7.4)      35.3 (7.1)  #>   Median                                         32.8          35.4            36.2            34.8     #>   Min - Max                                   22.4 - 48.0   23.3 - 57.5    23.0 - 58.3     22.4 - 58.3  #> Sex                                                                                                     #>   n                                               69            73              58             200      #>   F                                           38 (55.1%)    40 (54.8%)      32 (55.2%)      110 (55%)   #>   M                                           31 (44.9%)    33 (45.2%)      26 (44.8%)       90 (45%)   #> Race                                                                                                    #>   n                                               69            73              58             200      #>   ASIAN                                       38 (55.1%)    43 (58.9%)       29 (50%)       110 (55%)   #>   BLACK OR AFRICAN AMERICAN                   15 (21.7%)    13 (17.8%)      12 (20.7%)       40 (20%)   #>   WHITE                                       11 (15.9%)    12 (16.4%)       11 (19%)        34 (17%)   #>   AMERICAN INDIAN OR ALASKA NATIVE             4 (5.8%)      3 (4.1%)       6 (10.3%)       13 (6.5%)   #>   MULTIPLE                                     1 (1.4%)      1 (1.4%)           0             2 (1%)    #>   NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (1.4%)           0            1 (0.5%)   #>   OTHER                                            0             0              0               0       #>   UNKNOWN                                          0             0              0               0       #> Continous Level Biomarker 1                                                                             #>   n                                               69            73              58             200      #>   Mean (SD)                                    6.3 (3.6)     6.7 (3.5)      6.2 (3.3)       6.4 (3.5)   #>   Median                                          5.4           6.3            5.4             5.6      #>   Min - Max                                   0.4 - 17.8    1.0 - 18.5      2.4 - 19.1      0.4 - 19.1"},{"path":"https://insightsengineering.github.io/tern/articles/missing_values.html","id":"including-missing-values-in-rtables","dir":"Articles","previous_headings":"","what":"Including Missing Values in rtables","title":"Missing Values in Tern","text":"purposefully convert M values NA SEX variable. running df_explicit_na NA values encoded <Missing> included table. well, missing values included n count included denominator value calculating percent values. want Na values displayed table included n count denominator calculating percent values, use na_level argument.","code":"adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl)  vars <- c(\"AGE\", \"SEX\") var_labels <- c(   \"Age (yr)\",   \"Sex\" )  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) result #>                A: Drug X    B: Placebo    C: Combination   All Patients #>                 (N=69)        (N=73)          (N=58)         (N=200)    #> ——————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                #>   n               69            73              58             200      #>   Mean (SD)   34.1 (6.8)    35.8 (7.1)      36.1 (7.4)      35.3 (7.1)  #>   Median         32.8          35.4            36.2            34.8     #>   Min - Max   22.4 - 48.0   23.3 - 57.5    23.0 - 58.3     22.4 - 58.3  #> Sex                                                                     #>   n               38            40              32             110      #>   F            38 (100%)     40 (100%)      32 (100%)       110 (100%)  #>   M                0             0              0               0 adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\")  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) result #>                     A: Drug X    B: Placebo    C: Combination   All Patients #>                      (N=69)        (N=73)          (N=58)         (N=200)    #> ———————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                     #>   n                    69            73              58             200      #>   Mean (SD)        34.1 (6.8)    35.8 (7.1)      36.1 (7.4)      35.3 (7.1)  #>   Median              32.8          35.4            36.2            34.8     #>   Min - Max        22.4 - 48.0   23.3 - 57.5    23.0 - 58.3     22.4 - 58.3  #> Sex                                                                          #>   n                    69            73              58             200      #>   F                38 (55.1%)    40 (54.8%)      32 (55.2%)      110 (55%)   #>   M                     0             0              0               0       #>   Missing Values   31 (44.9%)    33 (45.2%)      26 (44.8%)       90 (45%)"},{"path":"https://insightsengineering.github.io/tern/articles/missing_values.html","id":"missing-values-in-numeric-variables","dir":"Articles","previous_headings":"","what":"Missing Values in Numeric Variables","title":"Missing Values in Tern","text":"Numeric variables missing values altered. means NA value numeric variable included summary statistics, included denominator value calculating percent values. make value less 30 missing AGE variable valued greater 30 included table .","code":"adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl)  vars <- c(\"AGE\", \"SEX\") var_labels <- c(   \"Age (yr)\",   \"Sex\" )  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) result #>                A: Drug X    B: Placebo    C: Combination   All Patients #>                 (N=69)        (N=73)          (N=58)         (N=200)    #> ——————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                #>   n               46            56              44             146      #>   Mean (SD)   37.8 (5.2)    38.3 (6.3)      39.1 (5.9)      38.3 (5.8)  #>   Median         37.2          37.3            37.5            37.5     #>   Min - Max   30.3 - 48.0   30.0 - 57.5    30.5 - 58.3     30.0 - 58.3  #> Sex                                                                     #>   n               69            73              58             200      #>   F           38 (55.1%)    40 (54.8%)      32 (55.2%)      110 (55%)   #>   M           31 (44.9%)    33 (45.2%)      26 (44.8%)       90 (45%)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"tern-tabulation","dir":"Articles","previous_headings":"","what":"tern Tabulation","title":"Tabulation","text":"tern R package provides functions create common analyses clinical trials R. core functionality tabulation built general purpose rtables package. New users first begin reading “Introduction tern” “Introduction rtables” vignettes. packages used vignette : datasets used vignette :","code":"library(rtables) library(tern) library(dplyr) adsl <- ex_adsl adae <- ex_adae adrs <- ex_adrs"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"tern-analyze-functions","dir":"Articles","previous_headings":"","what":"tern Analyze Functions","title":"Tabulation","text":"Analyze functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analyze functions wrappers around rtables::analyze function, offer various methods useful perspective clinical trials statistical projects. Examples tern analyze functions tern::count_occurrences, tern::summarize_ancova tern::analyze_vars. one prefix identify tern analyze functions recommended use tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"internals-of-tern-analyze-functions","dir":"Articles","previous_headings":"tern Analyze Functions","what":"Internals of tern Analyze Functions","title":"Tabulation","text":"Please skip subsection interested internals tern analyze functions. Internally tern analyze functions like tern::summarize_ancova mainly built 4 elements chain: descriptions function type: analysis helper functions h_*. functions useful help define analysis. statistics function s_*. Statistics functions computation numbers tabulated later. order separate computation formatting, take care rcell type formatting . formatted analysis functions a_*. arguments corresponding statistics functions, can customized calling rtables::make_afun() . used afun rtables::analyze(). analyze functions rtables::analyze(..., afun = make_afun(tern::a_*)). Analyze functions used combination rtables layout functions, pipeline creates table. last element chain. use native rtables::analyze function tern formatted analysis functions afun parameter. rtables::make_afun function helpful somebody wants attach format formatted analysis function.","code":"h_ancova() -> tern:::s_ancova() -> tern:::a_ancova() -> summarize_ancova() l <- basic_table() %>%     split_cols_by(var = \"ARM\") %>%     split_rows_by(var = \"AVISIT\") %>%     analyze(vars = \"AVAL\", afun = a_summary)  build_table(l, df = adrs) afun <- make_afun(     a_summary,     .stats = NULL,     .formats = c(median = \"xx.\"),     .labels = c(median = \"My median\"),     .indent_mods = c(median = 1L) )  l2 <- basic_table() %>%     split_cols_by(var = \"ARM\") %>%     split_rows_by(var = \"AVISIT\") %>%     analyze(vars = \"AVAL\", afun = afun)  build_table(l2, df = adrs)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"tabulation-examples","dir":"Articles","previous_headings":"","what":"Tabulation Examples","title":"Tabulation","text":"going create 3 different tables using tern analyze functions rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"demographic-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Demographic Table","title":"Tabulation","text":"Demographic tables provide summary characteristics patients enrolled clinical trial. Typically table columns represent treatment arms variables summarized table demographic properties age, sex, race, etc. example function tern analyze_vars() remaining layout functions rtables. change display order categorical variables table use factor variables explicitly set order levels. case display order columns rows. Note forcats package many useful functions help types data processing steps (used ). tern package includes many functions similar analyze_vars(). functions called layout creating functions used combination rtables layout functions just like examples . Layout creating functions wrapping calls rtables analyze(), analyze_colvars() summarize_row_groups() provide options easy formatting analysis modifications. customize display demographics table, can via arguments analyze_vars(). layout creating functions tern include standard arguments .stats, .formats, .labels .indent_mods control statistics displayed numbers formatted. Refer package help help(\"analyze_vars\") ?analyze_vars see full set options. example change default summary numeric variables include number records, mean standard deviation (single statistic, .e. within single cell). categorical variables modify summary include number records counts categories. also modify display format mean standard deviation print two decimal places instead just one. One feature layout can used different datasets create different summaries. example, can easily create summary demographics Brazil China subgroups, respectively:","code":"# Select variables to include in table. vars <- c(\"AGE\", \"SEX\") var_labels <- c(\"Age (yr)\", \"Sex\")  basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) #>                       A: Drug X    B: Placebo    C: Combination   All Patients #>                        (N=134)       (N=134)        (N=132)         (N=400)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                      134           134            132             400      #>   Mean (SD)          33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4)  #>   Median                33.0          35.0            35.0            34.0     #>   Min - Max          21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0  #> Sex                                                                            #>   n                      134           134            132             400      #>   F                   79 (59%)     77 (57.5%)       66 (50%)      222 (55.5%)  #>   M                  51 (38.1%)     55 (41%)       60 (45.5%)     166 (41.5%)  #>   U                   3 (2.2%)      2 (1.5%)         4 (3%)         9 (2.2%)   #>   UNDIFFERENTIATED    1 (0.7%)          0           2 (1.5%)        3 (0.8%) # Reorder the levels in the ARM variable. adsl$ARM <- factor(adsl$ARM, levels = c(\"B: Placebo\", \"A: Drug X\", \"C: Combination\")) # nolint  # Reorder the levels in the SEX variable. adsl$SEX <- factor(adsl$SEX, levels = c(\"M\", \"F\", \"U\", \"UNDIFFERENTIATED\")) # nolint  basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) #>                      B: Placebo     A: Drug X    C: Combination   All Patients #>                        (N=134)       (N=134)        (N=132)         (N=400)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                      134           134            132             400      #>   Mean (SD)          35.4 (7.9)    33.8 (6.6)      35.4 (7.7)      34.9 (7.4)  #>   Median                35.0          33.0            35.0            34.0     #>   Min - Max          21.0 - 62.0   21.0 - 50.0    20.0 - 69.0     20.0 - 69.0  #> Sex                                                                            #>   n                      134           134            132             400      #>   M                   55 (41%)     51 (38.1%)      60 (45.5%)     166 (41.5%)  #>   F                  77 (57.5%)     79 (59%)        66 (50%)      222 (55.5%)  #>   U                   2 (1.5%)      3 (2.2%)         4 (3%)         9 (2.2%)   #>   UNDIFFERENTIATED        0         1 (0.7%)        2 (1.5%)        3 (0.8%) # Select statistics and modify default formats. basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels,     .stats = c(\"n\", \"mean_sd\", \"count\"),     .formats = c(mean_sd = \"xx.xx (xx.xx)\")   ) %>%   build_table(adsl) #>                       B: Placebo     A: Drug X     C: Combination   All Patients #>                        (N=134)        (N=134)         (N=132)         (N=400)    #> ———————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                         #>   n                      134            134             132             400      #>   Mean (SD)          35.43 (7.90)   33.77 (6.55)    35.43 (7.72)    34.88 (7.44) #> Sex                                                                              #>   n                      134            134             132             400      #>   M                       55             51              60             166      #>   F                       77             79              66             222      #>   U                       2              3               4               9       #>   UNDIFFERENTIATED        0              1               2               3 lyt <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels   )  build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"BRA\")) #>                      B: Placebo     A: Drug X    C: Combination   All Patients #>                         (N=7)        (N=13)          (N=10)          (N=30)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                       7            13              10              30      #>   Mean (SD)          32.0 (6.1)    36.7 (6.4)     38.3 (10.6)      36.1 (8.1)  #>   Median                32.0          37.0            35.0            35.5     #>   Min - Max          25.0 - 42.0   24.0 - 47.0    25.0 - 64.0     24.0 - 64.0  #> Sex                                                                            #>   n                       7            13              10              30      #>   M                   4 (57.1%)     8 (61.5%)       5 (50%)        17 (56.7%)  #>   F                   3 (42.9%)     5 (38.5%)       5 (50%)        13 (43.3%)  #>   U                       0             0              0               0       #>   UNDIFFERENTIATED        0             0              0               0  build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"CHN\")) #>                      B: Placebo     A: Drug X    C: Combination   All Patients #>                        (N=81)        (N=74)          (N=64)         (N=219)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                      81            74              64             219      #>   Mean (SD)          35.7 (7.3)    33.0 (6.4)      35.2 (6.4)      34.6 (6.8)  #>   Median                36.0          32.0            35.0            34.0     #>   Min - Max          21.0 - 58.0   23.0 - 48.0    21.0 - 49.0     21.0 - 58.0  #> Sex                                                                            #>   n                      81            74              64             219      #>   M                  35 (43.2%)    27 (36.5%)      30 (46.9%)       92 (42%)   #>   F                  45 (55.6%)    44 (59.5%)      29 (45.3%)     118 (53.9%)  #>   U                   1 (1.2%)      2 (2.7%)        3 (4.7%)        6 (2.7%)   #>   UNDIFFERENTIATED        0         1 (1.4%)        2 (3.1%)        3 (1.4%)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"adverse-event-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Adverse Event Table","title":"Tabulation","text":"standard table adverse events summary system organ class preferred term. frequency counts preferred term, multiple occurrences AE individual count . create table need use combination several layout creating functions tabulation pipeline. start creating high-level summary. layout creating function tern can summarize_num_patients(): Note table, denominator used percentages shown header table (N = xx) defined based subject-level dataset adsl. done using alt_df_counts argument build_table(), provides alternative data set deriving counts header. often required work data sets include multiple records per patient df, adae .","code":"basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                  A: Drug X    B: Placebo    C: Combination   All Patients #>                                                   (N=134)       (N=134)        (N=132)         (N=400)    #> ————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE   122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%)  #> Overall total number of events                      609           622            703             1934"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"statistics-functions","dir":"Articles","previous_headings":"Tabulation Examples > Adverse Event Table","what":"Statistics Functions","title":"Tabulation","text":"building rest AE table helpful introduce tern package design conventions. layout creating function tern wrapper Statistics function. Statistics functions ones actual computation numbers table. functions always return named lists whose elements statistics available include layout via .stats argument layout creating function level. Statistics functions follow naming convention always begin s_* ease use documented page layout creating function counterpart. helpful review Statistic function understand logic used calculate numbers table see options may available modify analysis. example, Statistics function calculating numbers summarize_num_patients() s_num_patients(). results Statistics function list elements unique, nonunique unique_count: results can see unique nonunique statistics displayed “Patients” column initial AE table output . Also can see raw numbers formatted way. formatting functionality handled layout creating function level .formats argument. Now know types statistics can derived s_num_patients(), can try modifying default layout returned summarize_num_patients(). Instead reporting unique nonqunie statistics, specify analysis include unique_count statistic. result show counts unique patients. Note make update .stats .labels argument summarize_num_patients(). Let’s now continue building layout adverse event table. top-level summary, can repeat summary system organ class level. split analysis data split_rows_by() calling summarize_num_patients(). table looks almost ready. final step, need layout creating function can produce count table event frequencies. layout creating function count_occurrences(). Let’s first try using function simpler layout without row splits: Putting everything together, final AE table looks like :","code":"s_num_patients(x = adae$USUBJID, labelstr = \"\", .N_col = nrow(adae)) #> $unique #> [1] 365.000000   0.188728 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 1934 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 365 #> attr(,\"label\") #> [1] \"(n)\" basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = \"unique_count\",     .labels = c(unique_count = \"Total number of patients with at least one AE\")   ) %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                 A: Drug X   B: Placebo   C: Combination   All Patients #>                                                  (N=134)     (N=134)        (N=132)         (N=400)    #> —————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE      122         123            120             365 basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   split_rows_by(     \"AEBODSYS\",     child_labels = \"visible\",     nested = FALSE,     indent_mod = -1L,     split_fun = drop_split_levels   ) %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                    A: Drug X    B: Placebo    C: Combination   All Patients #>                                                     (N=134)       (N=134)        (N=132)         (N=400)    #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE     122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%)  #> Overall total number of events                        609           622            703             1934     #> cl A.1                                                                                                      #>   Total number of patients with at least one AE   78 (58.2%)    75 (56.0%)      89 (67.4%)     242 (60.5%)  #>   Overall total number of events                      132           130            160             422      #> cl B.1                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%)  #>   Overall total number of events                      56            60              62             178      #> cl B.2                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    74 (55.2%)      85 (64.4%)     238 (59.5%)  #>   Overall total number of events                      129           138            143             410      #> cl C.1                                                                                                      #>   Total number of patients with at least one AE   43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%)  #>   Overall total number of events                      55            63              64             182      #> cl C.2                                                                                                      #>   Total number of patients with at least one AE   35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%)  #>   Overall total number of events                      48            53              65             166      #> cl D.1                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    67 (50.0%)      80 (60.6%)     226 (56.5%)  #>   Overall total number of events                      127           106            135             368      #> cl D.2                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%)  #>   Overall total number of events                      62            72              74             208 basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   count_occurrences(vars = \"AEDECOD\") %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                 A: Drug X    B: Placebo   C: Combination   All Patients #>                  (N=134)      (N=134)        (N=132)         (N=400)    #> ——————————————————————————————————————————————————————————————————————— #> dcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%)  #> dcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%)  #> dcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%)  #> dcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%)  #> dcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%)  #> dcd C.1.1.1.3   43 (32.1%)   46 (34.3%)     43 (32.6%)     132 (33.0%)  #> dcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%)  #> dcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%)  #> dcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%)  #> dcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   split_rows_by(     \"AEBODSYS\",     child_labels = \"visible\",     nested = FALSE,     indent_mod = -1L,     split_fun = drop_split_levels   ) %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   count_occurrences(vars = \"AEDECOD\") %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                    A: Drug X    B: Placebo    C: Combination   All Patients #>                                                     (N=134)       (N=134)        (N=132)         (N=400)    #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE     122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%)  #> Overall total number of events                        609           622            703             1934     #> cl A.1                                                                                                      #>   Total number of patients with at least one AE   78 (58.2%)    75 (56.0%)      89 (67.4%)     242 (60.5%)  #>   Overall total number of events                      132           130            160             422      #>     dcd A.1.1.1.1                                 50 (37.3%)    45 (33.6%)      63 (47.7%)     158 (39.5%)  #>     dcd A.1.1.1.2                                 48 (35.8%)    48 (35.8%)      50 (37.9%)     146 (36.5%)  #> cl B.1                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%)  #>   Overall total number of events                      56            60              62             178      #>     dcd B.1.1.1.1                                 47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%)  #> cl B.2                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    74 (55.2%)      85 (64.4%)     238 (59.5%)  #>   Overall total number of events                      129           138            143             410      #>     dcd B.2.1.2.1                                 49 (36.6%)    44 (32.8%)      52 (39.4%)     145 (36.2%)  #>     dcd B.2.2.3.1                                 48 (35.8%)    54 (40.3%)      51 (38.6%)     153 (38.2%)  #> cl C.1                                                                                                      #>   Total number of patients with at least one AE   43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%)  #>   Overall total number of events                      55            63              64             182      #>     dcd C.1.1.1.3                                 43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%)  #> cl C.2                                                                                                      #>   Total number of patients with at least one AE   35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%)  #>   Overall total number of events                      48            53              65             166      #>     dcd C.2.1.2.1                                 35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%)  #> cl D.1                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    67 (50.0%)      80 (60.6%)     226 (56.5%)  #>   Overall total number of events                      127           106            135             368      #>     dcd D.1.1.1.1                                 50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%)  #>     dcd D.1.1.4.2                                 48 (35.8%)    42 (31.3%)      50 (37.9%)     140 (35.0%)  #> cl D.2                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%)  #>   Overall total number of events                      62            72              74             208      #>     dcd D.2.1.5.3                                 47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"response-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Response Table","title":"Tabulation","text":"typical response table binary clinical trial endpoint may composed several different analyses: Proportion responders treatment group Difference proportion responders comparison groups vs. control group Chi-Square test difference response rates comparison groups vs. control group can build table layout like following approach used AE table: table section produced using different layout creating function tern. First start data preparation steps set analysis dataset. select endpoint analyze PARAMCD define logical variable is_rsp indicates whether patient classified responder . create summary proportion responders treatment group, use estimate_proportion() layout creating function: specify arm table used reference, use argument ref_group split_cols_by(). change reference arm “B: Placebo” arm displayed first column: customize analysis, can use method conf_level arguments modify type confidence interval calculated: next table section needed summarize difference response rates reference arm comparison arm. Use estimate_proportion_diff() layout creating function : final section needed complete table includes statistical test difference response rates. Use test_proportion_diff() layout creating function : customize output, use method argument select Chi-Squared test Schouten correction. Now can put table sections together one layout pipeline. Note one small change needed. Since primary analysis variable table sections (is_rsp), need give sub-table unique name. done adding table_names argument providing unique names :","code":"# Preprocessing to select an analysis endpoint. anl <- adrs %>%   dplyr::filter(PARAMCD == \"BESRSPI\") %>%   dplyr::mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\",     table_names = \"est_prop\"   ) %>%   build_table(anl) #>                                   A: Drug X      B: Placebo    C: Combination #>                                    (N=134)        (N=134)         (N=132)     #> ————————————————————————————————————————————————————————————————————————————— #> Responders                       114 (85.1%)     90 (67.2%)     120 (90.9%)   #> 95% CI (Wald, with correction)   (78.7, 91.5)   (58.8, 75.5)    (85.6, 96.2) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\"   ) %>%   build_table(anl) #>                                   B: Placebo     A: Drug X     C: Combination #>                                    (N=134)        (N=134)         (N=132)     #> ————————————————————————————————————————————————————————————————————————————— #> Responders                        90 (67.2%)    114 (85.1%)     120 (90.9%)   #> 95% CI (Wald, with correction)   (58.8, 75.5)   (78.7, 91.5)    (85.6, 96.2) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\",     method = \"clopper-pearson\",     conf_level = 0.9   ) %>%   build_table(anl) #>                             B: Placebo     A: Drug X     C: Combination #>                              (N=134)        (N=134)         (N=132)     #> ——————————————————————————————————————————————————————————————————————— #> Responders                  90 (67.2%)    114 (85.1%)     120 (90.9%)   #> 90% CI (Clopper-Pearson)   (59.9, 73.9)   (79.1, 89.9)    (85.7, 94.7) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion_diff(     vars = \"is_rsp\",     show_labels = \"visible\",     var_labels = \"Unstratified Analysis\"   ) %>%   build_table(anl) #>                                      B: Placebo    A: Drug X    C: Combination #>                                       (N=134)       (N=134)        (N=132)     #> —————————————————————————————————————————————————————————————————————————————— #> Unstratified Analysis                                                          #>   Difference in Response rate (%)                    17.9            23.7      #>     95% CI (Wald, with correction)                (7.2, 28.6)    (13.7, 33.8) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   test_proportion_diff(vars = \"is_rsp\") %>%   build_table(anl) #>                                B: Placebo   A: Drug X   C: Combination #>                                 (N=134)      (N=134)       (N=132)     #> —————————————————————————————————————————————————————————————————————— #>   p-value (Chi-Squared Test)                 0.0006        <0.0001 basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   test_proportion_diff(     vars = \"is_rsp\",     method = \"schouten\"   ) %>%   build_table(anl) #>                                                         B: Placebo   A: Drug X   C: Combination #>                                                          (N=134)      (N=134)       (N=132)     #> ——————————————————————————————————————————————————————————————————————————————————————————————— #>   p-value (Chi-Squared Test with Schouten Correction)                 0.0008        <0.0001 basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\",     method = \"clopper-pearson\",     conf_level = 0.9,     table_names = \"est_prop\"   ) %>%   estimate_proportion_diff(     vars = \"is_rsp\",     show_labels = \"visible\",     var_labels = \"Unstratified Analysis\",     table_names = \"est_prop_diff\"   ) %>%   test_proportion_diff(     vars = \"is_rsp\",     method = \"schouten\",     table_names = \"test_prop_diff\"   ) %>%   build_table(anl) #>                                                          B: Placebo     A: Drug X     C: Combination #>                                                           (N=134)        (N=134)         (N=132)     #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> Responders                                               90 (67.2%)    114 (85.1%)     120 (90.9%)   #> 90% CI (Clopper-Pearson)                                (59.9, 73.9)   (79.1, 89.9)    (85.7, 94.7)  #> Unstratified Analysis                                                                                #>   Difference in Response rate (%)                                          17.9            23.7      #>     95% CI (Wald, with correction)                                     (7.2, 28.6)     (13.7, 33.8)  #>   p-value (Chi-Squared Test with Schouten Correction)                     0.0008         <0.0001"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Tabulation","text":"Tabulation tern builds top layout tabulation framework rtables. Complex tables built step step pipeline combining layout creating functions perform specific type analysis. tern analyze functions introduced vignette : analyze_vars() summarize_num_patients() count_occurrences() estimate_proportion() estimate_proportion_diff() test_proportion_diff() Layout creating functions build formatted layout controlling features labels, numerical display formats indentation. functions wrappers Statistics functions calculate raw summaries analysis. can easily spot Statistics functions documentation always begin prefix s_. can helpful inspect run Statistics functions understand ways analysis can customized.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"introduction-to-tern","dir":"Articles","previous_headings":"","what":"Introduction to tern","title":"Introduction to tern","text":"vignette shows general purpose syntax tern R package. tern R package contains analytical functions creating tables graphs useful clinical trials statistical analysis. main focus clinical trial reporting tables graphs related clinical trials also valuable. core functionality tabulation built top general purpose rtables package. strongly recommended start reading “Introduction rtables” vignette get familiar concept rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"common-clinical-trials-analyses","dir":"Articles","previous_headings":"","what":"Common Clinical Trials Analyses","title":"Introduction to tern","text":"package provides large range functionality create tables graphs used clinical trial statistical analysis. rtables tabulation extended clinical trials specific functions: demographics unique patients exposure across patients change baseline parameters statistical model fits: MMRM, logistic regression, Cox regression, … … rtables tabulation helper functions: pre-processing conversions transformations … data visualizations connected clinical trials: Kaplan-Meier plots forest plots line plots … data visualizations helper functions: arrange/stack multiple graphs embellishing graphs/tables metadata details, adding titles, footnotes, page number, etc. … reference tern functions available tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"analytical-functions-for-rtables","dir":"Articles","previous_headings":"","what":"Analytical Functions for rtables","title":"Introduction to tern","text":"Analytical functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analytical functions wrappers around rtables::analyze function; offer various methods useful perspective clinical trials statistical projects. Examples tern analytical functions tern::count_occurrences, tern::summarize_ancova tern::analyze_vars. one prefix identify tern analytical functions recommended use reference subsection tern website. rtables code first describe two tables assign descriptions variables lyt lyt2. built tables using actual data rtables::build_table. description table called table layout. analyze instruction adds layout ARM variable analyzed mean analysis function result rounded 1 decimal place. Hence, layout “pre-data”; , ’s description build table get data. Defining table layout pure rtables code. tern function analyze_vars replaces rtables::analyze function . see tern offers advanced analysis extending rtables function calls one additional function call. examples tabulation analyze functions presented Tabulation vignette.","code":"library(tern) library(dplyr) # Create table layout pure rtables lyt <- rtables::basic_table() %>%   rtables::split_cols_by(var = \"ARM\") %>%   rtables::split_rows_by(var = \"AVISIT\") %>%   rtables::analyze(vars = \"AVAL\", mean, format = \"xx.x\") # Create table layout with tern analyze_vars analyze function lyt2 <- rtables::basic_table() %>%   rtables::split_cols_by(var = \"ARM\") %>%   rtables::split_rows_by(var = \"AVISIT\") %>%   tern::analyze_vars(vars = \"AVAL\", .formats = c(\"mean_sd\" = \"(xx.xx, xx.xx)\")) # Apply table layout to data and produce `rtables` object  adrs <- formatters::ex_adrs  rtables::build_table(lyt, df = adrs) #>                    A: Drug X   B: Placebo   C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING                                                  #>   mean                3.0         3.0            3.0       #> BASELINE                                                   #>   mean                2.5         2.8            2.5       #> END OF INDUCTION                                           #>   mean                1.7         2.1            1.6       #> FOLLOW UP                                                  #>   mean                2.2         2.9            2.0 rtables::build_table(lyt2, df = adrs) #>                     A: Drug X      B: Placebo    C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING                                                       #>   n                    154            178             144       #>   Mean (SD)        (3.00, 0.00)   (3.00, 0.00)    (3.00, 0.00)  #>   Median               3.0            3.0             3.0       #>   Min - Max         3.0 - 3.0      3.0 - 3.0       3.0 - 3.0    #> BASELINE                                                        #>   n                    136            146             124       #>   Mean (SD)        (2.46, 0.88)   (2.77, 1.00)    (2.46, 1.08)  #>   Median               3.0            3.0             3.0       #>   Min - Max         1.0 - 4.0      1.0 - 5.0       1.0 - 5.0    #> END OF INDUCTION                                                #>   n                    218            205             217       #>   Mean (SD)        (1.75, 0.90)   (2.14, 1.28)    (1.65, 1.06)  #>   Median               2.0            2.0             1.0       #>   Min - Max         1.0 - 4.0      1.0 - 5.0       1.0 - 5.0    #> FOLLOW UP                                                       #>   n                    164            153             167       #>   Mean (SD)        (2.23, 1.26)   (2.89, 1.29)    (1.97, 1.01)  #>   Median               2.0            4.0             2.0       #>   Min - Max         1.0 - 4.0      1.0 - 4.0       1.0 - 4.0"},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"clinical-trials-visualizations","dir":"Articles","previous_headings":"","what":"Clinical Trials Visualizations","title":"Introduction to tern","text":"Clinical trial related plots complement rich palette tern tabulation analysis functions. Thus tern package delivers full-featured tool clinical trial reporting. tern plot functions return ggplot2 gTree objects, latter returned table attached plot. optional nestcolor package can loaded apply standardized NEST color palette tern plots. Line plot without table generated tern::g_lineplot function.  Line plot table generated tern::g_lineplot function.  first plot ggplot2 object second plot gTree object, latter contains table. second plot properly resized get clear readable table content. tern functions used plot generation mostly g_ prefixed. tern plot functions listed tern website functions reference.","code":"adsl <- formatters::ex_adsl adlb <- formatters::ex_adlb adlb <- dplyr::filter(adlb, PARAMCD == \"ALT\", AVISIT != \"SCREENING\") library(nestcolor) # Mean with CI tern::g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\") # Mean with CI, table and customized confidence level tern::g_lineplot(   adlb,   adsl,   table = c(\"n\", \"mean\", \"mean_ci\"),   title = \"Plot of Mean and 80% Confidence Limits by Visit\" )"},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"interactive-apps","dir":"Articles","previous_headings":"","what":"Interactive Apps","title":"Introduction to tern","text":"tern outputs easily accommodated shiny apps. recommend applying tern outputs teal apps. teal package shiny-based interactive exploration framework analyzing data. teal shiny apps tern outputs available teal.modules.clinical package.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to tern","text":"summary, tern contains many additional functions creating tables, listing graphs used clinical trials statistical analyses. design package gives users lot flexibility meet analysis needs regulatory exploratory reporting context. information please explore tern website.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"tern-formatting-functions-overview","dir":"Articles","previous_headings":"","what":"tern Formatting Functions Overview","title":"Formatting Functions","text":"tern R package provides functions create common analyses clinical trials R functions default formatting arguments displaying values output specific way. tern formatting differs compared formatting available formatters package tern formats capable handling logical statements, allowing fine-tuning output displayed. Depending type value displayed, value , format output change. Whereas using formatters package, specified format applied regardless value. see available formatting functions available tern see ?formatting_functions. see available format strings available formatters see formatters::list_valid_format_labels().","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"comparing-tern-formatters-formats","dir":"Articles","previous_headings":"","what":"Comparing tern & formatters Formats","title":"Formatting Functions","text":"packages used vignette : example demonstrates use tern formatting count_abnormal() function. example “low” category non-zero numerator value fraction percentage value displayed, “high” value numerator value zero fraction value displayed without also displaying redundant zero percentage value. following example count_abnormal() function utilized . time “low” values “high” values non-zero numerator show percentage. following example demonstrates difference formatters used instead format output. choose use \"xx / xx\" value format. “high” value zero numerator value “low” value non-zero numerator, yet displayed format. concept occurs using available formats formatters package. following example displays result using \"xx.x / xx.x\" format instead. Use formatters::list_valid_format_labels() see full list available formats formatters.","code":"library(rtables) library(formatters) library(tern) library(dplyr) df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = format_fraction)   ) %>%   build_table(df2) #>         all obs   #> ————————————————— #> low    2/2 (100%) #> high      0/2 df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = format_fraction)   ) %>%   build_table(df2) #>         all obs  #> ———————————————— #> low    1/2 (50%) #> high   1/2 (50%) df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE ) df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = \"xx / xx\")   ) %>%   build_table(df2) #>        all obs #> —————————————— #> low     2 / 2  #> high    0 / 2 df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE ) df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = \"xx.x / xx.x\")   ) %>%   build_table(df2) #>         all obs  #> ———————————————— #> low    2.0 / 2.0 #> high   0.0 / 2.0"},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"formatting-function-basics","dir":"Articles","previous_headings":"","what":"Formatting Function Basics","title":"Formatting Functions","text":"Current tern formatting functions consider following aspects setting custom behaviors: Missing values - custom value string can set display missing values instead NA. 0’s - cell value zero, tern fraction formatting functions exclude accompanying percentage value. Number decimal places display - number decimal places can fixed needed. Value thresholds - different format value can displayed depending whether value within certain threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"number-of-decimal-places-to-display","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Number of Decimal Places to Display","title":"Formatting Functions","text":"Two functions set fixed number decimal places (specifically 1) format_fraction_fixed_dp() format_count_fraction_fixed_dp(). default, formatting functions remove trailing zeros, two functions always one decimal place percentage, even digit zero. See following example:","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 3L)) #> [1] \"1/3 (33.3%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\"  format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.25)) #> [1] \"2 (25.0%)\""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"value-thresholds","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Value Thresholds","title":"Formatting Functions","text":"Functions set custom values according certain threshold include format_extreme_values(), format_extreme_values_ci(), format_fraction_threshold(). extreme value formats work similarly allow user specify maximum number digits include, large small values given special string value. example: format_fraction_threshold() function allows user specify lower percentage threshold, values instead assigned special string value. example: See documentation function specific details behavior customize .","code":"extreme_format <- format_extreme_values(digits = 2) extreme_format(0.235) #> [1] \"0.23\" extreme_format(0.001) #> [1] \"<0.01\" extreme_format(Inf) #> [1] \">999.99\" fraction_format <- format_fraction_threshold(0.05) fraction_format(x = c(20, 0.1)) #> [1] 10 fraction_format(x = c(2, 0.01)) #> [1] \"<5\""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"creating-custom-formatting-functions","dir":"Articles","previous_headings":"","what":"Creating Custom Formatting Functions","title":"Formatting Functions","text":"table requires customized output displayed using one pre-existing tern formatting functions, may want consider creating new formatting function. creating formatting function important consider aspects listed Formatting Function Customization section . section create custom formatting function derived format_fraction_fixed_dp() function. First take look function detail customize . see numerator value greater 0, fraction percentage displayed. numerator 0, fraction shown. Percent values always display 1 decimal place. create dummy dataset observe output value behavior formatting function applied. Now modify function make custom formatting function, custom_format. want display 3 decimal places percent value, numerator value 0 want display 0 value (without denominator).","code":"# First we will see how the format_fraction_fixed_dp code works and displays the outputs format_fraction_fixed_dp <- function(x, ...) {   attr(x, \"label\") <- NULL   checkmate::assert_vector(x)   checkmate::assert_count(x[\"num\"])   checkmate::assert_count(x[\"denom\"])    result <- if (x[\"num\"] == 0) {     paste0(x[\"num\"], \"/\", x[\"denom\"])   } else {     paste0(       x[\"num\"], \"/\", x[\"denom\"],       \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\"     )   }   return(result) } df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE ) %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = format_fraction_fixed_dp)   ) %>%   build_table(df2) #>          all obs    #> ——————————————————— #> low    2/2 (100.0%) #> high       0/2 custom_format <- function(x, ...) {   attr(x, \"label\") <- NULL   checkmate::assert_vector(x)   checkmate::assert_count(x[\"num\"])   checkmate::assert_count(x[\"denom\"])    result <- if (x[\"num\"] == 0) {     paste0(x[\"num\"]) # We remove the denominator on this line so that only a 0 is displayed   } else {     paste0(       x[\"num\"], \"/\", x[\"denom\"],       \" (\", sprintf(\"%.3f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" # We include 3 decimal places with %.3f     )   }   return(result) }  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = custom_format) # Here we implement our new custom_format function   ) %>%   build_table(df2) #>           all obs     #> ————————————————————— #> low    2/2 (100.000%) #> high         0"},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Formatting Functions","text":"tern analysis function pre-specified default format functions implement generating output, taken formatters package custom formatting functions stored tern. tern functions differ compared formatters logical statements can used set value-dependent customized formats. like create custom formatting function use tern, sure carefully consider rules want implement handle different input values.","code":""},{"path":"https://insightsengineering.github.io/tern/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Joe Zhu. Author, maintainer. Daniel Sabanés Bové. Author. Jana Stoilova. Author. Heng Wang. Author. Francois Collin. Author. Adrian Waddell. Author. Pawel Rucki. Author. Chendi Liao. Author. Jennifer Li. Author. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/tern/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Zhu J, Sabanés Bové D, Stoilova J, Wang H, Collin F, Waddell , Rucki P, Liao C, Li J (2023). tern: Create Common TLGs Used Clinical Trials. R package version 0.9.1.9013, https://github.com/insightsengineering/tern.","code":"@Manual{,   title = {tern: Create Common TLGs Used in Clinical Trials},   author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li},   year = {2023},   note = {R package version 0.9.1.9013},   url = {https://github.com/insightsengineering/tern}, }"},{"path":"https://insightsengineering.github.io/tern/index.html","id":"tern-","dir":"","previous_headings":"","what":"Create Common TLGs Used in Clinical Trials","title":"Create Common TLGs Used in Clinical Trials","text":"tern R package contains analysis functions create tables graphs used clinical trial reporting. package provides large range functionality, example: forest plots line plots Kaplan-Meier plots … logistic regression Cox regression … unique patients exposure across patients change baseline parameters … Many outputs available added teal applications interactive exploration data. teal modules available teal.modules.clinical package.","code":""},{"path":"https://insightsengineering.github.io/tern/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Create Common TLGs Used in Clinical Trials","text":"tern available CRAN can install latest released version : can install latest development version directly GitHub running following: Note recommended create use GITHUB_PAT installing GitHub. See package vignettes browseVignettes(package = \"tern\") usage package.","code":"install.packages(\"tern\") if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_github(\"insightsengineering/tern\")"},{"path":"https://insightsengineering.github.io/tern/index.html","id":"related","dir":"","previous_headings":"","what":"Related","title":"Create Common TLGs Used in Clinical Trials","text":"rtables - table engine used TLG Catalog - website showcasing many examples clinical trial tables, listings, graphs teal.modules.clinical - teal modules interactive data analysis","code":""},{"path":"https://insightsengineering.github.io/tern/index.html","id":"acknowledgment","dir":"","previous_headings":"","what":"Acknowledgment","title":"Create Common TLGs Used in Clinical Trials","text":"package result joint efforts many developers stakeholders. like thank everyone contributed far!","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/pull_request_template.html","id":null,"dir":"","previous_headings":"","what":"Pull Request","title":"Pull Request","text":"Fixes #nnn","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts with Abnormal Range Values — abnormal","title":"Patient Counts with Abnormal Range Values — abnormal","text":"Primary analysis variable .var indicates abnormal range result (character factor) additional analysis variables id (character factor) baseline (character factor). direction specified abnormal (e.g. high low) count patients numerator denominator follows: num : number patients abnormality recorded treatment. denom: number patients least one post-baseline assessment.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts with Abnormal Range Values — abnormal","text":"","code":"s_count_abnormal(   df,   .var,   abnormal = list(Low = \"LOW\", High = \"HIGH\"),   variables = list(id = \"USUBJID\", baseline = \"BNRIND\"),   exclude_base_abn = FALSE )  a_count_abnormal(   df,   .var,   abnormal = list(Low = \"LOW\", High = \"HIGH\"),   variables = list(id = \"USUBJID\", baseline = \"BNRIND\"),   exclude_base_abn = FALSE )  count_abnormal(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = var,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts with Abnormal Range Values — abnormal","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. abnormal (named list) list identifying abnormal range level(s) var. Defaults list(Low = \"LOW\", High = \"HIGH\") can also group different levels named list, example, abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")). variables (named list string) list additional analysis variables. exclude_base_abn (flag) whether exclude subjects baseline abnormality numerator denominator. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts with Abnormal Range Values — abnormal","text":"s_count_abnormal() returns statistic fraction vector num denom counts patients. a_count_abnormal() returns corresponding list formatted rtables::CellValue(). count_abnormal() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts with Abnormal Range Values — abnormal","text":"s_count_abnormal(): Statistics function counts patients abnormal range values single abnormal level. a_count_abnormal(): Formatted analysis function used afun count_abnormal(). count_abnormal(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Patient Counts with Abnormal Range Values — abnormal","text":"count_abnormal() works single variable containing multiple abnormal levels. df filtered include post-baseline records. denominator includes patients might abnormal levels baseline, patients missing baseline. Patients abnormalities baseline can optionally excluded numerator denominator.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts with Abnormal Range Values — abnormal","text":"","code":"library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:testthat’: #>  #>     matches #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union  df <- data.frame(   USUBJID = as.character(c(1, 1, 2, 2)),   ANRIND = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BNRIND = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  # Select only post-baseline records. df <- df %>%   filter(ONTRTFL == \"Y\") # Layout creating function. basic_table() %>%   count_abnormal(var = \"ANRIND\", abnormal = list(high = \"HIGH\", low = \"LOW\")) %>%   build_table(df) #>         all obs  #> ———————————————— #> high   1/2 (50%) #> low    1/2 (50%)  # Passing of statistics function and formatting arguments. df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  # Select only post-baseline records. df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\")   ) %>%   build_table(df2) #>         all obs  #> ———————————————— #> low    1/2 (50%) #> high   1/2 (50%)"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"Primary analysis variable .var indicates abnormal range result (character factor), additional analysis variables id (character factor) baseline (character factor). direction specified abnormal (e.g. high low) condition baseline range result count patients numerator denominator follows: <Abnormal> denom: number patients without abnormality baseline (excluding missing baseline) num:  number patients denom also least one abnormality post-baseline <Abnormal> denom: number patients abnormality baseline num: number patients denom also least one abnormality post-baseline Total denom: number patients least one valid measurement post-baseline num: number patients denom also least one abnormality post-baseline","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"","code":"s_count_abnormal_by_baseline(   df,   .var,   abnormal,   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\",   variables = list(id = \"USUBJID\", baseline = \"BNRIND\") )  a_count_abnormal_by_baseline(   df,   .var,   abnormal,   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\",   variables = list(id = \"USUBJID\", baseline = \"BNRIND\") )  count_abnormal_by_baseline(   lyt,   var,   abnormal,   na_str = \"<Missing>\",   nested = TRUE,   ...,   table_names = abnormal,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. abnormal (character) identifying abnormal range level(s) .var. na_level Please use na_str argument instead. na_str (string) explicit na_level argument used pre-processing steps (maybe df_explicit_na()). default \"<Missing>\". variables (named list string) list additional analysis variables. lyt (layout) input layout analyses added . nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"s_count_abnormal_by_baseline() returns statistic fraction named list 3 labeled elements: not_abnormal, abnormal, total. element contains vector num denom patient counts. a_count_abnormal_by_baseline() returns corresponding list formatted rtables::CellValue(). count_abnormal_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_baseline() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"s_count_abnormal_by_baseline(): Statistics function single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function used afun count_abnormal_by_baseline(). count_abnormal_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"df filtered include post-baseline records. baseline variable analysis variable contains NA, expected NA conveyed na_level appropriately beforehand df_explicit_na() explicit_na().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"","code":"df <- data.frame(   USUBJID = as.character(c(1:6)),   ANRIND = factor(c(rep(\"LOW\", 4), \"NORMAL\", \"HIGH\")),   BNRIND = factor(c(\"LOW\", \"NORMAL\", \"HIGH\", NA, \"LOW\", \"NORMAL\")) ) df <- df_explicit_na(df)  # Layout creating function. basic_table() %>%   count_abnormal_by_baseline(var = \"ANRIND\", abnormal = c(High = \"HIGH\")) %>%   build_table(df) #>                all obs   #> ———————————————————————— #> High                     #>   Not high    1/4 (25%)  #>   High           0/1     #>   Total      1/6 (16.7%)  # Passing of statistics function and formatting arguments. df2 <- data.frame(   ID = as.character(c(1, 2, 3, 4)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BLRANGE = factor(c(\"LOW\", \"HIGH\", \"HIGH\", \"NORMAL\")) )  basic_table() %>%   count_abnormal_by_baseline(     var = \"RANGE\",     abnormal = c(Low = \"LOW\"),     variables = list(id = \"ID\", baseline = \"BLRANGE\"),     .formats = c(fraction = \"xx / xx\"),     .indent_mods = c(fraction = 2L)   ) %>%   build_table(df2) #>                 all obs #> ——————————————————————— #> Low                     #>       Not low    1 / 3  #>       Low        0 / 1  #>       Total      1 / 4"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"Primary analysis variable .var indicates whether single, replicated last marked laboratory abnormality observed (factor). Additional analysis variables id (character factor) direction (factor) indicating direction abnormality. Denominator number patients least one valid measurement analysis. Single, last Last replicated: Numerator number patients Single, last Last replicated levels, respectively. : Numerator number patients either single replicated marked abnormalities.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"","code":"s_count_abnormal_by_marked(   df,   .var = \"AVALCAT1\",   .spl_context,   category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")),   variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") )  a_count_abnormal_by_marked(   df,   .var = \"AVALCAT1\",   .spl_context,   category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")),   variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") )  count_abnormal_by_marked(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables. category (list) different marked category names single last replicated. variables (named list string) list additional analysis variables. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"s_count_abnormal_by_marked() returns statistic count_fraction Single, last, Last replicated, results. a_count_abnormal_by_marked() returns corresponding list formatted rtables::CellValue(). count_abnormal_by_marked() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_marked() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"s_count_abnormal_by_marked(): Statistics function patients marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function used afun count_abnormal_by_marked(). count_abnormal_by_marked(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"Single, last Last replicated levels mutually exclusive. patient abnormalities meet Single, last Last replicated criteria, patient counted Last replicated category.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"","code":"library(dplyr)  df <- data.frame(   USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))),   ARMCD = factor(c(rep(\"ARM A\", 5), rep(\"ARM B\", 5), rep(\"ARM A\", 5), rep(\"ARM B\", 5))),   ANRIND = factor(c(     \"NORMAL\", \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"HIGH\",     \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"NORMAL\", \"HIGH HIGH\", \"NORMAL\", \"LOW\", \"LOW\", \"LOW LOW\", \"LOW\",     \"LOW\", \"LOW\", \"LOW LOW\", \"NORMAL\", \"LOW LOW\"   )),   ONTRTFL = rep(c(\"\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\"), 2),   PARAMCD = factor(c(rep(\"CRP\", 10), rep(\"ALT\", 10))),   AVALCAT1 = factor(rep(c(\"\", \"\", \"\", \"SINGLE\", \"REPLICATED\", \"\", \"\", \"LAST\", \"\", \"SINGLE\"), 2)),   stringsAsFactors = FALSE )  df <- df %>%   mutate(abn_dir = factor(     case_when(       ANRIND == \"LOW LOW\" ~ \"Low\",       ANRIND == \"HIGH HIGH\" ~ \"High\",       TRUE ~ \"\"     ),     levels = c(\"Low\", \"High\")   ))  # Select only post-baseline records. df <- df %>% filter(ONTRTFL == \"Y\") df_crp <- df %>%   filter(PARAMCD == \"CRP\") %>%   droplevels() full_parent_df <- list(df_crp, \"not_needed\") cur_col_subset <- list(rep(TRUE, nrow(df_crp)), \"not_needed\") spl_context <- data.frame(   split = c(\"PARAMCD\", \"GRADE_DIR\"),   full_parent_df = I(full_parent_df),   cur_col_subset = I(cur_col_subset) )  map <- unique(   df[df$abn_dir %in% c(\"Low\", \"High\") & df$AVALCAT1 != \"\", c(\"PARAMCD\", \"abn_dir\")] ) %>%   lapply(as.character) %>%   as.data.frame() %>%   arrange(PARAMCD, abn_dir)  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   split_rows_by(\"PARAMCD\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = \"unique_count\"   ) %>%   split_rows_by(     \"abn_dir\",     split_fun = trim_levels_to_map(map)   ) %>%   count_abnormal_by_marked(     var = \"AVALCAT1\",     variables = list(       id = \"USUBJID\",       param = \"PARAMCD\",       direction = \"abn_dir\"     )   ) %>%   build_table(df = df) #>                           ARM A      ARM B   #> ———————————————————————————————————————————— #> ALT (n)                     1          1     #>   Low                                        #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%) #> CRP (n)                     1          1     #>   High                                       #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%)  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   split_rows_by(\"PARAMCD\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = \"unique_count\"   ) %>%   split_rows_by(     \"abn_dir\",     split_fun = trim_levels_in_group(\"abn_dir\")   ) %>%   count_abnormal_by_marked(     var = \"AVALCAT1\",     variables = list(       id = \"USUBJID\",       param = \"PARAMCD\",       direction = \"abn_dir\"     )   ) %>%   build_table(df = df) #>                           ARM A      ARM B   #> ———————————————————————————————————————————— #> ALT (n)                     1          1     #>   Low                                        #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%) #> CRP (n)                     1          1     #>   High                                       #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%)"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"Primary analysis variable .var indicates toxicity grade (factor), additional analysis variables id (character factor), param (factor) grade_dir (factor). pre-processing steps crucial using function. certain direction (e.g. high low) function counts patients denominator number patients least one valid measurement treatment, patients numerator follows: 1 4: Numerator number patients worst grades 1-4 respectively; : Numerator number patients least one abnormality, means grade different 0.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"","code":"s_count_abnormal_by_worst_grade(   df,   .var = \"GRADE_ANL\",   .spl_context,   variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") )  a_count_abnormal_by_worst_grade(   df,   .var = \"GRADE_ANL\",   .spl_context,   variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") )  count_abnormal_by_worst_grade(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables. variables (named list string) list additional analysis variables. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"s_count_abnormal_by_worst_grade() returns single statistic count_fraction grades 1 4 \"\" results. a_count_abnormal_by_worst_grade() returns corresponding list formatted rtables::CellValue(). count_abnormal_by_worst_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_worst_grade() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"pre-processing steps crucial using function. standard lab grade variable ATOXGR, derive following two variables: grade direction variable (e.g. GRADE_DIR) required order obtain correct denominators building layout used define row splitting. toxicity grade variable (e.g. GRADE_ANL) negative values ATOXGR replaced absolute values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"s_count_abnormal_by_worst_grade(): Statistics function counts patients worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function used afun count_abnormal_by_worst_grade(). count_abnormal_by_worst_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"Prior tabulation, df must filtered include post-baseline records worst grade flags.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"","code":"library(dplyr) library(forcats) adlb <- tern_ex_adlb  # Data is modified in order to have some parameters with grades only in one direction # and simulate the real data. adlb$ATOXGR[adlb$PARAMCD == \"ALT\" & adlb$ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\")] <- \"-1\" adlb$ANRIND[adlb$PARAMCD == \"ALT\" & adlb$ANRIND == \"HIGH\"] <- \"LOW\" adlb$WGRHIFL[adlb$PARAMCD == \"ALT\"] <- \"\"  adlb$ATOXGR[adlb$PARAMCD == \"IGA\" & adlb$ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\")] <- \"1\" adlb$ANRIND[adlb$PARAMCD == \"IGA\" & adlb$ANRIND == \"LOW\"] <- \"HIGH\" adlb$WGRLOFL[adlb$PARAMCD == \"IGA\"] <- \"\"  # Here starts the real pre-processing. adlb_f <- adlb %>%   filter(!AVISIT %in% c(\"SCREENING\", \"BASELINE\")) %>%   mutate(     GRADE_DIR = factor(       case_when(         ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\") ~ \"LOW\",         ATOXGR == \"0\" ~ \"ZERO\",         ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\") ~ \"HIGH\"       ),       levels = c(\"LOW\", \"ZERO\", \"HIGH\")     ),     GRADE_ANL = fct_relevel(       fct_recode(ATOXGR, `1` = \"-1\", `2` = \"-2\", `3` = \"-3\", `4` = \"-4\"),       c(\"0\", \"1\", \"2\", \"3\", \"4\")     )   ) %>%   filter(WGRLOFL == \"Y\" | WGRHIFL == \"Y\") %>%   droplevels()  adlb_f_alt <- adlb_f %>%   filter(PARAMCD == \"ALT\") %>%   droplevels() full_parent_df <- list(adlb_f_alt, \"not_needed\") cur_col_subset <- list(rep(TRUE, nrow(adlb_f_alt)), \"not_needed\")  # This mimics a split structure on PARAM and GRADE_DIR for a total column spl_context <- data.frame(   split = c(\"PARAM\", \"GRADE_DIR\"),   full_parent_df = I(full_parent_df),   cur_col_subset = I(cur_col_subset) )  # Map excludes records without abnormal grade since they should not be displayed # in the table. map <- unique(adlb_f[adlb_f$GRADE_DIR != \"ZERO\", c(\"PARAM\", \"GRADE_DIR\", \"GRADE_ANL\")]) %>%   lapply(as.character) %>%   as.data.frame() %>%   arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   split_rows_by(\"PARAM\") %>%   split_rows_by(\"GRADE_DIR\", split_fun = trim_levels_to_map(map)) %>%   count_abnormal_by_worst_grade(     var = \"GRADE_ANL\",     variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\")   ) %>%   build_table(df = adlb_f) #>                                          ARM A        ARM B        ARM C    #> ——————————————————————————————————————————————————————————————————————————— #> Alanine Aminotransferase Measurement                                        #>   LOW                                                                       #>     1                                  12 (17.4%)    5 (6.8%)    8 (13.8%)  #>     2                                   9 (13%)     13 (17.8%)   6 (10.3%)  #>     3                                   6 (8.7%)     4 (5.5%)    6 (10.3%)  #>     4                                  7 (10.1%)     7 (9.6%)    6 (10.3%)  #>     Any                                34 (49.3%)   29 (39.7%)   26 (44.8%) #> C-Reactive Protein Measurement                                              #>   LOW                                                                       #>     1                                  11 (15.9%)   12 (16.4%)   7 (12.1%)  #>     2                                  8 (11.6%)     2 (2.7%)    6 (10.3%)  #>     3                                   4 (5.8%)    9 (12.3%)    6 (10.3%)  #>     4                                  7 (10.1%)     6 (8.2%)     4 (6.9%)  #>     Any                                30 (43.5%)   29 (39.7%)   23 (39.7%) #>   HIGH                                                                      #>     1                                  8 (11.6%)    11 (15.1%)    2 (3.4%)  #>     2                                   9 (13%)     11 (15.1%)    11 (19%)  #>     3                                  14 (20.3%)   10 (13.7%)    5 (8.6%)  #>     4                                   2 (2.9%)     4 (5.5%)    6 (10.3%)  #>     Any                                33 (47.8%)   36 (49.3%)   24 (41.4%) #> Immunoglobulin A Measurement                                                #>   HIGH                                                                      #>     1                                  7 (10.1%)     7 (9.6%)    6 (10.3%)  #>     2                                  8 (11.6%)     6 (8.2%)    8 (13.8%)  #>     3                                  7 (10.1%)     5 (6.8%)    9 (15.5%)  #>     4                                   6 (8.7%)     2 (2.7%)     3 (5.2%)  #>     Any                                28 (40.6%)   20 (27.4%)   26 (44.8%)"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"Patient count fraction laboratory events (worsen baseline) shift table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"","code":"s_count_abnormal_lab_worsen_by_baseline(   df,   .var = \"ATOXGR\",   variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") )  a_count_abnormal_lab_worsen_by_baseline(   df,   .var = \"ATOXGR\",   variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") )  count_abnormal_lab_worsen_by_baseline(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = NULL,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. variables (named list string) list additional analysis variables including: id (string) subject variable name. baseline_var (string) name data column containing baseline toxicity variable. direction_var (string) see direction_var details. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"s_count_abnormal_lab_worsen_by_baseline() returns counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\". a_count_abnormal_lab_worsen_by_baseline() returns corresponding list formatted rtables::CellValue(). count_abnormal_lab_worsen_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_lab_worsen_by_baseline() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"s_count_abnormal_lab_worsen_by_baseline(): Statistics function patients whose worst post-baseline lab grades worse baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function used afun count_abnormal_lab_worsen_by_baseline(). count_abnormal_lab_worsen_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"","code":"library(dplyr)  # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>%   mutate(     GRADDR = case_when(       PARAMCD == \"ALT\" ~ \"B\",       PARAMCD == \"CRP\" ~ \"L\",       PARAMCD == \"IGA\" ~ \"H\"     )   ) %>%   filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")  df <- h_adlb_worsen(   adlb,   worst_flag_low = c(\"WGRLOFL\" = \"Y\"),   worst_flag_high = c(\"WGRHIFL\" = \"Y\"),   direction_var = \"GRADDR\" )  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   add_colcounts() %>%   split_rows_by(\"PARAMCD\") %>%   split_rows_by(\"GRADDR\") %>%   count_abnormal_lab_worsen_by_baseline(     var = \"ATOXGR\",     variables = list(       id = \"USUBJID\",       baseline_var = \"BTOXGR\",       direction_var = \"GRADDR\"     )   ) %>%   append_topleft(\"Direction of Abnormality\") %>%   build_table(df = df, alt_counts_df = tern_ex_adsl) #>                                ARM A           ARM B           ARM C     #> Direction of Abnormality      (N=69)          (N=73)          (N=58)     #> ———————————————————————————————————————————————————————————————————————— #> IGA                                                                      #>   High                                                                   #>     1                       6/63 (9.5%)     6/64 (9.4%)      4/50 (8%)   #>     2                      8/64 (12.5%)     5/67 (7.5%)    8/53 (15.1%)  #>     3                      7/66 (10.6%)     5/68 (7.4%)    9/57 (15.8%)  #>     4                       6/68 (8.8%)     2/72 (2.8%)     3/58 (5.2%)  #>     Any                    27/68 (39.7%)    18/72 (25%)    24/58 (41.4%) #> ALT                                                                      #>   High                                                                   #>     1                      7/63 (11.1%)     6/62 (9.7%)     2/48 (4.2%)  #>     2                       12/63 (19%)      4/67 (6%)      11/50 (22%)  #>     3                       4/65 (6.2%)    11/71 (15.5%)   7/56 (12.5%)  #>     4                       1/67 (1.5%)    8/71 (11.3%)      4/57 (7%)   #>     Any                    24/67 (35.8%)   29/71 (40.8%)   24/57 (42.1%) #>   Low                                                                    #>     1                      12/67 (17.9%)    4/66 (6.1%)    7/52 (13.5%)  #>     2                      9/68 (13.2%)    12/69 (17.4%)   6/55 (10.9%)  #>     3                       6/69 (8.7%)     4/71 (5.6%)     5/56 (8.9%)  #>     4                      7/69 (10.1%)     7/73 (9.6%)    6/58 (10.3%)  #>     Any                    34/69 (49.3%)    27/73 (37%)    24/58 (41.4%) #> CRP                                                                      #>   Low                                                                    #>     1                      11/66 (16.7%)   10/67 (14.9%)    4/47 (8.5%)  #>     2                      8/66 (12.1%)     1/70 (1.4%)     6/50 (12%)   #>     3                       4/68 (5.9%)    9/70 (12.9%)     5/53 (9.4%)  #>     4                      7/69 (10.1%)     6/72 (8.3%)     4/55 (7.3%)  #>     Any                    30/69 (43.5%)   26/72 (36.1%)   19/55 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Function to Configure Risk Difference Column — add_riskdiff","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"Wrapper function rtables::add_combo_levels() configures settings risk difference column added rtables object. add risk difference column table, function used split_fun calls rtables::split_cols_by(), followed setting argument riskdiff TRUE following analyze function calls.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"","code":"add_riskdiff(   arm_x,   arm_y,   col_label = \"Risk Difference (%) (95% CI)\",   pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"arm_x (character) Name reference arm use risk difference calculations. arm_y (character) Name arm compare reference arm risk difference calculations. col_label (character) Label use rendering risk difference column within table. pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"closure suitable use split function (split_fun) within rtables::split_cols_by() creating table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"","code":"adae <- tern_ex_adae adae$AESEV <- factor(adae$AESEV)  lyt <- basic_table() %>%   split_cols_by(\"ARMCD\", split_fun = add_riskdiff(arm_x = \"ARM A\", arm_y = \"ARM B\")) %>%   count_occurrences_by_grade(     var = \"AESEV\",     riskdiff = TRUE   )  tbl <- build_table(lyt, df = adae) tbl #>              ARM A        ARM B        ARM C      Risk Difference (%) (95% CI) #> —————————————————————————————————————————————————————————————————————————————— #> MILD        6 (3.0%)     4 (2.3%)     2 (1.2%)          0.7 (-2.5 - 3.9)       #> MODERATE   19 (9.4%)    15 (8.5%)    14 (8.6%)          0.9 (-4.8 - 6.7)       #> SEVERE     34 (16.8%)   38 (21.5%)   32 (19.8%)        -4.6 (-12.6 - 3.3)"},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Layout Creating Function to Add Row Total Counts — add_rowcounts","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"works analogously rtables::add_colcounts() rows. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"","code":"add_rowcounts(lyt, alt_counts = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"lyt (layout) input layout analyses added . alt_counts (flag) whether row counts taken alt_counts_df (TRUE) df (FALSE). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"modified layout latest row split labels now row-wise total counts (.e. without column-based subsetting) attached parentheses.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"Row count values contained row count rows displayed considered zero rows default pruning.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"","code":"basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   split_rows_by(\"RACE\", split_fun = drop_split_levels) %>%   add_rowcounts() %>%   analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") %>%   build_table(DM) #>                                    A: Drug X   B: Placebo   C: Combination #>                                     (N=121)     (N=106)        (N=129)     #> —————————————————————————————————————————————————————————————————————————— #> ASIAN (N=231)                                                              #>   Min.                               20.00       21.00          22.00      #>   1st Qu.                            29.00       28.00          30.00      #>   Median                             33.00       32.50          33.50      #>   Mean                               34.20       32.68          34.63      #>   3rd Qu.                            38.50       36.00          38.00      #>   Max.                               58.00       55.00          53.00      #> BLACK OR AFRICAN AMERICAN (N=79)                                           #>   Min.                               23.00       21.00          24.00      #>   1st Qu.                            29.00       28.75          29.00      #>   Median                             33.00       30.00          32.00      #>   Mean                               34.68       31.71          34.00      #>   3rd Qu.                            37.25       36.25          39.00      #>   Max.                               60.00       42.00          51.00      #> WHITE (N=46)                                                               #>   Min.                               30.00       25.00          28.00      #>   1st Qu.                            38.00       31.00          30.25      #>   Median                             40.50       37.50          35.00      #>   Mean                               39.36       36.93          35.11      #>   3rd Qu.                            43.50       40.00          37.50      #>   Max.                               47.00       55.00          47.00"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Adverse Event Baskets — aesi_label","text":"","code":"aesi_label(aesi, scope = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Adverse Event Baskets — aesi_label","text":"aesi (character) standardized MedDRA query name (e.g. SMQzzNAM) customized query name (e.g. CQzzNAM). scope (character) scope query (e.g. SMQzzSC).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Adverse Event Baskets — aesi_label","text":"string standard label AE basket.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Labels for Adverse Event Baskets — aesi_label","text":"","code":"adae <- tern_ex_adae  # Standardized query label includes scope. aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC) #> [1] \"C.1.1.1.3/B.2.2.3.1 aesi (BROAD)\"  # Customized query label. aesi_label(adae$CQ01NAM) #> [1] \"D.2.1.5.3/A.1.1.1.1 aesi\""},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"risk difference column, function uses statistics function associated afun calculates risk difference values arm X (reference group) arm Y. arms specified configuring risk difference column done using add_riskdiff() split function previous call rtables::split_cols_by(). columns, applies afun usual. function utilizes stat_propdiff_ci() function perform risk difference calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"","code":"afun_riskdiff(   df,   labelstr = \"\",   .var,   .N_col,   .N_row,   .df_row,   .spl_context,   .all_col_counts,   .stats,   .indent_mods,   afun,   s_args = list() )"},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .var (string) single variable name passed rtables requested statistics function. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .df_row (data.frame) data frame across columns given row split. .spl_context (data.frame) gives information ancestor split states passed rtables. .all_col_counts (vector integer) value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .stats (character) statistics select table. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. afun (named list) named list containing one name-value pair name corresponds name statistics function used calculations value corresponding analysis function. s_args (named list) Additional arguments passed statistics function analysis function supplied afun.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"list formatted rtables::CellValue().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Selected Statistics Names — afun_selected_stats","title":"Get Selected Statistics Names — afun_selected_stats","text":"Helper function used creating afun.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Selected Statistics Names — afun_selected_stats","text":"","code":"afun_selected_stats(.stats, all_stats)"},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Selected Statistics Names — afun_selected_stats","text":".stats (vector NULL) input layout creating function. Note NULL means context default statistics used. all_stats (character) statistics can selected potentially.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Selected Statistics Names — afun_selected_stats","text":"character vector selected statistics.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_colvars_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze Functions on Columns — analyze_colvars_functions","title":"Analyze Functions on Columns — analyze_colvars_functions","text":"functions wrappers rtables::analyze_colvars() apply corresponding tern statistics functions add analysis given table layout. particular, functions designed analysis methods split different columns. analyze_vars_in_cols(): fundamental tabulation analysis methods onto columns. words, analysis methods defined column space, .e. become column labels. changing variable vector, list functions can applied different variables, caveat number statistical functions. tabulate_rsp_subgroups(): similarly analyze_vars_in_cols, function combines analyze_colvars summarize_row_groups compact way produce standard tables show analysis methods columns. tabulate_survival_subgroups(): function similar , used tables. analyze_patients_exposure_in_cols(): based analyze_colvars. needs summarize_patients_exposure_in_cols() leverage nesting label rows analysis rtables::summarize_row_groups(). summarize_coxreg(): generally based rtables::summarize_row_groups(), behaves similarly tabulate_* functions described designed provide specific standard tables may contain nested structure combination summarize_row_groups() rtables::analyze_colvars().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/analyze_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze Functions — analyze_functions","title":"Analyze Functions — analyze_functions","text":"functions wrappers rtables::analyze() apply corresponding tern statistics functions add analysis given table layout: analyze_num_patients() compare_vars() count_abnormal() count_abnormal_by_baseline() count_abnormal_by_marked() count_abnormal_by_worst_grade() count_cumulative() count_missed_doses() count_occurrences() count_occurrences_by_grade() count_patients_events_in_cols() count_patients_with_event() count_patients_with_flags() count_values() coxph_pairwise() estimate_incidence_rate() estimate_multinomial_rsp() estimate_odds_ratio() estimate_proportion() estimate_proportion_diff() summarize_ancova() summarize_colvars(): even function uses rtables::analyze_colvars(), applies analysis methods different rows one variables split different columns. comparison, analyze_colvars_functions leverage analyze_colvars context split rows analysis methods columns. summarize_change() analyze_vars(): formerly known summarize_vars(), renamed reflect core function rtables::analyze(). surv_time() surv_timepoint() test_proportion_diff()","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze Variables — analyze_variables","title":"Analyze Variables — analyze_variables","text":"use S3 generic function s_summary() implement summaries different x objects. used statistics function combination analyze function analyze_vars(). Deprecation cycle started summarize_vars going renamed analyze_vars. Intention reflect better core underlying rtables functions; case rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyze Variables — analyze_variables","text":"","code":"s_summary(x, na.rm = TRUE, denom, .N_row, .N_col, .var, ...)  # S3 method for numeric s_summary(   x,   na.rm = TRUE,   denom,   .N_row,   .N_col,   .var,   control = control_analyze_vars(),   ... )  # S3 method for factor s_summary(   x,   na.rm = TRUE,   denom = c(\"n\", \"N_row\", \"N_col\"),   .N_row,   .N_col,   ... )  # S3 method for character s_summary(   x,   na.rm = TRUE,   denom = c(\"n\", \"N_row\", \"N_col\"),   .N_row,   .N_col,   .var,   verbose = TRUE,   ... )  # S3 method for logical s_summary(   x,   na.rm = TRUE,   denom = c(\"n\", \"N_row\", \"N_col\"),   .N_row,   .N_col,   ... )  a_summary(   x,   .N_col,   .N_row,   .var = NULL,   .df_row = NULL,   .ref_group = NULL,   .in_ref_col = FALSE,   compare = FALSE,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL,   na.rm = TRUE,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   ... )  analyze_vars(   lyt,   vars,   var_labels = vars,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   nested = TRUE,   ...,   na.rm = TRUE,   show_labels = \"default\",   table_names = vars,   section_div = NA_character_,   .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )  summarize_vars(...)"},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyze Variables — analyze_variables","text":"x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .var (string) single variable name passed rtables requested statistics function. ... arguments passed s_summary(). control (list) parameters descriptive statistics details, specified using helper function control_analyze_vars(). possible parameter options : conf_level (proportion) confidence level interval mean median. quantiles (numeric) vector length two specify quantiles. quantile_type (numeric) 1 9 selecting quantile algorithms used. See type stats::quantile(). test_mean (numeric) value test mean null hypothesis calculating p-value. verbose (logical) Defaults TRUE, prints warnings messages. mainly used print information factor casting. .df_row (data.frame) data frame across columns given row split. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. compare (logical) Whether comparison statistics analyzed instead summary statistics (compare = TRUE adds pval statistic comparing reference group). .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analyze Variables — analyze_variables","text":"s_summary() returns different statistics depending class x. x class numeric, returns list following named numeric items: n: length() x. sum: sum() x. mean: mean() x. sd: stats::sd() x. se: standard error x mean, .e.: (sd(x) / sqrt(length(x))). mean_sd: mean() stats::sd() x. mean_se: mean() x standard error (see ). mean_ci: CI mean x (stat_mean_ci()). mean_sei: SE interval mean x, .e.: (mean() -/+ stats::sd() / sqrt()). mean_sdi: SD interval mean x, .e.: (mean() -/+ stats::sd()). mean_pval: two-sided p-value mean x (stat_mean_pval()). median: stats::median() x. mad: median absolute deviation x, .e.: (stats::median() xc, xc = x - stats::median()). median_ci: CI median x (stat_median_ci()). quantiles: Two sample quantiles x (stats::quantile()). iqr: stats::IQR() x. range: range_noinf() x. min: max() x. max: min() x. median_range: median() range_noinf() x. cv: coefficient variation x, .e.: (stats::sd() / mean() * 100). geom_mean: geometric mean x, .e.: (exp(mean(log(x)))). geom_cv: geometric coefficient variation x, .e.: (sqrt(exp(sd(log(x)) ^ 2) - 1) * 100). x class factor converted character, returns list named numeric items: n: length() x. count: list number cases level factor x. count_fraction: Similar count also includes proportion cases level factor x relative denominator, NA denominator zero. x class logical, returns list named numeric items: n: length() x (possibly removing NAs). count: Count TRUE x. count_fraction: Count proportion TRUE x relative denominator, NA denominator zero. Note NAs x never counted leading NA . a_summary() returns corresponding list formatted rtables::CellValue(). analyze_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_summary() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyze Variables — analyze_variables","text":"possible use \"auto\" analyze_vars subset methods. uses format_auto() determine automatically number digits analyzed variable (.vars), current row data (.df_row[[.var]], see ?rtables::additional_fun_params), whole data. Also column split considered.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Analyze Variables — analyze_variables","text":"s_summary(): S3 generic function produces variable summary. s_summary(numeric): Method numeric class. s_summary(factor): Method factor class. s_summary(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_summary(logical): Method logical class. a_summary(): Formatted analysis function used afun analyze_vars() compare_vars() cfun summarize_colvars(). analyze_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_vars():  Use analyze_vars instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Analyze Variables — analyze_variables","text":"x empty vector, NA returned. expected feature return rcell content rtables intersection column row delimits empty data selection. mean function applied empty vector, NA returned instead NaN, latter standard behavior R. x empty factor, list still returned counts one element per factor level. levels x, function fails. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"<Missing>\") also excluded na.rm set TRUE. Automatic conversion character factor guarantee table can generated correctly. particular sparse tables likely can fail. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table(). use comparison (additional p-value statistic), parameter compare must set TRUE. Ensure either NA values converted explicit NA level NA values left .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyze Variables — analyze_variables","text":"","code":"# `s_summary.numeric`  ## Basic usage: empty numeric returns NA-filled items. s_summary(numeric()) #> $n #> n  #> 0  #>  #> $sum #> sum  #>  NA  #>  #> $mean #> mean  #>   NA  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>   NA   NA  #>  #> $mean_se #> mean   se  #>   NA   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>     NA  #>  #> $mad #> mad  #>  NA  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>  NA  #>  #> $range #> min max  #>  NA  NA  #>  #> $min #> min  #>  NA  #>  #> $max #> max  #>  NA  #>  #> $median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>       NaN  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>   ## Management of NA values. x <- c(NA_real_, 1) s_summary(x, na.rm = TRUE) #> $n #> n  #> 1  #>  #> $sum #> sum  #>   1  #>  #> $mean #> mean  #>    1  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>    1   NA  #>  #> $mean_se #> mean   se  #>    1   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>      1  #>  #> $mad #> mad  #>   0  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>             1             1  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>   0  #>  #> $range #> min max  #>   1   1  #>  #> $min #> min  #>   1  #>  #> $max #> max  #>   1  #>  #> $median_range #> median    min    max  #>      1      1      1  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>         1  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>  s_summary(x, na.rm = FALSE) #> $n #> n  #> 2  #>  #> $sum #> sum  #>  NA  #>  #> $mean #> mean  #>   NA  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>   NA   NA  #>  #> $mean_se #> mean   se  #>   NA   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>     NA  #>  #> $mad #> mad  #>  NA  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>  NA  #>  #> $range #> min max  #>  NA  NA  #>  #> $min #> min  #>  NA  #>  #> $max #> max  #>  NA  #>  #> $median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>        NA  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>   x <- c(NA_real_, 1, 2) s_summary(x, stats = NULL) #> $n #> n  #> 2  #>  #> $sum #> sum  #>   3  #>  #> $mean #> mean  #>  1.5  #>  #> $sd #>        sd  #> 0.7071068  #>  #> $se #>  se  #> 0.5  #>  #> $mean_sd #>      mean        sd  #> 1.5000000 0.7071068  #>  #> $mean_se #> mean   se  #>  1.5  0.5  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>   -4.853102    7.853102  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>            1            2  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>    0.7928932    2.2071068  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #>   p_value  #> 0.2048328  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>    1.5  #>  #> $mad #> mad  #>   0  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>             1             2  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>   1  #>  #> $range #> min max  #>   1   2  #>  #> $min #> min  #>   1  #>  #> $max #> max  #>   2  #>  #> $median_range #> median    min    max  #>    1.5    1.0    2.0  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #>       cv  #> 47.14045  #>  #> $geom_mean #> geom_mean  #>  1.414214  #>  #> $geom_mean_ci #>  mean_ci_lwr  mean_ci_upr  #>   0.01729978 115.60839614  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #>  geom_cv  #> 52.10922  #>   ## Benefits in `rtables` contructions: require(rtables) dta_test <- data.frame(   Group = rep(LETTERS[1:3], each = 2),   sub_group = rep(letters[1:2], each = 3),   x = 1:6 )  ## The summary obtained in with `rtables`: basic_table() %>%   split_cols_by(var = \"Group\") %>%   split_rows_by(var = \"sub_group\") %>%   analyze(vars = \"x\", afun = s_summary) %>%   build_table(df = dta_test) #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #>                                                  A                        B                       C                   #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> a                                                                                                                     #>   n                                              2                        1                       0                   #>   sum                                            3                        3                       NA                  #>   mean                                          1.5                       3                       NA                  #>   sd                                     0.707106781186548               NA                       NA                  #>   se                                            0.5                      NA                       NA                  #>   mean_sd                              1.5, 0.707106781186548           3, NA                     NA                  #>   mean_se                                     1.5, 0.5                  3, NA                     NA                  #>   Mean 95% CI                   -4.85310236808735, 7.85310236808735      NA                       NA                  #>   Mean -/+ 1xSE                                 1, 2                     NA                       NA                  #>   Mean -/+ 1xSD                 0.792893218813452, 2.20710678118655      NA                       NA                  #>   Mean p-value (H0: mean = 0)            0.204832764699133               NA                       NA                  #>   median                                        1.5                       3                       NA                  #>   mad                                            0                        0                       NA                  #>   Median 95% CI                                  NA                      NA                       NA                  #>   25% and 75%-ile                               1, 2                    3, 3                      NA                  #>   iqr                                            1                        0                       NA                  #>   range                                         1, 2                    3, 3                      NA                  #>   min                                            1                        3                       NA                  #>   max                                            2                        3                       NA                  #>   Median (Min - Max)                         1.5, 1, 2                 3, 3, 3                    NA                  #>   cv                                      47.1404520791032               NA                       NA                  #>   geom_mean                               1.41421356237309                3                       NA                  #>   Geometric Mean 95% CI         0.0172997815631007, 115.608396135236     NA                       NA                  #>   geom_cv                                 52.1092246837487               NA                       NA                  #> b                                                                                                                     #>   n                                              0                        1                       2                   #>   sum                                            NA                       4                       11                  #>   mean                                           NA                       4                      5.5                  #>   sd                                             NA                      NA               0.707106781186548           #>   se                                             NA                      NA                      0.5                  #>   mean_sd                                        NA                     4, NA           5.5, 0.707106781186548        #>   mean_se                                        NA                     4, NA                  5.5, 0.5               #>   Mean 95% CI                                    NA                      NA      -0.853102368087347, 11.8531023680873 #>   Mean -/+ 1xSE                                  NA                      NA                      5, 6                 #>   Mean -/+ 1xSD                                  NA                      NA       4.79289321881345, 6.20710678118655  #>   Mean p-value (H0: mean = 0)                    NA                      NA               0.0577158767526089          #>   median                                         NA                       4                      5.5                  #>   mad                                            NA                       0                       0                   #>   Median 95% CI                                  NA                      NA                       NA                  #>   25% and 75%-ile                                NA                     4, 4                     5, 6                 #>   iqr                                            NA                       0                       1                   #>   range                                          NA                     4, 4                     5, 6                 #>   min                                            NA                       4                       5                   #>   max                                            NA                       4                       6                   #>   Median (Min - Max)                             NA                    4, 4, 4                5.5, 5, 6               #>   cv                                             NA                      NA                12.8564869306645           #>   geom_mean                                      NA                       4                5.47722557505166           #>   Geometric Mean 95% CI                          NA                      NA       1.71994304449266, 17.4424380482025  #>   geom_cv                                        NA                      NA                12.945835316564             ## By comparison with `lapply`: X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group))) lapply(X, function(x) s_summary(x$x)) #> $A.a #> $A.a$n #> n  #> 2  #>  #> $A.a$sum #> sum  #>   3  #>  #> $A.a$mean #> mean  #>  1.5  #>  #> $A.a$sd #>        sd  #> 0.7071068  #>  #> $A.a$se #>  se  #> 0.5  #>  #> $A.a$mean_sd #>      mean        sd  #> 1.5000000 0.7071068  #>  #> $A.a$mean_se #> mean   se  #>  1.5  0.5  #>  #> $A.a$mean_ci #> mean_ci_lwr mean_ci_upr  #>   -4.853102    7.853102  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $A.a$mean_sei #> mean_sei_lwr mean_sei_upr  #>            1            2  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $A.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>    0.7928932    2.2071068  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $A.a$mean_pval #>   p_value  #> 0.2048328  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $A.a$median #> median  #>    1.5  #>  #> $A.a$mad #> mad  #>   0  #>  #> $A.a$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $A.a$quantiles #> quantile_0.25 quantile_0.75  #>             1             2  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $A.a$iqr #> iqr  #>   1  #>  #> $A.a$range #> min max  #>   1   2  #>  #> $A.a$min #> min  #>   1  #>  #> $A.a$max #> max  #>   2  #>  #> $A.a$median_range #> median    min    max  #>    1.5    1.0    2.0  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $A.a$cv #>       cv  #> 47.14045  #>  #> $A.a$geom_mean #> geom_mean  #>  1.414214  #>  #> $A.a$geom_mean_ci #>  mean_ci_lwr  mean_ci_upr  #>   0.01729978 115.60839614  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $A.a$geom_cv #>  geom_cv  #> 52.10922  #>  #>  #> $B.a #> $B.a$n #> n  #> 1  #>  #> $B.a$sum #> sum  #>   3  #>  #> $B.a$mean #> mean  #>    3  #>  #> $B.a$sd #> sd  #> NA  #>  #> $B.a$se #> se  #> NA  #>  #> $B.a$mean_sd #> mean   sd  #>    3   NA  #>  #> $B.a$mean_se #> mean   se  #>    3   NA  #>  #> $B.a$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $B.a$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $B.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $B.a$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $B.a$median #> median  #>      3  #>  #> $B.a$mad #> mad  #>   0  #>  #> $B.a$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $B.a$quantiles #> quantile_0.25 quantile_0.75  #>             3             3  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $B.a$iqr #> iqr  #>   0  #>  #> $B.a$range #> min max  #>   3   3  #>  #> $B.a$min #> min  #>   3  #>  #> $B.a$max #> max  #>   3  #>  #> $B.a$median_range #> median    min    max  #>      3      3      3  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $B.a$cv #> cv  #> NA  #>  #> $B.a$geom_mean #> geom_mean  #>         3  #>  #> $B.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $B.a$geom_cv #> geom_cv  #>      NA  #>  #>  #> $C.a #> $C.a$n #> n  #> 0  #>  #> $C.a$sum #> sum  #>  NA  #>  #> $C.a$mean #> mean  #>   NA  #>  #> $C.a$sd #> sd  #> NA  #>  #> $C.a$se #> se  #> NA  #>  #> $C.a$mean_sd #> mean   sd  #>   NA   NA  #>  #> $C.a$mean_se #> mean   se  #>   NA   NA  #>  #> $C.a$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $C.a$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $C.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $C.a$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $C.a$median #> median  #>     NA  #>  #> $C.a$mad #> mad  #>  NA  #>  #> $C.a$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $C.a$quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $C.a$iqr #> iqr  #>  NA  #>  #> $C.a$range #> min max  #>  NA  NA  #>  #> $C.a$min #> min  #>  NA  #>  #> $C.a$max #> max  #>  NA  #>  #> $C.a$median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $C.a$cv #> cv  #> NA  #>  #> $C.a$geom_mean #> geom_mean  #>       NaN  #>  #> $C.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $C.a$geom_cv #> geom_cv  #>      NA  #>  #>  #> $A.b #> $A.b$n #> n  #> 0  #>  #> $A.b$sum #> sum  #>  NA  #>  #> $A.b$mean #> mean  #>   NA  #>  #> $A.b$sd #> sd  #> NA  #>  #> $A.b$se #> se  #> NA  #>  #> $A.b$mean_sd #> mean   sd  #>   NA   NA  #>  #> $A.b$mean_se #> mean   se  #>   NA   NA  #>  #> $A.b$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $A.b$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $A.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $A.b$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $A.b$median #> median  #>     NA  #>  #> $A.b$mad #> mad  #>  NA  #>  #> $A.b$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $A.b$quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $A.b$iqr #> iqr  #>  NA  #>  #> $A.b$range #> min max  #>  NA  NA  #>  #> $A.b$min #> min  #>  NA  #>  #> $A.b$max #> max  #>  NA  #>  #> $A.b$median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $A.b$cv #> cv  #> NA  #>  #> $A.b$geom_mean #> geom_mean  #>       NaN  #>  #> $A.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $A.b$geom_cv #> geom_cv  #>      NA  #>  #>  #> $B.b #> $B.b$n #> n  #> 1  #>  #> $B.b$sum #> sum  #>   4  #>  #> $B.b$mean #> mean  #>    4  #>  #> $B.b$sd #> sd  #> NA  #>  #> $B.b$se #> se  #> NA  #>  #> $B.b$mean_sd #> mean   sd  #>    4   NA  #>  #> $B.b$mean_se #> mean   se  #>    4   NA  #>  #> $B.b$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $B.b$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $B.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $B.b$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $B.b$median #> median  #>      4  #>  #> $B.b$mad #> mad  #>   0  #>  #> $B.b$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $B.b$quantiles #> quantile_0.25 quantile_0.75  #>             4             4  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $B.b$iqr #> iqr  #>   0  #>  #> $B.b$range #> min max  #>   4   4  #>  #> $B.b$min #> min  #>   4  #>  #> $B.b$max #> max  #>   4  #>  #> $B.b$median_range #> median    min    max  #>      4      4      4  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $B.b$cv #> cv  #> NA  #>  #> $B.b$geom_mean #> geom_mean  #>         4  #>  #> $B.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $B.b$geom_cv #> geom_cv  #>      NA  #>  #>  #> $C.b #> $C.b$n #> n  #> 2  #>  #> $C.b$sum #> sum  #>  11  #>  #> $C.b$mean #> mean  #>  5.5  #>  #> $C.b$sd #>        sd  #> 0.7071068  #>  #> $C.b$se #>  se  #> 0.5  #>  #> $C.b$mean_sd #>      mean        sd  #> 5.5000000 0.7071068  #>  #> $C.b$mean_se #> mean   se  #>  5.5  0.5  #>  #> $C.b$mean_ci #> mean_ci_lwr mean_ci_upr  #>  -0.8531024  11.8531024  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $C.b$mean_sei #> mean_sei_lwr mean_sei_upr  #>            5            6  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $C.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>     4.792893     6.207107  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $C.b$mean_pval #>    p_value  #> 0.05771588  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $C.b$median #> median  #>    5.5  #>  #> $C.b$mad #> mad  #>   0  #>  #> $C.b$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $C.b$quantiles #> quantile_0.25 quantile_0.75  #>             5             6  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $C.b$iqr #> iqr  #>   1  #>  #> $C.b$range #> min max  #>   5   6  #>  #> $C.b$min #> min  #>   5  #>  #> $C.b$max #> max  #>   6  #>  #> $C.b$median_range #> median    min    max  #>    5.5    5.0    6.0  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $C.b$cv #>       cv  #> 12.85649  #>  #> $C.b$geom_mean #> geom_mean  #>  5.477226  #>  #> $C.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>    1.719943   17.442438  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $C.b$geom_cv #>  geom_cv  #> 12.94584  #>  #>   # `s_summary.factor`  ## Basic usage: s_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\"))) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>   # Empty factor returns zero-filled items. s_summary(factor(levels = c(\"a\", \"b\", \"c\"))) #> $n #> [1] 0 #>  #> $count #> $count$a #> [1] 0 #>  #> $count$b #> [1] 0 #>  #> $count$c #> [1] 0 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 0 0 #>  #> $count_fraction$b #> [1] 0 0 #>  #> $count_fraction$c #> [1] 0 0 #>  #>  #> $n_blq #> [1] 0 #>   ## Management of NA values. x <- factor(c(NA, \"Female\")) x <- explicit_na(x) s_summary(x, na.rm = TRUE) #> $n #> [1] 1 #>  #> $count #> $count$Female #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$Female #> [1] 1 1 #>  #>  #> $n_blq #> [1] 0 #>  s_summary(x, na.rm = FALSE) #> $n #> [1] 2 #>  #> $count #> $count$Female #> [1] 1 #>  #> $count$`<Missing>` #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$Female #> [1] 1.0 0.5 #>  #> $count_fraction$`<Missing>` #> [1] 1.0 0.5 #>  #>  #> $n_blq #> [1] 0 #>   ## Different denominators. x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.3 #>  #> $count_fraction$b #> [1] 1.0 0.1 #>  #> $count_fraction$c #> [1] 1.0 0.1 #>  #>  #> $n_blq #> [1] 0 #>  s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.00 0.15 #>  #> $count_fraction$b #> [1] 1.00 0.05 #>  #> $count_fraction$c #> [1] 1.00 0.05 #>  #>  #> $n_blq #> [1] 0 #>   # `s_summary.character`  ## Basic usage: s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\"), .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\", \"\"), .var = \"x\", na.rm = FALSE, verbose = FALSE) #> $n #> [1] 6 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #> $count$`NA` #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #>  #> $count_fraction$b #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$c #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$`NA` #> [1] 1.0000000 0.1666667 #>  #>  #> $n_blq #> [1] 0 #>   # `s_summary.logical`  ## Basic usage: s_summary(c(TRUE, FALSE, TRUE, TRUE)) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.00 0.75 #>  #> $n_blq #> [1] 0 #>   # Empty factor returns zero-filled items. s_summary(as.logical(c())) #> $n #> [1] 0 #>  #> $count #> [1] 0 #>  #> $count_fraction #> [1] 0 0 #>  #> $n_blq #> [1] 0 #>   ## Management of NA values. x <- c(NA, TRUE, FALSE) s_summary(x, na.rm = TRUE) #> $n #> [1] 2 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0 0.5 #>  #> $n_blq #> [1] 0 #>  s_summary(x, na.rm = FALSE) #> $n #> [1] 3 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0000000 0.3333333 #>  #> $n_blq #> [1] 0 #>   ## Different denominators. x <- c(TRUE, FALSE, TRUE, TRUE) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.0 0.3 #>  #> $n_blq #> [1] 0 #>  s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.00 0.15 #>  #> $n_blq #> [1] 0 #>   a_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1        n              5          0         n #> 2        a              3          0         a #> 3        b              1          0         b #> 4        c              1          0         c #> 5        a        3 (60%)          0         a #> 6        b        1 (20%)          0         b #> 7        c        1 (20%)          0         c #> 8    n_blq              0          0     n_blq a_summary(   factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")),   .ref_group = factor(c(\"a\", \"a\", \"b\", \"c\")), compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                     row_name formatted_cell indent_mod #> 1                          n              5          0 #> 2                          a              3          0 #> 3                          b              1          0 #> 4                          c              1          0 #> 5                          a        3 (60%)          0 #> 6                          b        1 (20%)          0 #> 7                          c        1 (20%)          0 #> 8                      n_blq              0          0 #> 9 p-value (chi-squared test)         0.9560          0 #>                    row_label #> 1                          n #> 2                          a #> 3                          b #> 4                          c #> 5                          a #> 6                          b #> 7                          c #> 8                      n_blq #> 9 p-value (chi-squared test)  a_summary(c(\"A\", \"B\", \"A\", \"C\"), .var = \"x\", .N_col = 10, .N_row = 10, verbose = FALSE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1        n              4          0         n #> 2        A              2          0         A #> 3        B              1          0         B #> 4        C              1          0         C #> 5        A        2 (50%)          0         A #> 6        B        1 (25%)          0         B #> 7        C        1 (25%)          0         C #> 8    n_blq              0          0     n_blq a_summary(   c(\"A\", \"B\", \"A\", \"C\"),   .ref_group = c(\"B\", \"A\", \"C\"), .var = \"x\", compare = TRUE, verbose = FALSE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                     row_name formatted_cell indent_mod #> 1                          n              4          0 #> 2                          A              2          0 #> 3                          B              1          0 #> 4                          C              1          0 #> 5                          A        2 (50%)          0 #> 6                          B        1 (25%)          0 #> 7                          C        1 (25%)          0 #> 8                      n_blq              0          0 #> 9 p-value (chi-squared test)         0.9074          0 #>                    row_label #> 1                          n #> 2                          A #> 3                          B #> 4                          C #> 5                          A #> 6                          B #> 7                          C #> 8                      n_blq #> 9 p-value (chi-squared test)  a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>         row_name formatted_cell indent_mod      row_label #> 1              n              5          0              n #> 2          count              3          0          count #> 3 count_fraction        3 (60%)          0 count_fraction #> 4          n_blq              0          0          n_blq a_summary(   c(TRUE, FALSE, FALSE, TRUE, TRUE),   .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                     row_name formatted_cell indent_mod #> 1                          n              5          0 #> 2                      count              3          0 #> 3             count_fraction        3 (60%)          0 #> 4                      n_blq              0          0 #> 5 p-value (chi-squared test)                         0 #>                    row_label #> 1                          n #> 2                      count #> 3             count_fraction #> 4                      n_blq #> 5 p-value (chi-squared test)  a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = \"bla\") #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                       row_name    formatted_cell indent_mod #> 1                            n                10          0 #> 2                          Sum               0.4          0 #> 3                         Mean               0.0          0 #> 4                           SD               1.1          0 #> 5                           SE               0.4          0 #> 6                    Mean (SD)         0.0 (1.1)          0 #> 7                    Mean (SE)         0.0 (0.4)          0 #> 8                  Mean 95% CI     (-0.78, 0.85)          0 #> 9                Mean -/+ 1xSE     (-0.33, 0.40)          0 #> 10               Mean -/+ 1xSD     (-1.11, 1.18)          0 #> 11 Mean p-value (H0: mean = 0)              0.92          0 #> 12                      Median              -0.2          0 #> 13   Median Absolute Deviation               0.0          0 #> 14               Median 95% CI     (-0.85, 1.32)          0 #> 15             25% and 75%-ile        -0.8 - 1.1          0 #> 16                         IQR               1.9          0 #> 17                   Min - Max        -1.6 - 1.9          0 #> 18                     Minimum              -1.6          0 #> 19                     Maximum               1.9          0 #> 20          Median (Min - Max) -0.2 (-1.6 - 1.9)          0 #> 21                      CV (%)            3196.6          0 #> 22              Geometric Mean                NA          0 #> 23       Geometric Mean 95% CI                NA          0 #> 24         CV % Geometric Mean                NA          0 #>                      row_label #> 1                            n #> 2                          Sum #> 3                         Mean #> 4                           SD #> 5                           SE #> 6                    Mean (SD) #> 7                    Mean (SE) #> 8                  Mean 95% CI #> 9                Mean -/+ 1xSE #> 10               Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12                      Median #> 13   Median Absolute Deviation #> 14               Median 95% CI #> 15             25% and 75%-ile #> 16                         IQR #> 17                   Min - Max #> 18                     Minimum #> 19                     Maximum #> 20          Median (Min - Max) #> 21                      CV (%) #> 22              Geometric Mean #> 23       Geometric Mean 95% CI #> 24         CV % Geometric Mean a_summary(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .var = \"bla\", compare = TRUE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                       row_name  formatted_cell indent_mod #> 1                            n              10          0 #> 2                          Sum            53.0          0 #> 3                         Mean             5.3          0 #> 4                           SD             1.0          0 #> 5                           SE             0.3          0 #> 6                    Mean (SD)       5.3 (1.0)          0 #> 7                    Mean (SE)       5.3 (0.3)          0 #> 8                  Mean 95% CI    (4.62, 5.99)          0 #> 9                Mean -/+ 1xSE    (5.00, 5.61)          0 #> 10               Mean -/+ 1xSD    (4.34, 6.26)          0 #> 11 Mean p-value (H0: mean = 0)            0.00          0 #> 12                      Median             5.5          0 #> 13   Median Absolute Deviation             0.0          0 #> 14               Median 95% CI    (4.11, 6.12)          0 #> 15             25% and 75%-ile       4.7 - 6.0          0 #> 16                         IQR             1.3          0 #> 17                   Min - Max       3.5 - 6.5          0 #> 18                     Minimum             3.5          0 #> 19                     Maximum             6.5          0 #> 20          Median (Min - Max) 5.5 (3.5 - 6.5)          0 #> 21                      CV (%)            18.1          0 #> 22              Geometric Mean             5.2          0 #> 23       Geometric Mean 95% CI    (4.53, 6.01)          0 #> 24         CV % Geometric Mean            19.9          0 #> 25            p-value (t-test)         <0.0001          0 #>                      row_label #> 1                            n #> 2                          Sum #> 3                         Mean #> 4                           SD #> 5                           SE #> 6                    Mean (SD) #> 7                    Mean (SE) #> 8                  Mean 95% CI #> 9                Mean -/+ 1xSE #> 10               Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12                      Median #> 13   Median Absolute Deviation #> 14               Median 95% CI #> 15             25% and 75%-ile #> 16                         IQR #> 17                   Min - Max #> 18                     Minimum #> 19                     Maximum #> 20          Median (Min - Max) #> 21                      CV (%) #> 22              Geometric Mean #> 23       Geometric Mean 95% CI #> 24         CV % Geometric Mean #> 25            p-value (t-test)  ## Fabricated dataset. dta_test <- data.frame(   USUBJID = rep(1:6, each = 3),   PARAMCD = rep(\"lab\", 6 * 3),   AVISIT  = rep(paste0(\"V\", 1:3), 6),   ARM     = rep(LETTERS[1:3], rep(6, 3)),   AVAL    = c(9:1, rep(NA, 9)) )  # `analyze_vars()` in `rtables` pipelines ## Default output within a `rtables` pipeline. l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   split_rows_by(var = \"AVISIT\") %>%   analyze_vars(vars = \"AVAL\")  build_table(l, df = dta_test) #>                   A           B       C  #> ———————————————————————————————————————— #> V1                                       #>   n               2           1       0  #>   Mean (SD)   7.5 (2.1)   3.0 (NA)    NA #>   Median         7.5         3.0      NA #>   Min - Max   6.0 - 9.0   3.0 - 3.0   NA #> V2                                       #>   n               2           1       0  #>   Mean (SD)   6.5 (2.1)   2.0 (NA)    NA #>   Median         6.5         2.0      NA #>   Min - Max   5.0 - 8.0   2.0 - 2.0   NA #> V3                                       #>   n               2           1       0  #>   Mean (SD)   5.5 (2.1)   1.0 (NA)    NA #>   Median         5.5         1.0      NA #>   Min - Max   4.0 - 7.0   1.0 - 1.0   NA  ## Select and format statistics output. l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   split_rows_by(var = \"AVISIT\") %>%   analyze_vars(     vars = \"AVAL\",     .stats = c(\"n\", \"mean_sd\", \"quantiles\"),     .formats = c(\"mean_sd\" = \"xx.x, xx.x\"),     .labels = c(n = \"n\", mean_sd = \"Mean, SD\", quantiles = c(\"Q1 - Q3\"))   )  build_table(l, df = dta_test) #>                  A           B       C  #> ——————————————————————————————————————— #> V1                                      #>   n              2           1       0  #>   Mean, SD   7.5, 2.1     3.0, NA    NA #>   Q1 - Q3    6.0 - 9.0   3.0 - 3.0   NA #> V2                                      #>   n              2           1       0  #>   Mean, SD   6.5, 2.1     2.0, NA    NA #>   Q1 - Q3    5.0 - 8.0   2.0 - 2.0   NA #> V3                                      #>   n              2           1       0  #>   Mean, SD   5.5, 2.1     1.0, NA    NA #>   Q1 - Q3    4.0 - 7.0   1.0 - 1.0   NA  ## Use arguments interpreted by `s_summary`. l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   split_rows_by(var = \"AVISIT\") %>%   analyze_vars(vars = \"AVAL\", na.rm = FALSE)  build_table(l, df = dta_test) #>                   A       B    C  #> ————————————————————————————————— #> V1                                #>   n               2       2    2  #>   Mean (SD)   7.5 (2.1)   NA   NA #>   Median         7.5      NA   NA #>   Min - Max   6.0 - 9.0   NA   NA #> V2                                #>   n               2       2    2  #>   Mean (SD)   6.5 (2.1)   NA   NA #>   Median         6.5      NA   NA #>   Min - Max   5.0 - 8.0   NA   NA #> V3                                #>   n               2       2    2  #>   Mean (SD)   5.5 (2.1)   NA   NA #>   Median         5.5      NA   NA #>   Min - Max   4.0 - 7.0   NA   NA  ## Handle `NA` levels first when summarizing factors. dta_test$AVISIT <- NA_character_ dta_test <- df_explicit_na(dta_test) l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   analyze_vars(vars = \"AVISIT\", na.rm = FALSE)  build_table(l, df = dta_test) #>                A          B          C     #> —————————————————————————————————————————— #> n              6          6          6     #> <Missing>   6 (100%)   6 (100%)   6 (100%)  # auto format dt <- data.frame(\"VAR\" = c(0.001, 0.2, 0.0011000, 3, 4)) basic_table() %>%   analyze_vars(     vars = \"VAR\",     .stats = c(\"n\", \"mean\", \"mean_sd\", \"range\"),     .formats = c(\"mean_sd\" = \"auto\", \"range\" = \"auto\")   ) %>%   build_table(dt) #>                  all obs      #> ————————————————————————————— #> n                   5         #> Mean               1.4        #> Mean (SD)   1.44042 (1.91481) #> Min - Max    0.0010 - 4.0000"},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary numeric variables in columns — analyze_vars_in_cols","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"Layout-creating function can used creating column-wise summary tables. function sets analysis methods column labels wrapper rtables::analyze_colvars(). designed principally PK tables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"","code":"analyze_vars_in_cols(   lyt,   vars,   ...,   .stats = c(\"n\", \"mean\", \"sd\", \"se\", \"cv\", \"geom_cv\"),   .labels = c(n = \"n\", mean = \"Mean\", sd = \"SD\", se = \"SE\", cv = \"CV (%)\", geom_cv =     \"CV % Geometric Mean\"),   row_labels = NULL,   do_summarize_row_groups = FALSE,   split_col_vars = TRUE,   imp_rule = NULL,   avalcat_var = \"AVALCAT1\",   cache = FALSE,   .indent_mods = NULL,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   nested = TRUE,   .formats = NULL,   .aligns = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . ... additional arguments lower level functions. .stats (character) statistics select table. .labels (named character) labels statistics (without indent). row_labels (character) function works columns space, usual .labels character vector applies column space. can change row labels defining parameter named character vector names corresponding split values. defaults NULL contains one string, duplicate row label. do_summarize_row_groups (flag) defaults FALSE applies analysis current label rows. wrapper rtables::summarize_row_groups() can accept labelstr define row labels. behavior supported never need overload row labels. split_col_vars (flag) defaults TRUE puts analysis results onto columns. option allows add multiple instances functions, also nested fashion, without adding splits. split must happen one time single layout. imp_rule (character) imputation rule setting. Defaults NULL imputation rule. Can also \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. order use imputation rule, avalcat_var argument must specified. See imputation_rule() details imputation. avalcat_var (character) imp_rule NULL, name variable indicates whether row data corresponds analysis value category \"BLQ\", \"LTR\", \"<PCLLOQ\", none (defaults \"AVALCAT1\"). Variable must present data match variable used calculate n_blq statistic (included .stats). cache (flag) whether store computed values temporary caching environment. speed calculations large tables, set FALSE rtable layout used multiple tables different data. Defaults FALSE. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .aligns (character) alignment table contents (including labels). NULL, \"center\" applied. See formatters::list_valid_aligns() list currently supported alignments.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout summarize given variables, arrange output columns, add table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"experimental implementation rtables::summarize_row_groups() rtables::analyze_colvars() may subjected changes rtables extends support complex analysis pipelines column space. reasons, encourage read examples carefully file issues cases differ . labelstr behaves differently usual. defined (default NULL), row labels assigned automatically split values case rtables::analyze_colvars (do_summarize_row_groups = FALSE, default), group label do_summarize_row_groups = TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"","code":"library(dplyr)  # Data preparation adpp <- tern_ex_adpp %>% h_pkparam_sort()  lyt <- basic_table() %>%   split_rows_by(var = \"STRATA1\", label_pos = \"topleft\") %>%   split_rows_by(     var = \"SEX\",     label_pos = \"topleft\",     child_label = \"hidden\"   ) %>% # Removes duplicated labels   analyze_vars_in_cols(vars = \"AGE\") result <- build_table(lyt = lyt, df = adpp) result #> STRATA1                                                          #>   SEX      n    Mean    SD    SE    CV (%)   CV % Geometric Mean #> ———————————————————————————————————————————————————————————————— #> A                                                                #>   F       81    38.8   5.4    0.6    13.8           14.3         #>   M       81    38.9   5.9    0.7    15.1           14.9         #> B                                                                #>   F       90    36.0   6.4    0.7    17.7           17.9         #>   M       81    36.4   6.5    0.7    17.9           17.8         #> C                                                                #>   F       117   34.1   6.2    0.6    18.2           18.3         #>   M       72    33.2   11.5   1.4    34.6           31.6          # By selecting just some statistics and ad-hoc labels lyt <- basic_table() %>%   split_rows_by(var = \"ARM\", label_pos = \"topleft\") %>%   split_rows_by(     var = \"SEX\",     label_pos = \"topleft\",     child_labels = \"hidden\",     split_fun = drop_split_levels   ) %>%   analyze_vars_in_cols(     vars = \"AGE\",     .stats = c(\"n\", \"cv\", \"geom_mean\"),     .labels = c(       n = \"aN\",       cv = \"aCV\",       geom_mean = \"aGeomMean\"     )   ) result <- build_table(lyt = lyt, df = adpp) result #> ARM                                     #>   SEX            aN    aCV    aGeomMean #> ——————————————————————————————————————— #> A: Drug X                               #> B: Placebo                              #> C: Combination                          #>   F              288   17.6     35.5    #>   M              234   23.4     35.3     # Changing row labels lyt <- basic_table() %>%   analyze_vars_in_cols(     vars = \"AGE\",     row_labels = \"some custom label\"   ) result <- build_table(lyt, df = adpp) result #>                      n    Mean   SD    SE    CV (%)   CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————————— #> some custom label   522   36.1   7.4   0.3    20.4           20.6          # Pharmacokinetic parameters lyt <- basic_table() %>%   split_rows_by(     var = \"TLG_DISPLAY\",     split_label = \"PK Parameter\",     label_pos = \"topleft\",     child_label = \"hidden\"   ) %>%   analyze_vars_in_cols(     vars = \"AVAL\"   ) result <- build_table(lyt, df = adpp) result #> PK Parameter   n    Mean     SD    SE    CV (%)   CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax           58   29.7    5.6    0.7    19.0           19.3         #> AUCinf obs     58   207.5   34.9   4.6    16.8           17.3         #> CL obs         58    5.1    1.0    0.1    20.6           22.7         #> Ae             58    1.5    0.3    0.0    21.3           24.1         #> Fe             58   15.7    3.6    0.5    22.7           24.0         #> CLR            58    0.0    0.0    0.0    19.9           22.2         #> Rmax           58    9.6    2.0    0.3    21.1           21.6         #> Tonset         58    3.0    0.7    0.1    22.4           23.3         #> RENALCLD       58    0.0    0.0    0.0    19.0           19.4          # Multiple calls (summarize label and analyze underneath) lyt <- basic_table() %>%   split_rows_by(     var = \"TLG_DISPLAY\",     split_label = \"PK Parameter\",     label_pos = \"topleft\"   ) %>%   analyze_vars_in_cols(     vars = \"AVAL\",     do_summarize_row_groups = TRUE # does a summarize level   ) %>%   split_rows_by(\"SEX\",     child_label = \"hidden\",     label_pos = \"topleft\"   ) %>%   analyze_vars_in_cols(     vars = \"AVAL\",     split_col_vars = FALSE # avoids re-splitting the columns   ) result <- build_table(lyt, df = adpp) result #> PK Parameter                                                          #>   SEX          n    Mean     SD    SE    CV (%)   CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax           58   29.7    5.6    0.7    19.0           19.3         #>   F            32   30.1    6.1    1.1    20.4           21.0         #>   M            26   29.2    5.0    1.0    17.2           17.2         #> AUCinf obs     58   207.5   34.9   4.6    16.8           17.3         #>   F            32   209.5   43.3   7.7    20.7           21.6         #>   M            26   205.0   20.9   4.1    10.2           10.4         #> CL obs         58    5.1    1.0    0.1    20.6           22.7         #>   F            32    5.1    1.1    0.2    20.8           23.2         #>   M            26    5.1    1.1    0.2    20.7           22.5         #> Ae             58    1.5    0.3    0.0    21.3           24.1         #>   F            32    1.5    0.3    0.1    19.3           20.8         #>   M            26    1.5    0.4    0.1    23.9           27.9         #> Fe             58   15.7    3.6    0.5    22.7           24.0         #>   F            32   16.3    3.4    0.6    20.9           22.3         #>   M            26   15.0    3.7    0.7    24.7           25.6         #> CLR            58    0.0    0.0    0.0    19.9           22.2         #>   F            32    0.0    0.0    0.0    20.5           24.8         #>   M            26    0.0    0.0    0.0    19.5           18.9         #> Rmax           58    9.6    2.0    0.3    21.1           21.6         #>   F            32    9.6    2.2    0.4    22.4           23.5         #>   M            26    9.6    1.9    0.4    19.7           19.5         #> Tonset         58    3.0    0.7    0.1    22.4           23.3         #>   F            32    3.0    0.7    0.1    24.7           25.9         #>   M            26    3.0    0.6    0.1    19.8           20.0         #> RENALCLD       58    0.0    0.0    0.0    19.0           19.4         #>   F            32    0.0    0.0    0.0    19.6           20.4         #>   M            26    0.0    0.0    0.0    18.0           17.6"},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Variable Labels to Top Left Corner in Table — append_varlabels","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"Helper layout creating function just append variable labels given variables vector given dataset top left corner. variable label found variable name used instead. Multiple variable labels concatenated slashes.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"","code":"append_varlabels(lyt, df, vars, indent = 0L)"},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"lyt (layout) input layout analyses added . df (data.frame) data set containing analysis variables. vars (character) variable names labels looked df. indent (integer) non-negative number nested indent space, default 0L means indent. 1L means two spaces indent, 2L means four spaces indent .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"modified layout new variable label(s) added top-left material.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"optimal implementation course, since using data set layout creation. mature rtables implementation also improved necessary anymore.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"","code":"lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   split_rows_by(\"SEX\") %>%   append_varlabels(DM, \"SEX\") %>%   analyze(\"AGE\", afun = mean) %>%   append_varlabels(DM, \"AGE\", indent = 1) build_table(lyt, DM) #> SEX                   A: Drug X          B: Placebo       C: Combination  #>   Age                  (N=121)            (N=106)            (N=129)      #> ————————————————————————————————————————————————————————————————————————— #> F                                                                         #>   mean             33.7142857142857   33.8392857142857   34.8852459016393 #> M                                                                         #>   mean             36.5490196078431         32.1         34.2794117647059 #> U                                                                         #>   mean                    NA                 NA                 NA        #> UNDIFFERENTIATED                                                          #>   mean                    NA                 NA                 NA         lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"SEX\") %>%   analyze(\"AGE\", afun = mean) %>%   append_varlabels(DM, c(\"SEX\", \"AGE\")) build_table(lyt, DM) #> SEX / Age             A: Drug X          B: Placebo       C: Combination  #> ————————————————————————————————————————————————————————————————————————— #> F                                                                         #>   mean             33.7142857142857   33.8392857142857   34.8852459016393 #> M                                                                         #>   mean             36.5490196078431         32.1         34.2794117647059 #> U                                                                         #>   mean                    NA                 NA                 NA        #> UNDIFFERENTIATED                                                          #>   mean                    NA                 NA                 NA"},{"path":"https://insightsengineering.github.io/tern/reference/argument_convention.html","id":null,"dir":"Reference","previous_headings":"","what":"Standard Arguments — argument_convention","title":"Standard Arguments — argument_convention","text":"documentation function lists arguments tern used repeatedly express analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/argument_convention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standard Arguments — argument_convention","text":"... additional arguments lower level functions. .aligns (character) alignment table contents (including labels). NULL, \"center\" applied. See formatters::list_valid_aligns() list currently supported alignments. .all_col_counts (vector integer) value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .df_row (data.frame) data frame across columns given row split. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent). .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .ref_group (data.frame vector) data corresponding reference group. .spl_context (data.frame) gives information ancestor split states passed rtables. .stats (character) statistics select table. .var (string) single variable name passed rtables requested statistics function. add_total_level (flag) adds \"total\" level others includes levels constitute split. custom label can set level via custom_label argument. col_by (factor) defining column groups. conf_level (proportion) confidence level interval. data (data.frame) dataset containing variables summarize. df (data.frame) data set containing analysis variables. draw (flag) whether plot drawn. drop (flag) non appearing occurrence levels dropped resulting table. Note case remaining occurrence levels table sorted alphabetically. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. id (string) subject variable name. is_event (logical)TRUE event, FALSE time event censored. indent_mod Please use .indent_mods argument instead. label_all (string) label total population analysis. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. lyt (layout) input layout analyses added . method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed. na.rm (flag) whether NA values removed x prior analysis. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. newpage (flag) whether plot drawn new page. considered draw = TRUE used. prune_zero_rows (flag) whether prune zero rows. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. rsp (logical) whether subject responder . show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. tte (numeric) contains time--event duration values. var_labels (character) character label. variables (named list string) list additional analysis variables. vars (character) variable names primary analysis variable iterated . var (string) single variable name primary analysis variable. x (numeric) vector numbers want analyze.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/argument_convention.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Standard Arguments — argument_convention","text":"Although function just returns NULL two uses, tern users provides documentation arguments commonly consistently used framework. developer adds single reference point import roxygen argument description : @inheritParams argument_convention","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange Multiple Grobs — arrange_grobs","title":"Arrange Multiple Grobs — arrange_grobs","text":"Arrange grobs new grob n*m (rows*cols) layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange Multiple Grobs — arrange_grobs","text":"","code":"arrange_grobs(   ...,   grobs = list(...),   ncol = NULL,   nrow = NULL,   padding_ht = grid::unit(2, \"line\"),   padding_wt = grid::unit(2, \"line\"),   vp = NULL,   gp = NULL,   name = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange Multiple Grobs — arrange_grobs","text":"... grobs. grobs list grobs. ncol number columns layout. nrow number rows layout. padding_ht unit length 1, vertical space grob. padding_wt unit length 1, horizontal space grob. vp viewport() object (NULL). gp gpar() object. name character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange Multiple Grobs — arrange_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange Multiple Grobs — arrange_grobs","text":"","code":"library(grid)  # \\donttest{ num <- lapply(1:9, textGrob) grid::grid.newpage() grid.draw(arrange_grobs(grobs = num, ncol = 2))  showViewport()   g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, nrow = 2))  showViewport()   grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, ncol = 3))   grid::grid.newpage() grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2))) vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1))  showViewport()  # }"},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to rtable — as.rtable","title":"Convert to rtable — as.rtable","text":"new generic function convert objects rtable tables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to rtable — as.rtable","text":"","code":"as.rtable(x, ...)  # S3 method for data.frame as.rtable(x, format = \"xx.xx\", ...)"},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to rtable — as.rtable","text":"x object converted rtable. ... additional arguments methods. format format used columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to rtable — as.rtable","text":"rtables table object. Note concrete class depend method used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Convert to rtable — as.rtable","text":".rtable(data.frame): method converting data.frame contain numeric columns rtable.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to rtable — as.rtable","text":"","code":"x <- data.frame(   a = 1:10,   b = rnorm(10) ) as.rtable(x) #>        a       b   #> —————————————————— #> 1    1.00    -1.77 #> 2    2.00    0.29  #> 3    3.00    -0.50 #> 4    4.00    0.65  #> 5    5.00    0.66  #> 6    6.00    -2.82 #> 7    7.00    1.35  #> 8    8.00    0.50  #> 9    9.00    0.32  #> 10   10.00   1.76"},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of a Vector to a Factor — as_factor_keep_attributes","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"Converts x factor keeps attributes. Warns appropriately user can decide whether prefer converting factor manually (e.g. full control factor levels).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"","code":"as_factor_keep_attributes(   x,   x_name = deparse(substitute(x)),   na_level = \"<Missing>\",   verbose = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"x (atomic) object convert. x_name (string) name x. na_level (string) explicit missing level used converting character vector. verbose defaults TRUE. prints warnings messages.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"factor attributes (except class) x. modify x already factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional Assertions for checkmate — assertions","title":"Additional Assertions for checkmate — assertions","text":"Additional assertion functions can used together checkmate package.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional Assertions for checkmate — assertions","text":"","code":"assert_list_of_variables(x, .var.name = checkmate::vname(x), add = NULL)  assert_df_with_variables(   df,   variables,   na_level = NULL,   .var.name = checkmate::vname(df),   add = NULL )  assert_valid_factor(   x,   min.levels = 1,   max.levels = NULL,   null.ok = TRUE,   any.missing = TRUE,   n.levels = NULL,   len = NULL,   .var.name = checkmate::vname(x),   add = NULL )  assert_df_with_factors(   df,   variables,   min.levels = 1,   max.levels = NULL,   any.missing = TRUE,   na_level = NULL,   .var.name = checkmate::vname(df),   add = NULL )  assert_proportion_value(x, include_boundaries = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional Assertions for checkmate — assertions","text":"x () object test. .var.name [character(1)] Name checked object print assertions. Defaults heuristic implemented vname. add [AssertCollection] Collection store assertion messages. See AssertCollection. df (data.frame) data set test. variables (named list character) list variables test. na_level (character) string using represent NA missing data. NA values please consider using directly .na() similar approaches. min.levels [integer(1)] Minimum number factor levels. Default NULL (check). max.levels [integer(1)] Maximum number factor levels. Default NULL (check). null.ok [logical(1)] set TRUE, x may also NULL. case type check x performed, additional checks disabled. .missing [logical(1)] vectors missing values allowed? Default TRUE. n.levels [integer(1)] Exact number factor levels. Default NULL (check). len [integer(1)] Exact expected length x. include_boundaries (logical) whether include boundaries testing proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional Assertions for checkmate — assertions","text":"Nothing assertion passes, otherwise prints error message.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Additional Assertions for checkmate — assertions","text":"assert_list_of_variables(): Checks whether x valid list variable names. NULL elements list x dropped Filter(Negate(.null), x). assert_df_with_variables(): Check whether df data frame analysis variables. Please notice produces error variables present data.frame opposite required. assert_valid_factor(): Check whether x valid factor (.e. levels empty string levels). Note NULL NA elements allowed. assert_df_with_factors(): Check whether df data frame analysis variables factors. Note creation NA direct call factor() trim NA levels vector list . assert_proportion_value(): Check whether x proportion: number 0 1.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for Bins in Percent — bins_percent_labels","title":"Labels for Bins in Percent — bins_percent_labels","text":"creates labels quantile based bins percent. assumes right-closed intervals produced cut_quantile_bins().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Bins in Percent — bins_percent_labels","text":"","code":"bins_percent_labels(probs, digits = 0)"},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Bins in Percent — bins_percent_labels","text":"probs (proportion vector) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. digits (integer) number decimal places round percent numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Bins in Percent — bins_percent_labels","text":"character vector labels format [0%,20%], (20%,50%], etc.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Content Row Function to Add Row Total to Labels — c_label_n","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"takes label latest row split level adds row total df parentheses. function differs c_label_n_alt() taking row counts df rather alt_counts_df, used add_rowcounts() alt_counts set FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"","code":"c_label_n(df, labelstr, .N_row)"},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"important use df rather .N_row implementation, former already split columns refer first column data .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":null,"dir":"Reference","previous_headings":"","what":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"takes label latest row split level adds row total alt_counts_df parentheses. function differs c_label_n() taking row counts alt_counts_df rather df, used add_rowcounts() alt_counts set TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"","code":"c_label_n_alt(df, labelstr, .alt_df_row)"},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"can useful tabulating model results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"","code":"cfun_by_flag(analysis_var, flag_var, format = \"xx\", .indent_mods = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"analysis_var (string) variable name column containing values returned content function. flag_var (string) variable name logical column identifying row returned. format (string)rtables format use.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"content function gives df$analysis_var row identified .df_row$flag given format.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_diff_prop_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Check: Proportion Difference Arguments — check_diff_prop_ci","title":"Check: Proportion Difference Arguments — check_diff_prop_ci","text":"Verifies /convert arguments valid values used estimation difference responder proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_diff_prop_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check: Proportion Difference Arguments — check_diff_prop_ci","text":"","code":"check_diff_prop_ci(rsp, grp, strata = NULL, conf_level, correct = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/check_diff_prop_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check: Proportion Difference Arguments — check_diff_prop_ci","text":"rsp (logical) whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). strata (factor) variable one level per stratum length rsp. conf_level (proportion) confidence level interval. correct (logical) whether include continuity correction. information, see stats::prop.test().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Element Dimension — check_same_n","title":"Check Element Dimension — check_same_n","text":"Checks elements ... dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Element Dimension — check_same_n","text":"","code":"check_same_n(..., omit_null = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Element Dimension — check_same_n","text":"... (data.frames vectors) data frames/vectors. omit_null (logical) whether NULL elements ... omitted check.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Element Dimension — check_same_n","text":"logical value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Combination Functions Class — combination_function","title":"Combination Functions Class — combination_function","text":"CombinationFunction S4 class extends standard functions. special functions can combined negated logical operators.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combination Functions Class — combination_function","text":"","code":"# S4 method for CombinationFunction,CombinationFunction &amp;(e1, e2)  # S4 method for CombinationFunction,CombinationFunction |(e1, e2)  # S4 method for CombinationFunction !(x)"},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combination Functions Class — combination_function","text":"e1 (CombinationFunction) left hand side logical operator. e2 (CombinationFunction) right hand side logical operator. x (CombinationFunction) function negated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combination Functions Class — combination_function","text":"Returns logical value indicating whether left hand side equation equals right hand side.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Combination Functions Class — combination_function","text":"e1 & e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. e1 | e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. `!`(CombinationFunction): Logical negation CombinationFunction functions. resulting object class, evaluates original function. result opposite results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combination Functions Class — combination_function","text":"","code":"higher <- function(a) {   force(a)   CombinationFunction(     function(x) {       x > a     }   ) }  lower <- function(b) {   force(b)   CombinationFunction(     function(x) {       x < b     }   ) }  c1 <- higher(5) c2 <- lower(10) c3 <- higher(5) & lower(10) c3(7) #> [1] TRUE"},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Counts — combine_counts","title":"Combine Counts — combine_counts","text":"Simplifies estimation column counts, especially group combination required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Counts — combine_counts","text":"","code":"combine_counts(fct, groups_list = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Counts — combine_counts","text":"fct (factor) variable levels needs grouped. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Counts — combine_counts","text":"vector column counts.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Counts — combine_counts","text":"","code":"ref <- c(\"A: Drug X\", \"B: Placebo\") groups <- combine_groups(fct = DM$ARM, ref = ref)  col_counts <- combine_counts(   fct = DM$ARM,   groups_list = groups )  basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze_vars(\"AGE\") %>%   build_table(DM, col_counts = col_counts) #>             A: Drug X/B: Placebo   C: Combination #>                   (N=227)             (N=129)     #> ————————————————————————————————————————————————— #> n                   227                 129       #> Mean (SD)        34.0 (7.2)          34.6 (6.5)   #> Median              33.0                33.0      #> Min - Max       20.0 - 60.0         22.0 - 53.0    ref <- \"A: Drug X\" groups <- combine_groups(fct = DM$ARM, ref = ref) col_counts <- combine_counts(   fct = DM$ARM,   groups_list = groups )  basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze_vars(\"AGE\") %>%   build_table(DM, col_counts = col_counts) #>              A: Drug X    B: Placebo/C: Combination #>               (N=121)              (N=235)          #> ——————————————————————————————————————————————————— #> n               121                  235            #> Mean (SD)   34.9 (7.8)           33.9 (6.5)         #> Median         33.0                 33.0            #> Min - Max   20.0 - 60.0          21.0 - 55.0"},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Reference and Treatment Group Combination — combine_groups","title":"Reference and Treatment Group Combination — combine_groups","text":"Facilitate re-combination groups divided reference treatment groups; helps arranging groups columns rtables framework teal modules.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reference and Treatment Group Combination — combine_groups","text":"","code":"combine_groups(fct, ref = NULL, collapse = \"/\")"},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reference and Treatment Group Combination — combine_groups","text":"fct (factor) variable levels needs grouped. ref (string) reference level(s). collapse (string) character string separate fct ref.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reference and Treatment Group Combination — combine_groups","text":"list first item ref (reference) second item trt (treatment).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reference and Treatment Group Combination — combine_groups","text":"","code":"groups <- combine_groups(   fct = DM$ARM,   ref = c(\"B: Placebo\") )  basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze_vars(\"AGE\") %>%   build_table(DM) #>             B: Placebo    A: Drug X/C: Combination #>               (N=106)             (N=250)          #> —————————————————————————————————————————————————— #> n               106                 250            #> Mean (SD)   33.0 (6.3)           34.7 (7.1)        #> Median         32.0                 33.0           #> Min - Max   21.0 - 55.0         20.0 - 60.0"},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Factor Levels — combine_levels","title":"Combine Factor Levels — combine_levels","text":"Combine specified old factor Levels single new level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Factor Levels — combine_levels","text":"","code":"combine_levels(x, levels, new_level = paste(levels, collapse = \"/\"))"},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Factor Levels — combine_levels","text":"x factor levels level names combined new_level name new level","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Factor Levels — combine_levels","text":"factor new levels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Factor Levels — combine_levels","text":"","code":"x <- factor(letters[1:5], levels = letters[5:1]) combine_levels(x, levels = c(\"a\", \"b\")) #> [1] a/b a/b c   d   e   #> Levels: e d c a/b  combine_levels(x, c(\"e\", \"b\")) #> [1] a   e/b c   d   e/b #> Levels: e/b d c a"},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Two Vectors Element Wise — combine_vectors","title":"Combine Two Vectors Element Wise — combine_vectors","text":"Combine Two Vectors Element Wise","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Two Vectors Element Wise — combine_vectors","text":"","code":"combine_vectors(x, y)"},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Two Vectors Element Wise — combine_vectors","text":"x (vector) first vector combine. y (vector) second vector combine.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Two Vectors Element Wise — combine_vectors","text":"list element combines corresponding elements x y.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Two Vectors Element Wise — combine_vectors","text":"","code":"combine_vectors(1:3, 4:6) #> [[1]] #> [1] 1 4 #>  #> [[2]] #> [1] 2 5 #>  #> [[3]] #> [1] 3 6 #>"},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare Variables Between Groups — compare_variables","title":"Compare Variables Between Groups — compare_variables","text":"Comparison reference group different x objects.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare Variables Between Groups — compare_variables","text":"","code":"s_compare(x, .ref_group, .in_ref_col, ...)  # S3 method for numeric s_compare(x, .ref_group, .in_ref_col, ...)  # S3 method for factor s_compare(x, .ref_group, .in_ref_col, denom = \"n\", na.rm = TRUE, ...)  # S3 method for character s_compare(   x,   .ref_group,   .in_ref_col,   denom = \"n\",   na.rm = TRUE,   .var,   verbose = TRUE,   ... )  # S3 method for logical s_compare(x, .ref_group, .in_ref_col, na.rm = TRUE, denom = \"n\", ...)  a_compare(   x,   .N_col,   .N_row,   .var = NULL,   .df_row = NULL,   .ref_group = NULL,   .in_ref_col = FALSE,   ... )  compare_vars(   lyt,   vars,   var_labels = vars,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   nested = TRUE,   ...,   na.rm = TRUE,   show_labels = \"default\",   table_names = vars,   section_div = NA_character_,   .stats = c(\"n\", \"mean_sd\", \"count_fraction\", \"pval\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare Variables Between Groups — compare_variables","text":"x (numeric) vector numbers want analyze. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. ... arguments passed s_compare(). denom (string) choice denominator factor proportions, can n (number values row column intersection). na.rm (flag) whether NA values removed x prior analysis. .var (string) single variable name passed rtables requested statistics function. verbose (logical) Whether warnings messages printed. Mainly used print information factor casting. Defaults TRUE. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .df_row (data.frame) data frame across columns given row split. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare Variables Between Groups — compare_variables","text":"s_compare() returns output s_summary() comparisons versus reference group form p-values. a_compare() returns corresponding list formatted rtables::CellValue(). compare_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_compare() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Compare Variables Between Groups — compare_variables","text":"s_compare(): S3 generic function produce comparison summary. s_compare(numeric): Method numeric class. uses standard t-test calculate p-value. s_compare(factor): Method factor class. uses chi-squared test calculate p-value. s_compare(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_compare(logical): Method logical class. chi-squared test used. missing values removed, counted FALSE. a_compare(): Formatted analysis function used afun compare_vars(). compare_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compare Variables Between Groups — compare_variables","text":"factor variables, denom factor proportions can n since purpose compare proportions columns, therefore row-based proportion make sense. Proportion based N_col difficult since use counts chi-squared test statistic, therefore missing values accounted explicit factor levels. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"<Missing>\") also excluded na.rm set TRUE. character variables, automatic conversion factor guarantee table generated correctly. particular sparse tables likely can fail. Therefore always better manually convert character variables factors pre-processing. compare_vars(), column split must define reference group via ref_group comparison well defined. a_compare() deprecated favor a_summary() argument compare set TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare Variables Between Groups — compare_variables","text":"","code":"# `s_compare.numeric`  ## Usual case where both this and the reference group vector have more than 1 value. s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE) #> $n #>  n  #> 10  #>  #> $sum #>      sum  #> 51.15441  #>  #> $mean #>     mean  #> 5.115441  #>  #> $sd #>        sd  #> 0.9698247  #>  #> $se #>        se  #> 0.3066855  #>  #> $mean_sd #>      mean        sd  #> 5.1154408 0.9698247  #>  #> $mean_se #>      mean        se  #> 5.1154408 0.3066855  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.421670    5.809212  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>     4.808755     5.422126  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>     4.145616     6.085266  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #>      p_value  #> 4.472872e-08  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #>   median  #> 4.997242  #>  #> $mad #>          mad  #> 4.440892e-16  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>       4.09203       5.99734  #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>      4.276008      5.861620  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #>      iqr  #> 1.585612  #>  #> $range #>      min      max  #> 3.792829 6.848199  #>  #> $min #>      min  #> 3.792829  #>  #> $max #>      max  #> 6.848199  #>  #> $median_range #>   median      min      max  #> 4.997242 3.792829 6.848199  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #>       cv  #> 18.95877  #>  #> $geom_mean #> geom_mean  #>  5.033803  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.397113    5.762683  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #>  geom_cv  #> 19.07365  #>  #> $pval #> [1] 3.561901e-05 #>   ## If one group has not more than 1 value, then p-value is not calculated. s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE) #> $n #>  n  #> 10  #>  #> $sum #>      sum  #> 48.71622  #>  #> $mean #>     mean  #> 4.871622  #>  #> $sd #>        sd  #> 0.9660467  #>  #> $se #>        se  #> 0.3054908  #>  #> $mean_sd #>      mean        sd  #> 4.8716224 0.9660467  #>  #> $mean_se #>      mean        se  #> 4.8716224 0.3054908  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.180554    5.562691  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>     4.566132     5.177113  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>     3.905576     5.837669  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #>      p_value  #> 6.621947e-08  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #>   median  #> 4.925955  #>  #> $mad #>           mad  #> -4.440892e-16  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>      4.214380      5.713326  #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>      4.219359      5.446682  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #>      iqr  #> 1.227323  #>  #> $range #>      min      max  #> 3.166494 6.564464  #>  #> $min #>      min  #> 3.166494  #>  #> $max #>      max  #> 6.564464  #>  #> $median_range #>   median      min      max  #> 4.925955 3.166494 6.564464  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #>       cv  #> 19.83008  #>  #> $geom_mean #> geom_mean  #>  4.781985  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.125787    5.542552  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #>  geom_cv  #> 20.85449  #>  #> $pval #> character(0) #>   ## Empty numeric does not fail, it returns NA-filled items and no p-value. s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE) #> $n #> n  #> 0  #>  #> $sum #> sum  #>  NA  #>  #> $mean #> mean  #>   NA  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>   NA   NA  #>  #> $mean_se #> mean   se  #>   NA   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>     NA  #>  #> $mad #> mad  #>  NA  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>  NA  #>  #> $range #> min max  #>  NA  NA  #>  #> $min #> min  #>  NA  #>  #> $max #> max  #>  NA  #>  #> $median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>       NaN  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>  #> $pval #> character(0) #>   # `s_compare.factor`  ## Basic usage: x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) y <- factor(c(\"a\", \"b\", \"c\")) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>   ## Management of NA values. x <- explicit_na(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\", NA, NA))) y <- explicit_na(factor(c(\"a\", \"b\", \"c\", NA))) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>  s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 7 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #> $count$`<Missing>` #> [1] 2 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0000000 0.4285714 #>  #> $count_fraction$b #> [1] 1.0000000 0.1428571 #>  #> $count_fraction$c #> [1] 1.0000000 0.1428571 #>  #> $count_fraction$`<Missing>` #> [1] 2.0000000 0.2857143 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.9063036 #>   # `s_compare.character`  ## Basic usage: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\") y <- c(\"a\", \"b\", \"c\") s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>   ## Note that missing values handling can make a large difference: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\", NA) y <- c(\"a\", \"b\", \"c\", rep(NA, 20)) s_compare(x,   .ref_group = y, .in_ref_col = FALSE,   .var = \"x\", verbose = FALSE ) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>  s_compare(x,   .ref_group = y, .in_ref_col = FALSE, .var = \"x\",   na.rm = FALSE, verbose = FALSE ) #> $n #> [1] 6 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #> $count$`<Missing>` #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #>  #> $count_fraction$b #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$c #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$`<Missing>` #> [1] 1.0000000 0.1666667 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.005768471 #>   # `s_compare.logical`  ## Basic usage: x <- c(TRUE, FALSE, TRUE, TRUE) y <- c(FALSE, FALSE, TRUE) s_compare(x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.00 0.75 #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.2702894 #>   ## Management of NA values. x <- c(NA, TRUE, FALSE) y <- c(NA, NA, NA, NA, FALSE) s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 2 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0 0.5 #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.3864762 #>  s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 3 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0000000 0.3333333 #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.1675463 #>   # `a_compare` deprecated - use `a_summary()` instead a_compare(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .stats = c(\"n\", \"pval\")) #> Warning: `a_compare()` was deprecated in tern 0.8.3. #> ℹ Please use a_summary() with argument `compare` set to TRUE instead. #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>           row_name formatted_cell indent_mod        row_label #> 1                n             10          0                n #> 2 p-value (t-test)        <0.0001          0 p-value (t-test)  # `compare_vars()` in `rtables` pipelines  ## Default output within a `rtables` pipeline. lyt <- basic_table() %>%   split_cols_by(\"ARMCD\", ref_group = \"ARM B\") %>%   compare_vars(c(\"AGE\", \"SEX\")) build_table(lyt, tern_ex_adsl) #>                                  ARM B        ARM A        ARM C    #> ——————————————————————————————————————————————————————————————————— #> AGE                                                                 #>   n                                73           69           58     #>   Mean (SD)                    35.8 (7.1)   34.1 (6.8)   36.1 (7.4) #>   p-value (t-test)                            0.1446       0.8212   #> SEX                                                                 #>   n                                73           69           58     #>   F                            40 (54.8%)   38 (55.1%)   32 (55.2%) #>   M                            33 (45.2%)   31 (44.9%)   26 (44.8%) #>   p-value (chi-squared test)                  1.0000       1.0000    ## Select and format statistics output. lyt <- basic_table() %>%   split_cols_by(\"ARMCD\", ref_group = \"ARM C\") %>%   compare_vars(     vars = \"AGE\",     .stats = c(\"mean_sd\", \"pval\"),     .formats = c(mean_sd = \"xx.x, xx.x\"),     .labels = c(mean_sd = \"Mean, SD\")   ) build_table(lyt, df = tern_ex_adsl) #>                      ARM C       ARM A       ARM B   #> ———————————————————————————————————————————————————— #> Mean, SD           36.1, 7.4   34.1, 6.8   35.8, 7.1 #> p-value (t-test)                0.1176      0.8212"},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for Descriptive Statistics — control_analyze_vars","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"Sets list parameters summaries descriptive statistics. Typically used internally specify details s_summary(). function family mainly used analyze_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"","code":"control_analyze_vars(   conf_level = 0.95,   quantiles = c(0.25, 0.75),   quantile_type = 2,   test_mean = 0 )"},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"conf_level (proportion) confidence level interval. quantiles (numeric) length two specify quantiles calculate. quantile_type (numeric) 1 9 selecting quantile algorithms used. Default set 2 matches default quantile algorithm SAS proc univariate set QNTLDEF=5. differs R's default. See type stats::quantile(). test_mean (numeric) test mean null hypothesis calculating p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"Deprecation cycle started control_summarize_vars going renamed control_analyze_vars. Intention reflect better core underlying rtables functions; case analyze_vars() wraps rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for CoxPH Model — control_coxph","title":"Control Function for CoxPH Model — control_coxph","text":"auxiliary function controlling arguments CoxPH model, typically used internally specify details CoxPH model s_coxph_pairwise(). conf_level refers Hazard Ratio estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for CoxPH Model — control_coxph","text":"","code":"control_coxph(   pval_method = c(\"log-rank\", \"wald\", \"likelihood\"),   ties = c(\"efron\", \"breslow\", \"exact\"),   conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for CoxPH Model — control_coxph","text":"pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for CoxPH Model — control_coxph","text":"list components names arguments","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Controls for Cox Regression — control_coxreg","title":"Controls for Cox Regression — control_coxreg","text":"Sets list parameters Cox regression fit. Used internally.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Controls for Cox Regression — control_coxreg","text":"","code":"control_coxreg(   pval_method = c(\"wald\", \"likelihood\"),   ties = c(\"exact\", \"efron\", \"breslow\"),   conf_level = 0.95,   interaction = FALSE )"},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Controls for Cox Regression — control_coxreg","text":"pval_method (string) method used estimation p.values; wald (default) likelihood. ties (string) among exact (equivalent DISCRETE SAS), efron breslow, see survival::coxph(). Note: equivalent SAS EXACT method R. conf_level (proportion) confidence level interval. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Controls for Cox Regression — control_coxreg","text":"list items names corresponding arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Controls for Cox Regression — control_coxreg","text":"","code":"control_coxreg() #> $pval_method #> [1] \"wald\" #>  #> $ties #> [1] \"exact\" #>  #> $conf_level #> [1] 0.95 #>  #> $interaction #> [1] FALSE #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for incidence rate — control_incidence_rate","title":"Control function for incidence rate — control_incidence_rate","text":"auxiliary function controlling arguments incidence rate, used internally specify details s_incidence_rate().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate(   conf_level = 0.95,   conf_type = c(\"normal\", \"normal_log\", \"exact\", \"byar\"),   input_time_unit = c(\"year\", \"day\", \"week\", \"month\"),   num_pt_year = 100,   time_unit_input = lifecycle::deprecated(),   time_unit_output = lifecycle::deprecated() )"},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for incidence rate — control_incidence_rate","text":"conf_level (proportion) confidence level interval. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric) number patient-years use calculating adverse event rates. time_unit_input Please use input_time_unit argument instead. time_unit_output Please use num_pt_year argument instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for incidence rate — control_incidence_rate","text":"list components names arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate(0.9, \"exact\", \"month\", 100) #> $conf_level #> [1] 0.9 #>  #> $conf_type #> [1] \"exact\" #>  #> $input_time_unit #> [1] \"month\" #>  #> $num_pt_year #> [1] 100 #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for g_lineplot Function — control_lineplot_vars","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"Default values variables parameter g_lineplot function. variable's default value can overwritten variable.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"","code":"control_lineplot_vars(   x = \"AVISIT\",   y = \"AVAL\",   strata = \"ARM\",   paramcd = \"PARAMCD\",   y_unit = \"AVALU\",   cohort_id = \"USUBJID\" )"},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"x (character) x variable name. y (character) y variable name. strata (character NA) strata variable name. paramcd (character NA)paramcd variable name. y_unit (character NA)y_unit variable name. cohort_id (character NA) variable identify subjects cohorts.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"named character vector variable names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"","code":"control_lineplot_vars() #>         x         y    strata   paramcd    y_unit cohort_id  #>  \"AVISIT\"    \"AVAL\"     \"ARM\" \"PARAMCD\"   \"AVALU\" \"USUBJID\"  control_lineplot_vars(strata = NA) #>         x         y    strata   paramcd    y_unit cohort_id  #>  \"AVISIT\"    \"AVAL\"        NA \"PARAMCD\"   \"AVALU\" \"USUBJID\""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for Logistic Regression Model Fitting — control_logistic","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"auxiliary function controlling arguments logistic regression models. conf_level refers confidence level used Odds Ratio CIs.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"","code":"control_logistic(response_definition = \"response\", conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"response_definition (string) definition event terms response. used fitting logistic regression model left hand side formula. Note evaluated expression result either logical vector factor 2 levels. default just \"response\" original response variable used modified . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"","code":"# Standard options. control_logistic() #> $response_definition #> [1] \"response\" #>  #> $conf_level #> [1] 0.95 #>   # Modify confidence level. control_logistic(conf_level = 0.9) #> $response_definition #> [1] \"response\" #>  #> $conf_level #> [1] 0.9 #>   # Use a different response definition. control_logistic(response_definition = \"I(response %in% c('CR', 'PR'))\") #> $response_definition #> [1] \"I(response %in% c('CR', 'PR'))\" #>  #> $conf_level #> [1] 0.95 #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"auxiliary function controlling arguments STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"","code":"control_step(   biomarker = NULL,   use_percentile = TRUE,   bandwidth,   degree = 0L,   num_points = 39L )"},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"biomarker (numeric NULL) optional provision numeric biomarker variable, used infer bandwidth, see . use_percentile (flag) TRUE, running windows created according quantiles rather actual values, .e. bandwidth refers percentage data covered window. Suggest TRUE biomarker variable uniformly distributed. bandwidth (number NULL) indicating bandwidth window. Depending argument use_percentile, can either length actual-value windows real biomarker scale, percentage windows. use_percentile = TRUE, number 0 1. NULL, treat bandwidth infinity, means one global model fitted. default, 0.25 used percentage windows one quarter range biomarker variable actual-value windows. degree (count) degree polynomial function biomarker interaction term treatment arm fitted window. 0 (default), biomarker variable included model fitted biomarker window. num_points (count) number points hazard ratios estimated. smallest number 2.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"list components names arguments, except biomarker just used calculate bandwidth case actual biomarker windows requested.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"","code":"# Provide biomarker values and request actual values to be used, # so that bandwidth is chosen from range. control_step(biomarker = 1:10, use_percentile = FALSE) #> $use_percentile #> [1] FALSE #>  #> $bandwidth #> [1] 2.25 #>  #> $degree #> [1] 0 #>  #> $num_points #> [1] 39 #>   # Use a global model with quadratic biomarker interaction term. control_step(bandwidth = NULL, degree = 2) #> $use_percentile #> [1] TRUE #>  #> $bandwidth #> NULL #>  #> $degree #> [1] 2 #>  #> $num_points #> [1] 39 #>   # Reduce number of points to be used. control_step(num_points = 10) #> $use_percentile #> [1] TRUE #>  #> $bandwidth #> [1] 0.25 #>  #> $degree #> [1] 0 #>  #> $num_points #> [1] 10 #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for survfit Model for Survival Time — control_surv_time","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_time(). conf_level refers survival time estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"","code":"control_surv_time(   conf_level = 0.95,   conf_type = c(\"plain\", \"log\", \"log-log\"),   quantiles = c(0.25, 0.75) )"},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported. quantiles (numeric) length two specify quantiles survival time.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"list components names arguments","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_timepoint(). conf_level refers patient risk estimation time point.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"","code":"control_surv_timepoint(   conf_level = 0.95,   conf_type = c(\"plain\", \"log\", \"log-log\") )"},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"list components names arguments","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Counts with Thresholds — count_cumulative","title":"Cumulative Counts with Thresholds — count_cumulative","text":"Summarize cumulative counts (numeric) vector less , less equal , greater , greater equal user-specific thresholds.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Counts with Thresholds — count_cumulative","text":"","code":"s_count_cumulative(   x,   thresholds,   lower_tail = TRUE,   include_eq = TRUE,   .N_col,   ... )  a_count_cumulative(   x,   thresholds,   lower_tail = TRUE,   include_eq = TRUE,   .N_col,   ... )  count_cumulative(   lyt,   vars,   var_labels = vars,   show_labels = \"visible\",   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Counts with Thresholds — count_cumulative","text":"x (numeric) vector numbers want analyze. thresholds (numeric) vector cutoff value counts. lower_tail (logical) whether count lower tail, default TRUE. include_eq (logical) whether include value equal threshold count, default TRUE. .N_col (count) denominator fraction calculation. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Counts with Thresholds — count_cumulative","text":"s_count_cumulative() returns named list count_fractions: list thresholds value component, component containing vector count fraction. a_count_cumulative() returns corresponding list formatted rtables::CellValue(). count_cumulative() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_cumulative() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cumulative Counts with Thresholds — count_cumulative","text":"s_count_cumulative(): Statistics function produces named list given numeric vector thresholds. a_count_cumulative(): Formatted analysis function used afun count_cumulative(). count_cumulative(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Counts with Thresholds — count_cumulative","text":"","code":"basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_cumulative(     vars = \"AGE\",     thresholds = c(40, 60)   ) %>%   build_table(tern_ex_adsl) #>           A: Drug X    B: Placebo   C: Combination #>             (N=69)       (N=73)         (N=58)     #> —————————————————————————————————————————————————— #> AGE                                                #>   <= 40   52 (75.4%)   58 (79.5%)     41 (70.7%)   #>   <= 60   69 (100%)    73 (100%)      58 (100%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Missed Doses — count_missed_doses","title":"Counting Missed Doses — count_missed_doses","text":"specific functions count patients missed doses. difference count_cumulative() mainly special labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Missed Doses — count_missed_doses","text":"","code":"s_count_nonmissing(x)  s_count_missed_doses(x, thresholds, .N_col)  a_count_missed_doses(x, thresholds, .N_col)  count_missed_doses(   lyt,   vars,   var_labels = vars,   show_labels = \"visible\",   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Missed Doses — count_missed_doses","text":"x (numeric) vector numbers want analyze. thresholds (vector count) number missed doses patients least . .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Missed Doses — count_missed_doses","text":"s_count_nonmissing() returns statistic n count non-missing values x. s_count_missed_doses() returns statistics n count_fraction one element threshold. a_count_missed_doses() returns corresponding list formatted rtables::CellValue(). count_missed_doses() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_missed_doses() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Missed Doses — count_missed_doses","text":"s_count_nonmissing(): Statistics function count non-missing values. s_count_missed_doses(): Statistics function count patients missed doses. a_count_missed_doses(): Formatted analysis function used afun count_missed_doses(). count_missed_doses(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Missed Doses — count_missed_doses","text":"","code":"set.seed(1) x <- c(sample(1:10, 10), NA)  library(dplyr)  anl <- tern_ex_adsl %>%   distinct(STUDYID, USUBJID, ARM) %>%   mutate(     PARAMCD = \"TNDOSMIS\",     PARAM = \"Total number of missed doses during study\",     AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE),     AVALC = \"\"   )  basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_missed_doses(\"AVAL\", thresholds = c(1, 5, 10, 15), var_labels = \"Missed Doses\") %>%   build_table(anl, alt_counts_df = tern_ex_adsl) #>                              A: Drug X    B: Placebo   C: Combination #>                                (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————— #> Missed Doses                                                          #>   n                              69           73             58       #>   At least 1 missed dose     65 (94.2%)   67 (91.8%)     58 (100%)    #>   At least 5 missed doses    54 (78.3%)   51 (69.9%)     54 (93.1%)   #>   At least 10 missed doses   31 (44.9%)   40 (54.8%)     31 (53.4%)   #>   At least 15 missed doses   17 (24.6%)   23 (31.5%)     20 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Counts — count_occurrences","title":"Occurrence Counts — count_occurrences","text":"Functions analyzing frequencies fractions occurrences patients occurrence data. Primary analysis variables dictionary terms. occurrences counted total counts. Multiple occurrences within patient lowest term level displayed table counted .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Counts — count_occurrences","text":"","code":"s_count_occurrences(   df,   denom = c(\"N_col\", \"n\"),   .N_col,   .df_row,   drop = TRUE,   .var = \"MHDECOD\",   id = \"USUBJID\" )  a_count_occurrences(   df,   denom = c(\"N_col\", \"n\"),   .N_col,   .df_row,   drop = TRUE,   .var = \"MHDECOD\",   id = \"USUBJID\" )  count_occurrences(   lyt,   vars,   var_labels = vars,   show_labels = \"hidden\",   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = \"count_fraction\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Counts — count_occurrences","text":"df (data.frame) data set containing analysis variables. denom (string) choice denominator patient proportions. Can : N_col: total number patients column across rows n: number patients occurrences .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .df_row (data.frame) data frame across columns given row split. drop (flag) non appearing occurrence levels dropped resulting table. Note case remaining occurrence levels table sorted alphabetically. .var (string) single variable name passed rtables requested statistics function. id (string) subject variable name. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Counts — count_occurrences","text":"s_count_occurrences() returns list : count: list counts one element per occurrence. count_fraction: list counts fractions one element per occurrence. fraction: list numerators denominators one element per occurrence. a_count_occurrences() returns corresponding list formatted rtables::CellValue(). count_occurrences() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Counts — count_occurrences","text":"s_count_occurrences(): Statistics function counts number patients report occurrence. a_count_occurrences(): Formatted analysis function used afun count_occurrences(). count_occurrences(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Occurrence Counts — count_occurrences","text":"default, occurrences appear given row split dropped table occurrences table sorted alphabetically per row split. Therefore, corresponding layout needs use split_fun = drop_split_levels split_rows_by calls. Use drop = FALSE like show occurrences.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Counts — count_occurrences","text":"","code":"df <- data.frame(   USUBJID = as.character(c(1, 1, 2, 4, 4, 4)),   MHDECOD = c(\"MH1\", \"MH2\", \"MH1\", \"MH1\", \"MH1\", \"MH3\") )  N_per_col <- 4L  # Count unique occurrences per subject. s_count_occurrences(   df,   .N_col = N_per_col,   .df_row = df,   .var = \"MHDECOD\",   id = \"USUBJID\" ) #> $count #> $count$MH1 #> [1] 3 #>  #> $count$MH2 #> [1] 1 #>  #> $count$MH3 #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$MH1 #> [1] 3.00 0.75 #>  #> $count_fraction$MH2 #> [1] 1.00 0.25 #>  #> $count_fraction$MH3 #> [1] 1.00 0.25 #>  #>  #> $fraction #> $fraction$MH1 #>   num denom  #>     3     4  #>  #> $fraction$MH2 #>   num denom  #>     1     4  #>  #> $fraction$MH3 #>   num denom  #>     1     4  #>  #>   #  We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. afun <- make_afun(a_count_occurrences, .ungroup_stats = c(\"count\", \"count_fraction\", \"fraction\")) afun(   df,   .N_col = N_per_col,   .df_row = df,   .var = \"MHDECOD\",   id = \"USUBJID\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1      MH1              3          0       MH1 #> 2      MH2              1          0       MH2 #> 3      MH3              1          0       MH3 #> 4      MH1      3 (75.0%)          0       MH1 #> 5      MH2      1 (25.0%)          0       MH2 #> 6      MH3      1 (25.0%)          0       MH3 #> 7      MH1    3/4 (75.0%)          0       MH1 #> 8      MH2    1/4 (25.0%)          0       MH2 #> 9      MH3    1/4 (25.0%)          0       MH3  library(dplyr) df <- data.frame(   USUBJID = as.character(c(     1, 1, 2, 4, 4, 4,     6, 6, 6, 7, 7, 8   )),   MHDECOD = c(     \"MH1\", \"MH2\", \"MH1\", \"MH1\", \"MH1\", \"MH3\",     \"MH2\", \"MH2\", \"MH3\", \"MH1\", \"MH2\", \"MH4\"   ),   ARM = rep(c(\"A\", \"B\"), each = 6) ) df_adsl <- df %>%   select(USUBJID, ARM) %>%   unique()  # Create table layout lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_occurrences(vars = \"MHDECOD\", .stats = c(\"count_fraction\"))  # Apply table layout to data and produce `rtable` object lyt %>%   build_table(df, alt_counts_df = df_adsl) %>%   prune_table() #>           A           B     #>         (N=3)       (N=3)   #> ——————————————————————————— #> MH1   3 (100%)    1 (33.3%) #> MH2   1 (33.3%)   2 (66.7%) #> MH3   1 (33.3%)   1 (33.3%) #> MH4       0       1 (33.3%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Counts by Grade — count_occurrences_by_grade","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"Functions analyzing frequencies fractions occurrences grade patients occurrence data. Multiple occurrences within one individual counted greatest intensity/highest grade level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"","code":"s_count_occurrences_by_grade(   df,   .var,   .N_col,   id = \"USUBJID\",   grade_groups = list(),   remove_single = TRUE,   labelstr = \"\" )  a_count_occurrences_by_grade(   df,   .var,   .N_col,   id = \"USUBJID\",   grade_groups = list(),   remove_single = TRUE,   labelstr = \"\" )  count_occurrences_by_grade(   lyt,   var,   var_labels = var,   show_labels = \"default\",   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = var,   .stats = NULL,   .formats = NULL,   .indent_mods = NULL,   .labels = NULL )  summarize_occurrences_by_grade(   lyt,   var,   na_str = NA_character_,   ...,   .stats = NULL,   .formats = NULL,   .indent_mods = NULL,   .labels = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. id (string) subject variable name. grade_groups (named list character) containing groupings grades. remove_single (logical)TRUE include elements one-element grade groups output list; case grade groups names included output. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. lyt (layout) input layout analyses added . var_labels (character) labels show result table. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"s_count_occurrences_by_grade() returns list counts fractions one element per grade level grade level grouping. a_count_occurrences_by_grade() returns corresponding list formatted rtables::CellValue(). count_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences_by_grade() table layout. summarize_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_occurrences_by_grade() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"s_count_occurrences_by_grade(): Statistics function counts number patients highest grade. a_count_occurrences_by_grade(): Formatted analysis function used afun count_occurrences_by_grade(). count_occurrences_by_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function can take content function arguments additional format arguments. function wrapper rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"","code":"library(dplyr) df <- data.frame(   USUBJID = as.character(c(1:6, 1)),   ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"A\"), levels = c(\"A\", \"B\")),   AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)),   AESEV = factor(     x = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"MILD\", \"MILD\", \"MODERATE\", \"SEVERE\"),     levels = c(\"MILD\", \"MODERATE\", \"SEVERE\")   ),   stringsAsFactors = FALSE ) df_adsl <- df %>%   select(USUBJID, ARM) %>%   unique()  s_count_occurrences_by_grade(   df,   .N_col = 10L,   .var = \"AETOXGR\",   id = \"USUBJID\",   grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> $count_fraction #> $count_fraction$ANY #> [1] 6.0 0.6 #>  #> $count_fraction$`1` #> [1] 1.0 0.1 #>  #> $count_fraction$`2` #> [1] 2.0 0.2 #>  #> $count_fraction$`3` #> [1] 2.0 0.2 #>  #> $count_fraction$`4` #> [1] 1.0 0.1 #>  #> $count_fraction$`5` #> [1] 0 0 #>  #>   #  We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. afun <- make_afun(a_count_occurrences_by_grade, .ungroup_stats = \"count_fraction\") afun(   df,   .N_col = 10L,   .var = \"AETOXGR\",   id = \"USUBJID\",   grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1      ANY      6 (60.0%)          0       ANY #> 2        1      1 (10.0%)          0         1 #> 3        2      2 (20.0%)          0         2 #> 4        3      2 (20.0%)          0         3 #> 5        4      1 (10.0%)          0         4 #> 6        5              0          0         5  # Layout creating function with custom format. basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_occurrences_by_grade(     var = \"AESEV\",     .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\")   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                  A               B       #>                (N=3)           (N=3)     #> ———————————————————————————————————————— #> MILD       0.00 (0.00%)    2.00 (66.67%) #> MODERATE   1.00 (33.33%)   1.00 (33.33%) #> SEVERE     2.00 (66.67%)   0.00 (0.00%)   # Define additional grade groupings. grade_groups <- list(   \"-Any-\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),   \"Grade 1-2\" = c(\"1\", \"2\"),   \"Grade 3-5\" = c(\"3\", \"4\", \"5\") )  basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_occurrences_by_grade(     var = \"AETOXGR\",     grade_groups = grade_groups   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                 A           B     #>               (N=3)       (N=3)   #> ————————————————————————————————— #> -Any-       3 (100%)    3 (100%)  #> Grade 1-2   1 (33.3%)   2 (66.7%) #> 1               0       1 (33.3%) #> 2           1 (33.3%)   1 (33.3%) #> Grade 3-5   2 (66.7%)   1 (33.3%) #> 3           2 (66.7%)       0     #> 4               0       1 (33.3%) #> 5               0           0      # Layout creating function with custom format. basic_table() %>%   add_colcounts() %>%   split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>%   summarize_occurrences_by_grade(     var = \"AESEV\",     .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\")   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                 all obs    #>                  (N=6)     #> —————————————————————————— #> A                          #>   MILD       0.00 (0.00%)  #>   MODERATE   1.00 (16.67%) #>   SEVERE     2.00 (33.33%) #> B                          #>   MILD       2.00 (33.33%) #>   MODERATE   1.00 (16.67%) #>   SEVERE     0.00 (0.00%)   basic_table() %>%   add_colcounts() %>%   split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>%   summarize_occurrences_by_grade(     var = \"AETOXGR\",     grade_groups = grade_groups   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                all obs  #>                 (N=6)   #> ——————————————————————— #> A                       #>   -Any-       3 (50.0%) #>   Grade 1-2   1 (16.7%) #>   1               0     #>   2           1 (16.7%) #>   Grade 3-5   2 (33.3%) #>   3           2 (33.3%) #>   4               0     #>   5               0     #> B                       #>   -Any-       3 (50.0%) #>   Grade 1-2   2 (33.3%) #>   1           1 (16.7%) #>   2           1 (16.7%) #>   Grade 3-5   1 (16.7%) #>   3               0     #>   4           1 (16.7%) #>   5               0"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Patients and Events in Columns — count_patients_events_in_cols","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"Counting number unique patients total number specific events column table layout required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"","code":"s_count_patients_and_multiple_events(   df,   id,   filters_list,   empty_stats = character(),   labelstr = \"\",   custom_label = NULL )  summarize_patients_events_in_cols(   lyt,   id = \"USUBJID\",   filters_list = list(),   na_str = NA_character_,   ...,   .stats = c(\"unique\", \"all\", names(filters_list)),   .labels = c(unique = \"Patients (All)\", all = \"Events (All)\",     labels_or_names(filters_list)),   col_split = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"df (data.frame) data set containing analysis variables. id (string) subject variable name. filters_list (named list character) element list describes one type event describe filters, format s_count_patients_with_event(). label, used column title. empty_stats (character) optional names statistics returned empty corresponding table cells stay blank. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. custom_label (string NULL) provided labelstr empty used label. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. ... additional arguments lower level functions. .stats (character) statistics select table. .labels (named character) labels statistics (without indent). col_split (flag) whether columns split. Set FALSE required column split done already earlier layout pipe.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"s_count_patients_and_multiple_events() returns list statistics: unique: number unique patients df. : number rows df. one element name filters_list: number rows df, .e. events, fulfilling filter condition. summarize_patients_events_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_patients_and_multiple_events() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"s_count_patients_and_multiple_events(): Statistics function counts numbers patients multiple events defined filters. Used analysis function afun summarize_patients_events_in_cols(). summarize_patients_events_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"","code":"# `s_count_patients_and_multiple_events()` df <- data.frame(   USUBJID = rep(c(\"id1\", \"id2\", \"id3\", \"id4\"), c(2, 3, 1, 1)),   ARM = c(\"A\", \"A\", \"B\", \"B\", \"B\", \"B\", \"A\"),   AESER = rep(\"Y\", 7),   AESDTH = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"N\"),   AEREL = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"Y\"),   AEDECOD = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"C\", \"D\"),   AEBODSYS = rep(c(\"SOC1\", \"SOC2\", \"SOC3\"), c(3, 3, 1)) )  # `summarize_patients_events_in_cols()` basic_table() %>%   summarize_patients_events_in_cols(     filters_list = list(       related = formatters::with_label(c(AEREL = \"Y\"), \"Events (Related)\"),       fatal = c(AESDTH = \"Y\"),       fatal_related = c(AEREL = \"Y\", AESDTH = \"Y\")     ),     custom_label = \"%s Total number of patients and events\"   ) %>%   build_table(df) #>                                          Patients (All)   Events (All)   Events (Related)   fatal   fatal_related #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> %s Total number of patients and events         4               7                5             4           4"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the Number of Patients with a Particular Event — count_patients_with_event","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"primary analysis variable .var denotes unique patient identifier.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"","code":"s_count_patients_with_event(   df,   .var,   filters,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  a_count_patients_with_event(   df,   .var,   filters,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  count_patients_with_event(   lyt,   vars,   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = \"count_fraction\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"df (data.frame) data set containing analysis variables. .var (character) name column contains unique identifier. filters (character) character vector specifying column names flag variables used counting number unique identifiers satisfying conditions. Multiple column names flags accepted format c(\"column_name1\" = \"flag1\", \"column_name2\" = \"flag2\"). Note equality accepted condition. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"s_count_patients_with_event() returns count fraction unique identifiers defined event. a_count_patients_with_event() returns corresponding list formatted rtables::CellValue(). count_patients_with_event() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_event() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"s_count_patients_with_event(): Statistics function counts number patients defined event occurred. a_count_patients_with_event(): Formatted analysis function used afun count_patients_with_event(). count_patients_with_event(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"","code":"library(dplyr)  # `s_count_patients_with_event()`  s_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\") ) #> $n #> [1] 164 #>  #> $count #> [1] 164 #>  #> $count_fraction #> [1] 164   1 #>  #> $n_blq #> [1] 0 #>  s_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\") ) #> $n #> [1] 164 #>  #> $count #> [1] 79 #>  #> $count_fraction #> [1] 79.0000000  0.4817073 #>  #> $n_blq #> [1] 0 #>  s_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"),   denom = \"N_col\",   .N_col = 456 ) #> $n #> [1] 164 #>  #> $count #> [1] 79 #>  #> $count_fraction #> [1] 79.0000000  0.1732456 #>  #> $n_blq #> [1] 0 #>   # `a_count_patients_with_event()`  a_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\"),   .N_col = 100,   .N_row = 100 ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>         row_name formatted_cell indent_mod      row_label #> 1              n            164          0              n #> 2          count            164          0          count #> 3 count_fraction     164 (100%)          0 count_fraction #> 4          n_blq              0          0          n_blq  # `count_patients_with_event()`  lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_values(     \"STUDYID\",     values = \"AB12345\",     .stats = \"count\",     .labels = c(count = \"Total AEs\")   ) %>%   count_patients_with_event(     \"SUBJID\",     filters = c(\"TRTEMFL\" = \"Y\"),     .labels = c(count_fraction = \"Total number of patients with at least one adverse event\"),     table_names = \"tbl_all\"   ) %>%   count_patients_with_event(     \"SUBJID\",     filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"),     .labels = c(count_fraction = \"Total number of patients with fatal AEs\"),     table_names = \"tbl_fatal\"   ) %>%   count_patients_with_event(     \"SUBJID\",     filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\", \"AEREL\" = \"Y\"),     .labels = c(count_fraction = \"Total number of patients with related fatal AEs\"),     .indent_mods = c(count_fraction = 2L),     table_names = \"tbl_rel_fatal\"   ) build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) #>                                                            A: Drug X    B: Placebo   C: Combination #>                                                              (N=69)       (N=73)         (N=58)     #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs                                                     202          177            162       #> Total number of patients with at least one adverse event   59 (100%)    57 (100%)      48 (100%)    #> Total number of patients with fatal AEs                    28 (47.5%)   31 (54.4%)     20 (41.7%)   #>     Total number of patients with related fatal AEs        28 (47.5%)   31 (54.4%)     20 (41.7%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the Number of Patients with Particular Flags — count_patients_with_flags","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"primary analysis variable .var denotes unique patient identifier.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"","code":"s_count_patients_with_flags(   df,   .var,   flag_variables,   flag_labels = NULL,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  a_count_patients_with_flags(   df,   .var,   flag_variables,   flag_labels = NULL,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  count_patients_with_flags(   lyt,   var,   var_labels = var,   show_labels = \"hidden\",   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = paste0(\"tbl_flags_\", var),   .stats = \"count_fraction\",   .formats = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"df (data.frame) data set containing analysis variables. .var (character) name column contains unique identifier. flag_variables (character) character vector specifying names logical variables analysis dataset used counting number unique identifiers. flag_labels (character) vector labels use flag variables. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. lyt (layout) input layout analyses added . var (string) single variable name passed rtables requested statistics function. var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"s_count_patients_with_flags() returns count fraction unique identifiers particular flag list statistics n, count, count_fraction, n_blq, one element per flag. a_count_patients_with_flags() returns corresponding list formatted rtables::CellValue(). count_patients_with_flags() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_flags() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"s_count_patients_with_flags(): Statistics function counts number patients particular flag variable TRUE. a_count_patients_with_flags(): Formatted analysis function used afun count_patients_with_flags(). count_patients_with_flags(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"flag_labels specified, variables labels extracted df. variables labeled, variable names used instead. Alternatively, named vector can supplied flag_variables within name-value pair name corresponds variable name value label use variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"","code":"library(dplyr)  # `s_count_patients_with_flags()`  # Add labelled flag variables to analysis dataset. adae <- tern_ex_adae %>%   mutate(     fl1 = TRUE,     fl2 = TRTEMFL == \"Y\",     fl3 = TRTEMFL == \"Y\" & AEOUT == \"FATAL\",     fl4 = TRTEMFL == \"Y\" & AEOUT == \"FATAL\" & AEREL == \"Y\"   ) labels <- c(   \"fl1\" = \"Total AEs\",   \"fl2\" = \"Total number of patients with at least one adverse event\",   \"fl3\" = \"Total number of patients with fatal AEs\",   \"fl4\" = \"Total number of patients with related fatal AEs\" ) formatters::var_labels(adae)[names(labels)] <- labels  s_count_patients_with_flags(   adae,   \"SUBJID\",   flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"),   denom = \"N_col\",   .N_col = 1000 ) #> $n #> $n$`Total AEs` #> [1] 164 #>  #> $n$`Total number of patients with at least one adverse event` #> [1] 164 #>  #> $n$`Total number of patients with fatal AEs` #> [1] 164 #>  #> $n$`Total number of patients with related fatal AEs` #> [1] 164 #>  #>  #> $count #> $count$`Total AEs` #> [1] 164 #>  #> $count$`Total number of patients with at least one adverse event` #> [1] 164 #>  #> $count$`Total number of patients with fatal AEs` #> [1] 79 #>  #> $count$`Total number of patients with related fatal AEs` #> [1] 79 #>  #>  #> $count_fraction #> $count_fraction$`Total AEs` #> [1] 164.000   0.164 #>  #> $count_fraction$`Total number of patients with at least one adverse event` #> [1] 164.000   0.164 #>  #> $count_fraction$`Total number of patients with fatal AEs` #> [1] 79.000  0.079 #>  #> $count_fraction$`Total number of patients with related fatal AEs` #> [1] 79.000  0.079 #>  #>  #> $n_blq #> $n_blq$`Total AEs` #> [1] 0 #>  #> $n_blq$`Total number of patients with at least one adverse event` #> [1] 0 #>  #> $n_blq$`Total number of patients with fatal AEs` #> [1] 0 #>  #> $n_blq$`Total number of patients with related fatal AEs` #> [1] 0 #>  #>   #  We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly.  # `a_count_patients_with_flags()`  afun <- make_afun(a_count_patients_with_flags,   .stats = \"count_fraction\",   .ungroup_stats = \"count_fraction\" ) afun(   adae,   .N_col = 10L,   .N_row = 10L,   .var = \"USUBJID\",   flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\") ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                                                   row_name formatted_cell #> 1                                                Total AEs     164 (100%) #> 2 Total number of patients with at least one adverse event     164 (100%) #> 3                  Total number of patients with fatal AEs     79 (48.2%) #> 4          Total number of patients with related fatal AEs     79 (48.2%) #>   indent_mod                                                row_label #> 1          0                                                Total AEs #> 2          0 Total number of patients with at least one adverse event #> 3          0                  Total number of patients with fatal AEs #> 4          0          Total number of patients with related fatal AEs  # `count_patients_with_flags()`  lyt2 <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_patients_with_flags(     \"SUBJID\",     flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"),     denom = \"N_col\"   ) build_table(lyt2, adae, alt_counts_df = tern_ex_adsl) #>                                                            A: Drug X    B: Placebo   C: Combination #>                                                              (N=69)       (N=73)         (N=58)     #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs                                                  59 (85.5%)   57 (78.1%)     48 (82.8%)   #> Total number of patients with at least one adverse event   59 (85.5%)   57 (78.1%)     48 (82.8%)   #> Total number of patients with fatal AEs                    28 (40.6%)   31 (42.5%)     20 (34.5%)   #> Total number of patients with related fatal AEs            28 (40.6%)   31 (42.5%)     20 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Specific Values — count_values_funs","title":"Counting Specific Values — count_values_funs","text":"can count occurrence specific values variable interest.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Specific Values — count_values_funs","text":"","code":"s_count_values(   x,   values,   na.rm = TRUE,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  # S3 method for character s_count_values(x, values = \"Y\", na.rm = TRUE, ...)  # S3 method for factor s_count_values(x, values = \"Y\", ...)  # S3 method for logical s_count_values(x, values = TRUE, ...)  a_count_values(   x,   values,   na.rm = TRUE,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  count_values(   lyt,   vars,   values,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = \"count_fraction\",   .formats = NULL,   .labels = c(count_fraction = paste(values, collapse = \", \")),   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Specific Values — count_values_funs","text":"x (numeric) vector numbers want analyze. values (character) specific values counted. na.rm (flag) whether NA values removed x prior analysis. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Specific Values — count_values_funs","text":"s_count_values() returns output s_summary() specified values non-numeric variable. a_count_values() returns corresponding list formatted rtables::CellValue(). count_values() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_values() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Specific Values — count_values_funs","text":"s_count_values(): S3 generic function count values. s_count_values(character): Method character class. s_count_values(factor): Method factor class. makes automatic conversion character forwards method characters. s_count_values(logical): Method logical class. a_count_values(): Formatted analysis function used afun count_values(). count_values(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Counting Specific Values — count_values_funs","text":"factor variables, s_count_values checks whether values included levels x fails otherwise. count_values(), variable labels shown one element vars, otherwise hidden.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Specific Values — count_values_funs","text":"","code":"# `s_count_values.character` s_count_values(x = c(\"a\", \"b\", \"a\"), values = \"a\") #> $n #> [1] 3 #>  #> $count #> [1] 2 #>  #> $count_fraction #> [1] 2.0000000 0.6666667 #>  #> $n_blq #> [1] 0 #>  s_count_values(x = c(\"a\", \"b\", \"a\", NA, NA), values = \"b\", na.rm = FALSE) #> $n #> [1] 5 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0 0.2 #>  #> $n_blq #> [1] 0 #>   # `s_count_values.factor` s_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\") #> $n #> [1] 3 #>  #> $count #> [1] 2 #>  #> $count_fraction #> [1] 2.0000000 0.6666667 #>  #> $n_blq #> [1] 0 #>   # `s_count_values.logical` s_count_values(x = c(TRUE, FALSE, TRUE)) #> $n #> [1] 3 #>  #> $count #> [1] 2 #>  #> $count_fraction #> [1] 2.0000000 0.6666667 #>  #> $n_blq #> [1] 0 #>   # `a_count_values` a_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\", .N_col = 10, .N_row = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>         row_name formatted_cell indent_mod      row_label #> 1              n              3          0              n #> 2          count              2          0          count #> 3 count_fraction     2 (66.67%)          0 count_fraction #> 4          n_blq              0          0          n_blq  # `count_values` basic_table() %>%   count_values(\"Species\", values = \"setosa\") %>%   build_table(iris) #>            all obs   #> ———————————————————— #> setosa   50 (33.33%)"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox Proportional Hazards Regression — cox_regression","title":"Cox Proportional Hazards Regression — cox_regression","text":"Fits Cox regression model estimates hazard ratio describe effect size survival analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox Proportional Hazards Regression — cox_regression","text":"","code":"s_coxreg(model_df, .stats, .which_vars = \"all\", .var_nms = NULL)  a_coxreg(   df,   labelstr,   eff = FALSE,   var_main = FALSE,   multivar = FALSE,   variables,   at = list(),   control = control_coxreg(),   .spl_context,   .stats,   .formats,   .indent_mods = NULL,   na_level = lifecycle::deprecated(),   na_str = \"\",   cache_env = NULL )  summarize_coxreg(   lyt,   variables,   control = control_coxreg(),   at = list(),   multivar = FALSE,   common_var = \"STUDYID\",   .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\"),   .formats = c(n = \"xx\", hr = \"xx.xx\", ci = \"(xx.xx, xx.xx)\", pval =     \"x.xxxx | (<0.0001)\", pval_inter = \"x.xxxx | (<0.0001)\"),   varlabels = NULL,   .indent_mods = NULL,   na_level = lifecycle::deprecated(),   na_str = \"\",   .section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox Proportional Hazards Regression — cox_regression","text":"model_df (data.frame) contains resulting model fit fit_coxreg function tidying applied via broom::tidy(). .stats (character) name statistics reported among: n: number observations (univariate ) hr: hazard ratio ci: confidence interval pval: p-value treatment effect pval_inter: p-value interaction effect treatment covariate (univariate ) .which_vars (character) rows statistics returned given model. Defaults \"\". options include \"var_main\" main effects, \"inter\" interaction effects, \"multi_lvl\" multivariate model covariate level rows. .which_vars \"\" specific variables can selected specifying .var_nms. .var_nms (character) term value rows df .stats returned. Typically name variable. using variable labels, var vector desired variable name variable label order see .stats related variable. .which_vars \"var_main\" .var_nms variable name. df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. eff (flag) whether treatment effect calculated. Defaults FALSE. var_main (flag) whether main effects calculated. Defaults FALSE. multivar (flag) Defaults FALSE. TRUE multivariate Cox regression run, otherwise univariate Cox regression run. variables (named list string) list additional analysis variables. (list numeric) candidate covariate numeric, use specify value covariate effect estimated. control (list) list parameters returned helper function control_coxreg(). .spl_context (data.frame) gives information ancestor split states passed rtables. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_level Please use na_str argument instead. na_str (string) custom string replace NA values . Defaults \"\". cache_env (environment) environment object used cache regression model order avoid repeatedly fitting model every row table. Defaults NULL (caching). lyt (layout) input layout analyses added . common_var (character) name factor variable dataset takes value rows. created pre-processing variable currently exists. varlabels (list) named list corresponds names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. .section_div (character) string repeated section divider sections. Defaults NA section divider. vector two strings given, first used treatment covariate sections second different covariates.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox Proportional Hazards Regression — cox_regression","text":"s_coxreg() returns selected statistic Cox regression model selected variable(s). a_coxreg() returns formatted rtables::CellValue(). summarize_coxreg() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add Cox regression table containing chosen statistics table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox Proportional Hazards Regression — cox_regression","text":"Cox models commonly used methods estimate magnitude effect survival analysis. assumes proportional hazards: ratio hazards groups (e.g., two arms) constant time. ratio referred \"hazard ratio\" (HR) one commonly reported metrics describe effect size survival analysis (NEST Team, 2020).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox Proportional Hazards Regression — cox_regression","text":"s_coxreg(): Statistics function transforms results tabulated fit_coxreg_univar() fit_coxreg_multivar() list. a_coxreg(): Analysis function used afun rtables::analyze() cfun rtables::summarize_row_groups() within summarize_coxreg(). summarize_coxreg(): Layout-creating function creates Cox regression summary table layout. function wrapper several rtables layouting functions. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox Proportional Hazards Regression — cox_regression","text":"","code":"library(survival)  # Testing dataset [survival::bladder]. set.seed(1, kind = \"Mersenne-Twister\") dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   tibble::tibble(     TIME = stop,     STATUS = event,     ARM = as.factor(rx),     COVAR1 = as.factor(enum) %>% formatters::with_label(\"A Covariate Label\"),     COVAR2 = factor(       sample(as.factor(enum)),       levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\")     ) %>% formatters::with_label(\"Sex (F/M)\")   ) ) dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) dta_bladder$STUDYID <- factor(\"X\")  plot(   survfit(Surv(TIME, STATUS) ~ ARM + COVAR1, data = dta_bladder),   lty = 2:4,   xlab = \"Months\",   col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") )   # s_coxreg  # Univariate u1_variables <- list(   time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder) df1 <- broom::tidy(univar_model) s_coxreg(model_df = df1, .stats = \"hr\") #> $hr #> $hr$`2 vs control (1)` #> [1] 0.6386426 #>  #>  #> $hr #> $hr$`A Covariate Label` #> [1] 0.607037 #>  #>  #> $hr #> $hr$`Sex (F/M)` #> [1] 0.6242738 #>  #>   # Univariate with interactions univar_model_inter <- fit_coxreg_univar(   variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder ) df1_inter <- broom::tidy(univar_model_inter) s_coxreg(model_df = df1_inter, .stats = \"hr\", .which_vars = \"inter\", .var_nms = \"COVAR1\") #> $hr #> $hr$`  1` #> [1] 0.6284569 #>  #> $hr$`  2` #> [1] 0.5806499 #>  #> $hr$`  3` #> [1] 0.5486103 #>  #> $hr$`  4` #> [1] 0.6910725 #>  #>   # Univariate without treatment arm - only \"COVAR2\" covariate effects u2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder) df1_covs <- broom::tidy(univar_covs_model) s_coxreg(model_df = df1_covs, .stats = \"hr\", .var_nms = c(\"COVAR2\", \"Sex (F/M)\")) #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #>  #>  #> $hr #> $hr$M #> [1] 1.3271 #>  #>   # Multivariate. m1_variables <- list(   time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder) df2 <- broom::tidy(multivar_model) s_coxreg(model_df = df2, .stats = \"pval\", .which_vars = \"var_main\", .var_nms = \"COVAR1\") #> $pval #> $pval$`A Covariate Label (reference = 1)` #> [1] 7.209956e-09 #>  #>  s_coxreg(   model_df = df2, .stats = \"pval\", .which_vars = \"multi_lvl\",   .var_nms = c(\"COVAR1\", \"A Covariate Label\") ) #> $pval #> $pval$`2` #> [1] 0.001120332 #>  #> $pval$`3` #> [1] 6.293725e-06 #>  #> $pval$`4` #> [1] 3.013875e-08 #>  #>   # Multivariate without treatment arm - only \"COVAR1\" main effect m2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder) df2_covs <- broom::tidy(multivar_covs_model) s_coxreg(model_df = df2_covs, .stats = \"hr\") #> $hr #> $hr$`2` #> [1] 0.4600728 #>  #> $hr$`3` #> [1] 0.3100455 #>  #> $hr$`4` #> [1] 0.1854177 #>  #>  #> $hr #> $hr$`A Covariate Label (reference = 1)` #> numeric(0) #>  #>  #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #>  #>  #> $hr #> $hr$M #> [1] 1.285406 #>  #>   a_coxreg(   df = dta_bladder,   labelstr = \"Label 1\",   variables = u1_variables,   .spl_context = list(value = \"COVAR1\"),   .stats = \"n\",   .formats = \"xx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1  Label 1            340          0   Label 1  a_coxreg(   df = dta_bladder,   labelstr = \"\",   variables = u1_variables,   .spl_context = list(value = \"COVAR2\"),   .stats = \"pval\",   .formats = \"xx.xxxx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>    row_name formatted_cell indent_mod row_label #> 1 Sex (F/M)         0.0182          0 Sex (F/M)  # summarize_coxreg  result_univar <- basic_table() %>%   summarize_coxreg(variables = u1_variables) %>%   build_table(dta_bladder) result_univar #>                        n    Hazard Ratio      95% CI      p-value #> ————————————————————————————————————————————————————————————————— #> Treatment:                                                        #>   2 vs control (1)    340       0.64       (0.43, 0.94)   0.0242  #> Covariate:                                                        #>   A Covariate Label   340       0.61       (0.41, 0.90)   0.0126  #>   Sex (F/M)           340       0.62       (0.42, 0.92)   0.0182   result_multivar <- basic_table() %>%   summarize_coxreg(     variables = m1_variables,     multivar = TRUE,   ) %>%   build_table(dta_bladder) result_multivar #>                                       Hazard Ratio      95% CI      p-value #> ——————————————————————————————————————————————————————————————————————————— #> Treatment:                                                                  #>   ARM (reference = 1)                                                       #>     2                                     0.61       (0.41, 0.90)   0.0123  #> Covariate:                                                                  #>   A Covariate Label (reference = 1)                                 <0.0001 #>     2                                     0.46       (0.28, 0.73)   0.0011  #>     3                                     0.31       (0.18, 0.51)   <0.0001 #>     4                                     0.18       (0.10, 0.33)   <0.0001 #>   Sex (F/M) (reference = F)                                                 #>     M                                     1.29       (0.88, 1.89)   0.1911   result_univar_covs <- basic_table() %>%   summarize_coxreg(     variables = u2_variables,   ) %>%   build_table(dta_bladder) result_univar_covs #>                                       Hazard Ratio      95% CI      p-value #> ——————————————————————————————————————————————————————————————————————————— #> Covariate:                                                                  #>   A Covariate Label (reference = 1)                                 <0.0001 #>     2                                     0.45       (0.28, 0.71)   0.0007  #>     3                                     0.31       (0.19, 0.52)   <0.0001 #>     4                                     0.18       (0.10, 0.33)   <0.0001 #>   Sex (F/M) (reference = F)                                                 #>     M                                     1.33       (0.91, 1.94)   0.1414   result_multivar_covs <- basic_table() %>%   summarize_coxreg(     variables = m2_variables,     multivar = TRUE,     varlabels = c(\"Covariate 1\", \"Covariate 2\") # custom labels   ) %>%   build_table(dta_bladder) result_multivar_covs #>                                 Hazard Ratio      95% CI      p-value #> ————————————————————————————————————————————————————————————————————— #> Covariate:                                                            #>   Covariate 1 (reference = 1)                                 <0.0001 #>     2                               0.46       (0.29, 0.74)   0.0012  #>     3                               0.31       (0.19, 0.52)   <0.0001 #>     4                               0.19       (0.10, 0.34)   <0.0001 #>   Covariate 2 (reference = F)                                         #>     M                               1.29       (0.88, 1.88)   0.1958"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox Regression Helper: Interactions — cox_regression_inter","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"Test estimate effect treatment interaction covariate. effect estimated HR tested treatment given level covariate, comparison treatment control.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"","code":"h_coxreg_inter_effect(x, effect, covar, mod, label, control, ...)  # S3 method for numeric h_coxreg_inter_effect(x, effect, covar, mod, label, control, at, ...)  # S3 method for factor h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...)  # S3 method for character h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...)  h_coxreg_extract_interaction(effect, covar, mod, data, at, control)  h_coxreg_inter_estimations(   variable,   given,   lvl_var,   lvl_given,   mod,   conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"x (numeric factor) values covariate tested. effect (string) name effect tested estimated. covar (string) name covariate model. mod (coxph) fitted Cox regression model (see survival::coxph()). label (string) label returned term_label. control (list) list controls returned control_coxreg(). ... see methods. (list) list items named covariate, every item vector levels interaction estimated. data (data.frame) data frame model fit. variable, given (string) name variables interaction. seek estimation levels variable given levels given. lvl_var, lvl_given (character) corresponding levels given levels(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"h_coxreg_inter_effect() returns data.frame covariate interaction effects consisting following variables: effect, term, term_label, level, n, hr, lcl, ucl, pval, pval_inter. h_coxreg_extract_interaction() returns result interaction test estimated values. interaction, h_coxreg_univar_extract() applied instead. h_coxreg_inter_estimations() returns list matrices (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female) model abbreviated: y ~ Arm + Sex + Arm:Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) estimation Hazard Ratio arm C/sex M given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5). interaction coefficient deduced b2 + b5 standard error obtained $sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"h_coxreg_inter_effect(): S3 generic helper function determine interaction effect. h_coxreg_inter_effect(numeric): Method numeric class. Estimates interaction numeric covariate. h_coxreg_inter_effect(factor): Method factor class. Estimate interaction factor covariate. h_coxreg_inter_effect(character): Method character class. Estimate interaction character covariate. makes automatic conversion factor forwards method factors. h_coxreg_extract_interaction(): higher level function get results interaction test estimated values. h_coxreg_inter_estimations(): Hazard ratio estimation interactions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"Automatic conversion character factor guarantee results can generated correctly. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"","code":"library(survival)  set.seed(1, kind = \"Mersenne-Twister\")  # Testing dataset [survival::bladder]. dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   data.frame(     time = stop,     status = event,     armcd = as.factor(rx),     covar1 = as.factor(enum),     covar2 = factor(       sample(as.factor(enum)),       levels = 1:4,       labels = c(\"F\", \"F\", \"M\", \"M\")     )   ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)  plot(   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),   lty = 2:4,   xlab = \"Months\",   col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") )   mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) h_coxreg_extract_interaction(   mod = mod, effect = \"armcd\", covar = \"covar1\", data = dta_bladder,   control = control_coxreg() ) #>                    effect   term        term_label level   n        hr #> 1              Covariate: covar1 A Covariate Label       340        NA #> armcd2/covar11 Covariate: covar1                 1     1  NA 0.6341111 #> armcd2/covar12 Covariate: covar1                 2     2  NA 0.5845305 #> armcd2/covar13 Covariate: covar1                 3     3  NA 0.5507703 #> armcd2/covar14 Covariate: covar1                 4     4  NA 0.6910643 #>                      lcl      ucl      pval pval_inter #> 1                     NA       NA 0.1302825   0.988245 #> armcd2/covar11 0.3514676 1.144051        NA         NA #> armcd2/covar12 0.2716689 1.257692        NA         NA #> armcd2/covar13 0.2244668 1.351415        NA         NA #> armcd2/covar14 0.2315248 2.062715        NA         NA  mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) result <- h_coxreg_inter_estimations(   variable = \"armcd\", given = \"covar1\",   lvl_var = levels(dta_bladder$armcd),   lvl_given = levels(dta_bladder$covar1),   mod = mod, conf_level = .95 ) result #> $armcd2 #>                      coef  se(coef)        hr       lcl      ucl #> armcd2/covar11 -0.4555312 0.3010803 0.6341111 0.3514676 1.144051 #> armcd2/covar12 -0.5369464 0.3909383 0.5845305 0.2716689 1.257692 #> armcd2/covar13 -0.5964375 0.4579624 0.5507703 0.2244668 1.351415 #> armcd2/covar14 -0.3695225 0.5579418 0.6910643 0.2315248 2.062715 #>  #> attr(,\"details\") #> [1] \"Estimations of armcd hazard ratio given the level of covar1 compared to armcd level 1.\""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor Function for compare_vars() — create_afun_compare","title":"Constructor Function for compare_vars() — create_afun_compare","text":"Constructor function creates combined formatted analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor Function for compare_vars() — create_afun_compare","text":"","code":"create_afun_compare(   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor Function for compare_vars() — create_afun_compare","text":".stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor Function for compare_vars() — create_afun_compare","text":"Combined formatted analysis function use compare_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Constructor Function for compare_vars() — create_afun_compare","text":"function deprecated favor direct implementation a_summary() argument compare set TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"Constructor function creates combined formatted analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"","code":"create_afun_summary(.stats, .formats, .labels, .indent_mods)"},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":".stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"Combined formatted analysis function use analyze_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"function deprecated favor direct implementation a_summary().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":null,"dir":"Reference","previous_headings":"","what":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"cuts numeric vector sample quantile bins.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"","code":"cut_quantile_bins(   x,   probs = c(0.25, 0.5, 0.75),   labels = NULL,   type = 7,   ordered = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"x (numeric) continuous variable values cut quantile bins. may contain NA values, used quantile calculations, included return vector. probs (proportion vector) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. labels (character) unique labels quantile bins. n probabilities probs, must n + 1 long. type (integer) type quantiles use, see stats::quantile() details. ordered (flag) result ordered factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"factor variable appropriately-labeled bins levels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"Intervals closed right side. , first bin interval [-Inf, q1] q1 first quantile, second bin (q1, q2], etc., last bin (qn, +Inf] qn last quantile.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"","code":"# Default is to cut into quartile bins. cut_quantile_bins(cars$speed) #>  [1] [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   #>  [7] [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   #> [13] [0%,25%]   [0%,25%]   [0%,25%]   (25%,50%]  (25%,50%]  (25%,50%]  #> [19] (25%,50%]  (25%,50%]  (25%,50%]  (25%,50%]  (25%,50%]  (25%,50%]  #> [25] (25%,50%]  (25%,50%]  (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  #> [31] (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  #> [37] (50%,75%]  (50%,75%]  (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [43] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [49] (75%,100%] (75%,100%] #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%]  # Use custom quantiles. cut_quantile_bins(cars$speed, probs = c(0.1, 0.2, 0.6, 0.88)) #>  [1] [0%,10%]   [0%,10%]   [0%,10%]   [0%,10%]   [0%,10%]   (10%,20%]  #>  [7] (10%,20%]  (10%,20%]  (10%,20%]  (10%,20%]  (10%,20%]  (20%,60%]  #> [13] (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  #> [19] (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  #> [25] (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  #> [31] (20%,60%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  #> [37] (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  #> [43] (60%,88%]  (60%,88%]  (88%,100%] (88%,100%] (88%,100%] (88%,100%] #> [49] (88%,100%] (88%,100%] #> Levels: [0%,10%] < (10%,20%] < (20%,60%] < (60%,88%] < (88%,100%]  # Use custom labels. cut_quantile_bins(cars$speed, labels = paste0(\"Q\", 1:4)) #>  [1] Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 #> [26] Q2 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 #> Levels: Q1 < Q2 < Q3 < Q4  # NAs are preserved in result factor. ozone_binned <- cut_quantile_bins(airquality$Ozone) which(is.na(ozone_binned)) #>  [1]   5  10  25  26  27  32  33  34  35  36  37  39  42  43  45  46  52  53  54 #> [20]  55  56  57  58  59  60  61  65  72  75  83  84 102 103 107 115 119 150 # So you might want to make these explicit. explicit_na(ozone_binned) #>   [1] (50%,75%]  (50%,75%]  [0%,25%]   [0%,25%]   <Missing>  (25%,50%]  #>   [7] (25%,50%]  (25%,50%]  [0%,25%]   <Missing>  [0%,25%]   [0%,25%]   #>  [13] [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   (50%,75%]  [0%,25%]   #>  [19] (25%,50%]  [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   (50%,75%]  #>  [25] <Missing>  <Missing>  <Missing>  (25%,50%]  (50%,75%]  (75%,100%] #>  [31] (50%,75%]  <Missing>  <Missing>  <Missing>  <Missing>  <Missing>  #>  [37] <Missing>  (25%,50%]  <Missing>  (75%,100%] (50%,75%]  <Missing>  #>  [43] <Missing>  (25%,50%]  <Missing>  <Missing>  (25%,50%]  (50%,75%]  #>  [49] (25%,50%]  [0%,25%]   [0%,25%]   <Missing>  <Missing>  <Missing>  #>  [55] <Missing>  <Missing>  <Missing>  <Missing>  <Missing>  <Missing>  #>  [61] <Missing>  (75%,100%] (50%,75%]  (50%,75%]  <Missing>  (75%,100%] #>  [67] (50%,75%]  (75%,100%] (75%,100%] (75%,100%] (75%,100%] <Missing>  #>  [73] [0%,25%]   (25%,50%]  <Missing>  [0%,25%]   (50%,75%]  (50%,75%]  #>  [79] (50%,75%]  (75%,100%] (50%,75%]  [0%,25%]   <Missing>  <Missing>  #>  [85] (75%,100%] (75%,100%] (25%,50%]  (50%,75%]  (75%,100%] (50%,75%]  #>  [91] (75%,100%] (50%,75%]  (50%,75%]  [0%,25%]   [0%,25%]   (75%,100%] #>  [97] (50%,75%]  (75%,100%] (75%,100%] (75%,100%] (75%,100%] <Missing>  #> [103] <Missing>  (50%,75%]  (25%,50%]  (75%,100%] <Missing>  (25%,50%]  #> [109] (50%,75%]  (25%,50%]  (25%,50%]  (50%,75%]  (25%,50%]  [0%,25%]   #> [115] <Missing>  (50%,75%]  (75%,100%] (75%,100%] <Missing>  (75%,100%] #> [121] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [127] (75%,100%] (50%,75%]  (50%,75%]  (25%,50%]  (25%,50%]  (25%,50%]  #> [133] (25%,50%]  (50%,75%]  (25%,50%]  (25%,50%]  [0%,25%]   [0%,25%]   #> [139] (50%,75%]  [0%,25%]   [0%,25%]   (25%,50%]  [0%,25%]   [0%,25%]   #> [145] (25%,50%]  (50%,75%]  [0%,25%]   [0%,25%]   (25%,50%]  <Missing>  #> [151] [0%,25%]   [0%,25%]   (25%,50%]  #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%] < <Missing>"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Description function produces labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(abnormal)"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"abnormal (character) identifying abnormal range level(s) .var.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Abnormal category labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(\"LOW\") #> $not_abnormal #> [1] \"Not low\" #>  #> $abnormal #> [1] \"Low\" #>  #> $total #> [1] \"Total\" #>"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of Cumulative Count — d_count_cumulative","title":"Description of Cumulative Count — d_count_cumulative","text":"helper function describes analysis s_count_cumulative().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of Cumulative Count — d_count_cumulative","text":"","code":"d_count_cumulative(threshold, lower_tail, include_eq)"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of Cumulative Count — d_count_cumulative","text":"threshold (number) cutoff value threshold count values x. lower_tail (logical) whether count lower tail, default TRUE. include_eq (logical) whether include value equal threshold count, default TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of Cumulative Count — d_count_cumulative","text":"Labels s_count_cumulative().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description Function that Calculates Labels for s_count_missed_doses(). — d_count_missed_doses","text":"","code":"d_count_missed_doses(thresholds)"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description Function that Calculates Labels for s_count_missed_doses(). — d_count_missed_doses","text":"thresholds (vector count) number missed doses patients least .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description Function that Calculates Labels for s_count_missed_doses(). — d_count_missed_doses","text":"d_count_missed_doses() returns named character vector labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of Standard Oncology Response — d_onco_rsp_label","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"Describe oncology response standard way.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label(x)"},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"x (character) standard oncology code described.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"Response labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label(   c(\"CR\", \"PR\", \"SD\", \"NON CR/PD\", \"PD\", \"NE\", \"Missing\", \"<Missing>\", \"NE/Missing\") ) #>                           CR                           PR  #>       Complete Response (CR)        Partial Response (PR)  #>                           SD                    NON CR/PD  #>          Stable Disease (SD) Non-CR or Non-PD (NON CR/PD)  #>                           PD                           NE  #>     Progressive Disease (PD)           Not Evaluable (NE)  #>                      Missing                    <Missing>  #>                      Missing                    <Missing>  #>                   NE/Missing  #>       Missing or unevaluable  #> 9 Levels: Complete Response (CR) Partial Response (PR) ... <Missing>  # Adding some values not considered in d_onco_rsp_label  d_onco_rsp_label(   c(\"CR\", \"PR\", \"hello\", \"hi\") ) #>                     CR                     PR                  hello  #> Complete Response (CR)  Partial Response (PR)                  hello  #>                     hi  #>                     hi  #> Levels: Complete Response (CR) Partial Response (PR) hello hi"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_pkparam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate PK reference dataset — d_pkparam","text":"","code":"d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/reference/d_pkparam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate PK reference dataset — d_pkparam","text":"data.frame PK parameters","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_pkparam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate PK reference dataset — d_pkparam","text":"","code":"pk_reference_dataset <- d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the Proportion Summary — d_proportion","title":"Description of the Proportion Summary — d_proportion","text":"helper function describes analysis s_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the Proportion Summary — d_proportion","text":"","code":"d_proportion(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the Proportion Summary — d_proportion","text":"conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. long (flag) whether long short (default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the Proportion Summary — d_proportion","text":"String describing analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of Method Used for Proportion Comparison — d_proportion_diff","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"auxiliary function describes analysis s_proportion_diff.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"","code":"d_proportion_diff(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. long (logical) Whether long short (default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"string describing analysis.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"Internal function check variables included tabulate_rsp_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"","code":"d_rsp_subgroups_colvars(vars, conf_level = NULL, method = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"vars (character) variable names primary analysis variable iterated . conf_level (proportion) confidence level interval. method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"list variables tabulate labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"Internal function check variables included tabulate_survival_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"","code":"d_survival_subgroups_colvars(vars, conf_level, method, time_unit = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"vars (character) name statistics reported among: n_tot_events: Total number events per group. n_events: Number events per group. n_tot: Total number observations per group. n: Number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. conf_level (proportion) confidence level interval. method (character) p-value method testing hazard ratio = 1. time_unit (string) label unit median survival time. Default NULL skips displaying unit.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"list variables labels tabulate.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"least one n_tot n_tot_events must provided vars.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"auxiliary function describes analysis s_test_proportion_diff.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"","code":"d_test_proportion_diff(method)"},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"string describing test p-value derived.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of Days to Months — day2month","title":"Conversion of Days to Months — day2month","text":"Conversion Days Months","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of Days to Months — day2month","text":"","code":"day2month(x)"},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of Days to Months — day2month","text":"x (numeric) time days.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of Days to Months — day2month","text":"numeric vector time months.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of Days to Months — day2month","text":"","code":"x <- c(403, 248, 30, 86) day2month(x) #> [1] 13.2402464  8.1478439  0.9856263  2.8254620"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"function useful label grid grobs (also ggplot2, lattice plots) title, footnote, page numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"","code":"decorate_grob(   grob,   titles,   footnotes,   page = \"\",   width_titles = grid::unit(1, \"npc\") - grid::stringWidth(page),   width_footnotes = grid::unit(1, \"npc\") - grid::stringWidth(page),   border = TRUE,   margins = grid::unit(c(1, 0, 1, 0), \"lines\"),   padding = grid::unit(rep(1, 4), \"lines\"),   outer_margins = grid::unit(c(2, 1.5, 3, 1.5), \"cm\"),   gp_titles = grid::gpar(),   gp_footnotes = grid::gpar(fontsize = 8),   name = NULL,   gp = grid::gpar(),   vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"grob grid grob object, optionally NULL grob decoration shown. titles vector character strings. Vector elements separated newline strings wrapped according page width. footnotes vector character string. rules titles. page string page numeration, NULL page number displayed. width_titles unit object width_footnotes unit object border boolean, whether border drawn around plot . margins unit object length 4 padding unit object length 4 outer_margins unit object length 4 gp_titles gpar object gp_footnotes gpar object name character identifier grob.  Used find     grob display list /child another grob. gp \"gpar\" object, typically output     call function gpar.  basically     list graphical parameter settings. vp viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"grid grob (gTree).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"titles footnotes ragged, .e. title wrapped individually.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"","code":"library(grid)  titles <- c(   \"Edgar Anderson's Iris Data\",   paste(     \"This famous (Fisher's or Anderson's) iris data set gives the measurements\",     \"in centimeters of the variables sepal length and width and petal length\",     \"and width, respectively, for 50 flowers from each of 3 species of iris.\"   ) )  footnotes <- c(   \"The species are Iris setosa, versicolor, and virginica.\",   paste(     \"iris is a data frame with 150 cases (rows) and 5 variables (columns) named\",     \"Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.\"   ) )  ## empty plot grid.newpage()  grid.draw(   decorate_grob(     NULL,     titles = titles,     footnotes = footnotes,     page = \"Page 4 of 10\"   ) )   # grid p <- gTree(   children = gList(     rectGrob(),     xaxisGrob(),     yaxisGrob(),     textGrob(\"Sepal.Length\", y = unit(-4, \"lines\")),     textGrob(\"Petal.Length\", x = unit(-3.5, \"lines\"), rot = 90),     pointsGrob(iris$Sepal.Length, iris$Petal.Length, gp = gpar(col = iris$Species), pch = 16)   ),   vp = vpStack(plotViewport(), dataViewport(xData = iris$Sepal.Length, yData = iris$Petal.Length)) ) grid.newpage() grid.draw(p)   grid.newpage() grid.draw(   decorate_grob(     grob = p,     titles = titles,     footnotes = footnotes,     page = \"Page 6 of 129\"   ) )   ## with ggplot2 library(ggplot2)  p_gg <- ggplot2::ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) +   ggplot2::geom_point() p_gg  p <- ggplotGrob(p_gg) grid.newpage() grid.draw(   decorate_grob(     grob = p,     titles = titles,     footnotes = footnotes,     page = \"Page 6 of 129\"   ) )   ## with lattice library(lattice)  xyplot(Sepal.Length ~ Petal.Length, data = iris, col = iris$Species)  p <- grid.grab()  grid.newpage() grid.draw(   decorate_grob(     grob = p,     titles = titles,     footnotes = footnotes,     page = \"Page 6 of 129\"   ) )   # with gridExtra - no borders library(gridExtra) #>  #> Attaching package: ‘gridExtra’ #> The following object is masked from ‘package:dplyr’: #>  #>     combine grid.newpage() grid.draw(   decorate_grob(     tableGrob(       head(mtcars)     ),     titles = \"title\",     footnotes = \"footnote\",     border = FALSE   ) )"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Update Page Number — decorate_grob_factory","title":"Update Page Number — decorate_grob_factory","text":"Automatically updates page number.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update Page Number — decorate_grob_factory","text":"","code":"decorate_grob_factory(npages, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update Page Number — decorate_grob_factory","text":"npages number pages total ... passed decorate_grob()","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update Page Number — decorate_grob_factory","text":"Closure increments page number.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"Note uses decorate_grob_factory() function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"","code":"decorate_grob_set(grobs, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"grobs list grid grobs ... arguments passed decorate_grob().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"decorated grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"","code":"library(ggplot2) library(grid) g <- with(data = iris, {   list(     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Length, Sepal.Width, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Length, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Width, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Length, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Width, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Petal.Length, Petal.Width, col = Species)) +         ggplot2::geom_point()     )   ) }) lg <- decorate_grob_set(grobs = g, titles = \"Hello\\nOne\\nTwo\\nThree\", footnotes = \"\")  draw_grob(lg[[1]])  draw_grob(lg[[2]])  draw_grob(lg[[6]])"},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"Utility functions get valid statistic methods different method groups (.stats) associated formats (.formats) labels (.labels). utility used across tern, working principles can seen analyze_vars(). See notes understand experimental.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"","code":"get_stats(   method_groups = \"analyze_vars_numeric\",   stats_in = NULL,   add_pval = FALSE )  get_formats_from_stats(stats, formats_in = NULL)  get_labels_from_stats(stats, labels_in = NULL)  tern_default_formats  tern_default_labels  summary_formats(type = \"numeric\", include_pval = FALSE)  summary_labels(type = \"numeric\", include_pval = FALSE)  summary_custom(   type = \"numeric\",   include_pval = FALSE,   stats_custom = NULL,   formats_custom = NULL,   labels_custom = NULL,   indent_mods_custom = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"tern_default_formats list available formats, named relevant statistic. tern_default_labels character vector available labels, named relevant statistic.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"method_groups (character) indicates group statistical methods need defaults . character vector can used collect one group statistical methods. stats_in (character) desired stats picked selected method group. add_pval (flag) \"pval\" \"pval_counts\" (method_groups contains \"analyze_vars_counts\") added statistical methods? stats (character) statistical methods get defaults formats labels . formats_in (named vector)  inserted formats replace defaults. can character vector formatters::list_valid_format_labels() custom format function. labels_in (named vector)  inserted labels replace defaults. type (flag) going \"numeric\" \"counts\"? include_pval (flag) deprecated parameter. add_pval. stats_custom (named vector character) vector statistics include defaults. argument overrides include_pval custom value arguments settings statistics returned. formats_custom (named vector character) vector custom statistics formats use place defaults defined summary_formats(). Names subset statistics defined stats_custom (default statistics NULL). labels_custom (named vector character) vector custom statistics labels use place defaults defined summary_labels(). Names subset statistics defined stats_custom (default statistics NULL). indent_mods_custom (integer named vector integer) vector custom indentation modifiers statistics use instead default 0L statistics. Names subset statistics defined stats_custom (default statistics NULL). Alternatively, indentation modifier can applied statistics setting indent_mods_custom single integer value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"get_stats() returns character vector default statistical methods. get_formats_from_stats() returns named list formats, value formatters::list_valid_format_labels() custom function (e.g. formatting_functions). get_labels_from_stats() returns named character vector default labels (present otherwise NULL). summary_formats() returns named vector default statistic formats given data type. summary_labels returns named vector default statistic labels given data type. summary_custom returns list 4 named elements: stats, formats, labels, indent_mods.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"Current choices type counts numeric analyze_vars() affect get_stats().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"get_stats(): Get defaults statistical methods different groups methods. get_formats_from_stats(): Get formats vector statistical methods. present NULL returned. get_labels_from_stats(): Get labels vector statistical methods. tern_default_formats: Named list default formats tern. tern_default_labels: character vector contains default labels tern. summary_formats(): Quick function retrieve default formats summary statistics: analyze_vars() analyze_vars_in_cols() principally. summary_labels(): Quick function retrieve default labels summary statistics. Returns labels descriptive statistics understood rtables. Similar summary_formats summary_custom():  Function configure settings default custom summary statistics given data type. addition selecting custom subset statistics, user can also set custom formats, labels, indent modifiers statistics.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"defaults experimental use names functions retrieve default statistics. generalized groups methods according reasonable groupings. Formats tern rtables can functions take table cell value return string. well documented vignette(\"custom_appearance\", package = \"rtables\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"","code":"# analyze_vars is numeric num_stats <- get_stats(\"analyze_vars_numeric\") # also the default  # Other type cnt_stats <- get_stats(\"analyze_vars_counts\")  # Weirdly taking the pval from count_occurrences only_pval <- get_stats(\"count_occurrences\", add_pval = TRUE, stats_in = \"pval\")  # All count_occurrences all_cnt_occ <- get_stats(\"count_occurrences\")  # Multiple get_stats(c(\"count_occurrences\", \"analyze_vars_counts\")) #> [1] \"count\"                   \"count_fraction_fixed_dp\" #> [3] \"fraction\"                \"n\"                       #> [5] \"count_fraction\"          \"n_blq\"                    # Defaults formats get_formats_from_stats(num_stats) #> $n #> [1] \"xx.\" #>  #> $sum #> [1] \"xx.x\" #>  #> $mean #> [1] \"xx.x\" #>  #> $sd #> [1] \"xx.x\" #>  #> $se #> [1] \"xx.x\" #>  #> $mean_sd #> [1] \"xx.x (xx.x)\" #>  #> $mean_se #> [1] \"xx.x (xx.x)\" #>  #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_pval #> [1] \"xx.xx\" #>  #> $median #> [1] \"xx.x\" #>  #> $mad #> [1] \"xx.x\" #>  #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $quantiles #> [1] \"xx.x - xx.x\" #>  #> $iqr #> [1] \"xx.x\" #>  #> $range #> [1] \"xx.x - xx.x\" #>  #> $min #> [1] \"xx.x\" #>  #> $max #> [1] \"xx.x\" #>  #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #>  #> $cv #> [1] \"xx.x\" #>  #> $geom_mean #> [1] \"xx.x\" #>  #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $geom_cv #> [1] \"xx.x\" #>  get_formats_from_stats(cnt_stats) #> $n #> [1] \"xx.\" #>  #> $count #> [1] \"xx.\" #>  #> $count_fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else { #>     paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $n_blq #> [1] \"xx.\" #>  get_formats_from_stats(only_pval) #> $pval #> [1] \"x.xxxx | (<0.0001)\" #>  get_formats_from_stats(all_cnt_occ) #> $count #> [1] \"xx.\" #>  #> $count_fraction_fixed_dp #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else if (x[2] == 1) { #>     sprintf(\"%d (100%%)\", x[1]) #>   } else { #>     sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>   checkmate::assert_vector(x) #>   checkmate::assert_count(x[\"num\"]) #>   checkmate::assert_count(x[\"denom\"]) #>  #>   result <- if (x[\"num\"] == 0) { #>     paste0(x[\"num\"], \"/\", x[\"denom\"]) #>   } else { #>     paste0( #>       x[\"num\"], \"/\", x[\"denom\"], #>       \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #>     ) #>   } #>   return(result) #> } #> <environment: namespace:tern> #>   # Addition of customs get_formats_from_stats(all_cnt_occ, formats_in = c(\"fraction\" = c(\"xx\"))) #> $count #> [1] \"xx.\" #>  #> $count_fraction_fixed_dp #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else if (x[2] == 1) { #>     sprintf(\"%d (100%%)\", x[1]) #>   } else { #>     sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $fraction #> [1] \"xx\" #>  get_formats_from_stats(all_cnt_occ, formats_in = list(\"fraction\" = c(\"xx.xx\", \"xx\"))) #> $count #> [1] \"xx.\" #>  #> $count_fraction_fixed_dp #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else if (x[2] == 1) { #>     sprintf(\"%d (100%%)\", x[1]) #>   } else { #>     sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $fraction #> [1] \"xx.xx\" \"xx\"    #>   # Defaults labels get_labels_from_stats(num_stats) #>                             n                           sum  #>                           \"n\"                         \"Sum\"  #>                          mean                            sd  #>                        \"Mean\"                          \"SD\"  #>                            se                       mean_sd  #>                          \"SE\"                   \"Mean (SD)\"  #>                       mean_se                       mean_ci  #>                   \"Mean (SE)\"                 \"Mean 95% CI\"  #>                      mean_sei                      mean_sdi  #>               \"Mean -/+ 1xSE\"               \"Mean -/+ 1xSD\"  #>                     mean_pval                        median  #> \"Mean p-value (H0: mean = 0)\"                      \"Median\"  #>                           mad                     median_ci  #>   \"Median Absolute Deviation\"               \"Median 95% CI\"  #>                     quantiles                           iqr  #>             \"25% and 75%-ile\"                         \"IQR\"  #>                         range                           min  #>                   \"Min - Max\"                     \"Minimum\"  #>                           max                  median_range  #>                     \"Maximum\"          \"Median (Min - Max)\"  #>                            cv                     geom_mean  #>                      \"CV (%)\"              \"Geometric Mean\"  #>                  geom_mean_ci                       geom_cv  #>       \"Geometric Mean 95% CI\"         \"CV % Geometric Mean\"  get_labels_from_stats(cnt_stats) #>                n            count   count_fraction            n_blq  #>              \"n\"          \"count\" \"count_fraction\"          \"n_blq\"  get_labels_from_stats(only_pval) #>               pval  #> \"p-value (t-test)\"  get_labels_from_stats(all_cnt_occ) #>                   count count_fraction_fixed_dp                fraction  #>                 \"count\"                      \"\"                      \"\"   # Addition of customs get_labels_from_stats(all_cnt_occ, labels_in = c(\"fraction\" = \"Fraction\")) #>                   count count_fraction_fixed_dp                fraction  #>                 \"count\"                      \"\"              \"Fraction\"  get_labels_from_stats(all_cnt_occ, labels_in = list(\"fraction\" = c(\"Some more fractions\"))) #> $count #> [1] \"count\" #>  #> $count_fraction_fixed_dp #> [1] \"\" #>  #> $fraction #> [1] \"Some more fractions\" #>   summary_formats() #> $n #> [1] \"xx.\" #>  #> $sum #> [1] \"xx.x\" #>  #> $mean #> [1] \"xx.x\" #>  #> $sd #> [1] \"xx.x\" #>  #> $se #> [1] \"xx.x\" #>  #> $mean_sd #> [1] \"xx.x (xx.x)\" #>  #> $mean_se #> [1] \"xx.x (xx.x)\" #>  #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_pval #> [1] \"xx.xx\" #>  #> $median #> [1] \"xx.x\" #>  #> $mad #> [1] \"xx.x\" #>  #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $quantiles #> [1] \"xx.x - xx.x\" #>  #> $iqr #> [1] \"xx.x\" #>  #> $range #> [1] \"xx.x - xx.x\" #>  #> $min #> [1] \"xx.x\" #>  #> $max #> [1] \"xx.x\" #>  #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #>  #> $cv #> [1] \"xx.x\" #>  #> $geom_mean #> [1] \"xx.x\" #>  #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $geom_cv #> [1] \"xx.x\" #>  summary_formats(type = \"counts\", include_pval = TRUE) #> $n #> [1] \"xx.\" #>  #> $count #> [1] \"xx.\" #>  #> $count_fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else { #>     paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $n_blq #> [1] \"xx.\" #>  #> $pval_counts #> [1] \"x.xxxx | (<0.0001)\" #>   summary_labels() #>                             n                           sum  #>                           \"n\"                         \"Sum\"  #>                          mean                            sd  #>                        \"Mean\"                          \"SD\"  #>                            se                       mean_sd  #>                          \"SE\"                   \"Mean (SD)\"  #>                       mean_se                       mean_ci  #>                   \"Mean (SE)\"                 \"Mean 95% CI\"  #>                      mean_sei                      mean_sdi  #>               \"Mean -/+ 1xSE\"               \"Mean -/+ 1xSD\"  #>                     mean_pval                        median  #> \"Mean p-value (H0: mean = 0)\"                      \"Median\"  #>                           mad                     median_ci  #>   \"Median Absolute Deviation\"               \"Median 95% CI\"  #>                     quantiles                           iqr  #>             \"25% and 75%-ile\"                         \"IQR\"  #>                         range                           min  #>                   \"Min - Max\"                     \"Minimum\"  #>                           max                  median_range  #>                     \"Maximum\"          \"Median (Min - Max)\"  #>                            cv                     geom_mean  #>                      \"CV (%)\"              \"Geometric Mean\"  #>                  geom_mean_ci                       geom_cv  #>       \"Geometric Mean 95% CI\"         \"CV % Geometric Mean\"  summary_labels(type = \"counts\", include_pval = TRUE) #>                            n                        count  #>                          \"n\"                      \"count\"  #>               count_fraction                        n_blq  #>             \"count_fraction\"                      \"n_blq\"  #>                  pval_counts  #> \"p-value (chi-squared test)\"   summary_custom() #> Warning: `summary_custom()` was deprecated in tern 0.9.0.9001. #> ℹ Please use `get_stats`, `get_formats_from_stats`, and `get_labels_from_stats` #>   directly instead. #> $stats #>  [1] \"n\"            \"sum\"          \"mean\"         \"sd\"           \"se\"           #>  [6] \"mean_sd\"      \"mean_se\"      \"mean_ci\"      \"mean_sei\"     \"mean_sdi\"     #> [11] \"mean_pval\"    \"median\"       \"mad\"          \"median_ci\"    \"quantiles\"    #> [16] \"iqr\"          \"range\"        \"min\"          \"max\"          \"median_range\" #> [21] \"cv\"           \"geom_mean\"    \"geom_mean_ci\" \"geom_cv\"      #>  #> $formats #> $formats$n #> [1] \"xx.\" #>  #> $formats$sum #> [1] \"xx.x\" #>  #> $formats$mean #> [1] \"xx.x\" #>  #> $formats$sd #> [1] \"xx.x\" #>  #> $formats$se #> [1] \"xx.x\" #>  #> $formats$mean_sd #> [1] \"xx.x (xx.x)\" #>  #> $formats$mean_se #> [1] \"xx.x (xx.x)\" #>  #> $formats$mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$mean_sei #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$mean_sdi #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$mean_pval #> [1] \"xx.xx\" #>  #> $formats$median #> [1] \"xx.x\" #>  #> $formats$mad #> [1] \"xx.x\" #>  #> $formats$median_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$quantiles #> [1] \"xx.x - xx.x\" #>  #> $formats$iqr #> [1] \"xx.x\" #>  #> $formats$range #> [1] \"xx.x - xx.x\" #>  #> $formats$min #> [1] \"xx.x\" #>  #> $formats$max #> [1] \"xx.x\" #>  #> $formats$median_range #> [1] \"xx.x (xx.x - xx.x)\" #>  #> $formats$cv #> [1] \"xx.x\" #>  #> $formats$geom_mean #> [1] \"xx.x\" #>  #> $formats$geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$geom_cv #> [1] \"xx.x\" #>  #>  #> $labels #>                             n                           sum  #>                           \"n\"                         \"Sum\"  #>                          mean                            sd  #>                        \"Mean\"                          \"SD\"  #>                            se                       mean_sd  #>                          \"SE\"                   \"Mean (SD)\"  #>                       mean_se                       mean_ci  #>                   \"Mean (SE)\"                 \"Mean 95% CI\"  #>                      mean_sei                      mean_sdi  #>               \"Mean -/+ 1xSE\"               \"Mean -/+ 1xSD\"  #>                     mean_pval                        median  #> \"Mean p-value (H0: mean = 0)\"                      \"Median\"  #>                           mad                     median_ci  #>   \"Median Absolute Deviation\"               \"Median 95% CI\"  #>                     quantiles                           iqr  #>             \"25% and 75%-ile\"                         \"IQR\"  #>                         range                           min  #>                   \"Min - Max\"                     \"Minimum\"  #>                           max                  median_range  #>                     \"Maximum\"          \"Median (Min - Max)\"  #>                            cv                     geom_mean  #>                      \"CV (%)\"              \"Geometric Mean\"  #>                  geom_mean_ci                       geom_cv  #>       \"Geometric Mean 95% CI\"         \"CV % Geometric Mean\"  #>  #> $indent_mods #>            n          sum         mean           sd           se      mean_sd  #>            0            0            0            0            0            0  #>      mean_se      mean_ci     mean_sei     mean_sdi    mean_pval       median  #>            0            0            0            0            0            0  #>          mad    median_ci    quantiles          iqr        range          min  #>            0            0            0            0            0            0  #>          max median_range           cv    geom_mean geom_mean_ci      geom_cv  #>            0            0            0            0            0            0  #>  summary_custom(type = \"counts\", include_pval = TRUE) #> $stats #> [1] \"n\"              \"count\"          \"count_fraction\" \"n_blq\"          #> [5] \"pval_counts\"    #>  #> $formats #> $formats$n #> [1] \"xx.\" #>  #> $formats$count #> [1] \"xx.\" #>  #> $formats$count_fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else { #>     paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $formats$n_blq #> [1] \"xx.\" #>  #> $formats$pval_counts #> [1] \"x.xxxx | (<0.0001)\" #>  #>  #> $labels #>                            n                        count  #>                          \"n\"                      \"count\"  #>               count_fraction                        n_blq  #>             \"count_fraction\"                      \"n_blq\"  #>                  pval_counts  #> \"p-value (chi-squared test)\"  #>  #> $indent_mods #>              n          count count_fraction          n_blq    pval_counts  #>              0              0              0              0              0  #>  summary_custom(   include_pval = TRUE, stats_custom = c(\"n\", \"mean\", \"sd\", \"pval\"),   labels_custom = c(sd = \"Std. Dev.\"), indent_mods_custom = 3L ) #> $stats #> [1] \"n\"    \"mean\" \"sd\"   \"pval\" #>  #> $formats #> $formats$n #> [1] \"xx.\" #>  #> $formats$mean #> [1] \"xx.x\" #>  #> $formats$sd #> [1] \"xx.x\" #>  #> $formats$pval #> [1] \"x.xxxx | (<0.0001)\" #>  #>  #> $labels #>                  n               mean                 sd               pval  #>                \"n\"             \"Mean\"        \"Std. Dev.\" \"p-value (t-test)\"  #>  #> $indent_mods #>    n mean   sd pval  #>    3    3    3    3  #>"},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Intervals for a Difference of Binomials — desctools_binom","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"Several confidence intervals difference proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"","code":"desctools_binom(   x1,   n1,   x2,   n2,   conf.level = 0.95,   sides = c(\"two.sided\", \"left\", \"right\"),   method = c(\"ac\", \"wald\", \"waldcc\", \"score\", \"scorecc\", \"mn\", \"mee\", \"blj\", \"ha\", \"hal\",     \"jp\") )  desctools_binomci(   x,   n,   conf.level = 0.95,   sides = c(\"two.sided\", \"left\", \"right\"),   method = c(\"wilson\", \"wald\", \"waldcc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\",     \"wilsoncc\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\",     \"pratt\", \"midp\", \"lik\", \"blaker\"),   rand = 123,   tol = 1e-05 )"},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"conf.level (proportion) confidence level, defaults 0.95. sides (character) side confidence interval compute. Must one \"two-sided\" (default), \"left\", \"right\". method (character) method use. Can one : \"wald\", \"wilson\", \"wilsoncc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\", \"pratt\", \"midp\", \"lik\", \"blaker\". x (count) number successes n (count) number trials","code":""},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"matrix 3 values: est: estimate proportion difference. lwr.ci: estimate lower end confidence interval. upr.ci: estimate upper end confidence interval. matrix 3 columns containing: est: estimate proportion difference. lwr.ci: lower end confidence interval. upr.ci: upper end confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"desctools_binom(): Several confidence intervals difference proportions. desctools_binomci(): Compute confidence intervals binomial proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"helper function encode missing entries across groups categorical variables data frame.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"","code":"df_explicit_na(   data,   omit_columns = NULL,   char_as_factor = TRUE,   logical_as_factor = FALSE,   na_level = \"<Missing>\" )"},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"data (data.frame) data set. omit_columns (character) names variables data modified function. char_as_factor (flag) whether convert character variables data factors. logical_as_factor (flag) whether convert logical variables data factors. na_level (string) used replace NA empty values inside non-omit_columns columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"data.frame chosen modifications applied.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"Missing entries NA empty strings replaced specified value. factor variables include missing values, missing value inserted last level. Similarly, case character logical variables converted factors char_as_factor logical_as_factor options, missing values set last level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"","code":"my_data <- data.frame(   u = c(TRUE, FALSE, NA, TRUE),   v = factor(c(\"A\", NA, NA, NA), levels = c(\"Z\", \"A\")),   w = c(\"A\", \"B\", NA, \"C\"),   x = c(\"D\", \"E\", \"F\", NA),   y = c(\"G\", \"H\", \"I\", \"\"),   z = c(1, 2, 3, 4),   stringsAsFactors = FALSE )  # Example 1 # Encode missing values in all character or factor columns. df_explicit_na(my_data) #>       u         v         w         x         y z #> 1  TRUE         A         A         D         G 1 #> 2 FALSE <Missing>         B         E         H 2 #> 3    NA <Missing> <Missing>         F         I 3 #> 4  TRUE <Missing>         C <Missing> <Missing> 4 # Also convert logical columns to factor columns. df_explicit_na(my_data, logical_as_factor = TRUE) #>           u         v         w         x         y z #> 1      TRUE         A         A         D         G 1 #> 2     FALSE <Missing>         B         E         H 2 #> 3 <Missing> <Missing> <Missing>         F         I 3 #> 4      TRUE <Missing>         C <Missing> <Missing> 4 # Encode missing values in a subset of columns. df_explicit_na(my_data, omit_columns = c(\"x\", \"y\")) #>       u         v         w    x y z #> 1  TRUE         A         A    D G 1 #> 2 FALSE <Missing>         B    E H 2 #> 3    NA <Missing> <Missing>    F I 3 #> 4  TRUE <Missing>         C <NA>   4  # Example 2 # Here we purposefully convert all `M` values to `NA` in the `SEX` variable. # After running `df_explicit_na` the `NA` values are encoded as `<Missing>` but they are not # included when generating `rtables`. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl)  # If you want the `Na` values to be displayed in the table use the `na_level` argument. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\")  # Example 3 # Numeric variables that have missing values are not altered. This means that any `NA` value in # a numeric variable will not be included in the summary statistics, nor will they be included # in the denominator value for calculating the percent values. adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl)"},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw grob — draw_grob","title":"Draw grob — draw_grob","text":"Draw grob device page.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw grob — draw_grob","text":"","code":"draw_grob(grob, newpage = TRUE, vp = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw grob — draw_grob","text":"grob grid object newpage draw new page vp viewport() object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw grob — draw_grob","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw grob — draw_grob","text":"","code":"library(dplyr) library(grid)  # \\donttest{ rect <- rectGrob(width = grid::unit(0.5, \"npc\"), height = grid::unit(0.5, \"npc\")) rect %>% draw_grob(vp = grid::viewport(angle = 45))   num <- lapply(1:10, textGrob) num %>%   arrange_grobs(grobs = .) %>%   draw_grob() showViewport()  # }"},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Return an empty numeric if all elements are NA. — empty_vector_if_na","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"Return empty numeric elements NA.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"empty_vector_if_na(x)"},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"x (numeric) vector.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"empty numeric elements x NA, otherwise x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"x <- c(NA, NA, NA) # Internal function - empty_vector_if_na"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Hazard Ratio Estimation in Interactions — estimate_coef","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"function estimates hazard ratios arms interaction variable given specific values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"","code":"estimate_coef(   variable,   given,   lvl_var,   lvl_given,   coef,   mmat,   vcov,   conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"variable, given Names two variable interaction. seek estimation levels variable given levels given. lvl_var, lvl_given corresponding levels given levels. coef Numeric estimated coefficients. mmat name numeric filled 0 used template obtain design matrix. vcov Variance-covariance matrix underlying model. conf_level Single numeric confidence level estimate intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"list matrix (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female). model abbreviated: y ~ Arm + Sex + Arm x Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) Given want estimation Hazard Ratio arm C/sex M, estimation given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5), therefore interaction coefficient given b2 + b5 standard error obtained $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ confidence level 0.95.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"","code":"library(dplyr) library(survival)  ADSL <- tern_ex_adsl %>%   filter(SEX %in% c(\"F\", \"M\"))  adtte <- tern_ex_adtte %>% filter(PARAMCD == \"PFS\") adtte$ARMCD <- droplevels(adtte$ARMCD) adtte$SEX <- droplevels(adtte$SEX)  mod <- coxph(   formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2,   data = adtte )  mmat <- stats::model.matrix(mod)[1, ] mmat[!mmat == 0] <- 0"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"Estimate proportion along confidence interval proportion regarding level factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"","code":"s_length_proportion(x, .N_col, ...)  a_length_proportion(x, .N_col, ...)  estimate_multinomial_response(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = var,   .stats = \"prop_ci\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"x (numeric) vector numbers want analyze. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. ... additional arguments lower level functions. lyt (layout) input layout analyses added . var (string) single variable name passed rtables requested statistics function. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"s_length_proportion() returns statistics s_proportion(). a_length_proportion() returns corresponding list formatted rtables::CellValue(). estimate_multinomial_response() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_length_proportion() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"s_length_proportion(): Statistics function feeds length x number successes, .N_col total number successes failures s_proportion(). a_length_proportion(): Formatted analysis function used afun estimate_multinomial_response(). estimate_multinomial_response(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"","code":"s_length_proportion(rep(\"CR\", 10), .N_col = 100) #> $n_prop #> [1] 10.0  0.1 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #> [1]  3.620108 16.379892 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #>  s_length_proportion(factor(character(0)), .N_col = 100) #> $n_prop #> [1] 0 0 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #> [1] 0.0 0.5 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #>   a_length_proportion(rep(\"CR\", 10), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod                      row_label #> 1   n_prop     10 (10.0%)          0                     Responders #> 2  prop_ci  (3.62, 16.38)          0 95% CI (Wald, with correction) a_length_proportion(factor(character(0)), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod                      row_label #> 1   n_prop       0 (0.0%)          0                     Responders #> 2  prop_ci   (0.00, 0.50)          0 95% CI (Wald, with correction)  library(dplyr)  # Use of the layout creating function. dta_test <- data.frame(   USUBJID = paste0(\"S\", 1:12),   ARM     = factor(rep(LETTERS[1:3], each = 4)),   AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0)) ) %>% mutate(   AVALC = factor(AVAL,     levels = c(0, 1),     labels = c(\"Complete Response (CR)\", \"Partial Response (PR)\")   ) )  lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   estimate_multinomial_response(var = \"AVALC\")  tbl <- build_table(lyt, dta_test)  html <- as_html(tbl) html #> <div class=\"rtables-all-parts-block rtables-container\"> #>   <table class=\"table table-condensed table-hover\"> #>     <tr> #>       <th style=\"white-space:pre;\"><\/th> #>       <th class=\"text-center\">A<\/th> #>       <th class=\"text-center\">B<\/th> #>       <th class=\"text-center\">C<\/th> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\">Complete Response (CR)<\/td> #>       <td class=\"text-center\">0 (0.0%)<\/td> #>       <td class=\"text-center\">2 (50.0%)<\/td> #>       <td class=\"text-center\">4 (100.0%)<\/td> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\" style=\"padding-left: 3ch\">95% CI (Wald, with correction)<\/td> #>       <td class=\"text-center\">(0.00, 12.50)<\/td> #>       <td class=\"text-center\">(0.00, 100.00)<\/td> #>       <td class=\"text-center\">(87.50, 100.00)<\/td> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\">Partial Response (PR)<\/td> #>       <td class=\"text-center\">4 (100.0%)<\/td> #>       <td class=\"text-center\">2 (50.0%)<\/td> #>       <td class=\"text-center\">0 (0.0%)<\/td> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\" style=\"padding-left: 3ch\">95% CI (Wald, with correction)<\/td> #>       <td class=\"text-center\">(87.50, 100.00)<\/td> #>       <td class=\"text-center\">(0.00, 100.00)<\/td> #>       <td class=\"text-center\">(0.00, 12.50)<\/td> #>     <\/tr> #>     <caption style=\"caption-side:top;\"><div class=\"rtables-titles-block rtables-container\"> #>         <div class=\"rtables-main-titles-block rtables-container\"> #>           <p class=\"rtables-main-title\"><\/p> #>         <\/div> #>         <div class=\"rtables-subtitles-block rtables-container\"><\/div> #>       <\/div> #>     <\/caption> #>   <\/table> #>   <div class=\"rtables-footers-block rtables-container\"><\/div> #> <\/div> # \\donttest{ Viewer(html) # }"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimation of Proportions — estimate_proportions","title":"Estimation of Proportions — estimate_proportions","text":"Estimate proportion responders within studied population.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimation of Proportions — estimate_proportions","text":"","code":"s_proportion(   df,   .var,   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\",     \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"),   weights = NULL,   max_iterations = 50,   variables = list(strata = NULL),   long = FALSE )  a_proportion(   df,   .var,   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\",     \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"),   weights = NULL,   max_iterations = 50,   variables = list(strata = NULL),   long = FALSE )  estimate_proportion(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimation of Proportions — estimate_proportions","text":"df (logical data.frame) logical vector used, indicates whether subject responder . TRUE represents successful outcome. data.frame provided, also strata variable names must provided variables list element strata strings. case data.frame, logical vector responses must indicated variable name .var. .var (string) single variable name passed rtables requested statistics function. conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010)  minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights. variables (named list string) list additional analysis variables. long (flag) long description required. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments ultimately conveyed s_proportion(). show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimation of Proportions — estimate_proportions","text":"s_proportion() returns statistics n_prop (n proportion) prop_ci (proportion CI) given variable. a_proportion() returns corresponding list formatted rtables::CellValue(). estimate_proportion() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Estimation of Proportions — estimate_proportions","text":"s_proportion(): Statistics function estimating proportion along confidence interval. a_proportion(): Formatted analysis function used afun estimate_proportion(). estimate_proportion(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimation of Proportions — estimate_proportions","text":"","code":"# Case with only logical vector. rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0) s_proportion(rsp_v) #> $n_prop #> [1] 4.0 0.5 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #> [1]  9.102404 90.897596 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #>   # Example for Stratified Wilson CI nex <- 100 # Number of example rows dta <- data.frame(   \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE),   \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE),   \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE),   stringsAsFactors = TRUE )  s_proportion(   df = dta,   .var = \"rsp\",   variables = list(strata = c(\"f1\", \"f2\")),   conf_level = 0.90,   method = \"strat_wilson\" ) #> $n_prop #> [1] 49.00  0.49 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #>    lower    upper  #> 40.80675 56.65017  #> attr(,\"label\") #> [1] \"90% CI (Stratified Wilson, without correction)\" #>   dta_test <- data.frame(   USUBJID = paste0(\"S\", 1:12),   ARM     = rep(LETTERS[1:3], each = 4),   AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0)) )  basic_table() %>%   split_cols_by(\"ARM\") %>%   estimate_proportion(vars = \"AVAL\") %>%   build_table(df = dta_test) #>                                        A              B              C      #> ——————————————————————————————————————————————————————————————————————————— #> Responders                        4 (100.0%)      2 (50.0%)      0 (0.0%)   #> 95% CI (Wald, with correction)   (87.5, 100.0)   (0.0, 100.0)   (0.0, 12.5)"},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated CDISC Data for Examples — ex_data","title":"Simulated CDISC Data for Examples — ex_data","text":"Simulated CDISC Data Examples","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated CDISC Data for Examples — ex_data","text":"","code":"tern_ex_adsl  tern_ex_adae  tern_ex_adlb  tern_ex_adpp  tern_ex_adrs  tern_ex_adtte"},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated CDISC Data for Examples — ex_data","text":"rds (data.frame) object class tbl_df (inherits tbl, data.frame) 200 rows 21 columns. object class tbl_df (inherits tbl, data.frame) 541 rows 42 columns. object class tbl_df (inherits tbl, data.frame) 4200 rows 50 columns. object class tbl_df (inherits tbl, data.frame) 522 rows 25 columns. object class tbl_df (inherits tbl, data.frame) 1600 rows 29 columns. object class tbl_df (inherits tbl, data.frame) 1000 rows 28 columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Simulated CDISC Data for Examples — ex_data","text":"tern_ex_adsl: ADSL data tern_ex_adae: ADAE data tern_ex_adlb: ADLB data tern_ex_adpp: ADPP data tern_ex_adrs: ADRS data tern_ex_adtte: ADTTE data","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing Data — explicit_na","title":"Missing Data — explicit_na","text":"Substitute missing data string factor level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing Data — explicit_na","text":"","code":"explicit_na(x, label = \"<Missing>\")"},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing Data — explicit_na","text":"x (factor character vector) values missing values substituted. label (character) string missing data replaced .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing Data — explicit_na","text":"x NA values substituted label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing Data — explicit_na","text":"","code":"explicit_na(c(NA, \"a\", \"b\")) #> [1] \"<Missing>\" \"a\"         \"b\"         is.na(explicit_na(c(NA, \"a\", \"b\"))) #> [1] FALSE FALSE FALSE  explicit_na(factor(c(NA, \"a\", \"b\"))) #> [1] <Missing> a         b         #> Levels: a b <Missing> is.na(explicit_na(factor(c(NA, \"a\", \"b\")))) #> [1] FALSE FALSE FALSE  explicit_na(sas_na(c(\"a\", \"\"))) #> [1] \"a\"         \"<Missing>\""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Elements by Name — extract_by_name","title":"Extract Elements by Name — extract_by_name","text":"utility function extracts elements vector x names. Differences standard [ function :","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Elements by Name — extract_by_name","text":"","code":"extract_by_name(x, names)"},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Elements by Name — extract_by_name","text":"x (named vector) extract named elements . names (character) vector names extract.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Elements by Name — extract_by_name","text":"NULL x NULL, otherwise extracted elements x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Elements by Name — extract_by_name","text":"x NULL, still always NULL returned (base function). x NULL, intersection names made names elements returned. , names appear x returned NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"Prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates, subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"","code":"extract_rsp_biomarkers(   variables,   data,   groups_lists = list(),   control = control_logistic(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (named list) controls response definition confidence level produced control_logistic(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_rsp, prop, , lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"can also specify continuous variable rsp use response_definition control convert internally logical variable reflecting binary response.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   mutate(rsp = AVALC == \"CR\")  # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`, # in logistic regression models with one covariate `RACE`. The subgroups # are defined by the levels of `BMRKR2`. df <- extract_rsp_biomarkers(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adrs_f ) df #>   biomarker              biomarker_label n_tot n_rsp      prop        or #> 1    BMRKR1 Continuous Level Biomarker 1   200   164 0.8200000 0.9755036 #> 2       AGE                          Age   200   164 0.8200000 0.9952416 #> 3    BMRKR1 Continuous Level Biomarker 1    70    53 0.7571429 1.1524547 #> 4       AGE                          Age    70    53 0.7571429 0.9261012 #> 5    BMRKR1 Continuous Level Biomarker 1    68    58 0.8529412 0.8773122 #> 6       AGE                          Age    68    58 0.8529412 0.9867104 #> 7    BMRKR1 Continuous Level Biomarker 1    62    53 0.8548387 0.8792921 #> 8       AGE                          Age    62    53 0.8548387 1.0630262 #>         lcl      ucl conf_level      pval     pval_label     subgroup    var #> 1 0.8804862 1.080775       0.95 0.6352602 p-value (Wald) All Patients    ALL #> 2 0.9462617 1.046757       0.95 0.8530389 p-value (Wald) All Patients    ALL #> 3 0.9462127 1.403650       0.95 0.1584187 p-value (Wald)          LOW BMRKR2 #> 4 0.8487519 1.010500       0.95 0.0844837 p-value (Wald)          LOW BMRKR2 #> 5 0.7277189 1.057657       0.95 0.1699778 p-value (Wald)       MEDIUM BMRKR2 #> 6 0.8798911 1.106498       0.95 0.8189816 p-value (Wald)       MEDIUM BMRKR2 #> 7 0.7189748 1.075357       0.95 0.2103709 p-value (Wald)         HIGH BMRKR2 #> 8 0.9595973 1.177603       0.95 0.2418840 p-value (Wald)         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis  # Here we group the levels of `BMRKR2` manually, and we add a stratification # variable `STRATA1`. We also here use a continuous variable `EOSDY` # which is then binarized internally (response is defined as this variable # being larger than 500). df_grouped <- extract_rsp_biomarkers(   variables = list(     rsp = \"EOSDY\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     subgroups = \"BMRKR2\",     strat = \"STRATA1\"   ),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ),   control = control_logistic(     response_definition = \"I(response > 500)\"   ) ) #> Warning: Ran out of iterations and did not converge #> Warning: Ran out of iterations and did not converge #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: Ran out of iterations and did not converge df_grouped #>   biomarker              biomarker_label n_tot n_rsp prop or lcl ucl conf_level #> 1    BMRKR1 Continuous Level Biomarker 1   200   200    1  1   0 Inf       0.95 #> 2       AGE                          Age   200   200    1  1   0 Inf       0.95 #> 3    BMRKR1 Continuous Level Biomarker 1    70    70    1  1 NaN NaN       0.95 #> 4       AGE                          Age    70    70    1  1 NaN NaN       0.95 #> 5    BMRKR1 Continuous Level Biomarker 1   138   138    1  1 NaN NaN       0.95 #> 6       AGE                          Age   138   138    1  1 NaN NaN       0.95 #> 7    BMRKR1 Continuous Level Biomarker 1   200   200    1  1   0 Inf       0.95 #> 8       AGE                          Age   200   200    1  1   0 Inf       0.95 #>   pval     pval_label        subgroup    var                    var_label #> 1    1 p-value (Wald)    All Patients    ALL                 All Patients #> 2    1 p-value (Wald)    All Patients    ALL                 All Patients #> 3  NaN p-value (Wald)             low BMRKR2 Continuous Level Biomarker 2 #> 4  NaN p-value (Wald)             low BMRKR2 Continuous Level Biomarker 2 #> 5  NaN p-value (Wald)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 6  NaN p-value (Wald)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 7    1 p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> 8    1 p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #>   row_type #> 1  content #> 2  content #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis #> 7 analysis #> 8 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"Prepares response rates odds ratios population subgroups data frames. Simple wrapper h_odds_ratio_subgroups_df() h_proportion_subgroups_df(). Result list two data.frames: prop . variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"","code":"extract_rsp_subgroups(   variables,   data,   groups_lists = list(),   conf_level = 0.95,   method = NULL,   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. conf_level (proportion) confidence level interval. method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"named list two elements: prop: data.frame containing columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. : data.frame containing columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  # Unstratified analysis. df <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) df #> $prop #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142  2.714000 1.1804488  6.239827       0.95 All Patients    ALL #> 2        78 10.800000 2.2669576 51.452218       0.95            F    SEX #> 3        64  0.920000 0.2966470  2.853223       0.95            M    SEX #> 4        50  3.553846 1.0047370 12.570277       0.95          LOW BMRKR2 #> 5        49  2.705882 0.5911718 12.385232       0.95       MEDIUM BMRKR2 #> 6        43  2.250000 0.3970298 12.750933       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>   # Stratified analysis. df_strat <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strat = \"STRATA1\"),   data = adrs_f ) df_strat #> $prop #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142 2.6343899 1.1537821  6.015009       0.95 All Patients    ALL #> 2        78 9.5946605 2.0379337 45.171985       0.95            F    SEX #> 3        64 0.8947158 0.2936803  2.725809       0.95            M    SEX #> 4        50 3.5976656 1.0101319 12.813374       0.95          LOW BMRKR2 #> 5        49 2.6242168 0.5162572 13.339308       0.95       MEDIUM BMRKR2 #> 6        43 2.2816865 0.4116391 12.647228       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>   # Grouping of the BMRKR2 levels. df_grouped <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) df_grouped #> $prop #>           arm  n n_rsp      prop        subgroup    var #> 1  B: Placebo 73    50 0.6849315    All Patients    ALL #> 2   A: Drug X 69    59 0.8550725    All Patients    ALL #> 3  B: Placebo 40    25 0.6250000               F    SEX #> 4   A: Drug X 38    36 0.9473684               F    SEX #> 5  B: Placebo 33    25 0.7575758               M    SEX #> 6   A: Drug X 31    23 0.7419355               M    SEX #> 7  B: Placebo 24    13 0.5416667             low BMRKR2 #> 8   A: Drug X 26    21 0.8076923             low BMRKR2 #> 9  B: Placebo 47    30 0.6382979      low/medium BMRKR2 #> 10  A: Drug X 52    44 0.8461538      low/medium BMRKR2 #> 11 B: Placebo 73    50 0.6849315 low/medium/high BMRKR2 #> 12  A: Drug X 69    59 0.8550725 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or      lcl       ucl conf_level        subgroup    var #> 1       142  2.714000 1.180449  6.239827       0.95    All Patients    ALL #> 2        78 10.800000 2.266958 51.452218       0.95               F    SEX #> 3        64  0.920000 0.296647  2.853223       0.95               M    SEX #> 4        50  3.553846 1.004737 12.570277       0.95             low BMRKR2 #> 5        99  3.116667 1.193409  8.139385       0.95      low/medium BMRKR2 #> 6       142  2.714000 1.180449  6.239827       0.95 low/medium/high BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"Prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables), optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"","code":"extract_survival_biomarkers(   variables,   data,   groups_lists = list(),   control = control_coxreg(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) list parameters returned helper function control_coxreg(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_tot_events, median, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"","code":"# Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`, # in multiple regression models containing one covariate `RACE`, # as well as one stratification variable `STRATA1`. The subgroups # are defined by the levels of `BMRKR2`.  library(dplyr)  adtte <- tern_ex_adtte adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_biomarkers(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     strata = \"STRATA1\",     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adtte_f ) df #>   biomarker              biomarker_label n_tot n_tot_events   median        hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 2       AGE                          Age   200          141 753.5176 1.0106406 #> 3    BMRKR1 Continuous Level Biomarker 1    70           52 735.4722 0.9905065 #> 4       AGE                          Age    70           52 735.4722 1.0106279 #> 5    BMRKR1 Continuous Level Biomarker 1    68           42 858.9952 0.9623210 #> 6       AGE                          Age    68           42 858.9952 1.0360765 #> 7    BMRKR1 Continuous Level Biomarker 1    62           47 727.8043 1.0770946 #> 8       AGE                          Age    62           47 727.8043 1.0009890 #>         lcl      ucl conf_level      pval     pval_label     subgroup    var #> 1 0.9538978 1.050625       0.95 0.9646086 p-value (Wald) All Patients    ALL #> 2 0.9871004 1.034742       0.95 0.3787395 p-value (Wald) All Patients    ALL #> 3 0.9142220 1.073156       0.95 0.8155443 p-value (Wald)          LOW BMRKR2 #> 4 0.9621192 1.061582       0.95 0.6735773 p-value (Wald)          LOW BMRKR2 #> 5 0.8708694 1.063376       0.95 0.4509368 p-value (Wald)       MEDIUM BMRKR2 #> 6 0.9727439 1.103532       0.95 0.2707796 p-value (Wald)       MEDIUM BMRKR2 #> 7 0.9756250 1.189118       0.95 0.1412524 p-value (Wald)         HIGH BMRKR2 #> 8 0.9678535 1.035259       0.95 0.9541048 p-value (Wald)         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis  # Here we group the levels of `BMRKR2` manually. df_grouped <- extract_survival_biomarkers(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     strata = \"STRATA1\",     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) df_grouped #>   biomarker              biomarker_label n_tot n_tot_events   median        hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 2       AGE                          Age   200          141 753.5176 1.0106406 #> 3    BMRKR1 Continuous Level Biomarker 1    70           52 735.4722 0.9905065 #> 4       AGE                          Age    70           52 735.4722 1.0106279 #> 5    BMRKR1 Continuous Level Biomarker 1   138           94 777.8929 0.9801709 #> 6       AGE                          Age   138           94 777.8929 1.0236283 #> 7    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 8       AGE                          Age   200          141 753.5176 1.0106406 #>         lcl      ucl conf_level      pval     pval_label        subgroup    var #> 1 0.9538978 1.050625       0.95 0.9646086 p-value (Wald)    All Patients    ALL #> 2 0.9871004 1.034742       0.95 0.3787395 p-value (Wald)    All Patients    ALL #> 3 0.9142220 1.073156       0.95 0.8155443 p-value (Wald)             low BMRKR2 #> 4 0.9621192 1.061582       0.95 0.6735773 p-value (Wald)             low BMRKR2 #> 5 0.9235465 1.040267       0.95 0.5094582 p-value (Wald)      low/medium BMRKR2 #> 6 0.9859367 1.062761       0.95 0.2224475 p-value (Wald)      low/medium BMRKR2 #> 7 0.9538978 1.050625       0.95 0.9646086 p-value (Wald) low/medium/high BMRKR2 #> 8 0.9871004 1.034742       0.95 0.3787395 p-value (Wald) low/medium/high BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"Prepares estimates median survival times treatment hazard ratios population subgroups data frames. Simple wrapper h_survtime_subgroups_df() h_coxph_subgroups_df(). Result list two data.frames: survtime hr. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"","code":"extract_survival_subgroups(   variables,   data,   groups_lists = list(),   control = control_coxph(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"named list two elements: survtime: data.frame containing columns arm, n, n_events, median, subgroup, var, var_label, row_type. hr: data.frame containing columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(   \"ARM\" = adtte_labels[[\"ARM\"]],   \"SEX\" = adtte_labels[[\"SEX\"]],   \"AVALU\" = adtte_labels[[\"AVALU\"]],   \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) df #> $survtime #>           arm  n n_events    median     subgroup    var #> 1  B: Placebo 73       57  727.8043 All Patients    ALL #> 2   A: Drug X 69       44  974.6402 All Patients    ALL #> 3  B: Placebo 40       31  599.1772            F    SEX #> 4   A: Drug X 38       24 1016.2982            F    SEX #> 5  B: Placebo 33       26  888.4916            M    SEX #> 6   A: Drug X 31       20  974.6402            M    SEX #> 7  B: Placebo 24       21  735.4722          LOW BMRKR2 #> 8   A: Drug X 26       15  974.6402          LOW BMRKR2 #> 9  B: Placebo 23       14  731.8352       MEDIUM BMRKR2 #> 10  A: Drug X 26       17  964.2197       MEDIUM BMRKR2 #> 11 B: Placebo 26       22  654.8245         HIGH BMRKR2 #> 12  A: Drug X 17       12 1016.2982         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $hr #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        49           31 0.7651261 0.3641277 1.6077269       0.95 0.47860004 #> 6        43           34 0.6662356 0.3257413 1.3626456       0.95 0.26285846 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis #>   df_grouped <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) df_grouped #> $survtime #>           arm  n n_events    median        subgroup    var #> 1  B: Placebo 73       57  727.8043    All Patients    ALL #> 2   A: Drug X 69       44  974.6402    All Patients    ALL #> 3  B: Placebo 40       31  599.1772               F    SEX #> 4   A: Drug X 38       24 1016.2982               F    SEX #> 5  B: Placebo 33       26  888.4916               M    SEX #> 6   A: Drug X 31       20  974.6402               M    SEX #> 7  B: Placebo 24       21  735.4722             low BMRKR2 #> 8   A: Drug X 26       15  974.6402             low BMRKR2 #> 9  B: Placebo 47       35  735.4722      low/medium BMRKR2 #> 10  A: Drug X 52       32  964.2197      low/medium BMRKR2 #> 11 B: Placebo 73       57  727.8043 low/medium/high BMRKR2 #> 12  A: Drug X 69       44  974.6402 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $hr #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        99           67 0.7472958 0.4600419 1.2139136       0.95 0.23764314 #> 6       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #>           pval_label        subgroup    var                    var_label #> 1 p-value (log-rank)    All Patients    ALL                 All Patients #> 2 p-value (log-rank)               F    SEX                          Sex #> 3 p-value (log-rank)               M    SEX                          Sex #> 4 p-value (log-rank)             low BMRKR2 Continuous Level Biomarker 2 #> 5 p-value (log-rank)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 p-value (log-rank) low/medium/high BMRKR2 Continuous Level Biomarker 2 #>   row_type #> 1  content #> 2 analysis #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis #>"},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Extreme Values — extreme_format","title":"Formatting Extreme Values — extreme_format","text":"rtables formatting functions handle extreme values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Extreme Values — extreme_format","text":"","code":"h_get_format_threshold(digits = 2L)  h_format_threshold(x, digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Extreme Values — extreme_format","text":"digits (integer) number decimal places display. x (number) value format.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Extreme Values — extreme_format","text":"h_get_format_threshold() returns list 2 elements: threshold, low high thresholds, format_string, thresholds formatted strings. h_format_threshold() returns given value, value within digit threshold relation given value digit threshold, formatted string.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Formatting Extreme Values — extreme_format","text":"input, apply format specified number digits. value threshold, returns \"<0.01\" e.g. number digits 2. value threshold, returns \">999.99\" e.g. number digits 2. zero, returns \"0.00\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Formatting Extreme Values — extreme_format","text":"h_get_format_threshold(): Internal helper function calculate threshold create formatted strings used Formatting Functions. Returns list elements threshold format_string. h_format_threshold(): Internal helper function apply threshold format value. Creates formatted string used Formatting Functions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Extreme Values — extreme_format","text":"","code":"h_get_format_threshold(2L) #> $threshold #>    low   high  #>   0.01 999.99  #>  #> $format_string #>       low      high  #>   \"<0.01\" \">999.99\"  #>   h_format_threshold(0.001) #> [1] \"<0.01\" h_format_threshold(1000) #> [1] \">999.99\""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/f_conf_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for confidence interval — f_conf_level","text":"","code":"f_conf_level(conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/f_conf_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for confidence interval — f_conf_level","text":"conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/f_conf_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for confidence interval — f_conf_level","text":"string.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/f_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for p-value — f_pval","text":"","code":"f_pval(test_mean)"},{"path":"https://insightsengineering.github.io/tern/reference/f_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for p-value — f_pval","text":"test_mean (number) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/f_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for p-value — f_pval","text":"string.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"collapses levels keeps new group levels, order provided. returned factor levels order given, possible missing level last (included missing values).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"","code":"fct_collapse_only(.f, ..., .na_level = \"<Missing>\")"},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":".f (factor character) original vector. ... (named character vectors) levels vector provided collapsed new level given respective name. .na_level (string) level use levels, missing new factor. Note level must contained new levels specified ....","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"modified factor collapsed levels. Values levels included given character vector input set missing level .na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"existing NAs input vector replaced missing level. needed, explicit_na() can called separately result.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"","code":"fct_collapse_only(factor(c(\"a\", \"b\", \"c\", \"d\")), TRT = \"b\", CTRL = c(\"c\", \"d\")) #> [1] <Missing> TRT       CTRL      CTRL      #> Levels: TRT CTRL <Missing>"},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Certain Levels from a Factor — fct_discard","title":"Discard Certain Levels from a Factor — fct_discard","text":"discards observations well levels specified factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Certain Levels from a Factor — fct_discard","text":"","code":"fct_discard(x, discard)"},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Certain Levels from a Factor — fct_discard","text":"x (factor) original factor. discard (character) levels discard.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Certain Levels from a Factor — fct_discard","text":"modified factor observations well levels discard dropped.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discard Certain Levels from a Factor — fct_discard","text":"","code":"fct_discard(factor(c(\"a\", \"b\", \"c\")), \"c\") #> [1] a b #> Levels: a b"},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":null,"dir":"Reference","previous_headings":"","what":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"inserts explicit missings factor based condition. Additionally, existing NA values explicitly converted given na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(x, condition, na_level = \"<Missing>\")"},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"x (factor) original factor. condition (logical) insert missings. na_level (string) level use missings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"modified factor inserted existing NA converted na_level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(factor(c(\"a\", \"b\", NA)), c(TRUE, FALSE, FALSE)) #> [1] <Missing> b         <Missing> #> Levels: a b <Missing>"},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Fits for Cox Proportional Hazards Regression — fit_coxreg","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"Fitting functions univariate multivariate Cox regression models.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"","code":"fit_coxreg_univar(variables, data, at = list(), control = control_coxreg())  fit_coxreg_multivar(variables, data, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"variables (list) named list corresponds names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. data (data.frame) dataset containing variables fit models. (list numeric) candidate covariate numeric, use specify value covariate effect estimated. control (list) list parameters returned helper function control_coxreg().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"fit_coxreg_univar() returns coxreg.univar class object named list 5 elements: mod: Cox regression models fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model. : Value covariate effect estimated. fit_coxreg_multivar() returns coxreg.multivar class object named list 4 elements: mod: Cox regression model fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"fit_coxreg_univar(): Fit series univariate Cox regression models given inputs. fit_coxreg_multivar(): Fit multivariate Cox regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"using fit_coxreg_univar two study arms.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"","code":"library(survival)  set.seed(1, kind = \"Mersenne-Twister\")  # Testing dataset [survival::bladder]. dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   data.frame(     time = stop,     status = event,     armcd = as.factor(rx),     covar1 = as.factor(enum),     covar2 = factor(       sample(as.factor(enum)),       levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\")     )   ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)  plot(   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),   lty = 2:4,   xlab = \"Months\",   col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") )   # fit_coxreg_univar  ## Cox regression: arm + 1 covariate. mod1 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = \"covar1\"   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91) )  ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates. mod2 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91, interaction = TRUE) )  ## Cox regression: arm + 1 covariate, stratified analysis. mod3 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", strata = \"covar2\",     covariates = c(\"covar1\")   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91) )  ## Cox regression: no arm, only covariates. mod4 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder )  # fit_coxreg_multivar  ## Cox regression: multivariate Cox regression. multivar_model <- fit_coxreg_multivar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder )  # Example without treatment arm. multivar_covs_model <- fit_coxreg_multivar(   variables = list(     time = \"time\", event = \"status\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder )"},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit for Logistic Regression — fit_logistic","title":"Fit for Logistic Regression — fit_logistic","text":"Fit (conditional) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit for Logistic Regression — fit_logistic","text":"","code":"fit_logistic(   data,   variables = list(response = \"Response\", arm = \"ARMCD\", covariates = NULL, interaction =     NULL, strata = NULL),   response_definition = \"response\" )"},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit for Logistic Regression — fit_logistic","text":"data (data.frame) data frame model fit. variables (named list string) list additional analysis variables. response_definition (string) definition event terms response. used fitting (conditional) logistic regression model left hand side formula.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit for Logistic Regression — fit_logistic","text":"fitted logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"model-specification","dir":"Reference","previous_headings":"","what":"Model Specification","title":"Fit for Logistic Regression — fit_logistic","text":"variables list needs include following elements: arm: Treatment arm variable name. response: response arm variable name. Usually 0/1 variable. covariates: either NULL (covariates) character vector covariate variable names. interaction: either NULL (interaction) string single covariate variable name already included covariates. interaction treatment arm included model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit for Logistic Regression — fit_logistic","text":"","code":"library(dplyr)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred"},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"fits Subgroup Treatment Effect Pattern logistic regression models binary (response) outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated odds ratios comparison second level vs. first one. (conditional) logistic regression model fit : response ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"","code":"fit_rsp_step(variables, data, control = c(control_step(), control_logistic()))"},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"variables (named list character) list analysis variables: needs response, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_logistic().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"","code":"# Testing dataset with just two treatment arms. library(survival) library(dplyr)  adrs_f <- tern_ex_adrs %>%   filter(     PARAMCD == \"BESRSPI\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\")   ) %>%   mutate(     # Reorder levels of ARM to have Placebo as reference arm for Odds Ratio calculations.     ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")),     RSP = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     SEX = factor(SEX)   )  variables <- list(   arm = \"ARM\",   biomarker = \"BMRKR1\",   covariates = \"AGE\",   response = \"RSP\" )  # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. # We use a large enough bandwidth to avoid too small subgroups and linear separation in those. step_matrix <- fit_rsp_step(   variables = variables,   data = adrs_f,   control = c(control_logistic(), control_step(bandwidth = 0.5)) ) #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings dim(step_matrix) #> [1] 39 11 head(step_matrix) #>      Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,]             0.025                0            0.525        1.472755 #> [2,]             0.050                0            0.550        2.028546 #> [3,]             0.075                0            0.575        2.204086 #> [4,]             0.100                0            0.600        2.799776 #> [5,]             0.125                0            0.625        2.969998 #> [6,]             0.150                0            0.650        3.149068 #>      Interval Lower Interval Upper  n    logor       se   ci_lower ci_upper #> [1,]      0.4459546       6.057928 75 1.230538 1.185904 -1.0937914 3.554868 #> [2,]      0.4459546       6.371236 78 1.244538 1.183795 -1.0756577 3.564734 #> [3,]      0.4459546       6.701030 82 1.302066 1.182851 -1.0162794 3.620411 #> [4,]      0.4459546       6.886642 85 1.361186 1.182169 -0.9558222 3.678194 #> [5,]      0.4459546       7.026219 89 1.231372 1.179518 -1.0804407 3.543184 #> [6,]      0.4459546       7.144031 92 1.242238 1.180350 -1.0712045 3.555681  # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different logistic regression options, including confidence level. step_matrix2 <- fit_rsp_step(   variables = variables,   data = adrs_f,   control = c(control_logistic(conf_level = 0.9), control_step(bandwidth = 0.6, degree = 1)) ) #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings  # Use a global constant model. This is helpful as a reference for the subgroup models. step_matrix3 <- fit_rsp_step(   variables = variables,   data = adrs_f,   control = c(control_logistic(), control_step(bandwidth = NULL, num_points = 2L)) )  # It is also possible to use strata, i.e. use conditional logistic regression models. variables2 <- list(   arm = \"ARM\",   biomarker = \"BMRKR1\",   covariates = \"AGE\",   response = \"RSP\",   strata = c(\"STRATA1\", \"STRATA2\") )  step_matrix4 <- fit_rsp_step(   variables = variables2,   data = adrs_f,   control = c(control_logistic(), control_step(bandwidth = 0.6)) ) #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings"},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"fits Subgroup Treatment Effect Pattern models survival outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated hazard ratios comparison second level vs. first one. model fit : Surv(time, event) ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"","code":"fit_survival_step(   variables,   data,   control = c(control_step(), control_coxph()) )"},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"variables (named list character) list analysis variables: needs time, event, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_coxph().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"","code":"# Testing dataset with just two treatment arms. library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")),     is_event = CNSR == 0   ) labels <- c(\"ARM\" = \"Treatment Arm\", \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  variables <- list(   arm = \"ARM\",   biomarker = \"BMRKR1\",   covariates = c(\"AGE\", \"BMRKR2\"),   event = \"is_event\",   time = \"AVAL\" )  # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. step_matrix <- fit_survival_step(   variables = variables,   data = adtte_f ) dim(step_matrix) #> [1] 39 12 head(step_matrix) #>      Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,]             0.025                0            0.275        1.472755 #> [2,]             0.050                0            0.300        2.028546 #> [3,]             0.075                0            0.325        2.204086 #> [4,]             0.100                0            0.350        2.799776 #> [5,]             0.125                0            0.375        2.969998 #> [6,]             0.150                0            0.400        3.149068 #>      Interval Lower Interval Upper  n events      loghr        se   ci_lower #> [1,]      0.4459546       4.182444 39     29 0.03729601 0.4243013 -0.7943192 #> [2,]      0.4459546       4.349471 43     31 0.23246622 0.3881347 -0.5282639 #> [3,]      0.4459546       4.626913 46     32 0.07952492 0.3813113 -0.6678314 #> [4,]      0.4459546       4.805767 50     35 0.09020455 0.3638368 -0.6229026 #> [5,]      0.4459546       4.929816 53     37 0.15522702 0.3477999 -0.5264483 #> [6,]      0.4459546       5.020539 57     41 0.11135760 0.3332843 -0.5418676 #>       ci_upper #> [1,] 0.8689112 #> [2,] 0.9931963 #> [3,] 0.8268813 #> [4,] 0.8033117 #> [5,] 0.8369023 #> [6,] 0.7645828  # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different Cox regression options. step_matrix2 <- fit_survival_step(   variables = variables,   data = adtte_f,   control = c(control_coxph(conf_level = 0.9), control_step(degree = 2)) )  # Use a global model with cubic interaction and only 5 points. step_matrix3 <- fit_survival_step(   variables = variables,   data = adtte_f,   control = c(control_coxph(), control_step(bandwidth = NULL, degree = 3, num_points = 5L)) )"},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Viewport Tree for the Forest Plot — forest_viewport","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"Create Viewport Tree Forest Plot","code":""},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"","code":"forest_viewport(   tbl,   width_row_names = NULL,   width_columns = NULL,   width_forest = grid::unit(1, \"null\"),   gap_column = grid::unit(1, \"lines\"),   gap_header = grid::unit(1, \"lines\"),   mat_form = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"tbl (rtable) width_row_names (grid::unit) Width row names width_columns (grid::unit) Width column spans width_forest (grid::unit) Width forest plot gap_column (grid::unit) Gap width columns gap_header (grid::unit) Gap width header mat_form matrix print form table","code":""},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"viewport tree.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"","code":"library(grid)  tbl <- rtable(   header = rheader(     rrow(\"\", \"E\", rcell(\"CI\", colspan = 2)),     rrow(\"\", \"A\", \"B\", \"C\")   ),   rrow(\"row 1\", 1, 0.8, 1.1),   rrow(\"row 2\", 1.4, 0.8, 1.6),   rrow(\"row 3\", 1.2, 0.8, 1.2) )  # \\donttest{ v <- forest_viewport(tbl)  grid::grid.newpage() showViewport(v)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic formats from data significant digits — format_auto","title":"Automatic formats from data significant digits — format_auto","text":"Formatting function majority default methods used analyze_vars(). non-derived values, significant digits data used (e.g. range), derived values one digits (measure location dispersion like mean, standard deviation). function can called internally \"auto\" like, example, .formats = c(\"mean\" = \"auto\"). See details see works inner function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic formats from data significant digits — format_auto","text":"","code":"format_auto(dt_var, x_stat)"},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic formats from data significant digits — format_auto","text":"dt_var (numeric)  data statistics created upon. Used find significant digits. analyze_vars comes .df_row (see rtables::additional_fun_params), row data row splits. column split considered. x_stat (string)  string indicating current statistical method used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic formats from data significant digits — format_auto","text":"string rtables prints table cell.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic formats from data significant digits — format_auto","text":"internal function needed work rtables default structure format functions, .e. function(x, ...), x results statistical evaluation. can one element (e.g. .stats = \"mean_sd\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic formats from data significant digits — format_auto","text":"","code":"x_todo <- c(0.001, 0.2, 0.0011000, 3, 4) res <- c(mean(x_todo[1:3]), sd(x_todo[1:3]))  # x is the result coming into the formatting function -> res!! format_auto(dt_var = x_todo, x_stat = \"mean_sd\")(x = res) #> [1] \"0.06737 (0.11486)\" format_auto(x_todo, \"range\")(x = range(x_todo)) #> [1] \"0.0010 - 4.0000\" no_sc_x <- c(0.0000001, 1) format_auto(no_sc_x, \"range\")(x = no_sc_x) #> [1] \"0.0000001 - 1.0000000\""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Count and Fraction — format_count_fraction","title":"Formatting Count and Fraction — format_count_fraction","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Count and Fraction — format_count_fraction","text":"","code":"format_count_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Count and Fraction — format_count_fraction","text":"x (integer) vector length 2, count fraction. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Count and Fraction — format_count_fraction","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Count and Fraction — format_count_fraction","text":"","code":"format_count_fraction(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"x (integer) vector length 2, count fraction. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.5)) #> [1] \"2 (50.0%)\" format_count_fraction_fixed_dp(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"Formats count together fraction special consideration count less 10.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"x (integer) vector length 2, count fraction. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"string format count (fraction %). count less 10, count printed.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x = c(275, 0.9673)) #> [1] \"275 (96.7%)\" format_count_fraction_lt10(x = c(2, 0.6667)) #> [1] \"2\" format_count_fraction_lt10(x = c(9, 1)) #> [1] \"9\""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting a Single Extreme Value — format_extreme_values","title":"Formatting a Single Extreme Value — format_extreme_values","text":"Create Formatting Function single extreme value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting a Single Extreme Value — format_extreme_values","text":"","code":"format_extreme_values(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting a Single Extreme Value — format_extreme_values","text":"digits (integer) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting a Single Extreme Value — format_extreme_values","text":"rtables formatting function uses threshold digits return formatted extreme value.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting a Single Extreme Value — format_extreme_values","text":"","code":"format_fun <- format_extreme_values(2L) format_fun(x = 0.127) #> [1] \"0.13\" format_fun(x = Inf) #> [1] \">999.99\" format_fun(x = 0) #> [1] \"0.00\" format_fun(x = 0.009) #> [1] \"<0.01\""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"Formatting Function extreme values part confidence interval. Values formatted e.g. \"(xx.xx, xx.xx)\" number digits 2.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"","code":"format_extreme_values_ci(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"digits (integer) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"rtables formatting function uses threshold digits return formatted extreme values confidence interval.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"","code":"format_fun <- format_extreme_values_ci(2L) format_fun(x = c(0.127, Inf)) #> [1] \"(0.13, >999.99)\" format_fun(x = c(0, 0.009)) #> [1] \"(0.00, <0.01)\""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Fraction and Percentage — format_fraction","title":"Formatting Fraction and Percentage — format_fraction","text":"Formats fraction together ratio percent.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Fraction and Percentage — format_fraction","text":"","code":"format_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Fraction and Percentage — format_fraction","text":"x (integer) elements num denom. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Fraction and Percentage — format_fraction","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Fraction and Percentage — format_fraction","text":"","code":"format_fraction(x = c(num = 2L, denom = 3L)) #> [1] \"2/3 (66.7%)\" format_fraction(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"Formats fraction together ratio percent fixed single decimal place. Includes trailing zero case whole number percentages always keep one decimal place.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"x (integer) elements num denom. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 4L)) #> [1] \"1/4 (25.0%)\" format_fraction_fixed_dp(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Fraction with Lower Threshold — format_fraction_threshold","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"Formats fraction second element input x fraction. applies lower threshold, just stated fraction smaller .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"","code":"format_fraction_threshold(threshold)"},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"threshold (proportion) lower threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"rtables formatting function takes numeric input x second element fraction formatted. fraction equal threshold, displayed percentage. positive threshold, returns, e.g. \"<1\" threshold 0.01. zero, just \"0\" returned.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"","code":"format_fun <- format_fraction_threshold(0.05) format_fun(x = c(20, 0.1)) #> [1] 10 format_fun(x = c(2, 0.01)) #> [1] \"<5\" format_fun(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Numeric Values By Significant Figures — format_sigfig","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"Format numeric values print specified number significant figures.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"","code":"format_sigfig(sigfig, format = \"xx\", num_fmt = \"fg\")"},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"sigfig (integer) number significant figures display. format (character) format label (string) apply printing value. Decimal places string ignored favor formatting significant figures. Formats options : \"xx\", \"xx / xx\", \"(xx, xx)\", \"xx - xx\", \"xx (xx)\". num_fmt (character) numeric format modifiers apply value. Defaults \"fg\" standard significant figures formatting - fixed (non-scientific notation) format (\"f\") sigfig equal number significant figures instead decimal places (\"g\"). See formatC() format argument options.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"","code":"fmt_3sf <- format_sigfig(3) fmt_3sf(1.658) #> [1] \"1.66\" fmt_3sf(1e1) #> [1] \"10.0\"  fmt_5sf <- format_sigfig(5) fmt_5sf(0.57) #> [1] \"0.57000\" fmt_5sf(0.000025645) #> [1] \"0.000025645\""},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting: XX as Formatting Function — format_xx","title":"Formatting: XX as Formatting Function — format_xx","text":"Translate string x dots interpreted number place holders, others formatting elements.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting: XX as Formatting Function — format_xx","text":"","code":"format_xx(str)"},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting: XX as Formatting Function — format_xx","text":"str (string) template.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting: XX as Formatting Function — format_xx","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting: XX as Formatting Function — format_xx","text":"","code":"test <- list(c(1.658, 0.5761), c(1e1, 785.6))  z <- format_xx(\"xx (xx.x)\") sapply(test, z) #> [1] \"2 (0.6)\"    \"10 (785.6)\"  z <- format_xx(\"xx.x - xx.x\") sapply(test, z) #> [1] \"1.7 - 0.6\"  \"10 - 785.6\"  z <- format_xx(\"xx.x, incl. xx.x% NE\") sapply(test, z) #> [1] \"1.7, incl. 0.6% NE\"  \"10, incl. 785.6% NE\""},{"path":"https://insightsengineering.github.io/tern/reference/formatting_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Functions — formatting_functions","title":"Formatting Functions — formatting_functions","text":"See list formatting functions created tern work rtables. available formats can listed via formatters::list_valid_format_labels(). Additional custom formats can created via formatters::sprintf_format() function.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Forest Plot based on a Table — g_forest","text":"","code":"g_forest(   tbl,   col_x = attr(tbl, \"col_x\"),   col_ci = attr(tbl, \"col_ci\"),   vline = 1,   forest_header = attr(tbl, \"forest_header\"),   xlim = c(0.1, 10),   logx = TRUE,   x_at = c(0.1, 1, 10),   width_row_names = NULL,   width_columns = NULL,   width_forest = grid::unit(1, \"null\"),   col_symbol_size = attr(tbl, \"col_symbol_size\"),   col = getOption(\"ggplot2.discrete.colour\")[1],   gp = NULL,   draw = TRUE,   newpage = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Forest Plot based on a Table — g_forest","text":"tbl (rtable) col_x (integer) column index estimator. default tries get tbl attribute col_x, otherwise needs manually specified. col_ci (integer) column index confidence intervals. default tries get tbl attribute col_ci, otherwise needs manually specified. vline (number) x coordinate vertical line, NULL line omitted. forest_header (character, length 2) text displayed left right vline, respectively. vline = NULL forest_header needs NULL . default tries get tbl attribute forest_header. xlim (numeric) limits x axis. logx (flag) show x-values logarithm scale. x_at (numeric) x-tick locations, NULL get automatically chosen. width_row_names (unit) width row names. NULL widths get automatically calculated. See grid::unit(). width_columns (unit) widths table columns. NULL widths get automatically calculated. See grid::unit(). width_forest (unit) width forest column. NULL widths get automatically calculated. See grid::unit(). col_symbol_size (integer) column index tbl containing data used determine relative size estimator plot symbol. Typically, symbol size proportional sample size used calculate estimator. NULL, symbol size used subgroups. default tries get tbl attribute col_symbol_size, otherwise needs manually specified. col (character) color(s). gp \"gpar\" object, typically output     call function gpar.  basically     list graphical parameter settings. draw (flag) whether plot drawn. newpage (flag) whether plot drawn new page. considered draw = TRUE used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Forest Plot based on a Table — g_forest","text":"gTree object containing forest plot table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Forest Plot based on a Table — g_forest","text":"Create forest plot rtables::rtable() object column single value column 2 values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Forest Plot based on a Table — g_forest","text":"","code":"# \\donttest{ library(dplyr) library(forcats) library(nestcolor)  adrs <- tern_ex_adrs n_records <- 20 adrs_labels <- formatters::var_labels(adrs, fill = TRUE) adrs <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   slice(seq_len(n_records)) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs) <- c(adrs_labels, \"Response\") df <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA2\")),   data = adrs ) # Full commonly used response table.  tbl <- basic_table() %>%   tabulate_rsp_subgroups(df) p <- g_forest(tbl, gp = grid::gpar(fontsize = 10))  draw_grob(p)   # Odds ratio only table.  tbl_or <- basic_table() %>%   tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"or\", \"ci\")) tbl_or #>                                                                  #> Baseline Risk Factors     Total n   Odds Ratio       95% CI      #> ———————————————————————————————————————————————————————————————— #> All Patients                20         0.76       (0.10, 5.96)   #> Sex                                                              #>   F                         11       >999.99     (0.00, >999.99) #>   M                          9         0.22       (0.01, 3.98)   #> Stratification Factor 2                                          #>   S1                        10         1.00       (0.05, 22.18)  #>   S2                        10         0.67       (0.04, 11.29)  p <- g_forest(   tbl_or,   forest_header = c(\"Comparison\\nBetter\", \"Treatment\\nBetter\") )  draw_grob(p)   # Survival forest plot example. adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = TRUE) adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- list(   \"ARM\" = adtte_labels[\"ARM\"],   \"SEX\" = adtte_labels[\"SEX\"],   \"AVALU\" = adtte_labels[\"AVALU\"],   \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- as.character(labels) df <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) table_hr <- basic_table() %>%   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1]) g_forest(table_hr)  # Works with any `rtable`. tbl <- rtable(   header = c(\"E\", \"CI\", \"N\"),   rrow(\"\", 1, c(.8, 1.2), 200),   rrow(\"\", 1.2, c(1.1, 1.4), 50) ) g_forest(   tbl = tbl,   col_x = 1,   col_ci = 2,   xlim = c(0.5, 2),   x_at = c(0.5, 1, 2),   col_symbol_size = 3 )  tbl <- rtable(   header = rheader(     rrow(\"\", rcell(\"A\", colspan = 2)),     rrow(\"\", \"c1\", \"c2\")   ),   rrow(\"row 1\", 1, c(.8, 1.2)),   rrow(\"row 2\", 1.2, c(1.1, 1.4)) ) g_forest(   tbl = tbl,   col_x = 1,   col_ci = 2,   xlim = c(0.5, 2),   x_at = c(0.5, 1, 2),   vline = 1,   forest_header = c(\"Hello\", \"World\") )  # }"},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier Plot — g_km","title":"Kaplan-Meier Plot — g_km","text":"survival model, graphic rendered along tabulated annotation including number patient risk given time median survival per group.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier Plot — g_km","text":"","code":"g_km(   df,   variables,   control_surv = control_surv_timepoint(),   col = NULL,   lty = NULL,   lwd = 0.5,   censor_show = TRUE,   pch = 3,   size = 2,   max_time = NULL,   xticks = NULL,   xlab = \"Days\",   yval = c(\"Survival\", \"Failure\"),   ylab = paste(yval, \"Probability\"),   ylim = NULL,   title = NULL,   footnotes = NULL,   draw = TRUE,   newpage = TRUE,   gp = NULL,   vp = NULL,   name = NULL,   font_size = 12,   ci_ribbon = FALSE,   ggtheme = nestcolor::theme_nest(),   annot_at_risk = TRUE,   annot_at_risk_title = TRUE,   annot_surv_med = TRUE,   annot_coxph = FALSE,   annot_stats = NULL,   annot_stats_vlines = FALSE,   control_coxph_pw = control_coxph(),   position_coxph = c(-0.03, -0.02),   position_surv_med = c(0.95, 0.9),   width_annots = list(surv_med = grid::unit(0.3, \"npc\"), coxph = grid::unit(0.4, \"npc\")) )"},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier Plot — g_km","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strat (character NULL) variable names indicating stratification factors. control_surv (list) parameters comparison details, specified using helper function control_surv_timepoint(). possible parameter options : conf_level (proportion) confidence level interval survival rate. conf_type (string)\"plain\" (default), \"log\", \"log-log\" confidence interval type, see survival::survfit(). Note option \"none\" longer supported. col (character) lines colors. Length vector equal number strata survival::survfit(). lty (numeric) line type. Length vector equal number strata survival::survfit(). lwd (numeric) line width. Length vector equal number strata survival::survfit(). censor_show (flag) whether show censored. pch (numeric, string) value character points symbol indicate censored cases. size (numeric) size censored point, class unit. max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL). xticks (numeric, number, NULL) numeric vector ticks single number spacing ticks x axis. NULL (default), labeling::extended() used determine optimal tick position x axis. xlab (string) label x-axis. yval (string) value y-axis. Options Survival (default) Failure probability. ylab (string) label y-axis. ylim (vector numeric) vector length 2 containing lower upper limits y-axis. NULL (default), minimum maximum y-values displayed used limits. title (string) title plot. footnotes (string) footnotes plot. draw (flag) whether plot drawn. newpage (flag) whether plot drawn new page. considered draw = TRUE used. gp \"gpar\" object, typically output     call function gpar.  basically     list graphical parameter settings. vp viewport object (NULL). name character identifier grob.  Used find     grob display list /child another grob. font_size (number) font size used. ci_ribbon (flag) draw confidence interval around Kaplan-Meier curve. ggtheme (theme) graphical theme provided ggplot2 control outlook Kaplan-Meier curve. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE. annot_surv_med (flag) compute add annotation table Kaplan-Meier curve estimating median survival time per group. annot_coxph (flag) add annotation table survival::coxph() model. annot_stats (string) statistics annotations add plot. Options median (median survival follow-time) min (minimum survival follow-time). annot_stats_vlines (flag) add vertical lines corresponding statistics specified annot_stats. annot_stats NULL lines added. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. position_coxph (numeric) x y positions plotting survival::coxph() model. position_surv_med (numeric) x y positions plotting annotation table estimating median survival time per group. width_annots (named list units) named list widths annotation tables names surv_med (median survival time table) coxph (survival::coxph() model table), value width (units) implement printing annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier Plot — g_km","text":"grob class gTree.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier Plot — g_km","text":"","code":"# \\donttest{ library(dplyr) library(ggplot2) library(survival) library(grid) library(nestcolor)  df <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0) variables <- list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\")  # 1. Example - basic option  res <- g_km(df = df, variables = variables)  res <- g_km(df = df, variables = variables, yval = \"Failure\")  res <- g_km(   df = df,   variables = variables,   control_surv = control_surv_timepoint(conf_level = 0.9),   col = c(\"grey25\", \"grey50\", \"grey75\"),   annot_at_risk_title = FALSE )  res <- g_km(df = df, variables = variables, ggtheme = theme_minimal())  res <- g_km(df = df, variables = variables, ggtheme = theme_minimal(), lty = 1:3)  res <- g_km(df = df, variables = variables, max = 2000)  res <- g_km(   df = df,   variables = variables,   annot_stats = c(\"min\", \"median\"),   annot_stats_vlines = TRUE )   # 2. Example - Arrange several KM curve on a single graph device  # 2.1 Use case: A general graph on the top, a zoom on the bottom. grid.newpage() lyt <- grid.layout(nrow = 2, ncol = 1) %>%   viewport(layout = .) %>%   pushViewport()  res <- g_km(   df = df, variables = variables, newpage = FALSE, annot_surv_med = FALSE,   vp = viewport(layout.pos.row = 1, layout.pos.col = 1) ) res <- g_km(   df = df, variables = variables, max = 1000, newpage = FALSE, annot_surv_med = FALSE,   ggtheme = theme_dark(),   vp = viewport(layout.pos.row = 2, layout.pos.col = 1) )   # 2.1 Use case: No annotations on top, annotated graph on bottom grid.newpage() lyt <- grid.layout(nrow = 2, ncol = 1) %>%   viewport(layout = .) %>%   pushViewport()  res <- g_km(   df = df, variables = variables, newpage = FALSE,   annot_surv_med = FALSE, annot_at_risk = FALSE,   vp = viewport(layout.pos.row = 1, layout.pos.col = 1) ) res <- g_km(   df = df, variables = variables, max = 2000, newpage = FALSE, annot_surv_med = FALSE,   annot_at_risk = TRUE,   ggtheme = theme_dark(),   vp = viewport(layout.pos.row = 2, layout.pos.col = 1) )   # Add annotation from a pairwise coxph analysis g_km(   df = df, variables = variables,   annot_coxph = TRUE )   # Change widths/sizes of surv_med and coxph annotation tables. g_km(   df = df, variables = c(variables, list(strat = \"SEX\")),   annot_coxph = TRUE,   width_annots = list(surv_med = grid::unit(2, \"in\"), coxph = grid::unit(3, \"in\")) )   g_km(   df = df, variables = c(variables, list(strat = \"SEX\")),   font_size = 15,   annot_coxph = TRUE,   control_coxph = control_coxph(pval_method = \"wald\", ties = \"exact\", conf_level = 0.99),   position_coxph = c(0.5, 0.5) )   # Change position of the treatment group annotation table. g_km(   df = df, variables = c(variables, list(strat = \"SEX\")),   font_size = 15,   annot_coxph = TRUE,   control_coxph = control_coxph(pval_method = \"wald\", ties = \"exact\", conf_level = 0.99),   position_surv_med = c(1, 0.7) )  # }"},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Line plot with the optional table — g_lineplot","title":"Line plot with the optional table — g_lineplot","text":"Line plot optional table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Line plot with the optional table — g_lineplot","text":"","code":"g_lineplot(   df,   alt_counts_df = NULL,   variables = control_lineplot_vars(),   mid = \"mean\",   interval = \"mean_ci\",   whiskers = c(\"mean_ci_lwr\", \"mean_ci_upr\"),   table = NULL,   sfun = tern::s_summary,   ...,   mid_type = \"pl\",   mid_point_size = 2,   position = ggplot2::position_dodge(width = 0.4),   legend_title = NULL,   legend_position = \"bottom\",   ggtheme = nestcolor::theme_nest(),   y_lab = NULL,   y_lab_add_paramcd = TRUE,   y_lab_add_unit = TRUE,   title = \"Plot of Mean and 95% Confidence Limits by Visit\",   subtitle = \"\",   subtitle_add_paramcd = TRUE,   subtitle_add_unit = TRUE,   caption = NULL,   table_format = summary_formats(),   table_labels = summary_labels(),   table_font_size = 3,   newpage = TRUE,   col = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Line plot with the optional table — g_lineplot","text":"df (data.frame) data set containing analysis variables. alt_counts_df (data.frame NULL) data set used () counts objects strata. variables (named character vector) variable names df data set. Details : x (character) name x-axis variable. y (character) name y-axis variable. strata (character) name grouping variable, .e. treatment arm. Can NA indicate lack groups. cohort_id (character) name variable identifies group belonging. applies strata NULL. paramcd (character) name variable parameter's code. Used y-axis label plot's subtitle. Can NA paramcd added y-axis label subtitle. y_unit (character) name variable units y. Used y-axis label plot's subtitle. Can NA y unit added y-axis label subtitle. mid (character NULL) names statistics plotted midpoints. statistics indicated mid variable must present object returned sfun, double numeric type vector length one. interval (character NULL) names statistics plotted intervals. statistics indicated interval variable must present object returned sfun, double numeric type vector length two. whiskers (character) names interval whiskers plotted. Must match names attribute interval element list returned sfun. possible specify one whisker , lower upper. table (character NULL) names statistics displayed table plot. statistics indicated table variable must present object returned sfun. sfun (closure) function compute values required statistics. must return named list atomic vectors. names list elements refer names statistics used mid, interval, table. must able accept input vector data statistics computed. ... optional arguments sfun. mid_type (character) controls type mid plot, can point (p), line (l), point line (pl). mid_point_size (integer double) controls font size point mid plot. position (character call) geom element position adjustment, either string, result call position adjustment function. legend_title (character string) legend title. legend_position (character) position plot legend (none, left, right, bottom, top, two-element numeric vector). ggtheme (theme) graphical theme provided ggplot2 control styling plot. y_lab (character) y-axis label. equal NULL, label added. y_lab_add_paramcd (logical) paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added y-axis label y_lab? y_lab_add_unit (logical) y unit, .e. unique(df[[variables[\"y_unit\"]]]) added y-axis label y_lab? title (character) plot title. subtitle (character) plot subtitle. subtitle_add_paramcd (logical) paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added plot's subtitle subtitle? subtitle_add_unit (logical) y unit, .e. unique(df[[variables[\"y_unit\"]]]) added plot's subtitle subtitle? caption (character) optional caption plot. table_format (named character NULL) format patterns descriptive statistics used (optional) table appended plot. passed directly h_format_row function format parameter. Names table_format must match names statistics returned sfun function. table_labels (named character NULL) labels descriptive statistics used (optional) table appended plot. Names table_labels must match names statistics returned sfun function. table_font_size (integer double) controls font size values table. newpage (logical) plot drawn new page? col (character) colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Line plot with the optional table — g_lineplot","text":"ggplot line plot (statistics table applicable).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Line plot with the optional table — g_lineplot","text":"","code":"library(nestcolor)  adsl <- tern_ex_adsl adlb <- tern_ex_adlb %>% dplyr::filter(ANL01FL == \"Y\", PARAMCD == \"ALT\", AVISIT != \"SCREENING\") adlb$AVISIT <- droplevels(adlb$AVISIT) adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min))  # Mean with CI g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\")   # Mean with CI, no stratification g_lineplot(adlb, variables = control_lineplot_vars(strata = NA))   # Mean, upper whisker of CI, no strata counts N g_lineplot(   adlb,   whiskers = \"mean_ci_upr\",   title = \"Plot of Mean and Upper 95% Confidence Limit by Visit\" )   # Median with CI g_lineplot(   adlb,   adsl,   mid = \"median\",   interval = \"median_ci\",   whiskers = c(\"median_ci_lwr\", \"median_ci_upr\"),   title = \"Plot of Median and 95% Confidence Limits by Visit\" )   # Mean, +/- SD g_lineplot(adlb, adsl,   interval = \"mean_sdi\",   whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),   title = \"Plot of Median +/- SD by Visit\" )   # Mean with CI plot with stats table g_lineplot(adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\"))   # Mean with CI, table and customized confidence level g_lineplot(   adlb,   adsl,   table = c(\"n\", \"mean\", \"mean_ci\"),   control = control_analyze_vars(conf_level = 0.80),   title = \"Plot of Mean and 80% Confidence Limits by Visit\" )   # Mean with CI, table, filtered data adlb_f <- dplyr::filter(adlb, ARMCD != \"ARM A\" | AVISIT == \"BASELINE\") g_lineplot(adlb_f, table = c(\"n\", \"mean\"))"},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a STEP Graph — g_step","title":"Create a STEP Graph — g_step","text":"Based STEP results, creates ggplot graph showing estimated HR along continuous biomarker value subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a STEP Graph — g_step","text":"","code":"g_step(   df,   use_percentile = \"Percentile Center\" %in% names(df),   est = list(col = \"blue\", lty = 1),   ci_ribbon = list(fill = getOption(\"ggplot2.discrete.colour\")[1], alpha = 0.5),   col = getOption(\"ggplot2.discrete.colour\") )"},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a STEP Graph — g_step","text":"df (tibble) result tidy.step(). use_percentile (flag) whether use percentiles x axis actual biomarker values. est (named list)col lty settings estimate line. ci_ribbon (named list NULL)fill alpha settings confidence interval ribbon area, NULL plot CI ribbon. col (character) colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a STEP Graph — g_step","text":"ggplot STEP graph.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a STEP Graph — g_step","text":"","code":"library(nestcolor) library(survival) lung$sex <- factor(lung$sex)  # Survival example. vars <- list(   time = \"time\",   event = \"status\",   arm = \"sex\",   biomarker = \"age\" )  step_matrix <- fit_survival_step(   variables = vars,   data = lung,   control = c(control_coxph(), control_step(num_points = 10, degree = 2)) ) step_data <- broom::tidy(step_matrix)  # Default plot. g_step(step_data)   # Add the reference 1 horizontal line. library(ggplot2) g_step(step_data) +   ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2)   # Use actual values instead of percentiles, different color for estimate and no CI, # use log scale for y axis. g_step(   step_data,   use_percentile = FALSE,   est = list(col = \"blue\", lty = 1),   ci_ribbon = NULL ) + scale_y_log10()   # Adding another curve based on additional column. step_data$extra <- exp(step_data$`Percentile Center`) g_step(step_data) +   ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = \"green\")   # Response example. vars <- list(   response = \"status\",   arm = \"sex\",   biomarker = \"age\" )  step_matrix <- fit_rsp_step(   variables = vars,   data = lung,   control = c(     control_logistic(response_definition = \"I(response == 2)\"),     control_step()   ) ) step_data <- broom::tidy(step_matrix) g_step(step_data)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Horizontal Waterfall Plot — g_waterfall","text":"","code":"g_waterfall(   height,   id,   col_var = NULL,   col = getOption(\"ggplot2.discrete.colour\"),   xlab = NULL,   ylab = NULL,   col_legend_title = NULL,   title = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Horizontal Waterfall Plot — g_waterfall","text":"height (`numeric``) vector containing values plotted waterfall bars. id (character) vector containing IDs use x-axis label waterfall bars. col_var (factor, character NULL) categorical variable bar coloring. NULL default. col (character) colors. xlab (character) x label. Default \"ID\". ylab (character) y label. Default \"Value\". col_legend_title (character) text displayed legend title. title (character) text displayed plot title.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Horizontal Waterfall Plot — g_waterfall","text":"ggplot waterfall plot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Horizontal Waterfall Plot — g_waterfall","text":"basic waterfall plot visualizes quantity height ordered value markup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Horizontal Waterfall Plot — g_waterfall","text":"","code":"library(dplyr) library(nestcolor)  g_waterfall(height = c(3, 5, -1), id = letters[1:3])   g_waterfall(   height = c(3, 5, -1),   id = letters[1:3],   col_var = letters[1:3] )   adsl_f <- tern_ex_adsl %>%   select(USUBJID, STUDYID, ARM, ARMCD, SEX)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"OVRINV\") %>%   mutate(pchg = rnorm(n(), 10, 50))  adrs_f <- head(adrs_f, 30) adrs_f <- adrs_f[!duplicated(adrs_f$USUBJID), ] head(adrs_f) #> # A tibble: 5 × 30 #>   STUDYID COUNTRY SITEID SUBJID   AGE SEX   ARMCD ARM      ACTARMCD ACTARM RACE  #>   <chr>   <fct>   <chr>  <chr>  <dbl> <fct> <fct> <fct>    <fct>    <fct>  <fct> #> 1 AB12345 BRA     BRA-1  id-105  37.8 F     ARM A A: Drug… ARM A    A: Dr… ASIAN #> 2 AB12345 BRA     BRA-1  id-171  29.8 F     ARM B B: Plac… ARM B    B: Pl… ASIAN #> 3 AB12345 BRA     BRA-1  id-177  38.9 F     ARM B B: Plac… ARM B    B: Pl… ASIAN #> 4 AB12345 BRA     BRA-1  id-23   41.3 F     ARM A A: Drug… ARM A    A: Dr… AMER… #> 5 AB12345 BRA     BRA-1  id-59   26.7 F     ARM A A: Drug… ARM A    A: Dr… ASIAN #> # ℹ 19 more variables: TRTSDTM <dttm>, TRTEDTM <dttm>, EOSDY <dbl>, #> #   STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>, BMRKR2 <fct>, REGION1 <fct>, #> #   SAFFL <fct>, USUBJID <chr>, AVISIT <fct>, PARAMCD <fct>, PARAM <fct>, #> #   AVALC <fct>, AVAL <int>, ADTM <date>, AVISITN <dbl>, DTHFL <fct>, #> #   pchg <dbl>  g_waterfall(   height = adrs_f$pchg,   id = adrs_f$USUBJID,   col_var = adrs_f$AVALC )   g_waterfall(   height = adrs_f$pchg,   id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID),   col_var = adrs_f$SEX )   g_waterfall(   height = adrs_f$pchg,   id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID),   xlab = \"ID\",   ylab = \"Percentage Change\",   title = \"Waterfall plot\" )"},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility function to return a named list of covariate names. — get_covariates","title":"Utility function to return a named list of covariate names. — get_covariates","text":"Utility function return named list covariate names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to return a named list of covariate names. — get_covariates","text":"","code":"get_covariates(covariates)"},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to return a named list of covariate names. — get_covariates","text":"covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to return a named list of covariate names. — get_covariates","text":"named list character vector.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth Function with Optional Grouping — get_smooths","title":"Smooth Function with Optional Grouping — get_smooths","text":"produces loess smoothed estimates y Student confidence intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth Function with Optional Grouping — get_smooths","text":"","code":"get_smooths(df, x, y, groups = NULL, level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth Function with Optional Grouping — get_smooths","text":"df (data.frame) data set containing analysis variables. x (character) value x column name. y (character) value y column name. groups (character) vector optional grouping variables names. level (numeric) level confidence interval use (0.95 default).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Smooth Function with Optional Grouping — get_smooths","text":"data.frame original x, smoothed y, ylow, yhigh, optional groups variables formatted factor type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert List of Groups to Data Frame — groups_list_to_df","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"converts list group levels data frame format expected rtables::add_combo_levels().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"","code":"groups_list_to_df(groups_list)"},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"tibble::tibble() required format.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"","code":"grade_groups <- list(   \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),   \"Grade 3-4 (%)\" = c(\"3\", \"4\"),   \"Grade 5 (%)\" = \"5\" ) groups_list_to_df(grade_groups) #> # A tibble: 3 × 4 #>   valname  label         levelcombo exargs     #>   <chr>    <chr>         <list>     <list>     #> 1 AnyGrade Any Grade (%) <chr [5]>  <list [0]> #> 2 Grade34  Grade 3-4 (%) <chr [2]>  <list [0]> #> 3 Grade5   Grade 5 (%)   <chr [1]>  <list [0]>"},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"Helper function prepare df generate patient count shift table","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"","code":"h_adlb_worsen(   adlb,   worst_flag_low = NULL,   worst_flag_high = NULL,   direction_var )"},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"adlb (data.frame)ADLB dataframe worst_flag_low (named vector) Worst low post-baseline lab grade flag variable worst_flag_high (named vector) Worst high post-baseline lab grade flag variable direction_var (string) Direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"h_adlb_worsen() returns adlb  data.frame containing worst labs specified according worst_flag_low worst_flag_high direction specified according direction_var. instance, lab needed low direction , records flagged worst_flag_low selected. lab needed low high directions, worst low records selected low direction, worst high record selected high direction.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"","code":"library(dplyr)  # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>%   mutate(     GRADDR = case_when(       PARAMCD == \"ALT\" ~ \"B\",       PARAMCD == \"CRP\" ~ \"L\",       PARAMCD == \"IGA\" ~ \"H\"     )   ) %>%   filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")  df <- h_adlb_worsen(   adlb,   worst_flag_low = c(\"WGRLOFL\" = \"Y\"),   worst_flag_high = c(\"WGRHIFL\" = \"Y\"),   direction_var = \"GRADDR\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"Helper function merges ADSL ADLB datasets missing lab test records inserted output dataset. Remember na_level must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"","code":"h_adsl_adlb_merge_using_worst_flag(   adsl,   adlb,   worst_flag = c(WGRHIFL = \"Y\"),   by_visit = FALSE,   no_fillin_visits = c(\"SCREENING\", \"BASELINE\") )"},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"adsl (data.frame)ADSL dataframe. adlb (data.frame)ADLB dataframe. worst_flag (named vector) Worst post-baseline lab flag variable. by_visit (logical) defaults FALSE generate worst grade per patient. worst grade per patient per visit specified worst_flag, by_visit TRUE generate worst grade patient per visit. no_fillin_visits (named character) Visits considered post-baseline worst toxicity grade. Defaults c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"df containing variables shared adlb adsl along variables PARAM, PARAMCD, ATOXGR, BTOXGR relevant analysis. Optionally, AVISIT AVISITN included by_visit = TRUE no_fillin_visits = c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"result data missing records created following situations: Patients present adsl lab data adlb (baseline post-baseline). Patients post-baseline lab values. Patients without post-baseline values flagged worst.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"","code":"# `h_adsl_adlb_merge_using_worst_flag` adlb_out <- h_adsl_adlb_merge_using_worst_flag(   tern_ex_adsl,   tern_ex_adlb,   worst_flag = c(\"WGRHIFL\" = \"Y\") )  # `h_adsl_adlb_merge_using_worst_flag` by visit example adlb_out_by_visit <- h_adsl_adlb_merge_using_worst_flag(   tern_ex_adsl,   tern_ex_adlb,   worst_flag = c(\"WGRLOVFL\" = \"Y\"),   by_visit = TRUE )"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":"","code":"h_ancova(.var, .df_row, variables, interaction_item = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. variables (named list strings) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". interaction_item (character) name variable interactions arm. interaction needed, default option NULL.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":"summary linear model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":"","code":"h_ancova(   .var = \"Sepal.Length\",   .df_row = iris,   variables = list(arm = \"Species\", covariates = c(\"Petal.Length * Petal.Width\", \"Sepal.Width\")) ) #>  Species    emmean     SE  df lower.CL upper.CL #>  setosa       6.15 0.3371 143     5.49     6.82 #>  versicolor   5.72 0.0668 143     5.59     5.85 #>  virginica    5.41 0.1488 143     5.11     5.70 #>  #> Confidence level used: 0.95"},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Helper function s_count_occurrences_by_grade() insert grade groupings list individual grade frequencies. order final result follows order grade_groups. elements -grade group (), .e. grade group equal refs moved end. Grade groups names must unique.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups(grade_groups, refs, remove_single = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"grade_groups (named list character) containing groupings grades. refs (named list numeric) name corresponds reference grade level entry represents count. remove_single (logical)TRUE include elements one-element grade groups output list; case grade groups names included output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Formatted list grade groupings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups(   list(     \"Any Grade\" = as.character(1:5),     \"Grade 1-2\" = c(\"1\", \"2\"),     \"Grade 3-4\" = c(\"3\", \"4\")   ),   list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #>  #> $`Grade 1-2` #> [1] 30 #>  #> $`1` #> [1] 10 #>  #> $`2` #> [1] 20 #>  #> $`Grade 3-4` #> [1] 70 #>  #> $`3` #> [1] 30 #>  #> $`4` #> [1] 40 #>  #> $`5` #> [1] 50 #>   h_append_grade_groups(   list(     \"Any Grade\" = as.character(5:1),     \"Grade A\" = \"5\",     \"Grade B\" = c(\"4\", \"3\")   ),   list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #>  #> $`Grade A` #> [1] 50 #>  #> $`Grade B` #> [1] 70 #>  #> $`4` #> [1] 40 #>  #> $`3` #> [1] 30 #>  #> $`2` #> [1] 20 #>  #> $`1` #> [1] 10 #>   h_append_grade_groups(   list(     \"Any Grade\" = as.character(1:5),     \"Grade 1-2\" = c(\"1\", \"2\"),     \"Grade 3-4\" = c(\"3\", \"4\")   ),   list(\"1\" = 10, \"2\" = 5, \"3\" = 0) ) #> $`Any Grade` #> [1] 15 #>  #> $`Grade 1-2` #> [1] 15 #>  #> $`1` #> [1] 10 #>  #> $`2` #> [1] 5 #>  #> $`Grade 3-4` #> [1] 0 #>  #> $`3` #> [1] 0 #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Obtain Column Indices — h_col_indices","title":"Obtain Column Indices — h_col_indices","text":"Helper function extract column indices VTableTree given vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Obtain Column Indices — h_col_indices","text":"","code":"h_col_indices(table_tree, col_names)"},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Obtain Column Indices — h_col_indices","text":"table_tree (VTableTree) table extract indices . col_names (character) vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Obtain Column Indices — h_col_indices","text":"vector column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for s_count_cumulative() — h_count_cumulative","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"Helper function calculate count fraction x values lower upper tail given threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"","code":"h_count_cumulative(   x,   threshold,   lower_tail = TRUE,   include_eq = TRUE,   na.rm = TRUE,   .N_col )"},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"x (numeric) vector numbers want analyze. threshold (number) cutoff value threshold count values x. lower_tail (logical) whether count lower tail, default TRUE. include_eq (logical) whether include value equal threshold count, default TRUE. na.rm (flag) whether NA values removed x prior analysis. .N_col (count) denominator fraction calculation.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"named vector items: count: count values less , less equal , greater , greater equal threshold user specification. fraction: fraction count.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"","code":"set.seed(1, kind = \"Mersenne-Twister\") x <- c(sample(1:10, 10), NA) .N_col <- length(x) h_count_cumulative(x, 5, .N_col = .N_col) #>     count  fraction  #> 5.0000000 0.4545455  h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col) #>     count  fraction  #> 6.0000000 0.5454545  h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col) #>      count   fraction  #> 10.0000000  0.9090909  h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col) #>    count fraction  #>        0        0"},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"Helper functions used fit_coxreg_univar() fit_coxreg_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"","code":"h_coxreg_univar_formulas(variables, interaction = FALSE)  h_coxreg_multivar_formula(variables)  h_coxreg_univar_extract(effect, covar, data, mod, control = control_coxreg())  h_coxreg_multivar_extract(var, data, mod, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"variables (named list string) list additional analysis variables. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE. effect (string) treatment variable. covar (string) name covariate model. data (data.frame) dataset containing variables summarize. mod (coxph) Cox regression model fitted survival::coxph(). control (list) list controls returned control_coxreg(). var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"h_coxreg_univar_formulas() returns character vector coercible formulas (e.g stats::.formula()). h_coxreg_multivar_formula() returns string coercible formula (e.g stats::.formula()). h_coxreg_univar_extract() returns data.frame variables effect, term, term_label, level, n, hr, lcl, ucl, pval. h_coxreg_multivar_extract() returns data.frame variables pval, hr, lcl, ucl, level, n, term, term_label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"h_coxreg_univar_formulas(): Helper Cox regression formula. Creates list formulas. used internally fit_coxreg_univar() comparison univariate Cox regression models. h_coxreg_multivar_formula(): Helper multivariate Cox regression formula. Creates formulas string. used internally fit_coxreg_multivar() comparison multivariate Cox regression models. Interactions included multivariate Cox regression model. h_coxreg_univar_extract(): Utility function help tabulate result univariate Cox regression model. h_coxreg_multivar_extract(): Tabulation multivariate Cox regressions. Utility function help tabulate result multivariate Cox regression model treatment/covariate variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"","code":"# `h_coxreg_univar_formulas`  ## Simple formulas. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\")   ) ) #>                                        ref  #>     \"survival::Surv(time, status) ~ armcd\"  #>                                          X  #> \"survival::Surv(time, status) ~ armcd + X\"  #>                                          y  #> \"survival::Surv(time, status) ~ armcd + y\"   ## Addition of an optional strata. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"),     strata = \"SITE\"   ) ) #>                                                       ref  #>     \"survival::Surv(time, status) ~ armcd + strata(SITE)\"  #>                                                         X  #> \"survival::Surv(time, status) ~ armcd + X + strata(SITE)\"  #>                                                         y  #> \"survival::Surv(time, status) ~ armcd + y + strata(SITE)\"   ## Inclusion of the interaction term. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"),     strata = \"SITE\"   ),   interaction = TRUE ) #>                                                       ref  #>     \"survival::Surv(time, status) ~ armcd + strata(SITE)\"  #>                                                         X  #> \"survival::Surv(time, status) ~ armcd * X + strata(SITE)\"  #>                                                         y  #> \"survival::Surv(time, status) ~ armcd * y + strata(SITE)\"   ## Only covariates fitted in separate models. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", covariates = c(\"X\", \"y\")   ) ) #>                                      X                                      y  #> \"survival::Surv(time, status) ~ 1 + X\" \"survival::Surv(time, status) ~ 1 + y\"   # `h_coxreg_multivar_formula`  h_coxreg_multivar_formula(   variables = list(     time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\")   ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE\"  # Addition of an optional strata. h_coxreg_multivar_formula(   variables = list(     time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\"),     strata = \"SITE\"   ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE + strata(SITE)\"  # Example without treatment arm. h_coxreg_multivar_formula(   variables = list(     time = \"AVAL\", event = \"event\", covariates = c(\"RACE\", \"AGE\"),     strata = \"SITE\"   ) ) #> [1] \"survival::Surv(AVAL, event) ~ 1 + RACE + AGE + strata(SITE)\"  library(survival)  dta_simple <- data.frame(   time = c(5, 5, 10, 10, 5, 5, 10, 10),   status = c(0, 0, 1, 0, 0, 1, 1, 1),   armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c(\"A\", \"B\")),   var1 = c(45, 55, 65, 75, 55, 65, 85, 75),   var2 = c(\"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"U\") ) mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_univar_extract(   effect = \"armcd\", covar = \"armcd\", mod = mod, data = dta_simple ) result #>       effect  term       term_label level n       hr       lcl      ucl #> 1 Treatment: armcd B vs control (A)     B 8 6.551448 0.4606904 93.16769 #>       pval #> 1 0.165209  mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_multivar_extract(   var = \"var1\", mod = mod, data = dta_simple ) result #>        pval        hr      lcl      ucl level n term term_label #> 2 0.4456195 0.9423284 0.808931 1.097724  var1 8 var1       var1"},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function: tidy survival fit — h_data_plot","title":"Helper function: tidy survival fit — h_data_plot","text":"Convert survival fit data data frame designed plotting within g_km. starts broom::tidy() result, : Post-processes strata column factor. Extends stratum additional first row time 0 probability 1 downstream plot lines start coordinates. Adds censor column. Filters rows max_time.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function: tidy survival fit — h_data_plot","text":"","code":"h_data_plot(fit_km, armval = \"All\", max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function: tidy survival fit — h_data_plot","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function: tidy survival fit — h_data_plot","text":"tibble columns time, n.risk, n.event, n.censor, estimate, std.error, conf.high, conf.low, strata, censor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function: tidy survival fit — h_data_plot","text":"","code":"# \\donttest{ library(dplyr) library(survival)  # Test with multiple arms tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_data_plot() #> # A tibble: 203 × 10 #>     time n.risk n.event n.censor estimate std.error conf.high conf.low strata #>    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <fct>  #>  1   0       70       0        0    1        0          1        1     ARM A  #>  2  10.4     69       1        0    0.986    0.0146     1        0.958 ARM A  #>  3  20.5     68       1        0    0.971    0.0208     1        0.932 ARM A  #>  4  21.5     67       1        0    0.957    0.0257     1        0.910 ARM A  #>  5  25.3     66       0        1    0.957    0.0257     1        0.910 ARM A  #>  6  48.1     65       1        0    0.942    0.0300     0.999    0.888 ARM A  #>  7  66.6     64       0        1    0.942    0.0300     0.999    0.888 ARM A  #>  8  73.9     63       0        1    0.942    0.0300     0.999    0.888 ARM A  #>  9  78.9     62       0        1    0.942    0.0300     0.999    0.888 ARM A  #> 10  90.3     61       1        0    0.926    0.0342     0.991    0.866 ARM A  #> # ℹ 193 more rows #> # ℹ 1 more variable: censor <dbl>  # Test with single arm tern_ex_adtte %>%   filter(PARAMCD == \"OS\", ARMCD == \"ARM B\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_data_plot(armval = \"ARM B\") #> # A tibble: 74 × 10 #>      time n.risk n.event n.censor estimate std.error conf.high conf.low strata #>     <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <chr>  #>  1   0        74       0        0    1        0          1        1     ARM B  #>  2   2.17     73       1        0    0.986    0.0138     1        0.960 ARM B  #>  3  16.9      72       1        0    0.973    0.0196     1        0.936 ARM B  #>  4  22.6      71       1        0    0.959    0.0242     1        0.914 ARM B  #>  5  26.3      70       1        0    0.945    0.0282     0.999    0.894 ARM B  #>  6  57.5      69       1        0    0.932    0.0317     0.991    0.875 ARM B  #>  7  80.0      68       1        0    0.918    0.0350     0.983    0.857 ARM B  #>  8  81.6      67       1        0    0.904    0.0381     0.974    0.839 ARM B  #>  9  88.6      66       1        0    0.890    0.0411     0.965    0.822 ARM B  #> 10 102.       65       1        0    0.877    0.0439     0.955    0.804 ARM B  #> # ℹ 64 more rows #> # ℹ 1 more variable: censor <dbl> # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":null,"dir":"Reference","previous_headings":"","what":"ggplot Decomposition — h_decompose_gg","title":"ggplot Decomposition — h_decompose_gg","text":"elements composing ggplot extracted organized list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ggplot Decomposition — h_decompose_gg","text":"","code":"h_decompose_gg(gg)"},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ggplot Decomposition — h_decompose_gg","text":"gg (ggplot) graphic decompose.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ggplot Decomposition — h_decompose_gg","text":"named list elements: panel: panel. yaxis: y-axis. xaxis: x-axis. xlab: x-axis label. ylab: y-axis label. guide: legend.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ggplot Decomposition — h_decompose_gg","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   yval = \"Survival\",   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"tt\",   footnotes = \"ff\" )  g_el <- h_decompose_gg(gg) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"red\", fill = \"gray85\", lwd = 5)) grid::grid.draw(g_el$panel)   grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"royalblue\", fill = \"gray85\", lwd = 5)) grid::grid.draw(with(g_el, cbind(ylab, yaxis)))  # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"","code":"h_format_row(x, format, labels = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"x (named list) list numerical values formatted optionally labeled. Elements x must numeric vectors. format (named character NULL) format patterns x. Names format must match names x. parameter passed directly rtables::format_rcell function format parameter. labels (named character NULL) optional labels x. Names labels must match names x. label specified element x, function tries use label names (order) attribute element (depending one exists NULL NA NaN). none attributes attached given element x, label automatically generated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"single row data.frame object.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"","code":"mean_ci <- c(48, 51) x <- list(mean = 50, mean_ci = mean_ci) format <- c(mean = \"xx.x\", mean_ci = \"(xx.xx, xx.xx)\") labels <- c(mean = \"My Mean\") h_format_row(x, format, labels) #>   My Mean             V1 #> 1    50.0 (48.00, 51.00)  attr(mean_ci, \"label\") <- \"Mean 95% CI\" x <- list(mean = 50, mean_ci = mean_ci) h_format_row(x, format, labels) #>   My Mean    Mean 95% CI #> 1    50.0 (48.00, 51.00)"},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"Function generates simple line plot displaying parameter trends time.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"","code":"h_g_ipp(   df,   xvar,   yvar,   xlab,   ylab,   id_var,   title = \"Individual Patient Plots\",   subtitle = \"\",   caption = NULL,   add_baseline_hline = FALSE,   yvar_baseline = \"BASE\",   ggtheme = nestcolor::theme_nest(),   col = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (character scalar) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. col (character) lines colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"ggplot line plot.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"","code":"library(dplyr) library(nestcolor)  # Select a small sample of data to plot. adlb <- tern_ex_adlb %>%   filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>%   slice(1:36)  p <- h_g_ipp(   df = adlb,   xvar = \"AVISIT\",   yvar = \"AVAL\",   xlab = \"Visit\",   id_var = \"USUBJID\",   ylab = \"SGOT/ALT (U/L)\",   add_baseline_hline = TRUE ) p"},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function: KM plot — h_ggkm","title":"Helper function: KM plot — h_ggkm","text":"Draw Kaplan-Meier plot using ggplot2.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function: KM plot — h_ggkm","text":"","code":"h_ggkm(   data,   xticks = NULL,   yval = \"Survival\",   censor_show,   xlab,   ylab,   ylim = NULL,   title,   footnotes = NULL,   max_time = NULL,   lwd = 1,   lty = NULL,   pch = 3,   size = 2,   col = NULL,   ci_ribbon = FALSE,   ggtheme = nestcolor::theme_nest() )"},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function: KM plot — h_ggkm","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric, number, NULL) numeric vector ticks single number spacing ticks x axis. NULL (default), labeling::extended() used determine optimal tick position x axis. yval (string) value y-axis. Options Survival (default) Failure probability. censor_show (flag) whether show censored. xlab (string) label x-axis. ylab (string) label y-axis. ylim (vector numeric) vector length 2 containing lower upper limits y-axis. NULL (default), minimum maximum y-values displayed used limits. title (string) title plot. footnotes (string) footnotes plot. max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL). lwd (numeric) line width. Length vector equal number strata survival::survfit(). lty (numeric) line type. Length vector equal number strata survival::survfit(). pch (numeric, string) value character points symbol indicate censored cases. size (numeric) size censored point, class unit. col (character) lines colors. Length vector equal number strata survival::survfit(). ci_ribbon (flag) draw confidence interval around Kaplan-Meier curve. ggtheme (theme) graphical theme provided ggplot2 control outlook Kaplan-Meier curve.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function: KM plot — h_ggkm","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function: KM plot — h_ggkm","text":"","code":"# \\donttest{ library(dplyr) library(survival)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks,   xlab = \"Days\",   yval = \"Survival\",   ylab = \"Survival Probability\",   title = \"Survival\" ) gg  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Poisson Models. — h_glm_count","title":"Helper Functions for Poisson Models. — h_glm_count","text":"Helper functions can used return results various Poisson models.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Poisson Models. — h_glm_count","text":"","code":"h_glm_poisson(.var, .df_row, variables, weights)  h_glm_quasipoisson(.var, .df_row, variables, weights)  h_glm_count(.var, .df_row, variables, distribution, weights)  h_ppmeans(obj, .df_row, arm, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Poisson Models. — h_glm_count","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. variables (named list strings) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". offset (numeric) numeric vector scalar adding offset. weights (character) character vector specifying weights used averaging predictions. Number weights must equal number levels included covariates. Weights option passed emmeans::emmeans(). distribution (character) character value specifying distribution used regression (poisson, quasipoisson). obj (glm.fit) fitted model object used derive mean rate estimates treatment arm. arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. conf_level (numeric) value used derive confidence interval rate.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Poisson Models. — h_glm_count","text":"h_glm_poisson() returns results Poisson model. h_glm_quasipoisson() returns results Quasi-Poisson model. h_glm_count() returns results selected model. h_ppmeans() returns estimated means.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Poisson Models. — h_glm_count","text":"h_glm_poisson(): Helper function return results poisson model. h_glm_quasipoisson(): Helper function return results quasipoisson model. h_glm_count(): Helper function return results selected model (poisson, quasipoisson, negative binomial). h_ppmeans(): Helper function return estimated means.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: CoxPH Grob — h_grob_coxph","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"Grob rtable output h_tbl_coxph_pairwise()","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"","code":"h_grob_coxph(   ...,   x = 0,   y = 0,   width = grid::unit(0.4, \"npc\"),   ttheme = gridExtra::ttheme_default(padding = grid::unit(c(1, 0.5), \"lines\"), core =     list(bg_params = list(fill = c(\"grey95\", \"grey90\"), alpha = 0.5))) )"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"... arguments passed h_tbl_coxph_pairwise(). x (numeric) value 0 1 specifying x-location. y (numeric) value 0 1 specifying y-location. width (unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"grob table containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) data <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0) tbl_grob <- h_grob_coxph(   df = data,   variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\"),   control_coxph_pw = control_coxph(conf_level = 0.9), x = 0.5, y = 0.5 ) grid::grid.draw(tbl_grob)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: Survival Estimation Grob — h_grob_median_surv","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"survival fit transformed grob containing table groups rows characterized N, median 95% confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"","code":"h_grob_median_surv(   fit_km,   armval = \"All\",   x = 0.9,   y = 0.9,   width = grid::unit(0.3, \"npc\"),   ttheme = gridExtra::ttheme_default() )"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". x (numeric) value 0 1 specifying x-location. y (numeric) value 0 1 specifying y-location. width (unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"grob table containing statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_grob_median_surv() %>%   grid::grid.draw()  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"Two graphical objects obtained, one corresponding row labeling second table numbers patients risk. title = TRUE, third object corresponding table title also obtained.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"","code":"h_grob_tbl_at_risk(data, annot_tbl, xlim, title = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"data (data.frame) survival data pre-processed h_data_plot. annot_tbl (data.frame) annotation prepared survival::summary.survfit() includes number patients risk given time points. xlim (numeric) maximum value x-axis (used ensure risk table aligns KM graph). title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"named list two gTree objects title = FALSE: at_risk label, three gTree objects title = TRUE: at_risk, label, title.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)  data_plot <- h_data_plot(fit_km = fit_km)  xticks <- h_xticks(data = data_plot)  gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"tt\", footnotes = \"ff\", yval = \"Survival\" )  # The annotation table reports the patient at risk for a given strata and # time (`xticks`). annot_tbl <- summary(fit_km, time = xticks) if (is.null(fit_km$strata)) {   annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = \"All\")) } else {   strata_lst <- strsplit(sub(\"=\", \"equals\", levels(annot_tbl$strata)), \"equals\")   levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]   annot_tbl <- data.frame(     n.risk = annot_tbl$n.risk,     time = annot_tbl$time,     strata = annot_tbl$strata   ) }  # The annotation table is transformed into a grob. tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks))  # For the representation, the layout is estimated for which the decomposition # of the graphic element is necessary. g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\")  grid::grid.newpage() pushViewport(viewport(layout = lyt, height = .95, width = .95)) grid.rect(gp = grid::gpar(lty = 1, col = \"purple\", fill = \"gray85\", lwd = 1)) pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2)) grid.rect(gp = grid::gpar(lty = 1, col = \"orange\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$at_risk) popViewport() pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1)) grid.rect(gp = grid::gpar(lty = 1, col = \"green3\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$label)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"Build y-axis annotation decomposed ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"","code":"h_grob_y_annot(ylab, yaxis)"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"ylab (gtable) y-lab graphical object derived ggplot. yaxis (gtable) y-axis graphical object derived ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"gTree object containing y-axis annotation ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"title\", footnotes = \"footnotes\", yval = \"Survival\" )  g_el <- h_decompose_gg(gg)  grid::grid.newpage() pvp <- grid::plotViewport(margins = c(5, 4, 2, 20)) pushViewport(pvp) grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis)) grid.rect(gp = grid::gpar(lty = 1, col = \"gray35\", fill = NA))  # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"","code":"h_incidence_rate_normal(person_years, n_events, alpha = 0.05)  h_incidence_rate_normal_log(person_years, n_events, alpha = 0.05)  h_incidence_rate_exact(person_years, n_events, alpha = 0.05)  h_incidence_rate_byar(person_years, n_events, alpha = 0.05)  h_incidence_rate(person_years, n_events, control = control_incidence_rate())"},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"person_years (numeric) total person-years risk. n_events (integer) number events observed. alpha (numeric) two-sided alpha-level confidence interval. control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level: (proportion) confidence level estimated incidence rate. conf_type: (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit: (string)day, week, month, year (default) indicating time unit data input. num_pt_year: (numeric) time unit desired output (person-years).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"Estimated incidence rate rate associated confidence interval rate_ci.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"h_incidence_rate_normal(): Helper function estimate incidence rate associated confidence interval based normal approximation incidence rate. Unit one person-year. h_incidence_rate_normal_log(): Helper function estimate incidence rate associated confidence interval based normal approximation logarithm incidence rate. Unit one person-year. h_incidence_rate_exact(): Helper function estimate incidence rate associated exact confidence interval. Unit one person-year. h_incidence_rate_byar(): Helper function estimate incidence rate associated Byar's confidence interval. Unit one person-year. h_incidence_rate(): Helper function estimate incidence rate associated confidence interval.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"","code":"h_incidence_rate_normal(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] -0.003859038  0.023859038 #>   h_incidence_rate_normal_log(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] 0.002500977 0.039984382 #>   h_incidence_rate_exact(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] 0.001211046 0.036123438 #>   h_incidence_rate_byar(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] 0.001994207 0.032054171 #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper: KM Layout — h_km_layout","title":"Helper: KM Layout — h_km_layout","text":"Prepares (5 rows) x (2 cols) layout Kaplan-Meier curve.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper: KM Layout — h_km_layout","text":"","code":"h_km_layout(   data,   g_el,   title,   footnotes,   annot_at_risk = TRUE,   annot_at_risk_title = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper: KM Layout — h_km_layout","text":"data (data.frame) survival data pre-processed h_data_plot. g_el (list gtable) list obtained h_decompose_gg(). title (string) title plot. footnotes (string) footnotes plot. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper: KM Layout — h_km_layout","text":"grid layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper: KM Layout — h_km_layout","text":"layout corresponds grid two columns five rows unequal dimensions. dimension fixed, curve flexible accommodate remaining free space. left column gets annotation ggplot (y-axis) names strata patient risk tabulation. main constraint width columns must allow writing strata name. right column receive ggplot, legend, x-axis patient risk table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper: KM Layout — h_km_layout","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"tt\", footnotes = \"ff\", yval = \"Survival\" ) g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\") grid.show.layout(lyt)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"Helper functions used calculations logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"","code":"h_get_interaction_vars(fit_glm)  h_interaction_coef_name(   interaction_vars,   first_var_with_level,   second_var_with_level )  h_or_cat_interaction(   odds_ratio_var,   interaction_var,   fit_glm,   conf_level = 0.95 )  h_or_cont_interaction(   odds_ratio_var,   interaction_var,   fit_glm,   at = NULL,   conf_level = 0.95 )  h_or_interaction(   odds_ratio_var,   interaction_var,   fit_glm,   at = NULL,   conf_level = 0.95 )  h_simple_term_labels(terms, table)  h_interaction_term_labels(terms1, terms2, table, any = FALSE)  h_glm_simple_term_extract(x, fit_glm)  h_glm_interaction_extract(x, fit_glm)  h_glm_inter_term_extract(odds_ratio_var, interaction_var, fit_glm, ...)  h_logistic_simple_terms(x, fit_glm, conf_level = 0.95)  h_logistic_inter_terms(x, fit_glm, conf_level = 0.95, at = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"fit_glm (glm) logistic regression model fitted stats::glm() \"binomial\" family. Limited functionality also available conditional logistic regression models fitted survival::clogit(), currently used extract_rsp_biomarkers(). interaction_vars (character length 2) interaction variable names. first_var_with_level (character length 2) first variable name interaction level. second_var_with_level (character length 2) second variable name interaction level. odds_ratio_var (string) odds ratio variable. interaction_var (string) interaction variable. conf_level (proportion) confidence level interval. (NULL numeric) optional values interaction variable. Otherwise median used. terms (character) simple terms. table (table) table containing numbers terms. terms1 (character) terms first dimension (rows). terms2 (character) terms second dimension (rows). (flag) whether term1 term2 can fulfilled count number patients. case can scalar (strings). x (string character) variable interaction term fit_glm (depending helper function). ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"Vector names interaction variables. Name coefficient. Odds ratio. Odds ratio. Odds ratio. Term labels containing numbers patients. Term labels containing numbers patients. Tabulated main effect results logistic regression model. Tabulated interaction term results logistic regression model. data.frame tabulated interaction term results logistic regression model. Tabulated statistics given variable(s) logistic regression model. Tabulated statistics given variable(s) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"h_get_interaction_vars(): Helper function extract interaction variable names fitted model assuming one interaction term. h_interaction_coef_name(): Helper function get right coefficient name interaction variable names given levels. main value order first second variable checked interaction_vars input. h_or_cat_interaction(): Helper function calculate odds ratio estimates case odds ratio interaction variable categorical. h_or_cont_interaction(): Helper function calculate odds ratio estimates case either odds ratio interaction variable continuous. h_or_interaction(): Helper function calculate odds ratio estimates case interaction. wrapper h_or_cont_interaction() h_or_cat_interaction(). h_simple_term_labels(): Helper function construct term labels simple terms table numbers patients. h_interaction_term_labels(): Helper function construct term labels interaction terms table numbers patients. h_glm_simple_term_extract(): Helper function tabulate main effect results (conditional) logistic regression model. h_glm_interaction_extract(): Helper function tabulate interaction term results logistic regression model. h_glm_inter_term_extract(): Helper function tabulate interaction results logistic regression model. basically wrapper h_or_interaction() h_glm_simple_term_extract() puts results right data frame format. h_logistic_simple_terms(): Helper function tabulate results including odds ratios confidence intervals simple terms. h_logistic_inter_terms(): Helper function tabulate results including odds ratios confidence intervals interaction terms.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"provide function case variables continuous arise table, treatment arm variable always involved categorical.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"","code":"library(dplyr) library(broom)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred  h_glm_simple_term_extract(\"AGE\", mod1) #>   variable variable_label term term_label interaction interaction_label #> 1      AGE            Age  AGE        Age                               #>   reference reference_label  estimate  std_error df     pvalue #> 1                           0.1698216 0.09524116  1 0.07457501 #>   is_variable_summary is_term_summary #> 1               FALSE            TRUE h_glm_simple_term_extract(\"ARMCD\", mod1) #>   variable   variable_label  term              term_label interaction #> 1    ARMCD Planned Arm Code ARM A Reference ARM A, n = 64             #> 2    ARMCD Planned Arm Code ARM B           ARM B, n = 68             #> 3    ARMCD Planned Arm Code ARM C           ARM C, n = 52             #>   interaction_label reference reference_label  estimate std_error df    pvalue #> 1                                                                  2 0.3004308 #> 2                                             -1.774769  1.144405  1 0.1209443 #> 3                                               17.1922  3626.588  1 0.9962176 #>   is_variable_summary is_term_summary #> 1                TRUE           FALSE #> 2               FALSE            TRUE #> 3               FALSE            TRUE  h_glm_interaction_extract(\"ARMCD:AGE\", mod2) #>    variable                        variable_label  term              term_label #> 1 ARMCD:AGE Interaction of Planned Arm Code * Age ARM A Reference ARM A, n = 64 #> 2 ARMCD:AGE Interaction of Planned Arm Code * Age ARM B           ARM B, n = 68 #> 3 ARMCD:AGE Interaction of Planned Arm Code * Age ARM C           ARM C, n = 52 #>   interaction interaction_label reference reference_label   estimate std_error #> 1                                                                              #> 2                                                          0.3081205 0.2062392 #> 3                                                         0.02948826  548.5923 #>   df    pvalue is_variable_summary is_term_summary #> 1  2 0.3275837                TRUE           FALSE #> 2  1 0.1351767               FALSE            TRUE #> 3  1 0.9999571               FALSE            TRUE  h_glm_inter_term_extract(\"AGE\", \"ARMCD\", mod2) #>   variable variable_label term term_label interaction interaction_label #> 1      AGE            Age  AGE        Age                               #> 2      AGE            Age  AGE        Age       ARMCD  Planned Arm Code #> 3      AGE            Age  AGE        Age       ARMCD  Planned Arm Code #> 4      AGE            Age  AGE        Age       ARMCD  Planned Arm Code #>   reference reference_label    estimate std_error odds_ratio       lcl      ucl #> 1                           -0.03873898 0.1514322         NA        NA       NA #> 2     ARM A           ARM A          NA        NA  0.9620018 0.7149514 1.294420 #> 3     ARM B           ARM B          NA        NA  1.3091545 1.0021802 1.710157 #> 4     ARM C           ARM C          NA        NA  0.9907919 0.0000000      Inf #>   df   pvalue is_variable_summary is_term_summary is_reference_summary #> 1  1 0.798092               FALSE            TRUE                FALSE #> 2 NA       NA               FALSE           FALSE                 TRUE #> 3 NA       NA               FALSE           FALSE                 TRUE #> 4 NA       NA               FALSE           FALSE                 TRUE  h_logistic_simple_terms(\"AGE\", mod1) #>   variable variable_label term term_label interaction interaction_label #> 1      AGE            Age  AGE        Age                               #>   reference reference_label  estimate  std_error df     pvalue #> 1                           0.1698216 0.09524116  1 0.07457501 #>   is_variable_summary is_term_summary odds_ratio       lcl      ucl #> 1               FALSE            TRUE   1.185093 0.9832935 1.428308 #>                     ci #> 1 0.9832935, 1.4283084  h_logistic_inter_terms(c(\"RACE\", \"AGE\", \"ARMCD\", \"AGE:ARMCD\"), mod2) #>        variable                        variable_label                      term #> 1          RACE                                  Race                     ASIAN #> 2          RACE                                  Race BLACK OR AFRICAN AMERICAN #> 3          RACE                                  Race                     WHITE #> 13        ARMCD                      Planned Arm Code                     ARM A #> 23        ARMCD                      Planned Arm Code                     ARM B #> ARM B     ARMCD                      Planned Arm Code                     ARM B #> 33        ARMCD                      Planned Arm Code                     ARM C #> ARM C     ARMCD                      Planned Arm Code                     ARM C #> 11          AGE                                   Age                       AGE #> 21          AGE                                   Age                       AGE #> 31          AGE                                   Age                       AGE #> 4           AGE                                   Age                       AGE #> 12    AGE:ARMCD Interaction of Planned Arm Code * Age                     ARM A #> 22    AGE:ARMCD Interaction of Planned Arm Code * Age                     ARM B #> 32    AGE:ARMCD Interaction of Planned Arm Code * Age                     ARM C #>                              term_label interaction interaction_label reference #> 1              Reference ASIAN, n = 110                                         #> 2     BLACK OR AFRICAN AMERICAN, n = 40                                         #> 3                         WHITE, n = 34                                         #> 13              Reference ARM A, n = 64                                         #> 23                        ARM B, n = 68                                         #> ARM B                     ARM B, n = 68         AGE               Age        35 #> 33                        ARM C, n = 52                                         #> ARM C                     ARM C, n = 52         AGE               Age        35 #> 11                                  Age                                         #> 21                                  Age       ARMCD  Planned Arm Code     ARM A #> 31                                  Age       ARMCD  Planned Arm Code     ARM B #> 4                                   Age       ARMCD  Planned Arm Code     ARM C #> 12              Reference ARM A, n = 64                                         #> 22                        ARM B, n = 68                                         #> 32                        ARM C, n = 52                                         #>       reference_label    estimate std_error df     pvalue odds_ratio        lcl #> 1                                            2  0.9361139                       #> 2                        18.15018  3944.701  1  0.9963288   76299564          0 #> 3                      -0.3727152  1.025808  1  0.7163522  0.6888614 0.09224926 #> 13                                           2   0.253914         NA         NA #> 23                      -11.52715   6.96188  1 0.09777185         NA         NA #> ARM B              35          NA        NA NA         NA  0.4757148 0.03361147 #> 33                       16.33327  20278.39  1  0.9993573         NA         NA #> ARM C              35          NA        NA NA         NA   34808099          0 #> 11                    -0.03873898 0.1514322  1   0.798092         NA         NA #> 21              ARM A          NA        NA NA         NA  0.9620018  0.7149514 #> 31              ARM B          NA        NA NA         NA   1.309155    1.00218 #> 4               ARM C          NA        NA NA         NA  0.9907919          0 #> 12                                          NA         NA         NA         NA #> 22                      0.3081205 0.2062392  1  0.1351767         NA         NA #> 32                     0.02948826  548.5923  1  0.9999571         NA         NA #>            ucl is_variable_summary is_term_summary is_reference_summary #> 1                             TRUE           FALSE                FALSE #> 2          Inf               FALSE            TRUE                FALSE #> 3     5.143998               FALSE            TRUE                FALSE #> 13          NA                TRUE           FALSE                FALSE #> 23          NA               FALSE            TRUE                FALSE #> ARM B 6.732956               FALSE           FALSE                 TRUE #> 33          NA               FALSE            TRUE                FALSE #> ARM C      Inf               FALSE           FALSE                 TRUE #> 11          NA               FALSE            TRUE                FALSE #> 21     1.29442               FALSE           FALSE                 TRUE #> 31    1.710157               FALSE           FALSE                 TRUE #> 4          Inf               FALSE           FALSE                 TRUE #> 12          NA                TRUE           FALSE                FALSE #> 22          NA               FALSE            TRUE                FALSE #> 32          NA               FALSE            TRUE                FALSE #>                           ci #> 1                            #> 2                     0, Inf #> 3     0.09224926, 5.14399810 #> 13                    NA, NA #> 23                    NA, NA #> ARM B 0.03361147, 6.73295611 #> 33                    NA, NA #> ARM C                 0, Inf #> 11                    NA, NA #> 21      0.7149514, 1.2944200 #> 31        1.002180, 1.710157 #> 4                     0, Inf #> 12                    NA, NA #> 22                    NA, NA #> 32                    NA, NA"},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"Helper Function create map dataframe input dataset, can used argument trim_levels_to_map split function. Based different method, map constructed differently.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"","code":"h_map_for_count_abnormal(   df,   variables = list(anl = \"ANRIND\", split_rows = c(\"PARAM\"), range_low = \"ANRLO\",     range_high = \"ANRHI\"),   abnormal = list(low = c(\"LOW\", \"LOW LOW\"), high = c(\"HIGH\", \"HIGH HIGH\")),   method = c(\"default\", \"range\"),   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"df (data.frame) data set containing analysis variables. variables (named list string) list additional analysis variables. abnormal (named list) identifying abnormal range level(s) df. Based levels abnormality input dataset, can something like list(Low = \"LOW LOW\", High = \"HIGH HIGH\") abnormal = list(Low = \"LOW\", High = \"HIGH\")) method (string) indicates returned map constructed. Can \"default\" \"range\". na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"map data.frame.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"method \"default\", returned map abnormal directions observed df, records normal values excluded avoid error creating layout. method \"range\", returned map based rule least one observation low range > 0 low direction least one observation high range missing high direction.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"","code":"adlb <- df_explicit_na(tern_ex_adlb)  h_map_for_count_abnormal(   df = adlb,   variables = list(anl = \"ANRIND\", split_rows = c(\"LBCAT\", \"PARAM\")),   abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")),   method = \"default\",   na_str = \"<Missing>\" ) #>        LBCAT                                PARAM ANRIND #> 1  CHEMISTRY Alanine Aminotransferase Measurement    LOW #> 4  CHEMISTRY Alanine Aminotransferase Measurement   HIGH #> 7  CHEMISTRY Alanine Aminotransferase Measurement NORMAL #> 2  CHEMISTRY       C-Reactive Protein Measurement    LOW #> 3  CHEMISTRY       C-Reactive Protein Measurement   HIGH #> 8  CHEMISTRY       C-Reactive Protein Measurement NORMAL #> 5 IMMUNOLOGY         Immunoglobulin A Measurement    LOW #> 6 IMMUNOLOGY         Immunoglobulin A Measurement   HIGH #> 9 IMMUNOLOGY         Immunoglobulin A Measurement NORMAL  df <- data.frame(   USUBJID = c(rep(\"1\", 4), rep(\"2\", 4), rep(\"3\", 4)),   AVISIT = c(     rep(\"WEEK 1\", 2),     rep(\"WEEK 2\", 2),     rep(\"WEEK 1\", 2),     rep(\"WEEK 2\", 2),     rep(\"WEEK 1\", 2),     rep(\"WEEK 2\", 2)   ),   PARAM = rep(c(\"ALT\", \"CPR\"), 6),   ANRIND = c(     \"NORMAL\", \"NORMAL\", \"LOW\",     \"HIGH\", \"LOW\", \"LOW\", \"HIGH\", \"HIGH\", rep(\"NORMAL\", 4)   ),   ANRLO = rep(5, 12),   ANRHI = rep(20, 12) ) df$ANRIND <- factor(df$ANRIND, levels = c(\"LOW\", \"HIGH\", \"NORMAL\")) h_map_for_count_abnormal(   df = df,   variables = list(     anl = \"ANRIND\",     split_rows = c(\"PARAM\"),     range_low = \"ANRLO\",     range_high = \"ANRHI\"   ),   abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")),   method = \"range\",   na_str = \"<Missing>\" ) #>   PARAM ANRIND #> 1   ALT    LOW #> 3   ALT   HIGH #> 5   ALT NORMAL #> 2   CPR    LOW #> 4   CPR   HIGH #> 6   CPR NORMAL"},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"Functions calculate odds ratios estimate_odds_ratio().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"","code":"or_glm(data, conf_level)  or_clogit(data, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"data (data.frame) data frame containing least variables rsp grp, optionally strata or_clogit(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"named list elements or_ci n_tot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"or_glm(): Estimates odds ratio based stats::glm(). Note must exactly 2 groups data specified grp variable. or_clogit(): estimates odds ratio based survival::clogit(). done whole data set including groups, since results pairwise comparisons groups.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"","code":"# Data with 2 groups. data <- data.frame(   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)),   grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)],   strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)],   stringsAsFactors = TRUE )  # Odds ratio based on glm. or_glm(data, conf_level = 0.95) #> $or_ci #>        est        lcl        ucl  #> 0.33333333 0.01669735 6.65441589  #>  #> $n_tot #> n_tot  #>     8  #>   # Data with 3 groups. data <- data.frame(   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)),   grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)],   strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)],   stringsAsFactors = TRUE )  # Odds ratio based on stratified estimation by conditional logistic regression. or_clogit(data, conf_level = 0.95) #> $or_ci #> $or_ci$b #>        est        lcl        ucl  #> 0.28814553 0.02981009 2.78522598  #>  #> $or_ci$c #>       est       lcl       ucl  #> 0.5367919 0.0673365 4.2791881  #>  #>  #> $n_tot #> n_tot  #>    20  #>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"","code":"h_pkparam_sort(pk_data, key_var = \"PARAMCD\")"},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"pk_data (data.frame)Pharmacokinetics dataframe key_var (character) key variable used merge pk_data metadata created d_pkparam()","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"PK data.frame sorted PARAM variable.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"","code":"library(dplyr)  adpp <- tern_ex_adpp %>% mutate(PKPARAM = factor(paste0(PARAM, \" (\", AVALU, \")\"))) pk_ordered_data <- h_pkparam_sort(adpp)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"","code":"prop_diff_wald(rsp, grp, conf_level = 0.95, correct = FALSE)  prop_diff_ha(rsp, grp, conf_level)  prop_diff_nc(rsp, grp, conf_level, correct = FALSE)  prop_diff_cmh(rsp, grp, strata, conf_level = 0.95)  prop_diff_strat_nc(   rsp,   grp,   strata,   weights_method = c(\"cmh\", \"wilson_h\"),   conf_level = 0.95,   correct = FALSE )"},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"rsp (logical) whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). conf_level (proportion) confidence level interval. correct (logical) whether include continuity correction. information, see stats::prop.test(). strata (factor) variable one level per stratum length rsp. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"named list elements diff (proportion difference) diff_ci (proportion difference confidence interval).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"prop_diff_wald(): Wald interval follows usual textbook definition single proportion confidence interval using normal approximation. possible include continuity correction Wald's interval. prop_diff_ha(): Anderson-Hauck confidence interval. prop_diff_nc(): Newcombe confidence interval. based Wilson score confidence interval single binomial proportion. prop_diff_cmh(): Calculates weighted difference. defined difference response rates experimental treatment group control treatment group, adjusted stratification factors applying Cochran-Mantel-Haenszel (CMH) weights. CMH chi-squared test, use stats::mantelhaen.test(). prop_diff_strat_nc(): Calculates stratified Newcombe confidence interval difference response rates experimental treatment group control treatment group, adjusted stratification factors. implementation follows closely one proposed Yan Su (2010) . Weights can estimated heuristic proposed prop_strat_wilson() CMH-derived weights (see prop_diff_cmh()).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329--335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"","code":"# Wald confidence interval set.seed(2) rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20) grp <- factor(c(rep(\"A\", 10), rep(\"B\", 10))) prop_diff_wald(rsp = rsp, grp = grp, conf_level = 0.95, correct = FALSE) #> $diff #> [1] 0 #>  #> $diff_ci #> [1] -0.4382613  0.4382613 #>   # Anderson-Hauck confidence interval ## \"Mid\" case: 3/4 respond in group A, 1/2 respond in group B. rsp <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE) grp <- factor(c(\"A\", \"B\", \"A\", \"B\", \"A\", \"A\"), levels = c(\"B\", \"A\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.90) #> $diff #> [1] 0.25 #>  #> $diff_ci #> [1] -0.9195011  1.0000000 #>   ## Edge case: Same proportion of response in A and B. rsp <- c(TRUE, FALSE, TRUE, FALSE) grp <- factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.6) #> $diff #> [1] 0 #>  #> $diff_ci #> [1] -0.8451161  0.8451161 #>   # `Newcombe` confidence interval  set.seed(1) rsp <- c(   sample(c(TRUE, FALSE), size = 40, prob = c(3 / 4, 1 / 4), replace = TRUE),   sample(c(TRUE, FALSE), size = 40, prob = c(1 / 2, 1 / 2), replace = TRUE) ) grp <- factor(rep(c(\"A\", \"B\"), each = 40), levels = c(\"B\", \"A\")) table(rsp, grp) #>        grp #> rsp      B  A #>   FALSE 20 10 #>   TRUE  20 30 prop_diff_nc(rsp = rsp, grp = grp, conf_level = 0.9) #> $diff #> [1] 0.25 #>  #> $diff_ci #> [1] 0.07193388 0.40725819 #>   # Cochran-Mantel-Haenszel confidence interval  set.seed(2) rsp <- sample(c(TRUE, FALSE), 100, TRUE) grp <- sample(c(\"Placebo\", \"Treatment\"), 100, TRUE) grp <- factor(grp, levels = c(\"Placebo\", \"Treatment\")) strata_data <- data.frame(   \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   stringsAsFactors = TRUE )  prop_diff_cmh(   rsp = rsp, grp = grp, strata = interaction(strata_data),   conf_level = 0.90 ) #> $prop #>   Placebo Treatment  #> 0.5331117 0.3954251  #>  #> $prop_ci #> $prop_ci$Placebo #> [1] 0.4306536 0.6355698 #>  #> $prop_ci$Treatment #> [1] 0.2890735 0.5017768 #>  #>  #> $diff #> [1] -0.1376866 #>  #> $diff_ci #> [1] -0.285363076  0.009989872 #>  #> $weights #>       a.x       b.x       a.y       b.y       a.z       b.z  #> 0.1148388 0.2131696 0.1148388 0.2131696 0.1767914 0.1671918  #>  #> $n1 #> a.x b.x a.y b.y a.z b.z  #>   4  11   8  11  13  11  #>  #> $n2 #> a.x b.x a.y b.y a.z b.z  #>   8   9   4   9   6   6  #>   # Stratified `Newcombe` confidence interval  set.seed(2) data_set <- data.frame(   \"rsp\" = sample(c(TRUE, FALSE), 100, TRUE),   \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   \"grp\" = sample(c(\"Placebo\", \"Treatment\"), 100, TRUE),   stringsAsFactors = TRUE )  prop_diff_strat_nc(   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),   weights_method = \"cmh\",   conf_level = 0.90 ) #> $diff #> [1] -0.05777672 #>  #> $diff_ci #>      lower      upper  #> -0.2236537  0.1119331  #>   prop_diff_strat_nc(   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),   weights_method = \"wilson_h\",   conf_level = 0.90 ) #> $diff #> [1] -0.07771884 #>  #> $diff_ci #>      lower      upper  #> -0.2540844  0.1027720  #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions to Test Proportion Differences — h_prop_diff_test","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"Helper functions implement various tests difference two proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"","code":"prop_chisq(tbl)  prop_cmh(ary)  prop_schouten(tbl)  prop_fisher(tbl)"},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"tbl (matrix) matrix two groups rows binary response (TRUE/FALSE) columns. ary (array, 3 dimensions) array two groups rows, binary response (TRUE/FALSE) columns, strata third dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"prop_chisq(): performs Chi-Squared test. Internally calls stats::prop.test(). prop_cmh(): performs stratified Cochran-Mantel-Haenszel test. Internally calls stats::mantelhaen.test(). Note strata less two observations automatically discarded. prop_schouten(): performs Chi-Squared test Schouten correction. prop_fisher(): performs Fisher's exact test. Internally calls stats::fisher.test().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"Functions calculate different proportion confidence intervals use estimate_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"","code":"prop_wilson(rsp, conf_level, correct = FALSE)  prop_strat_wilson(   rsp,   strata,   weights = NULL,   conf_level = 0.95,   max_iterations = NULL,   correct = FALSE )  prop_clopper_pearson(rsp, conf_level)  prop_wald(rsp, conf_level, correct = FALSE)  prop_agresti_coull(rsp, conf_level)  prop_jeffreys(rsp, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"rsp (logical) whether subject responder . conf_level (proportion) confidence level interval. correct (flag) apply continuity correction. strata (factor) variable one level per stratum length rsp. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010)  minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"Confidence interval proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"prop_wilson(): Calculates Wilson interval calling stats::prop.test(). Also referred Wilson score interval. prop_strat_wilson(): Calculates stratified Wilson confidence interval unequal proportions described Yan Su (2010) prop_clopper_pearson(): Calculates Clopper-Pearson interval calling stats::binom.test(). Also referred exact method. prop_wald(): Calculates Wald interval following usual textbook definition single proportion confidence interval using normal approximation. prop_agresti_coull(): Calculates Agresti-Coull interval (created Alan Agresti Brent Coull) (95% CI) adding two successes two failures data using Wald formula construct CI. prop_jeffreys(): Calculates Jeffreys interval, equal-tailed interval based non-informative Jeffreys prior binomial proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329--335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"","code":"rsp <- c(   TRUE, TRUE, TRUE, TRUE, TRUE,   FALSE, FALSE, FALSE, FALSE, FALSE ) prop_wilson(rsp, conf_level = 0.9) #> [1] 0.2692718 0.7307282  # Stratified Wilson confidence interval with unequal probabilities  set.seed(1) rsp <- sample(c(TRUE, FALSE), 100, TRUE) strata_data <- data.frame(   \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   stringsAsFactors = TRUE ) strata <- interaction(strata_data) n_strata <- ncol(table(rsp, strata)) # Number of strata  prop_strat_wilson(   rsp = rsp, strata = strata,   conf_level = 0.90 ) #> $conf_int #>     lower     upper  #> 0.4072891 0.5647887  #>  #> $weights #>       a.x       b.x       a.y       b.y       a.z       b.z  #> 0.2074199 0.1776464 0.1915610 0.1604678 0.1351096 0.1277952  #>   # Not automatic setting of weights prop_strat_wilson(   rsp = rsp, strata = strata,   weights = rep(1 / n_strata, n_strata),   conf_level = 0.90 ) #> $conf_int #>     lower     upper  #> 0.4190436 0.5789733  #>   prop_clopper_pearson(rsp, conf_level = .95) #> [1] 0.3886442 0.5919637  prop_wald(rsp, conf_level = 0.95) #> [1] 0.3920214 0.5879786 prop_wald(rsp, conf_level = 0.95, correct = TRUE) #> [1] 0.3870214 0.5929786  prop_agresti_coull(rsp, conf_level = 0.95) #> [1] 0.3942193 0.5865206  prop_jeffreys(rsp, conf_level = 0.95) #> [1] 0.3934779 0.5870917"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"","code":"h_rsp_to_logistic_variables(variables, biomarker)  h_logistic_mult_cont_df(variables, data, control = control_logistic())  h_tab_rsp_one_biomarker(df, vars, .indent_mods = 0L)"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (named list) controls response definition confidence level produced control_logistic(). df (data.frame) results single biomarker, part returned extract_rsp_biomarkers() (needs couple columns added high-level function relative returned h_logistic_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables() returns named list elements response, arm, covariates, strata. h_logistic_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_rsp_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables(): helps converting \"response\" function variable list \"logistic regression\" variable list. reason currently inconsistency variable names accepted extract_rsp_subgroups() fit_logistic(). h_logistic_mult_cont_df(): prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates strat. h_tab_rsp_one_biomarker(): prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  # This is how the variable list is converted internally. h_rsp_to_logistic_variables(   variables = list(     rsp = \"RSP\",     covariates = c(\"A\", \"B\"),     strat = \"D\"   ),   biomarker = \"AGE\" ) #> $response #> [1] \"RSP\" #>  #> $arm #> [1] \"AGE\" #>  #> $covariates #> [1] \"A\" \"B\" #>  #> $strata #> [1] \"D\" #>   # For a single population, estimate separately the effects # of two biomarkers. df <- h_logistic_mult_cont_df(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\"   ),   data = adrs_f ) df #>   biomarker              biomarker_label n_tot n_rsp prop        or       lcl #> 1    BMRKR1 Continuous Level Biomarker 1   200   164 0.82 0.9755036 0.8804862 #> 2       AGE                          Age   200   164 0.82 0.9952416 0.9462617 #>        ucl conf_level      pval     pval_label #> 1 1.080775       0.95 0.6352602 p-value (Wald) #> 2 1.046757       0.95 0.8530389 p-value (Wald)  # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     strat = \"STRATA1\"   ),   data = adrs_f[NULL, ] ) #>   biomarker              biomarker_label n_tot n_tot_events median hr lcl ucl #> 1    BMRKR1 Continuous Level Biomarker 1     0            0     NA NA  NA  NA #> 2       AGE                          Age     0            0     NA NA  NA  NA #>   conf_level pval     pval_label #> 1       0.95   NA p-value (Wald) #> 2       0.95   NA p-value (Wald)  # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\"  h_tab_rsp_one_biomarker(   df1,   vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\") ) #>                Total n   Responders   Response (%)   Odds Ratio      95% CI      p-value (Wald) #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> All patients     200        164          82.0%          0.98      (0.88, 1.08)       0.6353"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"Helper functions tabulate data frame statistics response rate odds ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"","code":"h_proportion_df(rsp, arm)  h_proportion_subgroups_df(   variables,   data,   groups_lists = list(),   label_all = \"All Patients\" )  h_odds_ratio_df(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL)  h_odds_ratio_subgroups_df(   variables,   data,   groups_lists = list(),   conf_level = 0.95,   method = NULL,   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"rsp (logical) whether subject responder . arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame NULL) required stratified analysis performed. conf_level (proportion) confidence level interval. method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"h_proportion_df() returns data.frame columns arm, n, n_rsp, prop. h_proportion_subgroups_df() returns data.frame columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. h_odds_ratio_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, optionally pval pval_label. h_odds_ratio_subgroups_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"h_proportion_df(): helper prepare data frame binary responses arm. h_proportion_subgroups_df(): summarizes proportion binary responses arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_odds_ratio_df(): helper prepare data frame estimates odds ratio treatment control arm. h_odds_ratio_subgroups_df(): summarizes estimates odds ratio treatment control arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  h_proportion_df(   c(TRUE, FALSE, FALSE),   arm = factor(c(\"A\", \"A\", \"B\"), levels = c(\"A\", \"B\")) ) #>   arm n n_rsp prop #> 1   A 2     1  0.5 #> 2   B 1     0  0.0  h_proportion_subgroups_df(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Define groupings for BMRKR2 levels. h_proportion_subgroups_df(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>           arm  n n_rsp      prop        subgroup    var #> 1  B: Placebo 73    50 0.6849315    All Patients    ALL #> 2   A: Drug X 69    59 0.8550725    All Patients    ALL #> 3  B: Placebo 40    25 0.6250000               F    SEX #> 4   A: Drug X 38    36 0.9473684               F    SEX #> 5  B: Placebo 33    25 0.7575758               M    SEX #> 6   A: Drug X 31    23 0.7419355               M    SEX #> 7  B: Placebo 24    13 0.5416667             low BMRKR2 #> 8   A: Drug X 26    21 0.8076923             low BMRKR2 #> 9  B: Placebo 47    30 0.6382979      low/medium BMRKR2 #> 10  A: Drug X 52    44 0.8461538      low/medium BMRKR2 #> 11 B: Placebo 73    50 0.6849315 low/medium/high BMRKR2 #> 12  A: Drug X 69    59 0.8550725 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Unstratatified analysis. h_odds_ratio_df(   c(TRUE, FALSE, FALSE, TRUE),   arm = factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) ) #>   arm n_tot or        lcl      ucl conf_level #> 1         4  1 0.01984252 50.39681       0.95  # Include p-value. h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = \"chisq\") #>   arm n_tot    or      lcl      ucl conf_level       pval #> 1       142 2.714 1.180449 6.239827       0.95 0.01643036 #>                   pval_label #> 1 p-value (Chi-Squared Test)  # Stratatified analysis. h_odds_ratio_df(   rsp = adrs_f$rsp,   arm = adrs_f$ARM,   strata_data = adrs_f[, c(\"STRATA1\", \"STRATA2\")],   method = \"cmh\" ) #>   arm n_tot       or      lcl      ucl conf_level       pval #> 1       142 2.665586 1.146149 6.199324       0.95 0.02019665 #>                               pval_label #> 1 p-value (Cochran-Mantel-Haenszel Test)  # Unstratified analysis. h_odds_ratio_subgroups_df(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142  2.714000 1.1804488  6.239827       0.95 All Patients    ALL #> 2        78 10.800000 2.2669576 51.452218       0.95            F    SEX #> 3        64  0.920000 0.2966470  2.853223       0.95            M    SEX #> 4        50  3.553846 1.0047370 12.570277       0.95          LOW BMRKR2 #> 5        49  2.705882 0.5911718 12.385232       0.95       MEDIUM BMRKR2 #> 6        43  2.250000 0.3970298 12.750933       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis  # Stratified analysis. h_odds_ratio_subgroups_df(   variables = list(     rsp = \"rsp\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\"),     strat = c(\"STRATA1\", \"STRATA2\")   ),   data = adrs_f ) #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142 2.6655860 1.1461490  6.199324       0.95 All Patients    ALL #> 2        78 7.7065093 1.5817529 37.547132       0.95            F    SEX #> 3        64 0.9572284 0.2990954  3.063525       0.95            M    SEX #> 4        50 3.0323726 0.8833232 10.409875       0.95          LOW BMRKR2 #> 5        49 2.1264996 0.4312008 10.486995       0.95       MEDIUM BMRKR2 #> 6        43 2.5134820 0.4351747 14.517370       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis  # Define groupings of BMRKR2 levels. h_odds_ratio_subgroups_df(   variables = list(     rsp = \"rsp\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>   arm n_tot        or      lcl       ucl conf_level        subgroup    var #> 1       142  2.714000 1.180449  6.239827       0.95    All Patients    ALL #> 2        78 10.800000 2.266958 51.452218       0.95               F    SEX #> 3        64  0.920000 0.296647  2.853223       0.95               M    SEX #> 4        50  3.553846 1.004737 12.570277       0.95             low BMRKR2 #> 5        99  3.116667 1.193409  8.139385       0.95      low/medium BMRKR2 #> 6       142  2.714000 1.180449  6.239827       0.95 low/medium/high BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Dataframe by Subgroups — h_split_by_subgroups","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"Split dataframe non-nested list subsets.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"","code":"h_split_by_subgroups(data, subgroups, groups_lists = list())"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"data (data.frame) dataset split. subgroups (character) names factor variables data used create subsets. Unused levels present data dropped. Note order vector determines order downstream table. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"list subset data (df) metadata subset (df_labels).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"Main functionality prepare data use forest plot layouts.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"","code":"df <- data.frame(   x = c(1:5),   y = factor(c(\"A\", \"B\", \"A\", \"B\", \"A\"), levels = c(\"A\", \"B\", \"C\")),   z = factor(c(\"C\", \"C\", \"D\", \"D\", \"D\"), levels = c(\"D\", \"C\")) ) formatters::var_labels(df) <- paste(\"label for\", names(df))  h_split_by_subgroups(   data = df,   subgroups = c(\"y\", \"z\") ) #> $y.A #> $y.A$df #>   x y z #> 1 1 A C #> 2 3 A D #> 3 5 A D #>  #> $y.A$df_labels #>   subgroup var   var_label #> 1        A   y label for y #>  #>  #> $y.B #> $y.B$df #>   x y z #> 1 2 B C #> 2 4 B D #>  #> $y.B$df_labels #>   subgroup var   var_label #> 1        B   y label for y #>  #>  #> $z.D #> $z.D$df #>   x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #>  #> $z.D$df_labels #>   subgroup var   var_label #> 1        D   z label for z #>  #>  #> $z.C #> $z.C$df #>   x y z #> 1 1 A C #> 2 2 B C #>  #> $z.C$df_labels #>   subgroup var   var_label #> 1        C   z label for z #>  #>   h_split_by_subgroups(   data = df,   subgroups = c(\"y\", \"z\"),   groups_lists = list(     y = list(\"AB\" = c(\"A\", \"B\"), \"C\" = \"C\")   ) ) #> $y.AB #> $y.AB$df #>   x y z #> 1 1 A C #> 2 2 B C #> 3 3 A D #> 4 4 B D #> 5 5 A D #>  #> $y.AB$df_labels #>   subgroup var   var_label #> 1       AB   y label for y #>  #>  #> $z.D #> $z.D$df #>   x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #>  #> $z.D$df_labels #>   subgroup var   var_label #> 1        D   z label for z #>  #>  #> $z.C #> $z.C$df #>   x y z #> 1 1 A C #> 2 2 B C #>  #> $z.C$df_labels #>   subgroup var   var_label #> 1        C   z label for z #>  #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Split parameters — h_split_param","title":"Split parameters — h_split_param","text":"divides data vector param groups defined f based specified values. relevant rtables layers distribute parameters .stats ' .formats lists items corresponding specific analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split parameters — h_split_param","text":"","code":"h_split_param(param, value, f)"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split parameters — h_split_param","text":"param (vector) parameter split. value (vector) value used split. f (list vectors) reference make split","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split parameters — h_split_param","text":"named list element names f, containing elements specified .stats.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split parameters — h_split_param","text":"","code":"f <- list(   surv = c(\"pt_at_risk\", \"event_free_rate\", \"rate_se\", \"rate_ci\"),   surv_diff = c(\"rate_diff\", \"rate_diff_ci\", \"ztest_pval\") )  .stats <- c(\"pt_at_risk\", \"rate_diff\") h_split_param(.stats, .stats, f = f) #> $surv #> [1] \"pt_at_risk\" #>  #> $surv_diff #> [1] \"rate_diff\" #>   # $surv # [1] \"pt_at_risk\" # # $surv_diff # [1] \"rate_diff\"  .formats <- c(\"pt_at_risk\" = \"xx\", \"event_free_rate\" = \"xxx\") h_split_param(.formats, names(.formats), f = f) #> $surv #>      pt_at_risk event_free_rate  #>            \"xx\"           \"xxx\"  #>  #> $surv_diff #> NULL #>   # $surv # pt_at_risk event_free_rate # \"xx\"           \"xxx\" # # $surv_diff # NULL"},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"Helper Function create new SMQ variable ADAE consists adverse events belonging selected Standardized/Customized queries. new dataset contain records adverse events belonging selected baskets. Remember na_str must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"h_stack_by_baskets(   df,   baskets = grep(\"^(SMQ|CQ).+NAM$\", names(df), value = TRUE),   smq_varlabel = \"Standardized MedDRA Query\",   keys = c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"AEDECOD\", \"AESEQ\"),   aag_summary = NULL,   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"df (data.frame) data set containing analysis variables. baskets (character) variable names selected Standardized/Customized queries. smq_varlabel (string) label new variable created. keys (character) names key variables returned along new variable created. aag_summary (data.frame) containing SMQ baskets levels interest final SMQ variable. useful levels interest observed df dataset. two columns dataset named basket basket_name. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"data.frame variables keys taken df new variable SMQ containing records belonging baskets selected via baskets argument.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"adae <- tern_ex_adae[1:20, ] %>% df_explicit_na() h_stack_by_baskets(df = adae) #> # A tibble: 8 × 6 #>   STUDYID USUBJID               ASTDTM              AEDECOD       AESEQ SMQ      #>   <fct>   <fct>                 <dttm>              <fct>         <int> <fct>    #> 1 AB12345 AB12345-BRA-11-id-8   2021-12-05 02:02:07 dcd D.2.1.5.3     2 D.2.1.5… #> 2 AB12345 AB12345-BRA-12-id-120 2020-02-05 01:42:29 dcd D.2.1.5.3     2 D.2.1.5… #> 3 AB12345 AB12345-BRA-1-id-171  2022-11-29 12:18:31 dcd C.1.1.1.3     2 C.1.1.1… #> 4 AB12345 AB12345-BRA-1-id-23   2020-07-10 07:32:49 dcd B.2.2.3.1     3 C.1.1.1… #> 5 AB12345 AB12345-BRA-1-id-59   2021-10-10 23:54:46 dcd C.1.1.1.3     4 C.1.1.1… #> 6 AB12345 AB12345-BRA-1-id-9    2021-06-01 14:39:09 dcd C.1.1.1.3     1 C.1.1.1… #> 7 AB12345 AB12345-BRA-11-id-8   2021-12-21 02:02:07 dcd C.1.1.1.3     3 C.1.1.1… #> 8 AB12345 AB12345-BRA-12-id-120 2020-10-01 01:42:29 dcd C.1.1.1.3     3 C.1.1.1…  aag <- data.frame(   NAMVAR = c(\"CQ01NAM\", \"CQ02NAM\", \"SMQ01NAM\", \"SMQ02NAM\"),   REFNAME = c(     \"D.2.1.5.3/A.1.1.1.1 AESI\", \"X.9.9.9.9/Y.8.8.8.8 AESI\",     \"C.1.1.1.3/B.2.2.3.1 AESI\", \"C.1.1.1.3/B.3.3.3.3 AESI\"   ),   SCOPE = c(\"\", \"\", \"BROAD\", \"BROAD\"),   stringsAsFactors = FALSE )  basket_name <- character(nrow(aag)) cq_pos <- grep(\"^(CQ).+NAM$\", aag$NAMVAR) smq_pos <- grep(\"^(SMQ).+NAM$\", aag$NAMVAR) basket_name[cq_pos] <- aag$REFNAME[cq_pos] basket_name[smq_pos] <- paste0(   aag$REFNAME[smq_pos], \"(\", aag$SCOPE[smq_pos], \")\" )  aag_summary <- data.frame(   basket = aag$NAMVAR,   basket_name = basket_name,   stringsAsFactors = TRUE )  result <- h_stack_by_baskets(df = adae, aag_summary = aag_summary) #> Warning: There are 0 basket levels in common between aag_summary$basket_name and df. all(levels(aag_summary$basket_name) %in% levels(result$SMQ)) #> [1] TRUE  h_stack_by_baskets(   df = adae,   aag_summary = NULL,   keys = c(\"STUDYID\", \"USUBJID\", \"AEDECOD\", \"ARM\"),   baskets = \"SMQ01NAM\" ) #> # A tibble: 6 × 5 #>   STUDYID USUBJID               AEDECOD       ARM            SMQ                 #>   <fct>   <fct>                 <fct>         <fct>          <fct>               #> 1 AB12345 AB12345-BRA-1-id-171  dcd C.1.1.1.3 B: Placebo     C.1.1.1.3/B.2.2.3.… #> 2 AB12345 AB12345-BRA-1-id-23   dcd B.2.2.3.1 A: Drug X      C.1.1.1.3/B.2.2.3.… #> 3 AB12345 AB12345-BRA-1-id-59   dcd C.1.1.1.3 A: Drug X      C.1.1.1.3/B.2.2.3.… #> 4 AB12345 AB12345-BRA-1-id-9    dcd C.1.1.1.3 C: Combination C.1.1.1.3/B.2.2.3.… #> 5 AB12345 AB12345-BRA-11-id-8   dcd C.1.1.1.3 A: Drug X      C.1.1.1.3/B.2.2.3.… #> 6 AB12345 AB12345-BRA-12-id-120 dcd C.1.1.1.3 A: Drug X      C.1.1.1.3/B.2.2.3.…"},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"Helper functions used internally STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"","code":"h_step_window(x, control = control_step())  h_step_trt_effect(data, model, variables, x)  h_step_survival_formula(variables, control = control_step())  h_step_survival_est(   formula,   data,   variables,   x,   subset = rep(TRUE, nrow(data)),   control = control_coxph() )  h_step_rsp_formula(variables, control = c(control_step(), control_logistic()))  h_step_rsp_est(   formula,   data,   variables,   x,   subset = rep(TRUE, nrow(data)),   control = control_logistic() )"},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"x (numeric) biomarker value(s) use (without NA). control (named list) output control_step(). data (data.frame) dataset containing variables summarize. model regression model object. variables (named list string) list additional analysis variables. formula (formula) regression model formula. subset (logical) subset vector.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"h_step_window() returns list containing window-selection matrix sel interval information matrix interval. h_step_trt_effect() returns vector elements est se. h_step_survival_formula() returns model formula. h_step_survival_est() returns matrix number observations n, events, log hazard ratio estimates loghr, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x. h_step_rsp_formula() returns model formula. h_step_rsp_est() returns matrix number observations n, log odds ratio estimates logor, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"h_step_window(): creates windows STEP, based control settings provided. h_step_trt_effect(): calculates estimated treatment effect estimate linear predictor scale corresponding standard error STEP model fitted data given variables specification, single biomarker value x. works coxph glm models, .e. calculating log hazard ratio log odds ratio estimates. h_step_survival_formula(): builds model formula used survival STEP calculations. h_step_survival_est(): estimates model formula built based variables data given subset control parameters Cox regression. h_step_rsp_formula(): builds model formula used response STEP calculations. h_step_rsp_est(): estimates model formula built based variables data given subset control parameters logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"","code":"h_surv_to_coxreg_variables(variables, biomarker)  h_coxreg_mult_cont_df(variables, data, control = control_coxreg())  h_tab_surv_one_biomarker(df, vars, time_unit, .indent_mods = 0L)"},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (list) list parameters returned helper function control_coxreg(). df (data.frame) results single biomarker, part returned extract_survival_biomarkers() (needs couple columns added high-level function relative returned h_coxreg_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot_events: Total number events per group. n_tot: Total number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables() returns named list elements time, event, arm, covariates, strata. h_coxreg_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_surv_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables(): helps converting \"survival\" function variable list \"Cox regression\" variable list. reason currently inconsistency variable names accepted extract_survival_subgroups() fit_coxreg_multivar(). h_coxreg_mult_cont_df(): prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables) optionally subgroups strat. h_tab_surv_one_biomarker(): prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = FALSE)  adtte_f <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  # This is how the variable list is converted internally. h_surv_to_coxreg_variables(   variables = list(     tte = \"AVAL\",     is_event = \"EVNT\",     covariates = c(\"A\", \"B\"),     strata = \"D\"   ),   biomarker = \"AGE\" ) #> $time #> [1] \"AVAL\" #>  #> $event #> [1] \"EVNT\" #>  #> $arm #> [1] \"AGE\" #>  #> $covariates #> [1] \"A\" \"B\" #>  #> $strata #> [1] \"D\" #>   # For a single population, estimate separately the effects # of two biomarkers. df <- h_coxreg_mult_cont_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     strata = c(\"STRATA1\", \"STRATA2\")   ),   data = adtte_f ) df #>   biomarker              biomarker_label n_tot n_tot_events   median       hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.000189 #> 2       AGE                          Age   200          141 753.5176 1.008267 #>         lcl      ucl conf_level      pval     pval_label #> 1 0.9511092 1.051802       0.95 0.9941244 p-value (Wald) #> 2 0.9845155 1.032591       0.95 0.4984743 p-value (Wald)  # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"REGION1\",     strata = c(\"STRATA1\", \"STRATA2\")   ),   data = adtte_f[NULL, ] ) #>   biomarker              biomarker_label n_tot n_tot_events median hr lcl ucl #> 1    BMRKR1 Continuous Level Biomarker 1     0            0     NA NA  NA  NA #> 2       AGE                          Age     0            0     NA NA  NA  NA #>   conf_level pval     pval_label #> 1       0.95   NA p-value (Wald) #> 2       0.95   NA p-value (Wald)  # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\" h_tab_surv_one_biomarker(   df1,   vars = c(\"n_tot\", \"n_tot_events\", \"median\", \"hr\", \"ci\", \"pval\"),   time_unit = \"days\" ) #>                Total n   Total Events   Median (days)   Hazard Ratio   95% Wald CI    p-value (Wald) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> All patients     200         141            753.5           1.00       (0.95, 1.05)       0.9941"},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"Helper functions tabulate data frame statistics median survival time hazard ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"","code":"h_survtime_df(tte, is_event, arm)  h_survtime_subgroups_df(   variables,   data,   groups_lists = list(),   label_all = \"All Patients\" )  h_coxph_df(tte, is_event, arm, strata_data = NULL, control = control_coxph())  h_coxph_subgroups_df(   variables,   data,   groups_lists = list(),   control = control_coxph(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"tte (numeric) contains time--event duration values. is_event (logical)TRUE event, FALSE time event censored. arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame NULL) required stratified analysis performed. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"h_survtime_df() returns data.frame columns arm, n, n_events, median. h_survtime_subgroups_df() returns data.frame columns arm, n, n_events, median, subgroup, var, var_label, row_type. h_coxph_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval pval_label. h_coxph_subgroups_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"h_survtime_df(): helper prepare data frame median survival times arm. h_survtime_subgroups_df(): summarizes median survival times arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_coxph_df(): helper prepare data frame estimates treatment hazard ratio. h_coxph_subgroups_df(): summarizes estimates treatment hazard ratio across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     is_event = CNSR == 0   ) labels <- c(\"ARM\" = adtte_labels[[\"ARM\"]], \"SEX\" = adtte_labels[[\"SEX\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  # Extract median survival time for one group. h_survtime_df(   tte = adtte_f$AVAL,   is_event = adtte_f$is_event,   arm = adtte_f$ARM ) #>          arm  n n_events   median #> 1 B: Placebo 73       57 727.8043 #> 2  A: Drug X 69       44 974.6402  # Extract median survival time for multiple groups. h_survtime_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) #>           arm  n n_events    median     subgroup    var #> 1  B: Placebo 73       57  727.8043 All Patients    ALL #> 2   A: Drug X 69       44  974.6402 All Patients    ALL #> 3  B: Placebo 40       31  599.1772            F    SEX #> 4   A: Drug X 38       24 1016.2982            F    SEX #> 5  B: Placebo 33       26  888.4916            M    SEX #> 6   A: Drug X 31       20  974.6402            M    SEX #> 7  B: Placebo 24       21  735.4722          LOW BMRKR2 #> 8   A: Drug X 26       15  974.6402          LOW BMRKR2 #> 9  B: Placebo 23       14  731.8352       MEDIUM BMRKR2 #> 10  A: Drug X 26       17  964.2197       MEDIUM BMRKR2 #> 11 B: Placebo 26       22  654.8245         HIGH BMRKR2 #> 12  A: Drug X 17       12 1016.2982         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Define groupings for BMRKR2 levels. h_survtime_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>           arm  n n_events    median        subgroup    var #> 1  B: Placebo 73       57  727.8043    All Patients    ALL #> 2   A: Drug X 69       44  974.6402    All Patients    ALL #> 3  B: Placebo 40       31  599.1772               F    SEX #> 4   A: Drug X 38       24 1016.2982               F    SEX #> 5  B: Placebo 33       26  888.4916               M    SEX #> 6   A: Drug X 31       20  974.6402               M    SEX #> 7  B: Placebo 24       21  735.4722             low BMRKR2 #> 8   A: Drug X 26       15  974.6402             low BMRKR2 #> 9  B: Placebo 47       35  735.4722      low/medium BMRKR2 #> 10  A: Drug X 52       32  964.2197      low/medium BMRKR2 #> 11 B: Placebo 73       57  727.8043 low/medium/high BMRKR2 #> 12  A: Drug X 69       44  974.6402 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Extract hazard ratio for one group. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM) #>   arm n_tot n_tot_events        hr       lcl      ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.057337       0.95 0.09049511 #>           pval_label #> 1 p-value (log-rank)  # Extract hazard ratio for one group with stratification factor. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1) #>   arm n_tot n_tot_events        hr       lcl     ucl conf_level       pval #> 1       142          101 0.6646586 0.4399495 1.00414       0.95 0.05089188 #>           pval_label #> 1 p-value (log-rank)  # Extract hazard ratio for multiple groups. h_coxph_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        49           31 0.7651261 0.3641277 1.6077269       0.95 0.47860004 #> 6        43           34 0.6662356 0.3257413 1.3626456       0.95 0.26285846 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis  # Define groupings of BMRKR2 levels. h_coxph_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        99           67 0.7472958 0.4600419 1.2139136       0.95 0.23764314 #> 6       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #>           pval_label        subgroup    var                    var_label #> 1 p-value (log-rank)    All Patients    ALL                 All Patients #> 2 p-value (log-rank)               F    SEX                          Sex #> 3 p-value (log-rank)               M    SEX                          Sex #> 4 p-value (log-rank)             low BMRKR2 Continuous Level Biomarker 2 #> 5 p-value (log-rank)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 p-value (log-rank) low/medium/high BMRKR2 Continuous Level Biomarker 2 #>   row_type #> 1  content #> 2 analysis #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis  # Extract hazard ratio for multiple groups with stratification factors. h_coxph_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\"),     strat = c(\"STRATA1\", \"STRATA2\")   ),   data = adtte_f ) #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.6126133 0.3913507 0.9589739       0.95 0.03086774 #> 2        78           55 0.3934024 0.2027682 0.7632630       0.95 0.00469167 #> 3        64           46 0.9501768 0.4730073 1.9087145       0.95 0.88580522 #> 4        50           36 0.7378635 0.3140465 1.7336363       0.95 0.48408079 #> 5        49           31 0.9408062 0.4172095 2.1215148       0.95 0.88305965 #> 6        43           34 0.5125617 0.2125140 1.2362459       0.95 0.13124382 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"Please see h_tab_surv_one_biomarker() h_tab_rsp_one_biomarker(), use function examples. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"","code":"h_tab_one_biomarker(   df,   afuns,   colvars,   na_str = NA_character_,   .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"df (data.frame) results single biomarker. afuns (named list function) analysis functions. colvars (list vars labels) variables tabulate labels. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"rtables table object statistics columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"Create data.frame pairwise stratified unstratified CoxPH analysis results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"","code":"h_tbl_coxph_pairwise(df, variables, control_coxph_pw = control_coxph())"},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strat (character NULL) variable names indicating stratification factors. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"data.frame containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"","code":"# \\donttest{ library(dplyr)  adtte <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0)  h_tbl_coxph_pairwise(   df = adtte,   variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\"),   control_coxph_pw = control_coxph(conf_level = 0.9) ) #>                  HR       90% CI p-value (log-rank) #> B: Placebo     1.41 (1.01, 1.96)             0.0905 #> C: Combination 1.81 (1.24, 2.64)             0.0086 # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: Survival Estimations — h_tbl_median_surv","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"Transform survival fit table groups rows characterized N, median confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"","code":"h_tbl_median_surv(fit_km, armval = \"All\")"},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"summary table statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"","code":"# \\donttest{ library(dplyr) library(survival)  adtte <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") fit <- survfit(   form = Surv(AVAL, 1 - CNSR) ~ ARMCD,   data = adtte ) h_tbl_median_surv(fit_km = fit) #>        N Median        95% CI #> ARM A 69  974.6   (687, 1625) #> ARM B 73  727.8 (555.8, 1156) #> ARM C 58  632.3   (393, 1001) # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"Helper function count number patients fraction patients according highest post-baseline lab grade variable .var, baseline lab grade variable baseline_var, direction interest specified direction_var.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"h_worsen_counter(df, id, .var, baseline_var, direction_var)"},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"df (data.frame) data set containing analysis variables. id (string) subject variable name. .var (string) single variable name passed rtables requested statistics function. baseline_var (string) baseline lab grade variable direction_var (string) Direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"h_worsen_counter() returns counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\".","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"library(dplyr)  # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>%   mutate(     GRADDR = case_when(       PARAMCD == \"ALT\" ~ \"B\",       PARAMCD == \"CRP\" ~ \"L\",       PARAMCD == \"IGA\" ~ \"H\"     )   ) %>%   filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")  df <- h_adlb_worsen(   adlb,   worst_flag_low = c(\"WGRLOFL\" = \"Y\"),   worst_flag_high = c(\"WGRHIFL\" = \"Y\"),   direction_var = \"GRADDR\" )  # `h_worsen_counter` h_worsen_counter(   df %>% filter(PARAMCD == \"CRP\" & GRADDR == \"Low\"),   id = \"USUBJID\",   .var = \"ATOXGR\",   baseline_var = \"BTOXGR\",   direction_var = \"GRADDR\" ) #> $fraction #> $fraction$`1` #>   num denom  #>    25   180  #>  #> $fraction$`2` #>   num denom  #>    15   186  #>  #> $fraction$`3` #>   num denom  #>    18   191  #>  #> $fraction$`4` #>   num denom  #>    17   196  #>  #> $fraction$Any #>   num denom  #>    75   196  #>  #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function: x tick positions — h_xticks","title":"Helper function: x tick positions — h_xticks","text":"Calculate positions ticks x-axis. However, xticks already exists kept . based function ggplot2 relies , required graphic patient--risk annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function: x tick positions — h_xticks","text":"","code":"h_xticks(data, xticks = NULL, max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function: x tick positions — h_xticks","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric, number, NULL) numeric vector ticks single number spacing ticks x axis. NULL (default), labeling::extended() used determine optimal tick position x axis. max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function: x tick positions — h_xticks","text":"vector positions use x-axis ticks ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function: x tick positions — h_xticks","text":"","code":"# \\donttest{ library(dplyr) library(survival)  data <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_data_plot()  h_xticks(data) #> [1]    0 1000 2000 3000 4000 5000 h_xticks(data, xticks = seq(0, 3000, 500)) #> [1]    0  500 1000 1500 2000 2500 3000 h_xticks(data, xticks = 500) #>  [1]    0  500 1000 1500 2000 2500 3000 3500 4000 4500 h_xticks(data, xticks = 500, max_time = 6000) #>  [1]    0  500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 h_xticks(data, xticks = c(0, 500), max_time = 300) #> [1]   0 500 h_xticks(data, xticks = 500, max_time = 300) #>  [1]    0  500 1000 1500 2000 2500 3000 3500 4000 4500 # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"","code":"imputation_rule(   df,   x_stats,   stat,   imp_rule,   post = FALSE,   avalcat_var = \"AVALCAT1\" )"},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"df (data.frame) data set containing analysis variables. x_stats (named list) named list statistics, typically results s_summary(). stat (character) statistic return value/NA level according imputation rule applied. imp_rule (character) imputation rule setting. Set \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. post (flag) whether data corresponds post-dose time-point (defaults FALSE). parameter used imp_rule set \"1/3\". avalcat_var (character) name variable indicates whether row df corresponds analysis value category \"BLQ\", \"LTR\", \"<PCLLOQ\", none (defaults \"AVALCAT1\"). Variable avalcat_var must present df.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"list containing statistic value (val) NA level (na_str) displayed according specified imputation rule.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"","code":"set.seed(1) df <- data.frame(   AVAL = runif(50, 0, 1),   AVALCAT1 = sample(c(1, \"BLQ\"), 50, replace = TRUE) ) x_stats <- s_summary(df$AVAL) imputation_rule(df, x_stats, \"max\", \"1/3\") #> $val #>       max  #> 0.9919061  #>  #> $na_str #> [1] \"ND\" #>  imputation_rule(df, x_stats, \"geom_mean\", \"1/3\") #> $val #> [1] NA #>  #> $na_str #> [1] \"NE\" #>  imputation_rule(df, x_stats, \"mean\", \"1/2\") #> $val #> [1] NA #>  #> $na_str #> [1] \"ND\" #>"},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence Rate — incidence_rate","title":"Incidence Rate — incidence_rate","text":"Estimate event rate adjusted person-years risk, otherwise known incidence rate. Primary analysis variable person-years risk.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence Rate — incidence_rate","text":"","code":"s_incidence_rate(   df,   .var,   n_events,   is_event,   control = control_incidence_rate() )  a_incidence_rate(   df,   .var,   n_events,   is_event,   control = control_incidence_rate() )  estimate_incidence_rate(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence Rate — incidence_rate","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. n_events (integer) number events observed. is_event (logical)TRUE event, FALSE time event censored. control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level (proportion) confidence level estimated incidence rate. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric) time unit desired output (person-years). lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence Rate — incidence_rate","text":"s_incidence_rate() returns following statistics: person_years: Total person-years risk. n_events: Total number events observed. rate: Estimated incidence rate. rate_ci: Confidence interval incidence rate. a_incidence_rate() returns corresponding list formatted rtables::CellValue(). estimate_incidence_rate() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_incidence_rate() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Incidence Rate — incidence_rate","text":"s_incidence_rate(): Statistics function estimates incidence rate associated confidence interval. a_incidence_rate(): Formatted analysis function used afun estimate_incidence_rate(). estimate_incidence_rate(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence Rate — incidence_rate","text":"","code":"library(dplyr)  df <- data.frame(   USUBJID = as.character(seq(6)),   CNSR = c(0, 1, 1, 0, 0, 0),   AVAL = c(10.1, 20.4, 15.3, 20.8, 18.7, 23.4),   ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\")) ) %>%   mutate(is_event = CNSR == 0) %>%   mutate(n_events = as.integer(is_event))  basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   estimate_incidence_rate(     vars = \"AVAL\",     n_events = \"n_events\",     control = control_incidence_rate(       input_time_unit = \"month\",       num_pt_year = 100     )   ) %>%   build_table(df) #>                                            A                 B        #>                                          (N=3)             (N=3)      #> ————————————————————————————————————————————————————————————————————— #> Total patient-years at risk               3.8               5.2       #> Number of adverse events observed          1                 3        #> AE rate per 100 patient-years            26.20             57.23      #> 95% CI                              (-25.15, 77.55)   (-7.53, 122.00)"},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual Patient Plots — individual_patient_plot","title":"Individual Patient Plots — individual_patient_plot","text":"Line plot(s) displaying trend patients' parameter values time rendered. Patients' individual baseline values can added plot(s) reference.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual Patient Plots — individual_patient_plot","text":"","code":"g_ipp(   df,   xvar,   yvar,   xlab,   ylab,   id_var = \"USUBJID\",   title = \"Individual Patient Plots\",   subtitle = \"\",   caption = NULL,   add_baseline_hline = FALSE,   yvar_baseline = \"BASE\",   ggtheme = nestcolor::theme_nest(),   plotting_choices = c(\"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\"),   max_obs_per_plot = 4,   col = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual Patient Plots — individual_patient_plot","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (character scalar) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. plotting_choices (character) specifies options displaying plots. Must one \"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\". max_obs_per_plot (count) Number observations plotted one plot. Ignored plotting_choices \"separate_by_obs\". col (character) lines colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual Patient Plots — individual_patient_plot","text":"ggplot object list ggplot objects.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Individual Patient Plots — individual_patient_plot","text":"g_ipp(): Plotting function individual patient plots , depending user preference, renders single graphic compiles list graphics show trends individual's parameter values time.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual Patient Plots — individual_patient_plot","text":"","code":"library(dplyr) library(nestcolor)  # Select a small sample of data to plot. adlb <- tern_ex_adlb %>%   filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>%   slice(1:36)  plot_list <- g_ipp(   df = adlb,   xvar = \"AVISIT\",   yvar = \"AVAL\",   xlab = \"Visit\",   ylab = \"SGOT/ALT (U/L)\",   title = \"Individual Patient Plots\",   add_baseline_hline = TRUE,   plotting_choices = \"split_by_max_obs\",   max_obs_per_plot = 5 ) plot_list #> [[1]]  #>  #> [[2]]  #>"},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels or Names of List Elements — labels_or_names","title":"Labels or Names of List Elements — labels_or_names","text":"Internal helper function working nested statistic function results typically labels names can use.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels or Names of List Elements — labels_or_names","text":"","code":"labels_or_names(x)"},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels or Names of List Elements — labels_or_names","text":"x list","code":""},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels or Names of List Elements — labels_or_names","text":"character vector labels names list elements.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"Layout-creating function creates multivariate column layout summarizing logistic regression results. function wrapper rtables::split_cols_by_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"","code":"logistic_regression_cols(lyt, conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"lyt (layout) input layout analyses added . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"layout object suitable passing layouting functions. Adding function rtable layout split table columns corresponding statistics df, estimate, std_error, odds_ratio, ci, pvalue.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"Constructor content functions used summarize_logistic() summarize logistic regression results. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"","code":"logistic_summary_by_flag(flag_var, na_str = NA_character_, .indent_mods = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"flag_var (string) variable name identifying row used content function. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"content function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Names Without Dots — make_names","title":"Make Names Without Dots — make_names","text":"Make Names Without Dots","code":""},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Names Without Dots — make_names","text":"","code":"make_names(nams)"},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Names Without Dots — make_names","text":"nams (character) vector original names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Names Without Dots — make_names","text":"character  vector proper names, use dots contrast make.names().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of Months to Days — month2day","title":"Conversion of Months to Days — month2day","text":"Conversion Months Days. approximative calculation considers month average 30.4375 days.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of Months to Days — month2day","text":"","code":"month2day(x)"},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of Months to Days — month2day","text":"x (numeric) time months.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of Months to Days — month2day","text":"numeric vector time days.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of Months to Days — month2day","text":"","code":"x <- c(13.25, 8.15, 1, 2.834) month2day(x) #> [1] 403.29688 248.06563  30.43750  86.25988"},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":null,"dir":"Reference","previous_headings":"","what":"Muffled car::Anova — muffled_car_anova","title":"Muffled car::Anova — muffled_car_anova","text":"Applied survival models, car::Anova() signal strata terms dropped model formula present, function deliberately muffles message.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Muffled car::Anova — muffled_car_anova","text":"","code":"muffled_car_anova(mod, test_statistic)"},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Muffled car::Anova — muffled_car_anova","text":"mod (coxph) Cox regression model fitted survival::coxph(). test_statistic (string) method used estimation p.values; wald (default) likelihood.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Muffled car::Anova — muffled_car_anova","text":"Returns output car::Anova(), convergence message muffled.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of Available (Non-Missing Entries) in a Vector — n_available","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"Small utility function better readability.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"","code":"n_available(x)"},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"x () vector count non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"Number non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Odds Ratio Estimation — odds_ratio","title":"Odds Ratio Estimation — odds_ratio","text":"Compares bivariate responses two groups terms odds ratios along confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Odds Ratio Estimation — odds_ratio","text":"","code":"s_odds_ratio(   df,   .var,   .ref_group,   .in_ref_col,   .df_row,   variables = list(arm = NULL, strata = NULL),   conf_level = 0.95,   groups_list = NULL )  a_odds_ratio(   df,   .var,   .ref_group,   .in_ref_col,   .df_row,   variables = list(arm = NULL, strata = NULL),   conf_level = 0.95,   groups_list = NULL )  estimate_odds_ratio(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = vars,   .stats = \"or_ci\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Odds Ratio Estimation — odds_ratio","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. .df_row (data.frame) data frame across columns given row split. variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_odds_ratio(). show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Odds Ratio Estimation — odds_ratio","text":"s_odds_ratio() returns named list statistics or_ci (containing est, lcl, ucl) n_tot. a_odds_ratio() returns corresponding list formatted rtables::CellValue(). estimate_odds_ratio() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_odds_ratio() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Odds Ratio Estimation — odds_ratio","text":"function uses either logistic regression unstratified analyses, conditional logistic regression stratified analyses. Wald confidence interval specified confidence level calculated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Odds Ratio Estimation — odds_ratio","text":"s_odds_ratio(): Statistics function estimates odds ratio treatment control. variables list arm strata variable names must passed stratified analysis required. a_odds_ratio(): Formatted analysis function used afun estimate_odds_ratio(). estimate_odds_ratio(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Odds Ratio Estimation — odds_ratio","text":"stratified analyses, currently implementation conditional likelihood confidence intervals, therefore likelihood confidence interval yet available option. Besides, rsp contains responders non-responders, result values NA, odds ratio estimation possible.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Odds Ratio Estimation — odds_ratio","text":"","code":"set.seed(12) dta <- data.frame(   rsp = sample(c(TRUE, FALSE), 100, TRUE),   grp = factor(rep(c(\"A\", \"B\"), each = 50), levels = c(\"B\", \"A\")),   strata = factor(sample(c(\"C\", \"D\"), 100, TRUE)) )  # Unstratified analysis. s_odds_ratio(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   .df_row = dta ) #> $or_ci #>       est       lcl       ucl  #> 0.8484848 0.3831831 1.8788053  #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #>  #> $n_tot #> n_tot  #>   100  #> attr(,\"label\") #> [1] \"Total n\" #>   # Stratified analysis. s_odds_ratio(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   .df_row = dta,   variables = list(arm = \"grp\", strata = \"strata\") ) #> $or_ci #>       est       lcl       ucl  #> 0.7689750 0.3424155 1.7269154  #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #>  #> $n_tot #> n_tot  #>   100  #> attr(,\"label\") #> [1] \"Total n\" #>   a_odds_ratio(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   .df_row = dta ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name     formatted_cell indent_mod           row_label #> 1    or_ci 0.85 (0.38 - 1.88)          1 Odds Ratio (95% CI) #> 2    n_tot                100          0             Total n  dta <- data.frame(   rsp = sample(c(TRUE, FALSE), 100, TRUE),   grp = factor(rep(c(\"A\", \"B\"), each = 50)) )  l <- basic_table() %>%   split_cols_by(var = \"grp\", ref_group = \"B\") %>%   estimate_odds_ratio(vars = \"rsp\")  build_table(l, df = dta) #>                       B           A          #> ———————————————————————————————————————————— #> Odds Ratio (95% CI)       0.72 (0.33 - 1.60)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion Difference — prop_diff","text":"","code":"s_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\",     \"strat_newcombecc\"),   weights_method = \"cmh\" )  a_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\",     \"strat_newcombecc\"),   weights_method = \"cmh\" )  estimate_proportion_diff(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = vars,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion Difference — prop_diff","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_proportion_diff(). var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion Difference — prop_diff","text":"s_proportion_diff() returns named list elements diff diff_ci. a_proportion_diff() returns corresponding list formatted rtables::CellValue(). estimate_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion_diff() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Proportion Difference — prop_diff","text":"s_proportion_diff(): Statistics function estimating difference terms responder proportion. a_proportion_diff(): Formatted analysis function used afun estimate_proportion_diff(). estimate_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Proportion Difference — prop_diff","text":"performing unstratified analysis, methods \"cmh\", \"strat_newcombe\", \"strat_newcombecc\" permitted.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion Difference — prop_diff","text":"","code":"# Summary  ## \"Mid\" case: 4/4 respond in group A, 1/2 respond in group B. nex <- 100 # Number of example rows dta <- data.frame(   \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE),   \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE),   \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE),   stringsAsFactors = TRUE )  s_proportion_diff(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   conf_level = 0.90,   method = \"ha\" ) #> $diff #> [1] -8.05153 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #>  #> $diff_ci #> [1] -25.754477   9.651418 #> attr(,\"label\") #> [1] \"90% CI (Anderson-Hauck)\" #>   # CMH example with strata s_proportion_diff(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   variables = list(strata = c(\"f1\", \"f2\")),   conf_level = 0.90,   method = \"cmh\" ) #> $diff #> [1] -8.240459 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #>  #> $diff_ci #> [1] -24.002675   7.521757 #> attr(,\"label\") #> [1] \"90% CI (CMH, without correction)\" #>   a_proportion_diff(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   conf_level = 0.90,   method = \"ha\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod                       row_label #> 1     diff           -8.1          0 Difference in Response rate (%) #> 2  diff_ci   (-25.8, 9.7)          1         90% CI (Anderson-Hauck)  l <- basic_table() %>%   split_cols_by(var = \"grp\", ref_group = \"B\") %>%   estimate_proportion_diff(     vars = \"rsp\",     conf_level = 0.90,     method = \"ha\"   )  build_table(l, df = dta) #>                                   B        A       #> —————————————————————————————————————————————————— #> Difference in Response rate (%)           -8.1     #>   90% CI (Anderson-Hauck)             (-25.8, 9.7)"},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Difference Test for Two Proportions — prop_diff_test","title":"Difference Test for Two Proportions — prop_diff_test","text":"Various tests implemented test difference two proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Difference Test for Two Proportions — prop_diff_test","text":"","code":"s_test_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") )  a_test_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") )  test_proportion_diff(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = vars,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Difference Test for Two Proportions — prop_diff_test","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. variables (named list string) list additional analysis variables. method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_test_proportion_diff(). var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Difference Test for Two Proportions — prop_diff_test","text":"s_test_proportion_diff() returns named list single item pval attribute label describing method used. p-value tests null hypothesis proportions two groups . a_test_proportion_diff() returns corresponding list formatted rtables::CellValue(). test_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_test_proportion_diff() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Difference Test for Two Proportions — prop_diff_test","text":"s_test_proportion_diff(): Statistics function tests difference two proportions. a_test_proportion_diff(): Formatted analysis function used afun test_proportion_diff(). test_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Difference Test for Two Proportions — prop_diff_test","text":"","code":"dta <- data.frame(   rsp = sample(c(TRUE, FALSE), 100, TRUE),   grp = factor(rep(c(\"A\", \"B\"), each = 50)),   strat = factor(rep(c(\"V\", \"W\", \"X\", \"Y\", \"Z\"), each = 20)) )  # With `rtables` pipelines. l <- basic_table() %>%   split_cols_by(var = \"grp\", ref_group = \"B\") %>%   test_proportion_diff(     vars = \"rsp\",     method = \"cmh\", variables = list(strata = \"strat\")   )  build_table(l, df = dta) #>                                            B     A    #> ————————————————————————————————————————————————————— #>   p-value (Cochran-Mantel-Haenszel Test)       1.0000"},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Table Pruning — prune_occurrences","title":"Occurrence Table Pruning — prune_occurrences","text":"Family constructor condition functions flexibly prune occurrence tables. condition functions always return whether row result higher threshold. Since class CombinationFunction() can logically combined condition functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Table Pruning — prune_occurrences","text":"","code":"keep_rows(row_condition)  keep_content_rows(content_row_condition)  has_count_in_cols(atleast, ...)  has_count_in_any_col(atleast, ...)  has_fraction_in_cols(atleast, ...)  has_fraction_in_any_col(atleast, ...)  has_fractions_difference(atleast, ...)  has_counts_difference(atleast, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Table Pruning — prune_occurrences","text":"row_condition (CombinationFunction) condition function works individual analysis rows flags whether kept pruned table. content_row_condition (CombinationFunction) condition function works individual first content rows leaf tables flags whether leaf tables kept pruned table. atleast (count proportion) threshold met order keep row. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Table Pruning — prune_occurrences","text":"keep_rows() returns pruning function can used rtables::prune_table() prune rtables table. keep_content_rows() returns pruning function checks condition first content row leaf tables table. has_count_in_cols() returns condition function sums counts specified column. has_count_in_any_col() returns condition function compares counts specified columns threshold. has_fraction_in_cols() returns condition function sums counts specified column, computes fraction dividing total column counts. has_fraction_in_any_col() returns condition function looks fractions specified columns checks whether fulfill threshold. has_fractions_difference() returns condition function extracts fractions specified column, computes difference minimum maximum. has_counts_difference() returns condition function extracts counts specified column, computes difference minimum maximum.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Table Pruning — prune_occurrences","text":"keep_rows(): Constructor creating pruning functions based row condition function. removes analysis rows (TableRow) pruned, .e., fulfill row condition. removes sub-tree children left. keep_content_rows(): Constructor creating pruning functions based condition (first) content row leaf tables. removes leaf tables first content row fulfill condition. check individual rows. proceeds recursively removing sub tree children left. has_count_in_cols(): Constructor creating condition functions total counts specified columns. has_count_in_any_col(): Constructor creating condition functions counts specified columns satisfying threshold. has_fraction_in_cols(): Constructor creating condition functions total fraction specified columns. has_fraction_in_any_col(): Constructor creating condition functions fraction specified columns. has_fractions_difference(): Constructor creating condition function checks difference fractions reported specified column. has_counts_difference(): Constructor creating condition function checks difference counts reported specified column.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Occurrence Table Pruning — prune_occurrences","text":"Since table specifications worded positively, name constructor condition functions positively, . However, note result keep_rows() says pruned, conform rtables::prune_table() interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Table Pruning — prune_occurrences","text":"","code":"# \\donttest{ tab <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"RACE\") %>%   split_rows_by(\"STRATA1\") %>%   summarize_row_groups() %>%   analyze_vars(\"COUNTRY\", .stats = \"count_fraction\") %>%   build_table(DM) # }  # \\donttest{ # `keep_rows` is_non_empty <- !CombinationFunction(all_zero_or_na) prune_table(tab, keep_rows(is_non_empty)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # `keep_content_rows` # \\donttest{ more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab)) prune_table(tab, keep_content_rows(more_than_twenty)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>     CHE                         0            0              0        #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #>     GBR                         0            0              0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     RUS                         0            0              0        #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     NGA                         0            0              0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>     CHE                         0            0              0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #>     JPN                         0            0              0        #>     GBR                         0            0              0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        # }  # \\donttest{ more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # \\donttest{ # `has_count_in_any_col` any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(any_more_than_one)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # \\donttest{ # `has_fraction_in_cols` more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #> BLACK OR AFRICAN AMERICAN                                            #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    # }  # \\donttest{ # `has_fraction_in_any_col` any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #> BLACK OR AFRICAN AMERICAN                                            #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    # }  # \\donttest{ # `has_fractions_difference` more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent_diff)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # \\donttest{ more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one_diff)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }"},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-implemented range() Default S3 method for numerical objects — range_noinf","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"function returns c(NA, NA) instead c(-Inf, Inf) zero-length data without warnings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"","code":"range_noinf(x, na.rm = FALSE, finite = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"x (numeric) sequence numbers range computed. na.rm (logical) indicating NA omitted. finite (logical) indicating non-finite elements removed.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"2-element vector class numeric.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Reapply Variable Labels — reapply_varlabels","title":"Reapply Variable Labels — reapply_varlabels","text":"helper function used tests.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reapply Variable Labels — reapply_varlabels","text":"","code":"reapply_varlabels(x, varlabels, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reapply Variable Labels — reapply_varlabels","text":"x (vector) vector elements needs new labels. varlabels (character) vector labels x. ... parameters added list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reapply Variable Labels — reapply_varlabels","text":"x variable labels reapplied.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"Tabulate estimated effects multiple continuous biomarker variables binary response endpoint across population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"","code":"tabulate_rsp_biomarkers(   df,   vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\"),   .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"df (data.frame) containing analysis variables, returned extract_rsp_biomarkers(). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"rtables table summarizing biomarker effects binary response subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"functions create layout starting data frame contains required statistics. tables typically used input forest plots.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"contrast tabulate_rsp_subgroups() tabulation function start input layout lyt. internally table created combining multiple subtables.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  df <- extract_rsp_biomarkers(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adrs_f )  # \\donttest{ ## Table with default columns. tabulate_rsp_biomarkers(df) #>                                  Total n   Responders   Response (%)   Odds Ratio      95% CI      p-value (Wald) #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age                                                                                                               #>   All Patients                     200        164          82.0%          1.00      (0.95, 1.05)       0.8530     #>   Continuous Level Biomarker 2                                                                                    #>     LOW                            70          53          75.7%          0.93      (0.85, 1.01)       0.0845     #>     MEDIUM                         68          58          85.3%          0.99      (0.88, 1.11)       0.8190     #>     HIGH                           62          53          85.5%          1.06      (0.96, 1.18)       0.2419     #> Continuous Level Biomarker 1                                                                                      #>   All Patients                     200        164          82.0%          0.98      (0.88, 1.08)       0.6353     #>   Continuous Level Biomarker 2                                                                                    #>     LOW                            70          53          75.7%          1.15      (0.95, 1.40)       0.1584     #>     MEDIUM                         68          58          85.3%          0.88      (0.73, 1.06)       0.1700     #>     HIGH                           62          53          85.5%          0.88      (0.72, 1.08)       0.2104      ## Table with a manually chosen set of columns: leave out \"pval\", reorder. tab <- tabulate_rsp_biomarkers(   df = df,   vars = c(\"n_rsp\", \"ci\", \"n_tot\", \"prop\", \"or\") )  ## Finally produce the forest plot. g_forest(tab, xlim = c(0.7, 1.4))  # }"},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Binary Response by Subgroup — response_subgroups","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"Tabulate statistics response rate odds ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"","code":"a_response_subgroups(   .formats = list(n = \"xx\", n_rsp = \"xx\", prop = \"xx.x%\", n_tot = \"xx\", or =     list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)), pval =     \"x.xxxx | (<0.0001)\") )  tabulate_rsp_subgroups(lyt, df, vars = c(\"n_tot\", \"n\", \"prop\", \"or\", \"ci\"))"},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":".formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. lyt (layout) input layout analyses added . df (list) data frames containing analysis variables. List created using extract_rsp_subgroups(). vars (character) names statistics reported among: n: Total number observations per group. n_rsp: Number responders per group. prop: Proportion responders. n_tot: Total number observations. : Odds ratio. ci : Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"a_response_subgroups() returns corresponding list formatted rtables::CellValue(). rtables table summarizing binary response subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"functions create layout starting data frame contains required statistics. Tables typically used part forest plot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"a_response_subgroups(): Formatted analysis function used afun tabulate_rsp_subgroups(). tabulate_rsp_subgroups(): Table-creating function creates table summarizing binary response subgroup. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  # Unstratified analysis. df <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) df #> $prop #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142  2.714000 1.1804488  6.239827       0.95 All Patients    ALL #> 2        78 10.800000 2.2669576 51.452218       0.95            F    SEX #> 3        64  0.920000 0.2966470  2.853223       0.95            M    SEX #> 4        50  3.553846 1.0047370 12.570277       0.95          LOW BMRKR2 #> 5        49  2.705882 0.5911718 12.385232       0.95       MEDIUM BMRKR2 #> 6        43  2.250000 0.3970298 12.750933       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>   ## Table with default columns. basic_table() %>%   tabulate_rsp_subgroups(df) #>                                             B: Placebo           A: Drug X                                  #> Baseline Risk Factors          Total n   n    Response (%)   n    Response (%)   Odds Ratio      95% CI     #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                     142     73      68.5%       69      85.5%          2.71      (1.18, 6.24)  #> Sex                                                                                                         #>   F                              78      40      62.5%       38      94.7%         10.80      (2.27, 51.45) #>   M                              64      33      75.8%       31      74.2%          0.92      (0.30, 2.85)  #> Continuous Level Biomarker 2                                                                                #>   LOW                            50      24      54.2%       26      80.8%          3.55      (1.00, 12.57) #>   MEDIUM                         49      23      73.9%       26      88.5%          2.71      (0.59, 12.39) #>   HIGH                           43      26      76.9%       17      88.2%          2.25      (0.40, 12.75)  ## Table with selected columns. basic_table() %>%   tabulate_rsp_subgroups(     df = df,     vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\")   ) #>                                                    B: Placebo                       A: Drug X                                         #> Baseline Risk Factors          Total n   n    Responders   Response (%)   n    Responders   Response (%)   Odds Ratio      95% CI     #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                     142     73       50          68.5%       69       59          85.5%          2.71      (1.18, 6.24)  #> Sex                                                                                                                                   #>   F                              78      40       25          62.5%       38       36          94.7%         10.80      (2.27, 51.45) #>   M                              64      33       25          75.8%       31       23          74.2%          0.92      (0.30, 2.85)  #> Continuous Level Biomarker 2                                                                                                          #>   LOW                            50      24       13          54.2%       26       21          80.8%          3.55      (1.00, 12.57) #>   MEDIUM                         49      23       17          73.9%       26       23          88.5%          2.71      (0.59, 12.39) #>   HIGH                           43      26       20          76.9%       17       15          88.2%          2.25      (0.40, 12.75)"},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":null,"dir":"Reference","previous_headings":"","what":"rtables Access Helper Functions — rtables_access","title":"rtables Access Helper Functions — rtables_access","text":"couple functions help accessing data rtables objects. Currently work occurrence tables, defined count first element fraction second element cell.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"rtables Access Helper Functions — rtables_access","text":"","code":"h_row_first_values(table_row, col_names = NULL, col_indices = NULL)  h_row_counts(table_row, col_names = NULL, col_indices = NULL)  h_row_fractions(table_row, col_names = NULL, col_indices = NULL)  h_col_counts(table, col_names = NULL, col_indices = NULL)  h_content_first_row(table)  is_leaf_table(table)  check_names_indices(table_row, col_names = NULL, col_indices = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"rtables Access Helper Functions — rtables_access","text":"table_row (TableRow) analysis row occurrence table. col_names (character) names columns extract . col_indices (integer) indices columns extract . col_names provided, inferred names table_row. Note currently works well single column split. table (VTableNodeInfo) occurrence table row.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"rtables Access Helper Functions — rtables_access","text":"h_row_first_values() returns vector numeric values. h_row_counts() returns vector numeric values. h_row_fractions() returns vector proportions. h_col_counts() returns vector column counts. h_content_first_row() returns row rtables table. is_leaf_table() returns logical value indicating whether current table leaf. check_names_indices returns column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"rtables Access Helper Functions — rtables_access","text":"h_row_first_values(): Helper function extract first values content cell specified columns TableRow. Defaults columns. h_row_counts(): Helper function extracts row values checks convertible integers (integerish values). h_row_fractions(): helper function extract fractions specified columns TableRow. specifically extracts second values content cell checks fraction. h_col_counts(): Helper function extract column counts specified columns table. h_content_first_row(): Helper function get first row content table current table. is_leaf_table(): Helper function says whether current table leaf tree. check_names_indices(): Internal helper function tests standard inputs column indices.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"rtables Access Helper Functions — rtables_access","text":"","code":"tbl <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"RACE\") %>%   analyze(\"AGE\", function(x) {     list(       \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.x (xx.x)\"),       \"n\" = length(x),       \"frac\" = rcell(c(0.1, 0.1), format = \"xx (xx)\")     )   }) %>%   build_table(tern_ex_adsl) %>%   prune_table() tree_row_elem <- collect_leaves(tbl[2, ])[[1]] result <- max(h_row_first_values(tree_row_elem)) result #> [1] 35.31214  # Row counts (integer values) # h_row_counts(tree_row_elem) # Fails because there are no integers # Using values with integers tree_row_elem <- collect_leaves(tbl[3, ])[[1]] result <- h_row_counts(tree_row_elem) # result  # Row fractions tree_row_elem <- collect_leaves(tbl[4, ])[[1]] h_row_fractions(tree_row_elem) #>      A: Drug X     B: Placebo C: Combination  #>            0.1            0.1            0.1"},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"Analyses based multivariate Cox model usually performed Controlled Substance Reporting regulatory documents serve exploratory purposes (e.g., publication). practice, model usually includes main effects (without interaction terms). produces hazard ratio estimates covariates included model. analysis follows principles (e.g., stratified vs. unstratified analysis tie handling) usual Cox model analysis. Since usually pre-specified hypothesis testing analysis, p.values need interpreted caution. (Statistical Analysis Clinical Trials Data R, NEST's bookdown)","code":""},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"","code":"s_cox_multivariate(   formula,   data,   conf_level = 0.95,   pval_method = c(\"wald\", \"likelihood\"),   ... )"},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"formula (formula) formula corresponding investigated survival::Surv() survival model including covariates. data (data.frame) data frame includes variable formula covariates. conf_level (proportion) confidence level hazard ratio interval estimations. Default 0.95. pval_method (character) method used estimation p-values, one \"wald\" (default) \"likelihood\". ... Optional parameters passed survival::coxph(). Can include ties, character string specifying method tie handling, one exact (default), efron, breslow.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"list elements mod, msum, aov, coef_inter.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"output limited single effect terms. Work ongoing estimation interaction terms scope defined  Global Data Standards Repository (GDS_Standard_TLG_Specs_Tables_2.doc).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"","code":"library(dplyr)  adtte <- tern_ex_adtte adtte_f <- subset(adtte, PARAMCD == \"OS\") # _f: filtered adtte_f <- filter(   adtte_f,   PARAMCD == \"OS\" &     SEX %in% c(\"F\", \"M\") &     RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\") ) adtte_f$SEX <- droplevels(adtte_f$SEX) adtte_f$RACE <- droplevels(adtte_f$RACE)"},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Strings to NA — sas_na","title":"Convert Strings to NA — sas_na","text":"SAS imports missing data empty strings strings whitespaces . helper function can used convert values NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Strings to NA — sas_na","text":"","code":"sas_na(x, empty = TRUE, whitespaces = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Strings to NA — sas_na","text":"x (factor character vector) values missing values substituted. empty (logical) TRUE empty strings get replaced NA. whitespaces (logical) TRUE strings made whitespaces get replaced NA.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Strings to NA — sas_na","text":"x \"\" /whitespace-values substituted NA, depending values empty whitespaces.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Strings to NA — sas_na","text":"","code":"sas_na(c(\"1\", \"\", \" \", \"   \", \"b\")) #> [1] \"1\" NA  NA  NA  \"b\" sas_na(factor(c(\"\", \" \", \"b\"))) #> [1] <NA> <NA> b    #> Levels: b  is.na(sas_na(c(\"1\", \"\", \" \", \"   \", \"b\"))) #> [1] FALSE  TRUE  TRUE  TRUE FALSE"},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Table Sorting — score_occurrences","title":"Occurrence Table Sorting — score_occurrences","text":"Functions score occurrence table subtables rows can used sorting occurrence tables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Table Sorting — score_occurrences","text":"","code":"score_occurrences(table_row)  score_occurrences_cols(...)  score_occurrences_subtable(...)  score_occurrences_cont_cols(...)"},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Table Sorting — score_occurrences","text":"table_row (TableRow) analysis row occurrence table. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Table Sorting — score_occurrences","text":"score_occurrences() returns sum counts across columns table row. score_occurrences_cols() returns function sums counts across specified columns table row. score_occurrences_subtable() returns function sums counts subtable across specified columns. score_occurrences_cont_cols() returns function sums counts first content row specified columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Table Sorting — score_occurrences","text":"score_occurrences(): Scoring function sums counts across columns. fail anything else counts used. score_occurrences_cols(): Scoring functions can produced constructor include specific columns scoring. See h_row_counts() information. score_occurrences_subtable(): Scoring functions produced constructor can used subtables: sum specified column counts subtable. useful available content row summing counts. score_occurrences_cont_cols(): Produce score function sorting table summing first content row specified columns. Note extending rtables::cont_n_onecol() rtables::cont_n_allcols().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Table Sorting — score_occurrences","text":"","code":"lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   analyze_num_patients(     vars = \"USUBJID\",     .stats = c(\"unique\"),     .labels = c(\"Total number of patients with at least one event\")   ) %>%   split_rows_by(\"AEBODSYS\", child_labels = \"visible\", nested = FALSE) %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       \"Total number of patients with at least one event\",       \"Total number of events\"     )   ) %>%   count_occurrences(vars = \"AEDECOD\")  tbl <- build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) %>%   prune_table()  tbl_sorted <- tbl %>%   sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_occurrences)  tbl_sorted #>                                                      A: Drug X    B: Placebo   C: Combination #>                                                        (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event     59 (85.5%)   57 (78.1%)     48 (82.8%)   #> cl D.1                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     20 (34.5%)   #>   Total number of events                                 39           35             28       #>     dcd D.1.1.4.2                                    16 (23.2%)   13 (17.8%)     16 (27.6%)   #>     dcd D.1.1.1.1                                    17 (24.6%)   18 (24.7%)     7 (12.1%)    #> cl C.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   13 (17.8%)     18 (31.0%)   #>   Total number of events                                 17           15             19       #>     dcd C.1.1.1.3                                    15 (21.7%)   13 (17.8%)     18 (31.0%)   #> cl C.2                                                                                        #>   Total number of patients with at least one event   20 (29.0%)   14 (19.2%)     10 (17.2%)   #>   Total number of events                                 21           15             14       #>     dcd C.2.1.2.1                                    20 (29.0%)   14 (19.2%)     10 (17.2%)   #> cl B.2                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     24 (41.4%)   #>   Total number of events                                 40           38             33       #>     dcd B.2.2.3.1                                    17 (24.6%)   15 (20.5%)     16 (27.6%)   #>     dcd B.2.1.2.1                                    17 (24.6%)   16 (21.9%)     13 (22.4%)   #> cl B.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   19 (26.0%)     15 (25.9%)   #>   Total number of events                                 19           19             18       #>     dcd B.1.1.1.1                                    15 (21.7%)   19 (26.0%)     15 (25.9%)   #> cl D.2                                                                                        #>   Total number of patients with at least one event   21 (30.4%)   20 (27.4%)     12 (20.7%)   #>   Total number of events                                 27           22             15       #>     dcd D.2.1.5.3                                    21 (30.4%)   20 (27.4%)     12 (20.7%)   #> cl A.1                                                                                        #>   Total number of patients with at least one event   31 (44.9%)   24 (32.9%)     27 (46.6%)   #>   Total number of events                                 39           33             35       #>     dcd A.1.1.1.1                                    17 (24.6%)   17 (23.3%)     14 (24.1%)   #>     dcd A.1.1.1.2                                    17 (24.6%)   14 (19.2%)     17 (29.3%)    score_cols_a_and_b <- score_occurrences_cols(col_names = c(\"A: Drug X\", \"B: Placebo\"))  # Note that this here just sorts the AEDECOD inside the AEBODSYS. The AEBODSYS are not sorted. # That would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>%   sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_cols_a_and_b)  tbl_sorted #>                                                      A: Drug X    B: Placebo   C: Combination #>                                                        (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event     59 (85.5%)   57 (78.1%)     48 (82.8%)   #> cl D.1                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     20 (34.5%)   #>   Total number of events                                 39           35             28       #>     dcd D.1.1.1.1                                    17 (24.6%)   18 (24.7%)     7 (12.1%)    #>     dcd D.1.1.4.2                                    16 (23.2%)   13 (17.8%)     16 (27.6%)   #> cl C.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   13 (17.8%)     18 (31.0%)   #>   Total number of events                                 17           15             19       #>     dcd C.1.1.1.3                                    15 (21.7%)   13 (17.8%)     18 (31.0%)   #> cl C.2                                                                                        #>   Total number of patients with at least one event   20 (29.0%)   14 (19.2%)     10 (17.2%)   #>   Total number of events                                 21           15             14       #>     dcd C.2.1.2.1                                    20 (29.0%)   14 (19.2%)     10 (17.2%)   #> cl B.2                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     24 (41.4%)   #>   Total number of events                                 40           38             33       #>     dcd B.2.1.2.1                                    17 (24.6%)   16 (21.9%)     13 (22.4%)   #>     dcd B.2.2.3.1                                    17 (24.6%)   15 (20.5%)     16 (27.6%)   #> cl B.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   19 (26.0%)     15 (25.9%)   #>   Total number of events                                 19           19             18       #>     dcd B.1.1.1.1                                    15 (21.7%)   19 (26.0%)     15 (25.9%)   #> cl D.2                                                                                        #>   Total number of patients with at least one event   21 (30.4%)   20 (27.4%)     12 (20.7%)   #>   Total number of events                                 27           22             15       #>     dcd D.2.1.5.3                                    21 (30.4%)   20 (27.4%)     12 (20.7%)   #> cl A.1                                                                                        #>   Total number of patients with at least one event   31 (44.9%)   24 (32.9%)     27 (46.6%)   #>   Total number of events                                 39           33             35       #>     dcd A.1.1.1.1                                    17 (24.6%)   17 (23.3%)     14 (24.1%)   #>     dcd A.1.1.1.2                                    17 (24.6%)   14 (19.2%)     17 (29.3%)    score_subtable_all <- score_occurrences_subtable(col_names = names(tbl))  # Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That # would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>%   sort_at_path(path = c(\"AEBODSYS\"), scorefun = score_subtable_all, decreasing = FALSE)  tbl_sorted #>                                                      A: Drug X    B: Placebo   C: Combination #>                                                        (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event     59 (85.5%)   57 (78.1%)     48 (82.8%)   #> cl C.2                                                                                        #>   Total number of patients with at least one event   20 (29.0%)   14 (19.2%)     10 (17.2%)   #>   Total number of events                                 21           15             14       #>     dcd C.2.1.2.1                                    20 (29.0%)   14 (19.2%)     10 (17.2%)   #> cl C.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   13 (17.8%)     18 (31.0%)   #>   Total number of events                                 17           15             19       #>     dcd C.1.1.1.3                                    15 (21.7%)   13 (17.8%)     18 (31.0%)   #> cl B.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   19 (26.0%)     15 (25.9%)   #>   Total number of events                                 19           19             18       #>     dcd B.1.1.1.1                                    15 (21.7%)   19 (26.0%)     15 (25.9%)   #> cl D.2                                                                                        #>   Total number of patients with at least one event   21 (30.4%)   20 (27.4%)     12 (20.7%)   #>   Total number of events                                 27           22             15       #>     dcd D.2.1.5.3                                    21 (30.4%)   20 (27.4%)     12 (20.7%)   #> cl D.1                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     20 (34.5%)   #>   Total number of events                                 39           35             28       #>     dcd D.1.1.1.1                                    17 (24.6%)   18 (24.7%)     7 (12.1%)    #>     dcd D.1.1.4.2                                    16 (23.2%)   13 (17.8%)     16 (27.6%)   #> cl B.2                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     24 (41.4%)   #>   Total number of events                                 40           38             33       #>     dcd B.2.1.2.1                                    17 (24.6%)   16 (21.9%)     13 (22.4%)   #>     dcd B.2.2.3.1                                    17 (24.6%)   15 (20.5%)     16 (27.6%)   #> cl A.1                                                                                        #>   Total number of patients with at least one event   31 (44.9%)   24 (32.9%)     27 (46.6%)   #>   Total number of events                                 39           33             35       #>     dcd A.1.1.1.1                                    17 (24.6%)   17 (23.3%)     14 (24.1%)   #>     dcd A.1.1.1.2                                    17 (24.6%)   14 (19.2%)     17 (29.3%)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"","code":"split_cols_by_groups(lyt, var, groups_list = NULL, ref_group = NULL, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"lyt (layout) input layout analyses added . var (string) single variable name passed rtables requested statistics function. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. ref_group (data.frame vector) data corresponding reference group. ... additional arguments rtables::split_cols_by() order. instance, control formats (format), add joint column groups (incl_all).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"layout object suitable passing layouting functions. Adding function rtable layout add column split including given groups table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"","code":"# 1 - Basic use  # Without group combination `split_cols_by_groups` is # equivalent to [rtables::split_cols_by()]. basic_table() %>%   split_cols_by_groups(\"ARM\") %>%   add_colcounts() %>%   analyze(\"AGE\") %>%   build_table(DM) #>        A: Drug X   B: Placebo   C: Combination #>         (N=121)     (N=106)        (N=129)     #> —————————————————————————————————————————————— #> Mean     34.91       33.02          34.57       # Add a reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   analyze(     \"AGE\",     afun = function(x, .ref_group, .in_ref_col) {       if (.in_ref_col) {         in_rows(\"Diff Mean\" = rcell(NULL))       } else {         in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\"))       }     }   ) %>%   build_table(DM) #>             B: Placebo   A: Drug X   C: Combination #>              (N=106)      (N=121)       (N=129)     #> ——————————————————————————————————————————————————— #> Diff Mean                  1.89           1.55       # 2 - Adding group specification  # Manual preparation of the groups. groups <- list(   \"Arms A+B\" = c(\"A: Drug X\", \"B: Placebo\"),   \"Arms A+C\" = c(\"A: Drug X\", \"C: Combination\") )  # Use of split_cols_by_groups without reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze(\"AGE\") %>%   build_table(DM) #>        Arms A+B   Arms A+C #>        (N=227)    (N=250)  #> —————————————————————————— #> Mean    34.03      34.73    # Including differentiated output in the reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", groups_list = groups, ref_group = \"Arms A+B\") %>%   analyze(     \"AGE\",     afun = function(x, .ref_group, .in_ref_col) {       if (.in_ref_col) {         in_rows(\"Diff. of Averages\" = rcell(NULL))       } else {         in_rows(\"Diff. of Averages\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\"))       }     }   ) %>%   build_table(DM) #>                     Arms A+B   Arms A+C #> ——————————————————————————————————————— #> Diff. of Averages                0.71    # 3 - Binary list dividing factor levels into reference and treatment  # `combine_groups` defines reference and treatment. groups <- combine_groups(   fct = DM$ARM,   ref = c(\"A: Drug X\", \"B: Placebo\") ) groups #> $`A: Drug X/B: Placebo` #> [1] \"A: Drug X\"  \"B: Placebo\" #>  #> $`C: Combination` #> [1] \"C: Combination\" #>   # Use group definition without reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", groups_list = groups) %>%   add_colcounts() %>%   analyze(\"AGE\") %>%   build_table(DM) #>        A: Drug X/B: Placebo   C: Combination #>              (N=227)             (N=129)     #> ———————————————————————————————————————————— #> Mean          34.03               34.57       # Use group definition with reference column (first item of groups). basic_table() %>%   split_cols_by_groups(\"ARM\", groups, ref_group = names(groups)[1]) %>%   add_colcounts() %>%   analyze(     \"AGE\",     afun = function(x, .ref_group, .in_ref_col) {       if (.in_ref_col) {         in_rows(\"Diff Mean\" = rcell(NULL))       } else {         in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\"))       }     }   ) %>%   build_table(DM) #>             A: Drug X/B: Placebo   C: Combination #>                   (N=227)             (N=129)     #> ————————————————————————————————————————————————— #> Diff Mean                               0.54"},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Text According To Available Text Width — split_text_grob","title":"Split Text According To Available Text Width — split_text_grob","text":"Dynamically wrap text.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Text According To Available Text Width — split_text_grob","text":"","code":"split_text_grob(   text,   x = grid::unit(0.5, \"npc\"),   y = grid::unit(0.5, \"npc\"),   width = grid::unit(1, \"npc\"),   just = \"centre\",   hjust = NULL,   vjust = NULL,   default.units = \"npc\",   name = NULL,   gp = grid::gpar(),   vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Text According To Available Text Width — split_text_grob","text":"text character string x numeric vector unit object specifying x-values. y numeric vector unit object specifying y-values. width unit object specifying max width text just justification text     relative (x, y) location.  two values, first     value specifies horizontal justification second value specifies     vertical justification.  Possible string values : \"left\",     \"right\", \"centre\", \"center\", \"bottom\",     \"top\".  numeric values, 0 means left (bottom) alignment     1 means right (top) alignment. hjust numeric vector specifying horizontal justification.     specified, overrides just setting. vjust numeric vector specifying vertical justification.     specified, overrides just setting. default.units string indicating default units use     x y     given numeric vectors. name character identifier. gp object class \"gpar\", typically output     call function gpar.  basically     list graphical parameter settings. vp Grid viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Text According To Available Text Width — split_text_grob","text":"text grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Text According To Available Text Width — split_text_grob","text":"code taken R Graphics Paul Murell, 2nd edition","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack Multiple Grobs — stack_grobs","title":"Stack Multiple Grobs — stack_grobs","text":"Stack grobs new grob 1 column multiple rows layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack Multiple Grobs — stack_grobs","text":"","code":"stack_grobs(   ...,   grobs = list(...),   padding = grid::unit(2, \"line\"),   vp = NULL,   gp = NULL,   name = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack Multiple Grobs — stack_grobs","text":"... grobs. grobs list grobs. padding unit length 1, space grob. vp viewport() object (NULL). gp gpar() object. name character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack Multiple Grobs — stack_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stack Multiple Grobs — stack_grobs","text":"","code":"library(grid)  g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid.newpage() grid.draw(stack_grobs(g1, g2, g3))  showViewport()   grid.newpage() pushViewport(viewport(layout = grid.layout(1, 2))) vp1 <- viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(stack_grobs(g1, g2, g3, vp = vp1, name = \"test\"))  showViewport()  grid.ls(grobs = TRUE, viewports = TRUE, print = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Mean — stat_mean_ci","title":"Confidence Interval for Mean — stat_mean_ci","text":"Convenient function calculating mean confidence interval. calculates arithmetic well geometric mean. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Mean — stat_mean_ci","text":"","code":"stat_mean_ci(   x,   conf_level = 0.95,   na.rm = TRUE,   n_min = 2,   gg_helper = TRUE,   geom_mean = FALSE )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Mean — stat_mean_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. n_min (number) minimum number non-missing x estimate confidence interval mean. gg_helper (logical)TRUE output aligned use ggplot. geom_mean (logical)TRUE geometric mean calculated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Mean — stat_mean_ci","text":"named vector values mean_ci_lwr mean_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Mean — stat_mean_ci","text":"","code":"stat_mean_ci(sample(10), gg_helper = FALSE) #> mean_ci_lwr mean_ci_upr  #>    3.334149    7.665851   p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +   ggplot2::geom_point()  p + ggplot2::stat_summary(   fun.data = stat_mean_ci,   geom = \"errorbar\" )   p + ggplot2::stat_summary(   fun.data = stat_mean_ci,   fun.args = list(conf_level = 0.5),   geom = \"errorbar\" )   p + ggplot2::stat_summary(   fun.data = stat_mean_ci,   fun.args = list(conf_level = 0.5, geom_mean = TRUE),   geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":null,"dir":"Reference","previous_headings":"","what":"p-Value of the Mean — stat_mean_pval","title":"p-Value of the Mean — stat_mean_pval","text":"Convenient function calculating two-sided p-value mean.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"p-Value of the Mean — stat_mean_pval","text":"","code":"stat_mean_pval(x, na.rm = TRUE, n_min = 2, test_mean = 0)"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"p-Value of the Mean — stat_mean_pval","text":"x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. n_min (numeric) minimum number non-missing x estimate p-value mean. test_mean (numeric) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"p-Value of the Mean — stat_mean_pval","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"p-Value of the Mean — stat_mean_pval","text":"","code":"stat_mean_pval(sample(10)) #>     p_value  #> 0.000278196   stat_mean_pval(rnorm(10), test_mean = 0.5) #>    p_value  #> 0.01876644"},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Median — stat_median_ci","title":"Confidence Interval for Median — stat_median_ci","text":"Convenient function calculating median confidence interval. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Median — stat_median_ci","text":"","code":"stat_median_ci(x, conf_level = 0.95, na.rm = TRUE, gg_helper = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Median — stat_median_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. gg_helper (logical)TRUE output aligned use ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Median — stat_median_ci","text":"named vector values median_ci_lwr median_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence Interval for Median — stat_median_ci","text":"function adapted DescTools/versions/0.99.35/source","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Median — stat_median_ci","text":"","code":"stat_median_ci(sample(10), gg_helper = FALSE) #> median_ci_lwr median_ci_upr  #>             2             9  #> attr(,\"conf_level\") #> [1] 0.9785156  p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +   ggplot2::geom_point() p + ggplot2::stat_summary(   fun.data = stat_median_ci,   geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion Difference and Confidence Interval — stat_propdiff_ci","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"Function calculating proportion (risk) difference confidence interval arm X (reference group) arm Y. Risk difference calculated subtracting cumulative incidence arm Y cumulative incidence arm X.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci(   x,   y,   N_x,   N_y,   list_names = NULL,   conf_level = 0.95,   pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"x (list integer) list number occurrences arm X (reference group). y (list integer) list number occurrences arm Y. Must equal length x. N_x (numeric) total number records arm X. N_y (numeric) total number records arm Y. list_names (character) names variable/level corresponding pair proportions x y. Must equal length x y. conf_level (proportion) confidence level interval. pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"List proportion differences CIs corresponding pair number occurrences x y. list element consists 3 statistics: proportion difference, CI lower bound, CI upper bound.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci(   x = list(0.375), y = list(0.01), N_x = 5, N_y = 5, list_names = \"x\", conf_level = 0.9 ) #> $x #> [1]   7.30000 -12.35184  26.95184 #>   stat_propdiff_ci(   x = list(0.5, 0.75, 1), y = list(0.25, 0.05, 0.5), N_x = 10, N_y = 20, pct = FALSE ) #> [[1]] #> [1]  0.0375000 -0.1060891  0.1810891 #>  #> [[2]] #> [1]  0.07250000 -0.09220915  0.23720915 #>  #> [[3]] #> [1]  0.0750000 -0.1231285  0.2731285 #>"},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"function wraps estimation stratified percentiles assume approximation large numbers. necessary case proportions strata unequal.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"","code":"strata_normal_quantile(vars, weights, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"vars (character) variable names primary analysis variable iterated . weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010)  minimizes weighted squared length confidence interval. conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"Stratified quantile.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"","code":"strata_data <- table(data.frame(   \"f1\" = sample(c(TRUE, FALSE), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   stringsAsFactors = TRUE )) ns <- colSums(strata_data) ests <- strata_data[\"TRUE\", ] / ns vars <- ests * (1 - ests) / ns weights <- rep(1 / length(ns), length(ns)) strata_normal_quantile(vars, weights, 0.95) #> [1] 1.131693"},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":null,"dir":"Reference","previous_headings":"","what":"Indicate Study Arm Variable in Formula — study_arm","title":"Indicate Study Arm Variable in Formula — study_arm","text":"use study_arm indicate study arm variable tern formulas.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indicate Study Arm Variable in Formula — study_arm","text":"","code":"study_arm(x)"},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indicate Study Arm Variable in Formula — study_arm","text":"x arm information","code":""},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indicate Study Arm Variable in Formula — study_arm","text":"x","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"Summarize results ANCOVA. can used analyze multiple endpoints /multiple timepoints within response variable .var.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"","code":"s_ancova(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   conf_level,   interaction_y = FALSE,   interaction_item = NULL )  a_ancova(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   conf_level,   interaction_y = FALSE,   interaction_item = NULL )  summarize_ancova(   lyt,   vars,   var_labels,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"visible\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL,   interaction_y = FALSE,   interaction_item = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data frame across columns given row split. variables (named list string) list additional analysis variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. conf_level (proportion) confidence level interval. interaction_y (character) selected item inside interaction_item column used select specific ANCOVA results. interaction needed, default option FALSE. interaction_item (character) name variable interactions arm. interaction needed, default option NULL. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"s_ancova() returns named list 5 statistics: n: Count complete sample size group. lsmean: Estimated marginal means group. lsmean_diff: Difference estimated marginal means comparison reference group. working reference group, empty. lsmean_diff_ci: Confidence level difference estimated marginal means comparison reference group. pval: p-value (adjusted multiple comparisons). a_ancova() returns corresponding list formatted rtables::CellValue(). summarize_ancova() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_ancova() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"s_ancova(): Statistics function produces named list results investigated linear model. a_ancova(): Formatted analysis function used afun summarize_ancova(). summarize_ancova(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"","code":"library(dplyr)  df <- iris %>% filter(Species == \"virginica\") .df_row <- iris .var <- \"Petal.Length\" variables <- list(arm = \"Species\", covariates = \"Sepal.Length * Sepal.Width\") .ref_group <- iris %>% filter(Species == \"setosa\") conf_level <- 0.95  basic_table() %>%   split_cols_by(\"Species\", ref_group = \"setosa\") %>%   add_colcounts() %>%   summarize_ancova(     vars = \"Petal.Length\",     variables = list(arm = \"Species\", covariates = NULL),     table_names = \"unadj\",     conf_level = 0.95, var_labels = \"Unadjusted comparison\",     .labels = c(lsmean = \"Mean\", lsmean_diff = \"Difference in Means\")   ) %>%   summarize_ancova(     vars = \"Petal.Length\",     variables = list(arm = \"Species\", covariates = c(\"Sepal.Length\", \"Sepal.Width\")),     table_names = \"adj\",     conf_level = 0.95, var_labels = \"Adjusted comparison (covariates: Sepal.Length and Sepal.Width)\"   ) %>%   build_table(iris) #>                                                                  setosa    versicolor     virginica   #>                                                                  (N=50)      (N=50)         (N=50)    #> ————————————————————————————————————————————————————————————————————————————————————————————————————— #> Unadjusted comparison                                                                                 #>   n                                                                50          50             50      #>   Mean                                                            1.46        4.26           5.55     #>   Difference in Means                                                         2.80           4.09     #>     95% CI                                                                (2.63, 2.97)   (3.92, 4.26) #>     p-value                                                                 <0.0001        <0.0001    #> Adjusted comparison (covariates: Sepal.Length and Sepal.Width)                                        #>   n                                                                50          50             50      #>   Adjusted Mean                                                   2.02        4.19           5.07     #>   Difference in Adjusted Means                                                2.17           3.05     #>     95% CI                                                                (1.96, 2.38)   (2.81, 3.29) #>     p-value                                                                 <0.0001        <0.0001"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"primary analysis variable .var indicates numerical change baseline results, additional required secondary analysis variables value baseline_flag. Depending baseline flag, either absolute baseline values (baseline) change baseline values (post-baseline) summarized.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"","code":"s_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)  a_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)  summarize_change(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = c(\"n\", \"mean_sd\", \"median\", \"range\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. variables (named list string) list additional analysis variables. na.rm (flag) whether NA values removed x prior analysis. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"s_change_from_baseline() returns values returned s_summary.numeric(). a_change_from_baseline() returns corresponding list formatted rtables::CellValue(). summarize_change() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_change_from_baseline() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"s_change_from_baseline(): Statistics function summarizes baseline post-baseline visits. a_change_from_baseline(): Formatted analysis function used afun summarize_change(). summarize_change(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"data df must either baseline post-baseline visits. Otherwise error thrown. used split visits layout, data subset contains either baseline post-baseline data.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"","code":"df <- data.frame(   chg = c(1, 2, 3),   is_bl = c(TRUE, TRUE, TRUE),   val = c(4, 5, 6) )  # `summarize_change()`  ## Fabricated dataset. library(dplyr)  dta_test <- data.frame(   USUBJID = rep(1:6, each = 3),   AVISIT = rep(paste0(\"V\", 1:3), 6),   ARM = rep(LETTERS[1:3], rep(6, 3)),   AVAL = c(9:1, rep(NA, 9)) ) %>%   mutate(ABLFLL = AVISIT == \"V1\") %>%   group_by(USUBJID) %>%   mutate(     BLVAL = AVAL[ABLFLL],     CHG = AVAL - BLVAL   ) %>%   ungroup()  results <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   summarize_change(\"CHG\", variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\")) %>%   build_table(dta_test) # \\donttest{ Viewer(results) # }"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Variables in Columns — summarize_colvars","title":"Summarize Variables in Columns — summarize_colvars","text":"analyze function uses S3 generic function s_summary() summarize different variables arranged columns. Additional standard formatting arguments available. minimal wrapper rtables::analyze_colvars(). latter function meant add different analysis methods column variables different rows. analysis methods column labels, please refer analyze_vars_in_cols().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize Variables in Columns — summarize_colvars","text":"","code":"summarize_colvars(   lyt,   ...,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize Variables in Columns — summarize_colvars","text":"lyt (layout) input layout analyses added . ... arguments passed s_summary(). na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize Variables in Columns — summarize_colvars","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout summarize given variables, arrange output columns, add table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize Variables in Columns — summarize_colvars","text":"","code":"dta_test <- data.frame(   USUBJID = rep(1:6, each = 3),   PARAMCD = rep(\"lab\", 6 * 3),   AVISIT = rep(paste0(\"V\", 1:3), 6),   ARM = rep(LETTERS[1:3], rep(6, 3)),   AVAL = c(9:1, rep(NA, 9)),   CHG = c(1:9, rep(NA, 9)) )  ## Default output within a `rtables` pipeline. basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>%   summarize_colvars() %>%   build_table(dta_test) #>                         A                       B                 C      #>                 AVAL         CHG        AVAL         CHG      AVAL   CHG #> ———————————————————————————————————————————————————————————————————————— #> V1                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   7.5 (2.1)   2.5 (2.1)   3.0 (NA)    7.0 (NA)     NA    NA  #>   Median         7.5         2.5         3.0         7.0       NA    NA  #>   Min - Max   6.0 - 9.0   1.0 - 4.0   3.0 - 3.0   7.0 - 7.0    NA    NA  #> V2                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   6.5 (2.1)   3.5 (2.1)   2.0 (NA)    8.0 (NA)     NA    NA  #>   Median         6.5         3.5         2.0         8.0       NA    NA  #>   Min - Max   5.0 - 8.0   2.0 - 5.0   2.0 - 2.0   8.0 - 8.0    NA    NA  #> V3                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   5.5 (2.1)   4.5 (2.1)   1.0 (NA)    9.0 (NA)     NA    NA  #>   Median         5.5         4.5         1.0         9.0       NA    NA  #>   Min - Max   4.0 - 7.0   3.0 - 6.0   1.0 - 1.0   9.0 - 9.0    NA    NA   ## Selection of statistics, formats and labels also work. basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>%   summarize_colvars(     .stats = c(\"n\", \"mean_sd\"),     .formats = c(\"mean_sd\" = \"xx.x, xx.x\"),     .labels = c(n = \"n\", mean_sd = \"Mean, SD\")   ) %>%   build_table(dta_test) #>                       A                    B               C      #>                AVAL       CHG       AVAL       CHG     AVAL   CHG #> ————————————————————————————————————————————————————————————————— #> V1                                                                #>   n             2          2          1         1       0      0  #>   Mean, SD   7.5, 2.1   2.5, 2.1   3.0, NA   7.0, NA    NA    NA  #> V2                                                                #>   n             2          2          1         1       0      0  #>   Mean, SD   6.5, 2.1   3.5, 2.1   2.0, NA   8.0, NA    NA    NA  #> V3                                                                #>   n             2          2          1         1       0      0  #>   Mean, SD   5.5, 2.1   4.5, 2.1   1.0, NA   9.0, NA    NA    NA   ## Use arguments interpreted by `s_summary`. basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>%   summarize_colvars(na.rm = FALSE) %>%   build_table(dta_test) #>                         A                       B                 C      #>                 AVAL         CHG        AVAL         CHG      AVAL   CHG #> ———————————————————————————————————————————————————————————————————————— #> V1                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   7.5 (2.1)   2.5 (2.1)   3.0 (NA)    7.0 (NA)     NA    NA  #>   Median         7.5         2.5         3.0         7.0       NA    NA  #>   Min - Max   6.0 - 9.0   1.0 - 4.0   3.0 - 3.0   7.0 - 7.0    NA    NA  #> V2                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   6.5 (2.1)   3.5 (2.1)   2.0 (NA)    8.0 (NA)     NA    NA  #>   Median         6.5         3.5         2.0         8.0       NA    NA  #>   Min - Max   5.0 - 8.0   2.0 - 5.0   2.0 - 2.0   8.0 - 8.0    NA    NA  #> V3                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   5.5 (2.1)   4.5 (2.1)   1.0 (NA)    9.0 (NA)     NA    NA  #>   Median         5.5         4.5         1.0         9.0       NA    NA  #>   Min - Max   4.0 - 7.0   3.0 - 6.0   1.0 - 1.0   9.0 - 9.0    NA    NA"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Functions — summarize_functions","title":"Summarize Functions — summarize_functions","text":"functions wrappers rtables::summarize_row_groups(), applying corresponding tern content functions add summary rows given table layout: add_rowcounts() estimate_multinomial_response() (rtables::analyze()) h_tab_one_biomarker() (probably deprecate) logistic_summary_by_flag() summarize_num_patients() summarize_occurrences_by_grade() summarize_patients_events_in_cols() summarize_patients_exposure_in_cols() tabulate_rsp_subgroups() Additionally, summarize_coxreg() function utilizes rtables::summarize_row_groups() (combination several rtables functions like rtables::analyze_colvars()) output Cox regression summary table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary for Poisson Negative Binomial. — summarize_glm_count","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"Summarize results Poisson Negative Binomial Regression. can used analyze count /frequency data using linear model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"","code":"s_glm_count(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   distribution,   conf_level,   rate_mean_method,   weights,   scale = 1 )  a_glm_count(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   distribution,   conf_level,   rate_mean_method,   weights,   scale = 1 )  summarize_glm_count(   lyt,   vars,   var_labels,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"visible\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data frame across columns given row split. variables (named list string) list additional analysis variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. distribution (character) character value specifying distribution used regression (poisson, quasipoisson). conf_level (proportion) confidence level interval. weights (character) character vector specifying weights used averaging predictions. Number weights must equal number levels included covariates. Weights option passed emmeans::emmeans(). lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"s_glm_count() returns named list 5 statistics: n: Count complete sample size group. rate: Estimated event rate per follow-time. rate_ci: Confidence level estimated rate per follow-time. rate_ratio: Ratio event rates treatment arm reference arm. rate_ratio_ci: Confidence level rate ratio. pval: p-value. a_glm_count() returns corresponding list formatted rtables::CellValue(). summarize_glm_count() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_glm_count() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"s_glm_count(): Statistics function produces named list results investigated Poisson model. a_glm_count(): Formatted analysis function used afun summarize_glm_count(). summarize_glm_count(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"","code":"library(dplyr) anl <- tern_ex_adtte %>% filter(PARAMCD == \"TNE\") anl$AVAL_f <- as.factor(anl$AVAL)  lyt <- basic_table() %>%   split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   analyze_vars(     \"AVAL_f\",     var_labels = \"Number of exacerbations per patient\",     .stats = c(\"count_fraction\"),     .formats = c(\"count_fraction\" = \"xx (xx.xx%)\"),     .label = c(\"Number of exacerbations per patient\")   ) %>%   summarize_glm_count(     vars = \"AVAL\",     variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = NULL),     conf_level = 0.95,     distribution = \"poisson\",     rate_mean_method = \"emmeans\",     var_labels = \"Unadjusted exacerbation rate (per year)\",     table_names = \"unadj\",     .stats = c(\"rate\"),     .labels = c(rate = \"Rate\")   ) %>%   summarize_glm_count(     vars = \"AVAL\",     variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = c(\"REGION1\")),     conf_level = 0.95,     distribution = \"quasipoisson\",     rate_mean_method = \"ppmeans\",     var_labels = \"Adjusted (QP) exacerbation rate (per year)\",     table_names = \"adj\",     .stats = c(\"rate\", \"rate_ci\", \"rate_ratio\", \"rate_ratio_ci\", \"pval\"),     .labels = c(       rate = \"Rate\", rate_ci = \"Rate CI\", rate_ratio = \"Rate Ratio\",       rate_ratio_ci = \"Rate Ratio CI\", pval = \"p value\"     )   ) build_table(lyt = lyt, df = anl) #>                                                 B: Placebo         A: Drug X        C: Combination  #>                                                   (N=73)             (N=69)             (N=58)      #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Number of exacerbations per patient                                                                 #>   0                                             8 (10.96%)         3 (4.35%)          6 (10.34%)    #>   1                                             9 (12.33%)        11 (15.94%)         6 (10.34%)    #>   2                                            15 (20.55%)        18 (26.09%)         9 (15.52%)    #>   3                                            11 (15.07%)        14 (20.29%)        15 (25.86%)    #>   4                                             9 (12.33%)        10 (14.49%)         9 (15.52%)    #>   5                                             9 (12.33%)         7 (10.14%)         8 (13.79%)    #>   6                                             4 (5.48%)          4 (5.80%)          4 (6.90%)     #>   7                                             8 (10.96%)         2 (2.90%)          0 (0.00%)     #>   10                                            0 (0.00%)          0 (0.00%)          1 (1.72%)     #> Unadjusted exacerbation rate (per year)                                                             #>   Rate                                            9.1554             8.2061             7.8551      #> Adjusted (QP) exacerbation rate (per year)                                                          #>   Rate                                            3.4860             3.1214             2.6152      #>   Rate CI                                    (1.9833, 6.1272)   (1.7307, 5.6294)   (1.3661, 5.0065) #>   Rate Ratio                                                         0.8954             0.7502      #>   Rate Ratio CI                                                 (0.3975, 2.0170)   (0.3067, 1.8348) #>   p value                                                            0.7897             0.5288"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Logistic Regression Table — summarize_logistic","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"Layout-creating function summarizes logistic variable regression binary outcome categorical/continuous covariates model statement. covariate category (categorical) specified values (continuous), present degrees freedom, regression parameter estimate standard error (SE) relative reference group category. Report odds ratios covariate category specified values corresponding Wald confidence intervals default allow user specify confidence levels. Report p-value Wald chi-square test null hypothesis covariate effect response model containing specified covariates. Allow option include one two-way interaction present similar output interaction degree freedom.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"","code":"summarize_logistic(   lyt,   conf_level,   drop_and_remove_str = \"\",   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"lyt (layout) input layout analyses added . conf_level (proportion) confidence level interval. drop_and_remove_str (character) string dropped removed. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add logistic regression variable summary table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"formula, variable names need standard data.frame column names without special characters.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"","code":"library(dplyr) library(broom)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred  df <- tidy(mod1, conf_level = 0.99) df2 <- tidy(mod2, conf_level = 0.99)  # flagging empty strings with \"_\" df <- df_explicit_na(df, na_level = \"_\") df2 <- df_explicit_na(df2, na_level = \"_\")  result1 <- basic_table() %>%   summarize_logistic(     conf_level = 0.95,     drop_and_remove_str = \"_\"   ) %>%   build_table(df = df) result1 #>                                       Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Planned Arm Code                              2                                                                                 0.3004  #>   Reference ARM A, n = 64                                                                                                               #>   ARM B, n = 68                               1                  -1.775             1.144           0.17       (<0.01, 3.23)    0.1209  #>   ARM C, n = 52                               1                  17.192            3626.588       >999.99     (0.00, >999.99)   0.9962  #> Age                                                                                                                                     #>   Age                                         1                  0.170              0.095           1.19       (0.93, 1.51)     0.0746  #> Race                                          2                                                                                 0.7967  #>   Reference ASIAN, n = 110                                                                                                              #>   BLACK OR AFRICAN AMERICAN, n = 40           1                  17.923            4001.705       >999.99     (0.00, >999.99)   0.9964  #>   WHITE, n = 34                               1                  -0.656             0.974           0.52       (0.04, 6.37)     0.5002   result2 <- basic_table() %>%   summarize_logistic(     conf_level = 0.95,     drop_and_remove_str = \"_\"   ) %>%   build_table(df = df2) result2 #>                                         Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Race                                            2                                                                                 0.9361  #>   Reference ASIAN, n = 110                                                                                                                #>   BLACK OR AFRICAN AMERICAN, n = 40             1                  18.150            3944.701       >999.99     (0.00, >999.99)   0.9963  #>   WHITE, n = 34                                 1                  -0.373             1.026           0.69       (0.05, 9.68)     0.7164  #> Planned Arm Code                                2                                                                                 0.2539  #>   Reference ARM A, n = 64                                                                                                                 #>   ARM B, n = 68                                 1                 -11.527             6.962                                       0.0978  #>     Age                                                                                                                                   #>       35                                                                                              0.48       (0.01, 15.48)            #>   ARM C, n = 52                                 1                  16.333           20278.387                                     0.9994  #>     Age                                                                                                                                   #>       35                                                                                            >999.99     (0.00, >999.99)           #> Age                                                                                                                                       #>   Age                                           1                  -0.039             0.151                                       0.7981  #>     Planned Arm Code                                                                                                                      #>       ARM A                                                                                           0.96       (0.65, 1.42)             #>       ARM B                                                                                           1.31       (0.92, 1.86)             #>       ARM C                                                                                           0.99      (0.00, >999.99)           #> Interaction of Planned Arm Code * Age           2                                                                                 0.3276  #>   Reference ARM A, n = 64                                                                                                                 #>   ARM B, n = 68                                 1                  0.308              0.206                                       0.1352  #>   ARM C, n = 52                                 1                  0.029             548.592                                      1.0000"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of Patients — summarize_num_patients","title":"Number of Patients — summarize_num_patients","text":"Count number unique non-unique patients column (variable).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of Patients — summarize_num_patients","text":"","code":"s_num_patients(   x,   labelstr,   .N_col,   count_by = NULL,   unique_count_suffix = TRUE )  s_num_patients_content(   df,   labelstr = \"\",   .N_col,   .var,   required = NULL,   count_by = NULL,   unique_count_suffix = TRUE )  summarize_num_patients(   lyt,   var,   na_str = NA_character_,   .stats = NULL,   .formats = NULL,   .labels = c(unique = \"Number of patients with at least one event\", nonunique =     \"Number of events\"),   indent_mod = lifecycle::deprecated(),   .indent_mods = 0L,   riskdiff = FALSE,   ... )  analyze_num_patients(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   .stats = NULL,   .formats = NULL,   .labels = c(unique = \"Number of patients with at least one event\", nonunique =     \"Number of events\"),   show_labels = c(\"default\", \"visible\", \"hidden\"),   indent_mod = lifecycle::deprecated(),   .indent_mods = 0L,   riskdiff = FALSE,   ... )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of Patients — summarize_num_patients","text":"x (character factor) vector patient IDs. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. count_by (character factor) optional vector combined x counting nonunique records. unique_count_suffix (logical) \"(n)\" suffix added unique_count labels. Defaults TRUE. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. required (character NULL) optional name variable required non-missing. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). indent_mod Please use .indent_mods argument instead. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. ... additional arguments lower level functions. vars (character) variable names primary analysis variable iterated . nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of Patients — summarize_num_patients","text":"s_num_patients() returns named list 3 statistics: unique: Vector counts percentages. nonunique: Vector counts. unique_count: Counts. s_num_patients_content() returns values s_num_patients(). summarize_num_patients() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_num_patients_content() table layout. analyze_num_patients() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_num_patients_content() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of Patients — summarize_num_patients","text":"general, functions starts analyze* expected work like rtables::analyze(), functions starts summarize* based upon rtables::summarize_row_groups(). latter provides value dividing split row column space, , bound fundamental splits, repeated design every page pagination involved.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Number of Patients — summarize_num_patients","text":"s_num_patients(): Statistics function counts number unique patients, corresponding percentage taken respect total number patients, number non-unique patients. s_num_patients_content(): Statistics function counts number unique patients column (variable), corresponding percentage taken respect total number patients, number non-unique patients column. summarize_num_patients(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). analyze_num_patients(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Number of Patients — summarize_num_patients","text":"opposed summarize_num_patients(), function repeat produced rows.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Number of Patients — summarize_num_patients","text":"","code":"# Use the statistics function to count number of unique and nonunique patients. s_num_patients(x = as.character(c(1, 1, 1, 2, 4, NA)), labelstr = \"\", .N_col = 6L) #> $unique #> [1] 3.0 0.5 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 5 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>  s_num_patients(   x = as.character(c(1, 1, 1, 2, 4, NA)),   labelstr = \"\",   .N_col = 6L,   count_by = as.character(c(1, 1, 2, 1, 1, 1)) ) #> $unique #> [1] 3.0 0.5 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 4 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>   # Count number of unique and non-unique patients. df <- data.frame(   USUBJID = as.character(c(1, 2, 1, 4, NA)),   EVENT = as.character(c(10, 15, 10, 17, 8)) ) s_num_patients_content(df, .N_col = 5, .var = \"USUBJID\") #> $unique #> [1] 3.0 0.6 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 4 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>   df_by_event <- data.frame(   USUBJID = as.character(c(1, 2, 1, 4, NA)),   EVENT = as.character(c(10, 15, 10, 17, 8)) ) s_num_patients_content(df_by_event, .N_col = 5, .var = \"USUBJID\") #> $unique #> [1] 3.0 0.6 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 4 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>  s_num_patients_content(df_by_event, .N_col = 5, .var = \"USUBJID\", count_by = \"EVENT\") #> $unique #> [1] 3.0 0.6 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 3 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>   df_tmp <- data.frame(   USUBJID = as.character(c(1, 2, 1, 4, NA, 6, 6, 8, 9)),   ARM = c(\"A\", \"A\", \"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"B\"),   AGE = c(10, 15, 10, 17, 8, 11, 11, 19, 17) ) tbl <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   analyze_num_patients(\"USUBJID\", .stats = c(\"unique\")) %>%   build_table(df_tmp) tbl #>                                                  A           B     #>                                                (N=5)       (N=4)   #> —————————————————————————————————————————————————————————————————— #> Number of patients with at least one event   3 (60.0%)   3 (75.0%)"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"Counting number patients summing analysis value (.e exposure values) across patients column table layout required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"","code":"s_count_patients_sum_exposure(   df,   ex_var = \"AVAL\",   id = \"USUBJID\",   labelstr = \"\",   .stats = c(\"n_patients\", \"sum_exposure\"),   .N_col,   custom_label = NULL )  a_count_patients_sum_exposure(   df,   var = NULL,   ex_var = \"AVAL\",   id = \"USUBJID\",   labelstr = \"\",   add_total_level = FALSE,   .N_col,   .stats,   .formats = list(n_patients = \"xx (xx.x%)\", sum_exposure = \"xx\"),   custom_label = NULL )  summarize_patients_exposure_in_cols(   lyt,   var,   na_str = NA_character_,   ...,   .stats = c(\"n_patients\", \"sum_exposure\"),   .labels = c(n_patients = \"Patients\", sum_exposure = \"Person time\"),   .indent_mods = NULL,   col_split = TRUE )  analyze_patients_exposure_in_cols(   lyt,   var = NULL,   ex_var = \"AVAL\",   col_split = TRUE,   add_total_level = FALSE,   .stats = c(\"n_patients\", \"sum_exposure\"),   .labels = c(n_patients = \"Patients\", sum_exposure = \"Person time\"),   .indent_mods = 0L,   ... )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"df (data.frame) data set containing analysis variables. ex_var (character) name variable within df containing exposure values. id (string) subject variable name. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .stats (character) statistics select table. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. custom_label (string NULL) provided labelstr empty used label. var (string) single variable name passed rtables requested statistics function. add_total_level (flag) adds \"total\" level others includes levels constitute split. custom label can set level via custom_label argument. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. ... additional arguments lower level functions. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. col_split (flag) whether columns split. Set FALSE required column split done already earlier layout pipe.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"s_count_patients_sum_exposure() returns named list statistics: n_patients: Number unique patients df. sum_exposure: Sum ex_var across patients df. a_count_patients_sum_exposure() returns formatted rtables::CellValue(). summarize_patients_exposure_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows, statistics s_count_patients_sum_exposure() arranged columns, table layout. analyze_patients_exposure_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted data rows, statistics s_count_patients_sum_exposure() arranged columns, table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"s_count_patients_sum_exposure(): Statistics function counts numbers patients sum exposure across patients. a_count_patients_sum_exposure(): Analysis function used afun rtables::analyze_colvars() within analyze_patients_exposure_in_cols() cfun rtables::summarize_row_groups() within summarize_patients_exposure_in_cols(). summarize_patients_exposure_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::split_cols_by_multivar() rtables::summarize_row_groups(). analyze_patients_exposure_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::split_cols_by_multivar() rtables::analyze_colvars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"opposed summarize_patients_exposure_in_cols() generates content rows, analyze_patients_exposure_in_cols() generates data rows repeated multiple pages pagination used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"","code":"set.seed(1) df <- data.frame(   USUBJID = c(paste(\"id\", seq(1, 12), sep = \"\")),   ARMCD = c(rep(\"ARM A\", 6), rep(\"ARM B\", 6)),   SEX = c(rep(\"Female\", 6), rep(\"Male\", 6)),   AVAL = as.numeric(sample(seq(1, 20), 12)),   stringsAsFactors = TRUE ) adsl <- data.frame(   USUBJID = c(paste(\"id\", seq(1, 12), sep = \"\")),   ARMCD = c(rep(\"ARM A\", 2), rep(\"ARM B\", 2)),   SEX = c(rep(\"Female\", 2), rep(\"Male\", 2)),   stringsAsFactors = TRUE )  a_count_patients_sum_exposure(   df = df,   var = \"SEX\",   .N_col = nrow(df),   .stats = \"n_patients\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1   Female      6 (50.0%)          0    Female #> 2     Male      6 (50.0%)          0      Male  lyt <- basic_table() %>%   summarize_patients_exposure_in_cols(var = \"AVAL\", col_split = TRUE) result <- build_table(lyt, df = df, alt_counts_df = adsl) result #>                                       Patients     Person time #> —————————————————————————————————————————————————————————————— #> Total patients numbers/person time   12 (100.0%)       114      lyt2 <- basic_table() %>%   summarize_patients_exposure_in_cols(var = \"AVAL\", col_split = TRUE, .stats = \"sum_exposure\") result2 <- build_table(lyt2, df = df, alt_counts_df = adsl) result2 #>                                      Person time #> ———————————————————————————————————————————————— #> Total patients numbers/person time       114      lyt3 <- basic_table() %>%   split_cols_by(\"ARMCD\", split_fun = add_overall_level(\"Total\", first = FALSE)) %>%   summarize_patients_exposure_in_cols(var = \"AVAL\", col_split = TRUE) %>%   analyze_patients_exposure_in_cols(var = \"SEX\", col_split = FALSE) result3 <- build_table(lyt3, df = df, alt_counts_df = adsl) result3 #>                                               ARM A                      ARM B                       Total           #>                                       Patients    Person time    Patients    Person time    Patients     Person time #> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total patients numbers/person time   6 (100.0%)       46        6 (100.0%)       68        12 (100.0%)       114     #>   Female                             6 (100.0%)       46         0 (0.0%)         0         6 (50.0%)        46      #>   Male                                0 (0.0%)         0        6 (100.0%)       68         6 (50.0%)        68       lyt4 <- basic_table() %>%   split_cols_by(\"ARMCD\", split_fun = add_overall_level(\"Total\", first = FALSE)) %>%   summarize_patients_exposure_in_cols(     var = \"AVAL\", col_split = TRUE,     .stats = \"n_patients\", custom_label = \"some custom label\"   ) %>%   analyze_patients_exposure_in_cols(var = \"SEX\", col_split = FALSE, ex_var = \"AVAL\") result4 <- build_table(lyt4, df = df, alt_counts_df = adsl) result4 #>                       ARM A        ARM B         Total    #>                      Patients     Patients     Patients   #> ————————————————————————————————————————————————————————— #> some custom label   6 (100.0%)   6 (100.0%)   12 (100.0%) #>   Female            6 (100.0%)    0 (0.0%)     6 (50.0%)  #>   Male               0 (0.0%)    6 (100.0%)    6 (50.0%)   lyt5 <- basic_table() %>%   analyze_patients_exposure_in_cols(var = \"SEX\", col_split = TRUE, ex_var = \"AVAL\") result5 <- build_table(lyt5, df = df, alt_counts_df = adsl) result5 #>          Patients    Person time #> ———————————————————————————————— #> Female   6 (50.0%)       46      #> Male     6 (50.0%)       68       # Adding total levels and custom label lyt <- basic_table(   show_colcounts = TRUE ) %>%   analyze_patients_exposure_in_cols(     var = \"ARMCD\",     col_split = TRUE,     add_total_level = TRUE,     custom_label = \"TOTAL\"   ) %>%   append_topleft(c(\"\", \"Sex\"))  tbl <- build_table(lyt, df = df, alt_counts_df = adsl) tbl #>          Patients     Person time #> Sex       (N=12)        (N=12)    #> ————————————————————————————————— #> ARM A    6 (50.0%)        46      #> ARM B    6 (50.0%)        68      #> TOTAL   12 (100.0%)       114"},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"Tabulate estimated effects multiple continuous biomarker variables across population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"","code":"tabulate_survival_biomarkers(   df,   vars = c(\"n_tot\", \"n_tot_events\", \"median\", \"hr\", \"ci\", \"pval\"),   time_unit = NULL,   .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"df (data.frame) containing analysis variables, returned extract_survival_biomarkers(). vars (character) names statistics reported among: n_tot_events: Total number events per group. n_tot: Total number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"rtables table summarizing biomarker effects survival subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"functions create layout starting data frame contains required statistics. tables typically used input forest plots.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"tabulate_survival_biomarkers(): Table-creating function creates table summarizing biomarker effects survival subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"contrast tabulate_survival_subgroups() tabulation function start input layout lyt. internally table created combining multiple subtables.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"","code":"library(dplyr)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_biomarkers(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     strata = \"STRATA1\",     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adtte_f ) df #>   biomarker              biomarker_label n_tot n_tot_events   median        hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 2       AGE                          Age   200          141 753.5176 1.0106406 #> 3    BMRKR1 Continuous Level Biomarker 1    70           52 735.4722 0.9905065 #> 4       AGE                          Age    70           52 735.4722 1.0106279 #> 5    BMRKR1 Continuous Level Biomarker 1    68           42 858.9952 0.9623210 #> 6       AGE                          Age    68           42 858.9952 1.0360765 #> 7    BMRKR1 Continuous Level Biomarker 1    62           47 727.8043 1.0770946 #> 8       AGE                          Age    62           47 727.8043 1.0009890 #>         lcl      ucl conf_level      pval     pval_label     subgroup    var #> 1 0.9538978 1.050625       0.95 0.9646086 p-value (Wald) All Patients    ALL #> 2 0.9871004 1.034742       0.95 0.3787395 p-value (Wald) All Patients    ALL #> 3 0.9142220 1.073156       0.95 0.8155443 p-value (Wald)          LOW BMRKR2 #> 4 0.9621192 1.061582       0.95 0.6735773 p-value (Wald)          LOW BMRKR2 #> 5 0.8708694 1.063376       0.95 0.4509368 p-value (Wald)       MEDIUM BMRKR2 #> 6 0.9727439 1.103532       0.95 0.2707796 p-value (Wald)       MEDIUM BMRKR2 #> 7 0.9756250 1.189118       0.95 0.1412524 p-value (Wald)         HIGH BMRKR2 #> 8 0.9678535 1.035259       0.95 0.9541048 p-value (Wald)         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis  ## Table with default columns. tabulate_survival_biomarkers(df) #>                                  Total n   Total Events   Median   Hazard Ratio   95% Wald CI    p-value (Wald) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age                                                                                                             #>   All Patients                     200         141        753.5        1.01       (0.99, 1.03)       0.3787     #>   Continuous Level Biomarker 2                                                                                  #>     LOW                            70           52        735.5        1.01       (0.96, 1.06)       0.6736     #>     MEDIUM                         68           42        859.0        1.04       (0.97, 1.10)       0.2708     #>     HIGH                           62           47        727.8        1.00       (0.97, 1.04)       0.9541     #> Continuous Level Biomarker 1                                                                                    #>   All Patients                     200         141        753.5        1.00       (0.95, 1.05)       0.9646     #>   Continuous Level Biomarker 2                                                                                  #>     LOW                            70           52        735.5        0.99       (0.91, 1.07)       0.8155     #>     MEDIUM                         68           42        859.0        0.96       (0.87, 1.06)       0.4509     #>     HIGH                           62           47        727.8        1.08       (0.98, 1.19)       0.1413      ## Table with a manually chosen set of columns: leave out \"pval\", reorder. tab <- tabulate_survival_biomarkers(   df = df,   vars = c(\"n_tot_events\", \"ci\", \"n_tot\", \"median\", \"hr\"),   time_unit = as.character(adtte_f$AVALU[1]) )  ## Finally produce the forest plot. # \\donttest{ g_forest(tab, xlim = c(0.8, 1.2))  # }"},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise CoxPH model — survival_coxph_pairwise","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"Summarize p-value, HR CIs stratified unstratified CoxPH model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"","code":"s_coxph_pairwise(   df,   .ref_group,   .in_ref_col,   .var,   is_event,   strat = NULL,   control = control_coxph() )  a_coxph_pairwise(   df,   .ref_group,   .in_ref_col,   .var,   is_event,   strat = NULL,   control = control_coxph() )  coxph_pairwise(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = \"CoxPH\",   show_labels = \"visible\",   table_names = vars,   .stats = c(\"pvalue\", \"hr\", \"hr_ci\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"df (data.frame) data set containing analysis variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. .var (string) single variable name passed rtables requested statistics function. is_event (logical)TRUE event, FALSE time event censored. strat (character NULL) variable names indicating stratification factors. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"s_coxph_pairwise() returns statistics: pvalue: p-value test HR = 1. hr: Hazard ratio. hr_ci: Confidence interval hazard ratio. n_tot: Total number observations. n_tot_events: Total number events. a_coxph_pairwise() returns corresponding list formatted rtables::CellValue(). coxph_pairwise() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_coxph_pairwise() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"s_coxph_pairwise(): Statistics function analyzes HR, CIs HR p-value coxph model. a_coxph_pairwise(): Formatted analysis function used afun coxph_pairwise(). coxph_pairwise(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"","code":"library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0) df <- adtte_f %>%   filter(ARMCD == \"ARM A\") df_ref_group <- adtte_f %>%   filter(ARMCD == \"ARM B\")  basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   coxph_pairwise(     vars = \"AVAL\",     is_event = \"is_event\",     var_labels = \"Unstratified Analysis\"   ) %>%   build_table(df = adtte_f) #>                         ARM A       ARM B          ARM C     #>                         (N=69)      (N=73)         (N=58)    #> ———————————————————————————————————————————————————————————— #> Unstratified Analysis                                        #>   p-value (log-rank)                0.0905         0.0086    #>   Hazard Ratio                       1.41           1.81     #>   95% CI                         (0.95, 2.09)   (1.16, 2.84)  basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   coxph_pairwise(     vars = \"AVAL\",     is_event = \"is_event\",     var_labels = \"Stratified Analysis\",     strat = \"SEX\",     control = control_coxph(pval_method = \"wald\")   ) %>%   build_table(df = adtte_f) #>                       ARM A       ARM B          ARM C     #>                       (N=69)      (N=73)         (N=58)    #> —————————————————————————————————————————————————————————— #> Stratified Analysis                                        #>   p-value (wald)                  0.0784         0.0066    #>   Hazard Ratio                     1.44           1.89     #>   95% CI                       (0.96, 2.15)   (1.19, 2.98)"},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"Tabulate statistics median survival time hazard ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"","code":"a_survival_subgroups(   .formats = list(n = \"xx\", n_events = \"xx\", n_tot_events = \"xx\", median = \"xx.x\", n_tot     = \"xx\", hr = list(format_extreme_values(2L)), ci =     list(format_extreme_values_ci(2L)), pval = \"x.xxxx | (<0.0001)\") )  tabulate_survival_subgroups(   lyt,   df,   vars = c(\"n_tot_events\", \"n_events\", \"median\", \"hr\", \"ci\"),   time_unit = NULL,   na_str = NA_character_ )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":".formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. lyt (layout) input layout analyses added . df (list) data frames containing analysis variables. List created using extract_survival_subgroups(). vars (character) name statistics reported among: n_tot_events: Total number events per group. n_events: Number events per group. n_tot: Total number observations per group. n: Number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"a_survival_subgroups() returns corresponding list formatted rtables::CellValue(). rtables table summarizing survival subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"functions create layout starting data frame contains required statistics. Tables typically used part forest plot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"a_survival_subgroups(): Formatted analysis function used afun tabulate_survival_subgroups(). tabulate_survival_subgroups(): Table-creating function creates table summarizing survival subgroup. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(   \"ARM\" = adtte_labels[[\"ARM\"]],   \"SEX\" = adtte_labels[[\"SEX\"]],   \"AVALU\" = adtte_labels[[\"AVALU\"]],   \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) df #> $survtime #>           arm  n n_events    median     subgroup    var #> 1  B: Placebo 73       57  727.8043 All Patients    ALL #> 2   A: Drug X 69       44  974.6402 All Patients    ALL #> 3  B: Placebo 40       31  599.1772            F    SEX #> 4   A: Drug X 38       24 1016.2982            F    SEX #> 5  B: Placebo 33       26  888.4916            M    SEX #> 6   A: Drug X 31       20  974.6402            M    SEX #> 7  B: Placebo 24       21  735.4722          LOW BMRKR2 #> 8   A: Drug X 26       15  974.6402          LOW BMRKR2 #> 9  B: Placebo 23       14  731.8352       MEDIUM BMRKR2 #> 10  A: Drug X 26       17  964.2197       MEDIUM BMRKR2 #> 11 B: Placebo 26       22  654.8245         HIGH BMRKR2 #> 12  A: Drug X 17       12 1016.2982         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $hr #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        49           31 0.7651261 0.3641277 1.6077269       0.95 0.47860004 #> 6        43           34 0.6662356 0.3257413 1.3626456       0.95 0.26285846 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis #>   ## Table with default columns. basic_table() %>%   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1]) #>                                                     B: Placebo               A: Drug X                                      #> Baseline Risk Factors          Total Events   Events   Median (DAYS)   Events   Median (DAYS)   Hazard Ratio   95% Wald CI  #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                       101          57         727.8         44         974.6           0.71       (0.48, 1.06) #> Sex                                                                                                                         #>   F                                 55          31         599.2         24        1016.3           0.56       (0.32, 0.96) #>   M                                 46          26         888.5         20         974.6           0.91       (0.50, 1.65) #> Continuous Level Biomarker 2                                                                                                #>   LOW                               36          21         735.5         15         974.6           0.76       (0.39, 1.51) #>   MEDIUM                            31          14         731.8         17         964.2           0.77       (0.36, 1.61) #>   HIGH                              34          22         654.8         12        1016.3           0.67       (0.33, 1.36)  ## Table with a manually chosen set of columns: adding \"pval\". basic_table() %>%   tabulate_survival_subgroups(     df = df,     vars = c(\"n_tot_events\", \"n_events\", \"median\", \"hr\", \"ci\", \"pval\"),     time_unit = adtte_f$AVALU[1]   ) #>                                                     B: Placebo               A: Drug X                                                           #> Baseline Risk Factors          Total Events   Events   Median (DAYS)   Events   Median (DAYS)   Hazard Ratio   95% Wald CI    p-value (log-rank) #> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                       101          57         727.8         44         974.6           0.71       (0.48, 1.06)         0.0905       #> Sex                                                                                                                                              #>   F                                 55          31         599.2         24        1016.3           0.56       (0.32, 0.96)         0.0341       #>   M                                 46          26         888.5         20         974.6           0.91       (0.50, 1.65)         0.7558       #> Continuous Level Biomarker 2                                                                                                                     #>   LOW                               36          21         735.5         15         974.6           0.76       (0.39, 1.51)         0.4324       #>   MEDIUM                            31          14         731.8         17         964.2           0.77       (0.36, 1.61)         0.4786       #>   HIGH                              34          22         654.8         12        1016.3           0.67       (0.33, 1.36)         0.2629"},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival Time Analysis — survival_time","title":"Survival Time Analysis — survival_time","text":"Summarize median survival time CIs, percentiles survival times, survival time range censored/event patients.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival Time Analysis — survival_time","text":"","code":"s_surv_time(df, .var, is_event, control = control_surv_time())  a_surv_time(df, .var, is_event, control = control_surv_time())  surv_time(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = \"Time to Event\",   table_names = vars,   .stats = c(\"median\", \"median_ci\", \"quantiles\", \"range_censor\", \"range_event\"),   .formats = NULL,   .labels = NULL,   .indent_mods = c(median = 0L, median_ci = 1L, quantiles = 0L, range_censor = 0L,     range_event = 0L, range = 0L) )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Survival Time Analysis — survival_time","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. is_event (logical)TRUE event, FALSE time event censored. control (list) parameters comparison details, specified using helper function control_surv_time(). possible parameter options : conf_level (proportion) confidence level interval survival time. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" supported. quantiles (numeric) vector length two specify quantiles survival time. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) character label. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Survival Time Analysis — survival_time","text":"s_surv_time() returns statistics: median: Median survival time. median_ci: Confidence interval median time. quantiles: Survival time two specified quantiles. range_censor: Survival time range censored observations. range_event: Survival time range observations events. range: Survival time range observations. a_surv_time() returns corresponding list formatted rtables::CellValue(). surv_time() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_surv_time() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Survival Time Analysis — survival_time","text":"s_surv_time(): Statistics function analyzes survival times. a_surv_time(): Formatted analysis function used afun surv_time(). surv_time(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Survival Time Analysis — survival_time","text":"","code":"library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVAL = day2month(AVAL),     is_event = CNSR == 0   ) df <- adtte_f %>% filter(ARMCD == \"ARM A\")  basic_table() %>%   split_cols_by(var = \"ARMCD\") %>%   add_colcounts() %>%   surv_time(     vars = \"AVAL\",     var_labels = \"Survival Time (Months)\",     is_event = \"is_event\",     control = control_surv_time(conf_level = 0.9, conf_type = \"log-log\")   ) %>%   build_table(df = adtte_f) #>                             ARM A          ARM B          ARM C     #>                             (N=69)         (N=73)         (N=58)    #> ——————————————————————————————————————————————————————————————————— #> Survival Time (Months)                                              #>   Median                     32.0           23.9           20.8     #>     90% CI               (22.6, 46.5)   (18.3, 29.2)   (12.9, 25.9) #>   25% and 75%-ile         17.4, 65.3     9.8, 42.0      7.3, 37.1   #>   Range (censored)       0.8 to 63.5    6.2 to 78.9    3.4 to 52.4  #>   Range (event)          0.3 to 155.5   0.1 to 154.1   0.6 to 80.7"},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival Time Point Analysis — survival_timepoint","title":"Survival Time Point Analysis — survival_timepoint","text":"Summarize patients' survival rate difference survival rates groups time point.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival Time Point Analysis — survival_timepoint","text":"","code":"s_surv_timepoint(   df,   .var,   time_point,   is_event,   control = control_surv_timepoint() )  a_surv_timepoint(   df,   .var,   time_point,   is_event,   control = control_surv_timepoint() )  s_surv_timepoint_diff(   df,   .var,   .ref_group,   .in_ref_col,   time_point,   control = control_surv_timepoint(),   ... )  a_surv_timepoint_diff(   df,   .var,   .ref_group,   .in_ref_col,   time_point,   control = control_surv_timepoint(),   ... )  surv_timepoint(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   table_names_suffix = \"\",   var_labels = \"Time\",   show_labels = \"visible\",   method = c(\"surv\", \"surv_diff\", \"both\"),   .stats = c(\"pt_at_risk\", \"event_free_rate\", \"rate_ci\", \"rate_diff\", \"rate_diff_ci\",     \"ztest_pval\"),   .formats = NULL,   .labels = NULL,   .indent_mods = if (method == \"both\") {      c(rate_diff = 1L, rate_diff_ci = 2L,     ztest_pval = 2L)  } else {      c(rate_diff_ci = 1L, ztest_pval = 1L)  } )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Survival Time Point Analysis — survival_timepoint","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. time_point (number) survival time point interest. is_event (logical)TRUE event, FALSE time event censored. control (list) parameters comparison details, specified using helper function control_surv_timepoint(). possible parameter options : conf_level (proportion) confidence level interval survival rate. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported. time_point (number) survival time point interest. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names_suffix (string) optional suffix table_names used rtables avoid warnings duplicate table names. var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". method (string) either surv (survival estimations), surv_diff (difference survival control) . .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Survival Time Point Analysis — survival_timepoint","text":"s_surv_timepoint() returns statistics: pt_at_risk: Patients remaining risk. event_free_rate: Event-free rate (%). rate_se: Standard error event free rate. rate_ci: Confidence interval event free rate. a_surv_timepoint() returns corresponding list formatted rtables::CellValue(). s_surv_timepoint_diff() returns statistics: rate_diff: Event-free rate difference two groups. rate_diff_ci: Confidence interval difference. ztest_pval: p-value test difference 0. a_surv_timepoint_diff() returns corresponding list formatted rtables::CellValue(). surv_timepoint() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_surv_timepoint() /s_surv_timepoint_diff() table layout depending value method.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Survival Time Point Analysis — survival_timepoint","text":"s_surv_timepoint(): Statistics function analyzes survival rate. a_surv_timepoint(): Formatted analysis function used afun surv_timepoint() method = \"surv\". s_surv_timepoint_diff(): Statistics function analyzes difference two survival rates. a_surv_timepoint_diff(): Formatted analysis function used afun surv_timepoint() method = \"surv_diff\". surv_timepoint(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Survival Time Point Analysis — survival_timepoint","text":"","code":"library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVAL = day2month(AVAL),     is_event = CNSR == 0   ) df <- adtte_f %>%   filter(ARMCD == \"ARM A\")  df_ref_group <- adtte_f %>%   filter(ARMCD == \"ARM B\")  # Survival at given time points. basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   surv_timepoint(     vars = \"AVAL\",     var_labels = \"Months\",     is_event = \"is_event\",     time_point = 7   ) %>%   build_table(df = adtte_f) #>                                    ARM A            ARM B            ARM C      #>                                    (N=69)           (N=73)           (N=58)     #> ——————————————————————————————————————————————————————————————————————————————— #> 7 Months                                                                        #>   Patients remaining at risk         54               57               42       #>   Event Free Rate (%)              84.89            79.43            75.50      #>   95% CI                       (76.24, 93.53)   (70.15, 88.71)   (64.33, 86.67)  # Difference in survival at given time points. basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   surv_timepoint(     vars = \"AVAL\",     var_labels = \"Months\",     is_event = \"is_event\",     time_point = 9,     method = \"surv_diff\",     .indent_mods = c(\"rate_diff\" = 0L, \"rate_diff_ci\" = 2L, \"ztest_pval\" = 2L)   ) %>%   build_table(df = adtte_f) #>                                   ARM A        ARM B            ARM C      #>                                   (N=69)       (N=73)           (N=58)     #> —————————————————————————————————————————————————————————————————————————— #> 9 Months                                                                   #>   Difference in Event Free Rate                -9.64            -13.03     #>       95% CI                               (-22.80, 3.52)   (-27.59, 1.53) #>       p-value (Z-test)                         0.1511           0.0794      # Survival and difference in survival at given time points. basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   surv_timepoint(     vars = \"AVAL\",     var_labels = \"Months\",     is_event = \"is_event\",     time_point = 9,     method = \"both\"   ) %>%   build_table(df = adtte_f) #>                                       ARM A            ARM B            ARM C      #>                                       (N=69)           (N=73)           (N=58)     #> —————————————————————————————————————————————————————————————————————————————————— #> 9 Months                                                                           #>   Patients remaining at risk            53               53               39       #>   Event Free Rate (%)                 84.89            75.25            71.86      #>   95% CI                          (76.24, 93.53)   (65.32, 85.17)   (60.14, 83.57) #>   Difference in Event Free Rate                        -9.64            -13.03     #>     95% CI                                         (-22.80, 3.52)   (-27.59, 1.53) #>     p-value (Z-test)                                   0.1511           0.0794"},{"path":"https://insightsengineering.github.io/tern/reference/tern-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tern Package — tern-package","title":"tern Package — tern-package","text":"Package create tables, listings graphs analyze clinical trials data.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tern-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tern Package — tern-package","text":"Maintainer: Joe Zhu joe.zhu@roche.com Authors: Daniel Sabanés Bové daniel.sabanes_bove@roche.com Jana Stoilova jana.stoilova@roche.com Heng Wang wang.heng@gene.com Francois Collin Adrian Waddell adrian.waddell@gene.com Pawel Rucki pawel.rucki@roche.com Chendi Liao chendi.liao@roche.com Jennifer Li li.jing@gene.com contributors: F. Hoffmann-La Roche AG [copyright holder, funder]","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom Tidy Method for Binomial GLM Results — tidy.glm","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"Helper method (broom::tidy()) prepare data frame glm object binomial family.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"","code":"# S3 method for glm tidy(x, conf_level = 0.95, at = NULL, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"x logistic regression model fitted stats::glm() \"binomial\" family. conf_level (proportion) confidence level interval. (NULL numeric) optional values interaction variable. Otherwise median used. ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"data.frame containing tidied model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"","code":"library(dplyr) library(broom)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred  df <- tidy(mod1, conf_level = 0.99) df2 <- tidy(mod2, conf_level = 0.99)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom Tidy Method for STEP Results — tidy.step","title":"Custom Tidy Method for STEP Results — tidy.step","text":"Tidy STEP results tibble format ready plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom Tidy Method for STEP Results — tidy.step","text":"","code":"# S3 method for step tidy(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom Tidy Method for STEP Results — tidy.step","text":"x (step matrix) results fit_survival_step(). ... used .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom Tidy Method for STEP Results — tidy.step","text":"tibble one row per STEP subgroup. estimates CIs HR scale, respectively. Additional attributes carry metadata also used plotting.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Tidy Method for STEP Results — tidy.step","text":"","code":"library(survival) lung$sex <- factor(lung$sex) vars <- list(   time = \"time\",   event = \"status\",   arm = \"sex\",   biomarker = \"age\" ) step_matrix <- fit_survival_step(   variables = vars,   data = lung,   control = c(control_coxph(), control_step(num_points = 10, degree = 2)) ) broom::tidy(step_matrix) #> # A tibble: 10 × 12 #>    `Percentile Center` `Percentile Lower` `Percentile Upper` `Interval Center` #>  *               <dbl>              <dbl>              <dbl>             <dbl> #>  1              0.0909             0                   0.341              50   #>  2              0.182              0                   0.432              54   #>  3              0.273              0.0227              0.523              57   #>  4              0.364              0.114               0.614              59.5 #>  5              0.455              0.205               0.705              62   #>  6              0.545              0.295               0.795              64   #>  7              0.636              0.386               0.886              67   #>  8              0.727              0.477               0.977              69   #>  9              0.818              0.568               1                  71   #> 10              0.909              0.659               1                  74   #> # ℹ 8 more variables: `Interval Lower` <dbl>, `Interval Upper` <dbl>, n <dbl>, #> #   events <dbl>, `Hazard Ratio` <dbl>, se <dbl>, ci_lower <dbl>, #> #   ci_upper <dbl>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"","code":"# S3 method for summary.coxph tidy(x, ...)  # S3 method for coxreg.univar tidy(x, ...)  # S3 method for coxreg.multivar tidy(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"x (list) Result Cox regression model fitted fit_coxreg_univar() (univariate models) fit_coxreg_multivar() (multivariate models). ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"tidy() returns: summary.coxph objects,  data.frame columns: Pr(>|z|), exp(coef), exp(-coef), lower .95, upper .95, level, n. coxreg.univar objects, data.frame columns: effect, term, term_label, level, n, hr, lcl, ucl, pval, ci. coxreg.multivar objects, data.frame columns: term, pval, term_label, hr, lcl, ucl, level, ci.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"tidy(summary.coxph): Custom tidy method survival::coxph() summary results. Tidy survival::coxph() results data.frame extract model results. tidy(coxreg.univar): Custom tidy method univariate Cox regression. Tidy result Cox regression model fitted fit_coxreg_univar(). tidy(coxreg.multivar): Custom tidy method multivariate Cox regression. Tidy result Cox regression model fitted fit_coxreg_multivar().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"","code":"library(survival) library(broom)  set.seed(1, kind = \"Mersenne-Twister\")  dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   data.frame(     time = stop,     status = event,     armcd = as.factor(rx),     covar1 = as.factor(enum),     covar2 = factor(       sample(as.factor(enum)),       levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\")     )   ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)  formula <- \"survival::Surv(time, status) ~ armcd + covar1\" msum <- summary(coxph(stats::as.formula(formula), data = dta_bladder)) tidy(msum) #>       Pr(>|z|) exp(coef) exp(-coef)  lower .95 upper .95   level   n #> 1 1.287954e-02 0.6110123   1.636628 0.41442417 0.9008549  armcd2 340 #> 2 6.407916e-04 0.4460731   2.241785 0.28061816 0.7090818 covar12 340 #> 3 5.272933e-06 0.3075864   3.251119 0.18517346 0.5109230 covar13 340 #> 4 2.125359e-08 0.1808795   5.528541 0.09943722 0.3290258 covar14 340  ## Cox regression: arm + 1 covariate. mod1 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = \"covar1\"   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91) )  ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates. mod2 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91, interaction = TRUE) )  tidy(mod1) #>            effect   term        term_label level   n        hr       lcl #> ref    Treatment:  armcd  2 vs control (1)     2 340 0.6386426 0.4557586 #> covar1 Covariate: covar1 A Covariate Label     2 340  0.607037 0.4324571 #>              ucl       pval                   ci #> ref    0.8949131 0.02423805 0.4557586, 0.8949131 #> covar1 0.8520935 0.01257339 0.4324571, 0.8520935 tidy(mod2) #>                           effect   term        term_label level   n        hr #> ref                   Treatment:  armcd  2 vs control (1)     2 340 0.6386426 #> covar1.1              Covariate: covar1 A Covariate Label       340           #> covar1.armcd2/covar11 Covariate: covar1                 1     1     0.6284569 #> covar1.armcd2/covar12 Covariate: covar1                 2     2     0.5806499 #> covar1.armcd2/covar13 Covariate: covar1                 3     3     0.5486103 #> covar1.armcd2/covar14 Covariate: covar1                 4     4     0.6910725 #> covar2.1              Covariate: covar2         Sex (F/M)       340           #> covar2.armcd2/covar2F Covariate: covar2                 F     F     0.6678243 #> covar2.armcd2/covar2M Covariate: covar2                 M     M     0.5954021 #>                             lcl       ucl       pval pval_inter #> ref                   0.4557586 0.8949131 0.02423805            #> covar1.1                     NA        NA             0.9883021 #> covar1.armcd2/covar11 0.3450471 1.1446499                       #> covar1.armcd2/covar12 0.2684726 1.2558239                       #> covar1.armcd2/covar13 0.2226814 1.3515868                       #> covar1.armcd2/covar14 0.2308006 2.0692373                       #> covar2.1                     NA        NA             0.7759013 #> covar2.armcd2/covar2F 0.3649842 1.2219413                       #> covar2.armcd2/covar2M 0.3572772 0.9922368                       #>                                         ci #> ref                   0.4557586, 0.8949131 #> covar1.1                                   #> covar1.armcd2/covar11 0.3450471, 1.1446499 #> covar1.armcd2/covar12 0.2684726, 1.2558239 #> covar1.armcd2/covar13 0.2226814, 1.3515868 #> covar1.armcd2/covar14 0.2308006, 2.0692373 #> covar2.1                                   #> covar2.armcd2/covar2F 0.3649842, 1.2219413 #> covar2.armcd2/covar2M 0.3572772, 0.9922368  multivar_model <- fit_coxreg_multivar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder ) broom::tidy(multivar_model) #>                       term         pval                        term_label #> armcd.1              armcd                            ARM (reference = 1) #> armcd.2                ARM   0.01232761                                 2 #> covar1.1            covar1 7.209956e-09 A Covariate Label (reference = 1) #> covar1.2 A Covariate Label  0.001120332                                 2 #> covar1.3 A Covariate Label 6.293725e-06                                 3 #> covar1.4 A Covariate Label 3.013875e-08                                 4 #> covar2.1            covar2                      Sex (F/M) (reference = F) #> covar2.2         Sex (F/M)    0.1910521                                 M #>                 hr        lcl       ucl level                     ci #> armcd.1                    NA        NA  <NA>                        #> armcd.2  0.6062777 0.40970194 0.8971710     2   0.4097019, 0.8971710 #> covar1.1                   NA        NA  <NA>                        #> covar1.2 0.4564763 0.28481052 0.7316115     2   0.2848105, 0.7316115 #> covar1.3 0.3069612 0.18386073 0.5124813     3   0.1838607, 0.5124813 #> covar1.4 0.1817017 0.09939435 0.3321668     4 0.09939435, 0.33216684 #> covar2.1                   NA        NA  <NA>                        #> covar2.2  1.289373 0.88087820 1.8873019     M   0.8808782, 1.8873019"},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Replicate Entries of a Vector if Required — to_n","title":"Replicate Entries of a Vector if Required — to_n","text":"Replicate entries vector required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replicate Entries of a Vector if Required — to_n","text":"","code":"to_n(x, n)"},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replicate Entries of a Vector if Required — to_n","text":"x (numeric) vector numbers want analyze. n (count) many entries need.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replicate Entries of a Vector if Required — to_n","text":"x required length already NULL, otherwise scalar replicated version n entries.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Replicate Entries of a Vector if Required — to_n","text":"function fail x length n /scalar.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Table into Matrix of Strings — to_string_matrix","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"Helper function use mostly within tests. with_spacesparameter allows test content also indentation table structure. print_txt_to_copy instead facilitate testing development returning well formatted text needs copied pasted expected output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"","code":"to_string_matrix(x, with_spaces = FALSE, print_txt_to_copy = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"x rtables table. with_spaces tested table keep indentation relevant spaces? print_txt_to_copy Utility way copy input table directly expected variable instead copying manually.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"matrix strings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":null,"dir":"Reference","previous_headings":"","what":"tryCatch around car::Anova — try_car_anova","title":"tryCatch around car::Anova — try_car_anova","text":"Captures warnings executing car::Anova.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"tryCatch around car::Anova — try_car_anova","text":"","code":"try_car_anova(mod, test.statistic)"},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"tryCatch around car::Anova — try_car_anova","text":"mod lm, aov, glm, multinom, polr     mlm, coxph, coxme, lme, mer, merMod, svyglm, svycoxph,     rlm, suitable model object. test.statistic generalized linear model, whether calculate     \"LR\" (likelihood-ratio), \"Wald\", \"F\" tests; Cox     Cox mixed-effects model, whether calculate \"LR\" (partial-likelihood ratio)     \"Wald\" tests; default case linear mixed models fit     lmer, whether calculate Wald \"Chisq\" Kenward-Roger     \"F\" tests Satterthwaite degrees freedom (warning: KR F-tests     can time-consuming).     multivariate linear model, multivariate test statistic compute --- one     \"Pillai\", \"Wilks\", \"Hotelling-Lawley\", \"Roy\",     \"Pillai\" default. summary method Anova.mlm     objects permits specification one multivariate     test statistic, default report four.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"tryCatch around car::Anova — try_car_anova","text":"list item aov result model error_text captured warnings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"tryCatch around car::Anova — try_car_anova","text":"","code":"# `car::Anova` on cox regression model including strata and expected # a likelihood ratio test triggers a warning as only `Wald` method is # accepted.  library(survival)  mod <- coxph(   formula = Surv(time = futime, event = fustat) ~ factor(rx) + strata(ecog.ps),   data = ovarian )"},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Ungroup Non-Numeric Statistics — ungroup_stats","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"Ungroups grouped non-numeric statistics within input vectors .formats, .labels, .indent_mods.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"","code":"ungroup_stats(x, .formats, .labels, .indent_mods)"},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"x (named list numeric) list numeric statistics containing statistics ungroup. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"list modified elements x, .formats, .labels, .indent_mods.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Univariate Formula Special Term — univariate","title":"Univariate Formula Special Term — univariate","text":"special term univariate indicate model fitted individually every variable included univariate.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Univariate Formula Special Term — univariate","text":"","code":"univariate(x)"},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Univariate Formula Special Term — univariate","text":"x vector variable name separated commas.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Univariate Formula Special Term — univariate","text":"used within model formula, produces univariate models variable provided.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Univariate Formula Special Term — univariate","text":"provided alongside pairwise specification, model y ~ ARM + univariate(SEX, AGE, RACE) lead study comparison models y ~ ARM y ~ ARM + SEX y ~ ARM + AGE y ~ ARM + RACE","code":""},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Blank for Missing Input — unlist_and_blank_na","title":"Blank for Missing Input — unlist_and_blank_na","text":"Helper function use tabulating model results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blank for Missing Input — unlist_and_blank_na","text":"","code":"unlist_and_blank_na(x)"},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blank for Missing Input — unlist_and_blank_na","text":"x (vector) input cell.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Blank for Missing Input — unlist_and_blank_na","text":"empty character vector entries x missing (NA), otherwise unlisted version x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"function wraps iteration procedure allows estimate weights proportional strata. assumes minimize weighted squared length confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"","code":"update_weights_strat_wilson(   vars,   strata_qnorm,   initial_weights,   n_per_strata,   max_iterations = 50,   conf_level = 0.95,   tol = 0.001 )"},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"vars (numeric) normalized proportions strata. strata_qnorm (numeric) initial estimation identical weights quantiles. initial_weights (numeric) initial weights used calculate strata_qnorm. can optimized future need estimate better initial weights. n_per_strata (numeric) number elements strata. max_iterations (count) maximum number iterations tried. Convergence always checked. conf_level (proportion) confidence level interval. tol (number) tolerance threshold convergence.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"list 3 elements: n_it, weights, diff_v.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"","code":"vs <- c(0.011, 0.013, 0.012, 0.014, 0.017, 0.018) sq <- 0.674 ws <- rep(1 / length(vs), length(vs)) ns <- c(22, 18, 17, 17, 14, 12)  update_weights_strat_wilson(vs, sq, ws, ns, 100, 0.95, 0.001) #> $n_it #> [1] 3 #>  #> $weights #> [1] 0.2067191 0.1757727 0.1896962 0.1636346 0.1357615 0.1284160 #>  #> $diff_v #> [1] 1.458717e-01 1.497223e-03 1.442189e-06 #>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-9-1-9013","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.9.1.9013","text":"Added na_str argument analyze & summarize_row_groups wrapper functions count_abnormal, count_abnormal_by_baseline, count_abnormal_by_marked, count_abnormal_by_worst_grade, count_abnormal_lab_worsen_by_baseline, count_cumulative, count_missed_doses, count_occurrences, count_occurrences_by_grade, summarize_occurrences_by_grade, summarize_patients_events_in_cols, count_patients_with_event, count_patients_with_flags, count_values, estimate_multinomial_response, estimate_proportion, h_tab_one_biomarker, estimate_incidence_rate, logistic_summary_by_flag, estimate_odds_ratio, estimate_proportion_diff, test_proportion_diff, summarize_ancova, summarize_change, summarize_glm_count, summarize_num_patients, analyze_num_patients, summarize_patients_exposure_in_cols, coxph_pairwise, tabulate_survival_subgroups, surv_time, surv_timepoint.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-9-1-9013","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.9.1.9013","text":"Added formatting function format_count_fraction_lt10 formatting count_fraction special consideration count less 10. Updated s_summary.logical output count_fraction denominator zero display NA instead 0 tables. Updated analyze_vars_in_cols allow character input indicate whether nominal time point post-dose pre-dose applying 1/3 imputation rule.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-9-1-9013","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.9.1.9013","text":"Fixed bug g_km causing error converting certain annotation width units.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-9-1-9013","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.9.1.9013","text":"Began deprecation na_level argument s_count_abnormal_by_baseline, a_summary, analyze_vars, analyze_vars_in_cols, compare_vars, h_map_for_count_abnormal, h_stack_by_baskets, summarize_colvars, a_coxreg, summarize_coxreg replaced na_str argument.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-9-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.9.1","text":"Added imputation_rule function apply imputation rule data. Added new format function format_sigfig allow numeric value formatting specified number significant figures. Added vectors containing default statistic formats labels tern_default_formats tern_default_labels, respectively. Added function get_stats return methods given statistical method groups. Added function get_formats_from_stats return formats get_labels_from_stats return labels given set statistics. Added \"auto\" option .formats. uses format_auto determine automatically number digits. Added title argument h_grob_tbl_at_risk annot_at_risk_title argument g_km h_km_layout allows user add “Patients Risk” title Kaplan-Meier risk annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-9-1","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.9.1","text":"Refactored tabulate_rsp_subgroups pass sanitation checks preventing creation degenerate subtables. Updated analyze_vars_in_cols use caching, allow implementation imputation rule via imp_rule argument, allow user specify cell alignment via .aligns argument. Updated add_rowcounts allow addition row counts alt_counts_df using alt_counts argument. Added gp argument g_forest control graphical parameters font size.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-9-1","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.9.1","text":"Grouped functions relating valid method names default formats labels new source file utils_defaults_handling.R. Started deprecation summary_custom() a_summary() S3 method. Renamed statistical method p-value discrete case pval_counts. Removed a_summary_internal() favor one main a_summary().","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-090","dir":"Changelog","previous_headings":"","what":"tern 0.9.0","title":"tern 0.9.0","text":"CRAN release: 2023-09-01","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-9-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.9.0","text":"Added stat_propdiff_ci function calculate proportion/risk difference CI. Added risk difference column functionality via riskdiff argument functions count_occurrences, count_occurrences_by_grade, count_patients_with_event, count_patients_with_flags, analyze_num_patients, summarize_num_patients.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-9-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.9.0","text":"Refactored function a_summary longer use helper function create_afun_summary. Refactored functions summarize_vars compare_vars use refactored a_summary function. Created new internal helper functions ungroup_stats ungroup statistics calculated factor variables, a_summary_internal perform calculations a_summary.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.9.0","text":"Fixed bug s_count_occurrences_by_grade “missing” grade always appears final level. Fixed bug analyze_vars_in_cols categorical data used. Fixed bug s_count_occurrences_by_grade levels relabeled reordering account “missing” grades.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-9-0","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.9.0","text":"Fixed swapped descriptions .N_row .N_col parameters. Removed internal calls df_explicit_na. Changes NA values happen externally tern functions, depending users’ needs. Reinstated correct soft deprecation create_afun_summary create_afun_compare.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-5","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.5","text":"Added ylim argument g_km allow user set custom limits y-axis. Added assertion g_km checks whether one arm present data annot_coxph true. Added flag_labels argument s_count_patients_with_flags enable label handling options count_patients_by_flags. Added nested argument analyze wrapper functions count_abnormal, count_abnormal_by_baseline, count_abnormal_by_marked, count_abnormal_by_worst_grade, count_abnormal_lab_worsen_by_baseline, count_cumulative, count_missed_doses, count_occurrences, count_occurrences_by_grade, count_patients_with_event, count_patients_with_flags, count_values, estimate_multinomial_response, estimate_proportion, estimate_incidence_rate, estimate_odds_ratio, estimate_proportion_diff, test_proportion_diff, summarize_ancova, summarize_change, summarize_glm_count, analyze_num_patients, coxph_pairwise, surv_time, surv_timepoint.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-5","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.5","text":"Started deprecation cycle summarize_vars control_summarize_vars. Renamed analyze_vars control_analyze_vars reflect underlying rtables machinery keeping backward compatibility aliases.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-084","dir":"Changelog","previous_headings":"","what":"tern 0.8.4","title":"tern 0.8.4","text":"CRAN release: 2023-06-27","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-4","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.4","text":"Added method character class h_coxreg_inter_effect enabling character covariates summarize_coxreg.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-4","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.4","text":"Began deprecation time_unit_input time_unit_output arguments replaced input_time_unit num_pt_year, respectively, control_incidence_rate. Removed deprecated pairwise function. Deprecated a_compare replaced a_summary argument compare = TRUE. Deprecated helper functions create_afun_summary create_afun_compare longer used a_summary a_compare respectively.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-4","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.4","text":"Fixed long double assertion check sum(weights) M1mac installation.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-083","dir":"Changelog","previous_headings":"","what":"tern 0.8.3","title":"tern 0.8.3","text":"CRAN release: 2023-06-19","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.3","text":"Added explicit zero counts g_km plot “risk” annotation tables. Added flag total level split analyze_patients_exposure_in_cols. Implemented .indent_mods argument functions h_tab_one_biomarker, h_tab_rsp_one_biomarker, h_tab_surv_one_biomarker, summarize_logistic, logistic_summary_by_flag, tabulate_rsp_biomarkers, a_coxreg, summarize_coxreg, tabulate_survival_biomarkers, surv_time, surv_timepoint, cfun_by_flag. Updated summarize_coxreg print covariates data rows univariate Cox regression interactions content rows otherwise. Removed “baseline status” text d_count_abnormal_by_baseline labels. Improved default sizing annotation tables g_km added dynamic scaling surv_med coxph annotation tables, customization via width_annots argument.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-3","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.3","text":"Fixed bug split_text_grob preventing titles footnotes properly formatted printed decorate_grob. Fixed bug g_lineplot preventing addition lines plot midpoint statistic calculations result NA value(s). Fixed tern:::tidy.glm formals respect broom:::tidy.default formals.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.3","text":"Updated README include installation instructions CRAN. Remove examples unexported functions. Export functions has_count_in_cols, has_counts_difference, combine_counts, h_tab_rsp_one_biomarker, arrange_grobs, a_count_patients_sum_exposure, a_coxreg, groups_list_to_df, forest_viewport. Updated README include installation instructions CRAN. Began deprecation indent_mod argument replaced .indent_mods argument summarize_num_patients analyze_num_patients.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"breaking-changes-0-8-2","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"tern 0.8.2","text":"Refactored s_coxreg summarize_coxregto work new analysis function a_coxreg.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-2","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.2","text":"Added section_div na_level arguments summarize_vars. Added median_range numeric variable statistic option summarize_vars. Corrected ordering factor levels d_onco_rsp_label. Added new “Analyze Functions”, “Summarize Functions”, “Analyze Functions Columns” overview pages. Consolidated KM plot documentation within g_km function. Added a_count_patients_sum_exposure summarize_patients_exposure_in_cols new analyze function analyze_patients_exposure_in_cols. Added informative error user selects invalid method unstratified analyses s_proportion_diff. Updated s_summary s_compare allow NA values input variables. factor variables NAs, na.rm = FALSE explicit NA level automatically added. na.rm = TRUE also consider \"<Missing>\" values exclude . Updated purpose na_level parameter s_summary s_compare align tern functions. Instead string consider NA setting na.rm = TRUE, now defines string print place NA values output table.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-2","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.2","text":"Fixed missing label TRTEDTM tern datasets. Fixed improper implementation na_level argument summarize_vars preventing effect.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-2","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.2","text":"Implemented lubridate package date variables tern datasets. Organized .gitignore .Rbuildignore files. Removed deprecated footnotes functions related files. Started deprecation cycle pairwise function. Moved count_patients_with_flags functions count_patients_with_event.R count_patients_with_flags.R.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.0","text":"Added summarize_glm_count function analyze count data using linear model. Added legend g_step. Added formatting functions format_fraction_fixed_dp format_count_fraction_fixed_dp fixed single decimal place percentages. Added na_level labelstr arguments summarize_vars_in_cols. Added analyze_num_patients include summary beginning repeat paginating. Added h_row_first_values function general helper function retrieve first values specific rows. Added option remove \"(n)\" suffix unique_count labels s_num_patients. Added options g_km annotate statistics (annot_stats) add corresponding vertical lines (annot_stats_lines).","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.0","text":"Fixed bug causing incorrect ordering numeric grade levels missing grades present s_count_occurrences_by_grade. Refactored summarize_vars_in_cols work pagination machinery. Fixed bug allow passing conf_level argument emmeans::contrast() s_ancova. Fixed bugs rtables_access.R caused checking specific combinations (also standard values never used) column indices names. Fixed single applicable record bug count_abnormal_by_grade. Fixed bug add_rowcounts caused row count row values count zero. Fixed bug h_col_indices causing error pruning combination columns. Fixed bug test_proportion_diff missing argument var_labels.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"documentation-and-tests-0-8-0","dir":"Changelog","previous_headings":"","what":"Documentation and Tests","title":"tern 0.8.0","text":"Added tests increase code coverage. Created separate documentation files functions different sections pkgdown reference. Created separate .R files logistic regression cox regression helper functions. Fixed table tests using analyze_num_patients generate initial summary repetition paginating. Updated tests use testthat 3rd edition replaced applicable tests snapshot testing. Updated summarize_ancova examples use iris dataset instead scda data. Created vignette saves cached synthetic CDISC dataset files data/ folder generated cached synthetic datasets. Updated examples/tests use datasets data/ folder instead scda datasets. Removed template tests tern. tests internal repo scda.test.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-0","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.0","text":"Renamed summarize_vars_in_cols analyze_vars_in_cols reflect appropriate analyze logic. Removed redundant summary_in_cols helper functions. Exported function format_xx. Replaced deprecated ggplot2 functions/arguments fix warnings. Replaced deprecated function forcats::fct_explicit_na forcats::fct_na_value_to_level. Removed deprecated wrap_text function related files. Started deprecation cycle footnotes functions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-10","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.7.10","text":"Added stratified Newcombe stratified Wilson statistics estimate_proportion estimate_proportion_diff relative tests. Added stat_mean_pval, new summary statistic calculate p-value mean. Added statistic mean_se (mean standard error) summarize_variables related functions. Introduced Rdpack references.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-10","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.10","text":"Redesign data handling tests removing repetitive data loads library calls. Added DescTools::BinomDiffCI function within tern. Added new parameter summarize_logistic specify pivoted value use analysis. Updated s_coxph_pairwise generate log-rank p-value using original log-rank test instead Cox Proportional-Hazards Model. Implemented nestcolor examples adapting g_km, g_ipp, g_waterfall, g_step, g_lineplot, g_forest. Added parameters interaction_y interaction_item ANCOVA make interaction calculations available. Added new parameter footnotes add footnotes g_km.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"migration-from-assertthat-to-checkmate-0-7-10","dir":"Changelog","previous_headings":"","what":"Migration from assertthat to checkmate","title":"tern 0.7.10","text":"Implemented checkmate::assert_vector, checkmate::assert_set_equal, checkmate::assert_int check vector type, length, values. Replaced standard assertions checkmate following functions: all_elements_in_ref, is_df_with_nlevels_factor, is_df_with_no_na_level, is_proportion_vector, is_quantiles_vector, is_character_or_factor, is_nonnegative_count, is_valid_character, assert_character_or_factor, assert_equal_length has_tabletree_colnames. Modified is_proportion, is_equal_length, is_df_with_no_na_level, is_df_with_nlevels_factor, is_variables, is_df_with_variables, is_df_with_factors, is_valid_factor use assertion logic. Added verbose warnings as_factor_keep_attributes. Made assert_df_with_factors assert_proportion_value internal functions. Renamed assertthat.R test-assertthat.R utils_checkmate.R test-utils_checkmate.R.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"documentation-and-namespace-polishing-0-7-10","dir":"Changelog","previous_headings":"","what":"Documentation and NAMESPACE Polishing","title":"tern 0.7.10","text":"count_abnormal_by_marked (reference abnormal_by_marked), count_abnormal_lab_worsen_by_baseline h_adlb_worsen (reference abnormal_by_worst_grade_worsen_from_baseline), count_abnormal_by_worst_grade (reference abnormal_by_worst_grade), to_string_matrix, tidy.summary.coxph, tidy.step, surv_timepoint, (reference survival_timepoint), surv_time (reference survival_time), coxph_pairwise (reference survival_coxph_pairwise), extract_survival_subgroups tabulate_survival_subgroups (reference survival_duration_subgroups), extract_survival_biomarkers tabulate_survival_biomarkers (reference survival_biomarkers_subgroups), control_summarize_vars, s_summary a_summary (reference summarize_variables) kept S3 method tree. summarize_patients_exposure_in_cols, summarize_num_patients s_num_patients, s_num_patients_content, summarize_num_patients. count_cumulative, count_missed_doses, count_patients_events_in_cols, summarize_colvars, summarize_change, summarize_ancova,.rtable, color_palette, add_footnotes. (statistical function controls) control_coxreg, control_coxph, control_incidence_rate, control_lineplot_vars, control_surv_time, control_surv_timepoint, control_logisitic, control_step. stat_mean_ci, stat_median_ci, split_cols_by_groups, explicit_na, sas_na, extract_rsp_subgroups, tabulate_rsp_subgroups, extract_rsp_biomarkers, tabulate_rsp_biomarkers, keep_rows, keep_content_rows, has_count_in_any_col, has_fraction_in_cols, has_fraction_in_any_col, has_fractions_difference, test_proportion_diff, pairwise, logistic_regression, estimate_incidence_rate, control_incidence_rate (reference incidence_rate), cut_quantile_bins, estimate_multinomial_rsp, decorate_grob_set, extreme_format, fit_rsp_step, fit_survival_step, footnotes, footnotes-set, format_count_fraction, format_fraction_threshold, formatting_functions, format_fraction, combination_function (S4 method), compare_variables (S3 method), kaplan_meier. (chain functions, reference abnormal_by_marked) s_count_abnormal_by_marked, a_count_abnormal_by_marked. (chain functions, reference abnormal_by_worst_grade_worsen_from_baseline) a_count_abnormal_lab_worsen_by_baseline, s_count_abnormal_lab_worsen_by_baseline. (chain functions, reference abnormal_by_worst_grade) s_count_abnormal_by_worst_grade, a_count_abnormal_by_worst_grade. (chain functions, reference survival_timepoint) s_surv_timepoint, s_surv_timepoint_diff, a_surv_timepoint, a_surv_timepoint_diff. (chain functions, reference survival_time) s_surv_time, a_surv_time. (chain functions, reference survival_coxph_pairwise) s_coxph_pairwise, a_coxph_pairwise. (chain functions, reference survival_duration_subgroups) a_survival_subgroups. (chain functions, reference count_cumulative) s_count_cumulative, a_count_cumulative. (chain functions, reference count_missed_doses) s_count_nonmissing, s_count_missed_doses, a_count_missed_doses. (chain functions, reference count_patients_events_in_cols) s_count_patients_and_multiple_events, summarize_patients_events_in_cols. (chain functions, reference incidence_rate) s_incidence_rate, a_incidence_rate. (cox regression helper) cox_regression_inter, decorate_grob_factory, draw_grob, estimate_coef. summary_labels, summary_formats, s_count_patients_sum_exposure, a_change_from_baseline s_change_from_baseline, a_ancova, s_ancova, arrange_grobs, as_factor_keep_attributes, combine_levels, split_text_grob, groups_list_to_df, s_cox_multivariate, is_leaf_table, a_response_subgroups, range_noinf, has_count_in_cols, has_counts_difference, prop_chisq, prop_cmh, prop_schouten, prop_fisher, s_test_proportion_diff, a_test_proportion_diff, fct_discard, fct_explicit_na_if.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-10","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.10","text":"Fixed retrieval method stats::ancova output due version inconsistency. Fixed tests respect new standard print NA coming rtables. Fixed error tests coming changes formatters::var_labels. Fixed prop_diff functions respect success responses (TRUE values). Fixed error coming comparing factors vector characters vector. Fixed empty vector exception cut_quantile_bins. Fixed exception error empty strings present pivoted columns (rtables split functions) Fixed bug s_ancova causing error first level arm factor control arm. Fixed bug s_abnormal_by_worst_grade one PARAM level. Fixed bug prop_diff_wald selecting responders, updated tests accordingly. Fixed bug h_ancova caused error deselecting covariates.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-10","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.10","text":"Added deprecated badge g_mmrm. Removed internal function calls examples (tern:::) added dontrun internal function examples. Removed warnings messages console occurring examples tests. Deprecated functions color_palette h_set_nest_theme favor nestcolor::color_palette nestcolor::theme_nest, respectively. Removed deprecated functions: color_palette, color_palette_core, h_set_nest_theme, s_cox_univariate. Removed deprecated mmrm functions: fit_mmrm, g_mmrm_diagnostic, g_mmrm_lsmeans, .rtable.mmrm, h_mmrm_fixed, h_mmrm_cov, h_mmrm_diagnostic, tidy.mmrm, s_mmrm_lsmeans, s_mmrm_lsmeans_single, summarize_lsmeans. Renamed functions arm study_arm extract extract_by_name. Renamed rtables.R utils_rtables.R. Extracted cox_regression_inter separate file cox_regression. Renamed estimate_incidence_rate.R incidence_rate.R match documentation grouping name. Extracted control_incidence_rate separate file produces separate documentation file. Added @md removed @order incidence_rate.R. Modified examples accordingly. Removed hyperlink prop_schouten function documentation. Exported draw_grob function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"fix-0-7-8","dir":"Changelog","previous_headings":"","what":"Fix","title":"tern 0.7.8","text":"h_split_by_subgroups documentation warning fix wrong placing example block","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"documentation-and-namespace-polishing-0-7-8","dir":"Changelog","previous_headings":"","what":"Documentation and NAMESPACE polishing","title":"tern 0.7.8","text":"Adopting standard badges @description instead every @descriptionIn function. Corrected accordingly summarize_variables_in_cols Added stable badge g_lineplot, g_step, g_waterfall, cox_regression, score_occurrences, add_rowcounts, odds_ratio, count_occurrences, count_occurrences_by_grade, explicit_na, df_explicit_na, count_patients_with_event, decorate_grob, combine_groups, append_varlabels, univariate, stack_grobs, count_abnormal (reference abnormal), count_abnormal_by_baseline (reference abnormal_by_baseline). (helper functions) h_format_row, h_map_for_count_abnormal (utils functions) make_names, month2day, day2month empty_vector_if_na, aesi_label, n_available, format_xx, arm. count_values_funs, prop_difference, combine_counts. (chain functions) s_count_abnormal, a_count_abnormal. (chain functions) s_count_abnormal_by_baseline, a_count_abnormal_by_baseline, d_count_abnormal_by_baseline. Deprecated s_cox_univariate function now deprecated badge.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-8","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.8","text":"Enhanced g_lineplot table automatically scale table height return ggplot object. Enhanced g_ipp caption argument adjust position. Enhanced prop_diff, tern function related functions able apply continuity correction Newcombe method. Enhanced summarize_numeric_in_columns summarize_variables allow factor/character summary able summarize number BLQs AVALC ADPC dataset. Updated order summarize variables stats manual order consistency. Added sum option summarize_variables. Use consistent color palette plotting (stream default). Enhanced h_pkparam_sort function argument key_var allow data different column names.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-8","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.8","text":"Updated test-table_aet02.R variant 12. Changed scda data version ‘2022-02-28’. Added template pkgdown site. Removed package dependencies grDevices, stringr, viridisLite. Renaming summarize_numeric_in_columns summarize_variables_in_columns. Renaming summarize_vars_numeric_in_cols summarize_vars_in_cols. Fixed bug points g_lineplot plot connected missing values. Updated package authors.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"breaking-changes-0-7-7","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tern 0.7.7","text":"Move MMRM separate package tern.mmrm.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-7","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.7","text":"Added h_pkparam_sort order PK PARAM value based order dataset generated d_pkparam(). Added d_pkparam generate PK parameter map sorting.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-7","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.7","text":"Changed nudge_y argument h_g_ipp dependent data, fixing issue whereby baseline labels offset incorrectly. Enhanced stat_mean_ci s_summary.numeric calculate geometric mean confidence intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-7","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.7","text":"Updated dependencies internal adjustments rtables package refactor. Removed with_label, var_labels, var_labels<- resolve conflict formatters package, new dependency. Added new “Introduction tern” “tern tabulation” vignettes.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-6","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.6","text":"Added h_map_for_count_abnormal create map used trim_levels_to_map split function calling helper function. supports two methods: one observed mapping, one least low limit zero least one non missing high limit. Added s_summary_numeric_in_cols summarize_vars_numeric_in_cols functions generate summary statistics columns, mainly used PK datasets. Added five statistics s_summary.numeric use s_summary_numeric_in_cols.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-6","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.6","text":"Enhanced functions tabulate_survival_subgroups tabulate_rsp_subgroups (Survival Duration Best Response analyses) calculate N-s based records considered create model. Enhanced function estimate_proportion related functions able apply continuity correction Wilson method. Refactored count_abnormal_by_marked related statistics formatting functions use efficient layout .spl_context argument used determining denominators trim_levels_to_map split function split_rows_by show desired levels table. breaking change. Refactored count_abnormal_by_worst_grade related statistics formatting functions use efficient layout .spl_context argument used determining denominators trim_levels_to_map split function split_rows_by show desired levels table. breaking change. Refactored count_abnormal function related statistics formatting functions use efficient layout trim_levels_to_map split function split_rows_by show desired levels table. Also updated abnormal argument able consider one level direction. breaking change. Enhanced function estimate_incidence_rate related functions consider week time unit data input.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-6","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tern 0.7.6","text":"Fixed bug assertthat functions output wrong data frame names limited length failure message outputs.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-6","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.6","text":"Removed dependency utils.nest using checkmate purrr packages validation moved get_free_cores skip_if_too_deep functions utils.nest tern.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-5","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.5","text":"Added functions estimate continuous biomarker effects across subgroups survival binary response endpoints, used produce corresponding forest plots, see survival_biomarkers_subgroups response_biomarkers_subgroups. Added g_lineplot plot function, including new h_format_row helper function control_lineplot_vars function. Removed g_summary_by. Added new safety helper function h_stack_by_baskets stack events SMQ /CQ basket flag ADAE data set.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-5","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.5","text":"Added couple new statistics s_summary.numeric. Added names attribute element final list returned s_summary.numeric function. Added summary_formats summary_labels helper functions. Added option also convert logical variables factor variables df_explicit_na. Refactored h_append_grade_groups improve flexibility, robustness clearness, make sure result ordering according order grade_groups. Also, added remove_single argument controls whether elements one-element grade groups output removed. Added var_labels show_labels arguments count_occurrences count_patients_with_flags allow creation title row. Added na_level argument count_abnormal_by_baseline. Updated h_append_grade_groups longer fill-empty grade groups zeros.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-5","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.5","text":"Fixed prop_diff_cmh handle edge case FALSE (TRUE) responses. Enhanced g_mmrm_diagnostic improve error handling data amenable Locally Weighted Scatterplot Smoothing. Plot can now display combination annotation tables number patients risk, median survival time, CoxPH summary. Function return warning instead error arm variable includes single level annot_coxph = TRUE. Lines plot now start time 0 probability 1. Category labels can include equals sign.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-5","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.5","text":"Fixed day2month month2day work NA data. Added parameters stat_mean_ci stat_median_ci may return different outputs. Added functionality h_row_counts handle analysis rows NULL cells. Updated LICENCE README new package references. Added error_on_lint: TRUE .lintr.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-4","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.4","text":"count_abnormal_by_marked tabulates marked laboratory abnormalities. summarize_patients_exposure_in_cols tabulates patient counts sum exposure across patients.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-4","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.4","text":"Enhanced mmrm related functions fitting models without arm variable. Updated cox_regression work without covariates. Also case interaction model summary, p-values main effect coefficients longer displayed. Descriptive statistics returned summarize_vars now include quantiles. summarize_vars now accepts control function control_summarize_vars specify details confidence level mean median quantile details. control argument replaces conf_level. Added var_labels show_labels arguments count_occurrences_by_grade. Changed indent argument append_varlabels accept non-negative integer represent indent space defined user. Previous calls Boolean indent integer conversion produce warning.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-4","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.4","text":"Corrected tabulate_survival_subgroups related survival forest plot functions use total number events, instead observations, default scaling symbol sizes plot. (user might still use total number observations manually wish .) Helper function h_adsl_adlb_merge_using_worst_flag now impute BTOXGR missing visits.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-4","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.4","text":"Deleted count_abnormal_by_worst_grade_by_baseline related statistic analysis functions simpler design create lab abnormality tables. Switched data examples use scda instead random.cdisc.data package.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-3","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.7.3","text":"Added Subgroup Treatment Effect Pattern (STEP) model fitting functions fit_rsp_step fit_survival_step, corresponding tidy method tidy.step well graph function g_step. Added new layout function compare_vars compares variables different types columns produces p-value comparison reference column. Function built top summarize_vars functionality. cut_quantile_bins cuts numeric vector quantile bins. fct_collapse_only collapses levels factor keeps order provided. fct_explicit_na_if inserts explicit missings factor based condition. range_noinf kind wrapper function base::range. returns c(NA, NA) instead c(-Inf, Inf) zero-length data.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.3","text":"Cox regression via fit_coxreg_univar fit_coxreg_multivar now also possible without treatment arm. univariate case means fits separate univariate models provided covariates tabulation corresponding effect estimates can later occur. Added fraction result returned s_count_occurrences. contains list numerators denominators one element per occurrence. Updated sum_num_patients count_occurrences result unique count_fraction return (0, 0) input empty. Added new argument groups_lists extract_survival_subgroups, extract_rsp_subgroups associated helper functions allows group factor levels subgroup variables manually defined groups, enhancing flexibility resulting forest graphs. Forest graph function g_forest now extracts default arguments attributes input table produced tabulate_rsp_subgroups tabulate_survival_subgroups user manually anymore. Remove arm variable name arm labels plot legend annotation tables. Show symbol used mark censored cases match order arm variable factor levels legend. Display hazard ratio confidence interval two decimal places. Updated default position hazard ratio table stay left bottom corner x-axis. Use arm value strata name treatment arm variable single level. Updated s_surv_time function use newly created function range_noinf instead base::range. New argument no_fillin_visits added h_adsl_adlb_merge_using_worst_flag specify excluded visits post-baseline worst toxicity grade output. Improved h_adsl_adlb_merge_using_worst_flag include variables shared adsl adlb, along PARAM, PARAMCD, ATOXGR, BTOXGR optionally AVISIT, AVISITN by_visit = TRUE. Prior output contained USUBJID, ARMCD, PARAMCD, ATOXGR, BTOXGR.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-3","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.3","text":"Fix bug s_surv_timepoint cases zero patients risk. Modified stat_median_ci function passing empty var empty name, row names contain missing values error show.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.3","text":"Deprecated s_cox_univariate function, use fit_coxreg_univar function instead. Updated default formats hr hr_ci a_coxph_pairwise median s_surv_time align STREAM. Updated pre-processing code files test-table_ttet01.R test-table_dort01.R make sure analysis variable EVNT1 levels factor defined. Improved error message number levels factor variable data frame expected.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-072","dir":"Changelog","previous_headings":"","what":"tern 0.7.2","title":"tern 0.7.2","text":"Fixed column label internal test errors.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-071","dir":"Changelog","previous_headings":"","what":"tern 0.7.1","title":"tern 0.7.1","text":"New argument position_surv_med added g_km move position annotation table median survival times. Fixed bug g_km related ignored arguments pch size passed helper function h_ggkm. Updated xticks max_time arguments g_km greater functionality. max_time added argument h_xticks allow . Fixed bug prop_diff_cmh led NaN weighted proportion difference estimates missing confidence intervals. change, including patients one treatment arm least one stratum estimation lead numeric results. Fixed bug prop_cmh giving error case least one stratum containing less two observations. New argument n_events added estimate_incidence_rate. New argument denom added count_occurrences. New arguments yval ci_ribbon added g_km. Add new individual patient plot function g_ipp along helpers h_g_ipp h_set_nest_theme. Fixed bug count_patients_with_events, now shows zero counts without percentage. Fixed bug get_mmrm_lsmeans allow MMRM analysis 3000 observations. Updated stat_mean_ci stat_median_ci handle edge cases number elements input series equal 1. cases, NA_real_ now returned, instead NA +/-Inf confidence interval (CI) estimates. Rename n_lim argument stat_mean_ci n_min better reflect desired meaning.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-070","dir":"Changelog","previous_headings":"","what":"tern 0.7.0","title":"tern 0.7.0","text":"version tern introduces major rewriting tern due change layout based tabulation rtables. tern now build tables directly anymore, instead provides analysis functions build tables, see examples. * Counting patients abnormal values post-baseline count_abnormal. * Counting patients graded abnormal values count_abnormal_by_worst_grade. * Counting patients abnormal values baseline status count_abnormal_by_baseline. * Counting patients missed doses s_count_missed_doses count_missed_doses. * Counting patients event flags count_patients_with_event count_patients_with_flags. * Summarizing variables summarize_vars (supports numeric, factor, character logical variables). Note factors need NAs converted na_level use. * Summarizing change baseline summarize_change. * Summarizing variables columns summarize_colvars. * Estimating difference responder proportions estimate_proportion_diff. * Estimating difference Odds Ratio estimate_odds_ratio. * Testing difference responder proportions test_proportion_diff. * Estimating responder proportion level factor estimate_multinomial_response. * Fitting tabulating results Cox regressions fit_coxreg_univar, fit_coxreg_multivar summarize_coxreg, respectively. * Pruning occurrence tables (tables counts fractions) flexible rules, see ?prune_occurrences details. * Sorting occurrence tables using different options, see ?score_occurrences details. * Fitting tabulating MMRM models fit_mmrm .rtable summarize_lsmeans, see ?tabulate_mmrm details. * Counting number unique non-unique patients summarize_num_patients. * Counting occurrences count_occurrences. * Counting occurrences grade summarize_occurrences_by_grade count_occurrences_by_grade. * Counting patients events columns summarize_patients_events_in_cols. * Tabulating binary outcome response subgroup extract_rsp_subgroups tabulate_rsp_subgroups. * Tabulating survival duration subgroup extract_survival_subgroups tabulate_survival_subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-069","dir":"Changelog","previous_headings":"","what":"tern 0.6.9","title":"tern 0.6.9","text":"Removing used imports.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-068","dir":"Changelog","previous_headings":"","what":"tern 0.6.8","title":"tern 0.6.8","text":"Improved handling missing data aggregation functions: a_mean_sd, a_median, a_n_true_and_freq, a_count, a_q1q3, a_iqr, a_range. New default p-value method s_test_proportion_diff: Chi-Squared Test Schouten Correction. Add new function t_contingency contingency tables. Renamed class splitText dynamicSplitText resolve name conflict package ggpubr. Add rreplace_format tabulation post-processing. Add new tern function t_ancova create ANCOVA tables, well corresponding elementary table function t_el_ancova summary function s_ancova. Add new tern function s_odds_ratio estimate Odds Ratio response categories, well corresponding elementary table function t_el_odds_ratio. Added new CI methods (Agresti-Coull, Jeffreys) s_proportion. Added new CI methods anderson-hauck newcombe s_proportion_diff. Added new p-value methods (Fisher’s Exact, Chi-Squared Test Schouten Correction) s_test_proportion_diff. binary summary table function t_binary_outcome takes now lists (instead character vectors) specified helper function control_binary_comparison arguments strat_analysis unstrat_analysis. Odds Ratio estimates CIs now removable included default, similarly subsections arm comparison analyses. Also added argument rsp_multinomial. Add new table function t_el_multinomial_proportion. Add new table function t_abn_shift. Add new MMRM analysis function s_mmrm, well corresponding table functions t_mmrm_lsmeans, t_mmrm_cov, t_mmrm_diagnostic, t_mmrm_fixed, plot functions g_mmrm_lsmeans, g_mmrm_diagnostic. results match SAS results (numeric precision). Deprecated old MMRM functions a_mmrm t_mmrm (give deprecation warning still work) remove next release. reason results functions don’t match SAS results. Fix bug g_km related numbers patients risk table correct numbers integer time--event variable inputs.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-067","dir":"Changelog","previous_headings":"","what":"tern 0.6.7","title":"tern 0.6.7","text":"functions row_by argument, inputs longer require use nested_by. Add stat_mean_ci stat_median_ci error bars ggplot2. Add new tern function t_coxreg single interface diverse cox regression types. Add compound table binary endpoint: t_binary_endpoint elementary functions: t_el_proportion, t_el_proportion_diff t_el_test_proportion_diff. supporting summary functions added : s_proportion, s_adj_proportion_diff, s_proportion_diff s_test_proportion_diff. Added new tern function t_events_patyear create event table adjusted person-years. Added new tern function t_abnormality elementary table function t_el_abnormality. Removed grade_levels argument t_events_term_grade_id functions. Post-processing reordering leaves table tree creates different ordering rows required. Creating helper function occur later time. Added prune_zero_rows argument t_events_per_term_grade_id t_max_grade_per_id show rows zeros can clutter visualization Shiny app make slower. Fixed position (N=xx) t_summary_by output summarizing numeric columns parallel compare_in_header. Rename t_coxph t_coxph_pairwise reflect model process, add details documentation. Remove test.nest dependency. Keep column labels splitting data tree.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-066","dir":"Changelog","previous_headings":"","what":"tern 0.6.6","title":"tern 0.6.6","text":"Remove test.nest dependency.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-065","dir":"Changelog","previous_headings":"","what":"tern 0.6.5","title":"tern 0.6.5","text":"Change default option denominator N t_summary. Fix IQR bug: IQR Q3 minus Q1. Add new function t_logistic multi-variable logistic regression table. Add new function df_explicit_na replace NA explicit values. Added possibility t_tte specify confidence level independent survfit, coxph, ztest, see manual. Fixed bug t_rsp showing p-value, odds ratio CIs strata_data NULL. Added stratified analysis t_forest_rsp t_forest_tte, footnotes g_forest. Added footnotes, footnotes<- add_footnotes<- functions deal footnotes. Added argument conf_int confidence interval level t_el_forest_rps, t_forest_rsp, t_el_forest_tte, t_forest_tte. Added argument col_symbol_size g_forest control relative size symbols used plot. Added s_coxph_pairwise function perform pairwise testing, used t_tte t_coxph. Added methods t_count_true replacing t_summary_true. Updated t_count_unique create analysis subsets, added t_el_count_unique vectors. Fixed bug t_events_term_id table sort order decreasing frequency instead alphabetical. Added function color_palette new nest color palette.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-064","dir":"Changelog","previous_headings":"","what":"tern 0.6.4","title":"tern 0.6.4","text":"Refactored renaming functions utils.nest. Added event_type argument t_events_per_term_grade_id. Added one/two-arm t-test functions. Improvements t_summary_by. Internal code refactoring.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-063","dir":"Changelog","previous_headings":"","what":"tern 0.6.3","title":"tern 0.6.3","text":"related tree functions see reference trees. Moved label functions rtables. Deleted keys keys<- functions. Exported tabulate_pairwise. get_N, col_N_add_total, check_id. na_as_level. as_factor_keep_attributes. r_by. Disposition elementary table t_el_disposition. t_el_forest_tte, t_el_forest_rsp. Added table_tree argument returns node object. Added f_numeric choose statistics calculate. denominator now also allows omit wanting omit percentages. Renamed row_by. Changed functionality group_data using row_by_list. Removed na_omit_group. Removed indent argument, use indent function rtables. instead","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-062","dir":"Changelog","previous_headings":"","what":"tern 0.6.2","title":"tern 0.6.2","text":"Use cached data random.cdisc.data speed testing. Added t_summary.Date method. Added save_join.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-061","dir":"Changelog","previous_headings":"","what":"tern 0.6.1","title":"tern 0.6.1","text":"Fixed colors Kaplan-Meier Plot Changed width_row.names argument g_forest function width_row_names. Changed censor.show argument g_km function censor_show. Changed col.legend.title argument g_waterfall function col_legend_title. Changed na.rm argument t_count_unique function na_rm. Changed row.name argument t_count_unique function row_name. Changed na.omit.group argument t_forest_rsp function na_omit_group. Changed na.omit.group argument t_forest_tte function na_omit_group. Changed row.name.TRUE row.name.FALSE arguments t_summary.logical row_name_true row_name_false respectively. Rename splotTextGrob split_text_grob. Fix examples. Refactor internal functions code.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-060","dir":"Changelog","previous_headings":"","what":"tern 0.6.0","title":"tern 0.6.0","text":"Removed functions addTable, t_summarize_by_visit, t_summarize_variables. Added t_summary_by function. Refactor g_km function, renamed kmGrob kmCurveGrob. Refactor t_events_* family functions. Updated examples.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-tlgs-0-5-0-3","dir":"Changelog","previous_headings":"","what":"New TLGs","title":"tern 0.5.0.3","text":"t_summary methods data.frame, numeric, logical, character, factor, Date objects. t_events_per_term_id, t_events_per_term_grade_id: Adverse Events & Concomitant Treatment Tables. t_max_grade_per_id, t_count_unique, t_events_summary elementary tables used Adverse Events & Concomitant Treatment Tables. g_waterfall: Horizontal Waterfall Plot.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-helper-functions-0-5-0-3","dir":"Changelog","previous_headings":"","what":"New Helper Functions","title":"tern 0.5.0.3","text":"decorate_grob, decorate_grob_set, decorate_grob_factory, splitTextGrob. stack_grobs, arrange_grobs, draw_grob.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tlg-changes-0-5-0-3","dir":"Changelog","previous_headings":"","what":"TLG changes","title":"tern 0.5.0.3","text":"t_tte now shows two rows ranges event censored times, respectively. g_km works one arm survfit objects. forest plot functions, added formatting display extreme values “>999.9”. t_summarise_variables uses now n instead N denominator calculating percentages factors default. t_rsp now works response values TRUE FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"deprecated-functions-0-5-0-3","dir":"Changelog","previous_headings":"","what":"Deprecated Functions","title":"tern 0.5.0.3","text":"Deprecated t_summarize_variables t_summary powerful. Replacing t_summarize_by_visit t_summary_by occur upcoming release.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-050","dir":"Changelog","previous_headings":"","what":"tern 0.5.0","title":"tern 0.5.0","text":"First version harmonized analysis functions names arguments.","code":""}]
+[{"path":[]},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [INSERT CONTACT METHOD]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://insightsengineering.github.io/tern/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contribution Guidelines","title":"Contribution Guidelines","text":"🙏 Thank taking time contribute! input deeply valued, whether issue, pull request, even feedback, regardless size, content scope.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of contents","title":"Contribution Guidelines","text":"👶 Getting started 📔 Code Conduct 🗃 License 📜 Issues 🚩 Pull requests 💻 Coding guidelines 🏆 Recognition model ❓ Questions","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Contribution Guidelines","text":"Please refer project documentation brief introduction. Please also see articles within project documentation additional information.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contribution Guidelines","text":"Code Conduct governs project. Participants contributors expected follow rules outlined therein.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Contribution Guidelines","text":"contributions covered project’s license.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"issues","dir":"","previous_headings":"","what":"Issues","title":"Contribution Guidelines","text":"use GitHub track issues, feature requests, bugs. submitting new issue, please check issue already reported. issue already exists, please upvote existing issue 👍. new feature requests, please elaborate context benefit feature users, developers, relevant personas.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"github-flow","dir":"","previous_headings":"Pull requests","what":"GitHub Flow","title":"Contribution Guidelines","text":"repository uses GitHub Flow model collaboration. submit pull request: Create branch Please see branch naming convention . don’t write access repository, please fork . Make changes Make sure code passes checks imposed GitHub Actions well documented well tested unit tests sufficiently covering changes introduced Create pull request (PR) pull request description, please link relevant issue (), provide detailed description change, include assumptions. Address review comments, Post approval Merge PR write access. Otherwise, reviewer merge PR behalf. Pat back Congratulations! 🎉 now official contributor project! grateful contribution.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"branch-naming-convention","dir":"","previous_headings":"Pull requests","what":"Branch naming convention","title":"Contribution Guidelines","text":"Suppose changes related current issue current project; please name branch follows: <issue_id>_<short_description>. Please use underscore (_) delimiter word separation. example, 420_fix_ui_bug suitable branch name change resolving UI-related bug reported issue number 420 current project. change affects multiple repositories, please name branches follows: <issue_id>_<issue_repo>_<short description>. example, 69_awesomeproject_fix_spelling_error reference issue 69 reported project awesomeproject aims resolve one spelling errors multiple (likely related) repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"monorepo-and-stageddependencies","dir":"","previous_headings":"Pull requests","what":"monorepo and staged.dependencies","title":"Contribution Guidelines","text":"Sometimes might need change upstream dependent package(s) able submit meaningful change. using staged.dependencies functionality simulate monorepo behavior. dependency configuration already specified project’s staged_dependencies.yaml file. need name feature branches appropriately. exception branch naming convention described . Please refer staged.dependencies package documentation details.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"coding-guidelines","dir":"","previous_headings":"","what":"Coding guidelines","title":"Contribution Guidelines","text":"repository follows unified processes standards adopted maintainers ensure software development carried consistently within teams cohesively across repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"style-guide","dir":"","previous_headings":"Coding guidelines","what":"Style guide","title":"Contribution Guidelines","text":"repository follows standard tidyverse style guide uses lintr lint checks. Customized lint configurations available repository’s .lintr file.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"dependency-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency management","title":"Contribution Guidelines","text":"Lightweight right weight. repository follows tinyverse recommedations limiting dependencies minimum.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"dependency-version-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency version management","title":"Contribution Guidelines","text":"code compatible (!) historical versions given dependenct package, required specify minimal version DESCRIPTION file. particular: development version requires (imports) development version another package - required put abc (>= 1.2.3.9000).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"r--package-versions","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"R & package versions","title":"Contribution Guidelines","text":"continuously test packages newest R version along recent dependencies CRAN BioConductor. recommend working environment also set way. can find details R version packages used R CMD check GitHub Action execution log - step prints R sessionInfo(). discover bugs older R versions older set dependencies, please create relevant bug reports.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"pre-commit","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"pre-commit","title":"Contribution Guidelines","text":"highly recommend use pre-commit tool combined R hooks pre-commit execute checks committing pushing changes. Pre-commit hooks already available repository’s .pre-commit-config.yaml file.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"recognition-model","dir":"","previous_headings":"","what":"Recognition model","title":"Contribution Guidelines","text":"mentioned previously, contributions deeply valued appreciated. contribution data available part repository insights, recognize significant contribution hence add contributor package authors list, following rules enforced: Minimum 5% lines code authored* (determined git blame query) top 5 contributors terms number commits lines added lines removed* *Excluding auto-generated code, including limited roxygen comments renv.lock files. package maintainer also reserves right adjust criteria recognize contributions.","code":""},{"path":"https://insightsengineering.github.io/tern/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contribution Guidelines","text":"questions regarding contribution guidelines, please contact package/repository maintainer.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/SECURITY.html","id":"reporting-security-issues","dir":"","previous_headings":"","what":"Reporting Security Issues","title":"Security Policy","text":"believe found security vulnerability repositories organization, please report us coordinated disclosure. Please report security vulnerabilities public GitHub issues, discussions, pull requests. Instead, please send email vulnerability.management[@]roche.com. Please include much information listed can help us better understand resolve issue: type issue (e.g., buffer overflow, SQL injection, cross-site scripting) Full paths source file(s) related manifestation issue location affected source code (tag/branch/commit direct URL) special configuration required reproduce issue Step--step instructions reproduce issue Proof--concept exploit code (possible) Impact issue, including attacker might exploit issue information help us triage report quickly.","code":""},{"path":"https://insightsengineering.github.io/tern/SECURITY.html","id":"data-security-standards-dss","dir":"","previous_headings":"","what":"Data Security Standards (DSS)","title":"Security Policy","text":"Please make sure reporting issues form bug, feature, pull request, sensitive information PII, PHI, PCI completely removed text attachments, including pictures videos.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/missing_values.html","id":"variable-class-conversion","dir":"Articles","previous_headings":"","what":"Variable Class Conversion","title":"Missing Values in Tern","text":"rtables requires split variables factors. try split variable isn’t, warning message appear. purposefully convert SEX variable character demonstrate happens try splitting rows variable. fix , df_explict_na convert factor resulting table generated.","code":"adsl <- tern_ex_adsl adsl$SEX <- as.character(adsl$SEX)  vars <- c(\"AGE\", \"SEX\", \"RACE\", \"BMRKR1\") var_labels <- c(   \"Age (yr)\",   \"Sex\",   \"Race\",   \"Continous Level Biomarker 1\" )  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures  #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures  #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures  #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures result #>                                                A: Drug X    B: Placebo    C: Combination   All Patients #>                                                 (N=69)        (N=73)          (N=58)         (N=200)    #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                                                #>   n                                               69            73              58             200      #>   Mean (SD)                                   34.1 (6.8)    35.8 (7.1)      36.1 (7.4)      35.3 (7.1)  #>   Median                                         32.8          35.4            36.2            34.8     #>   Min - Max                                   22.4 - 48.0   23.3 - 57.5    23.0 - 58.3     22.4 - 58.3  #> Sex                                                                                                     #>   n                                               69            73              58             200      #>   F                                           38 (55.1%)    40 (54.8%)      32 (55.2%)      110 (55%)   #>   M                                           31 (44.9%)    33 (45.2%)      26 (44.8%)       90 (45%)   #> Race                                                                                                    #>   n                                               69            73              58             200      #>   ASIAN                                       38 (55.1%)    43 (58.9%)       29 (50%)       110 (55%)   #>   BLACK OR AFRICAN AMERICAN                   15 (21.7%)    13 (17.8%)      12 (20.7%)       40 (20%)   #>   WHITE                                       11 (15.9%)    12 (16.4%)       11 (19%)        34 (17%)   #>   AMERICAN INDIAN OR ALASKA NATIVE             4 (5.8%)      3 (4.1%)       6 (10.3%)       13 (6.5%)   #>   MULTIPLE                                     1 (1.4%)      1 (1.4%)           0             2 (1%)    #>   NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (1.4%)           0            1 (0.5%)   #>   OTHER                                            0             0              0               0       #>   UNKNOWN                                          0             0              0               0       #> Continous Level Biomarker 1                                                                             #>   n                                               69            73              58             200      #>   Mean (SD)                                    6.3 (3.6)     6.7 (3.5)      6.2 (3.3)       6.4 (3.5)   #>   Median                                          5.4           6.3            5.4             5.6      #>   Min - Max                                   0.4 - 17.8    1.0 - 18.5      2.4 - 19.1      0.4 - 19.1"},{"path":"https://insightsengineering.github.io/tern/articles/missing_values.html","id":"including-missing-values-in-rtables","dir":"Articles","previous_headings":"","what":"Including Missing Values in rtables","title":"Missing Values in Tern","text":"purposefully convert M values NA SEX variable. running df_explicit_na NA values encoded <Missing> included table. well, missing values included n count included denominator value calculating percent values. want Na values displayed table included n count denominator calculating percent values, use na_level argument.","code":"adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl)  vars <- c(\"AGE\", \"SEX\") var_labels <- c(   \"Age (yr)\",   \"Sex\" )  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) result #>                A: Drug X    B: Placebo    C: Combination   All Patients #>                 (N=69)        (N=73)          (N=58)         (N=200)    #> ——————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                #>   n               69            73              58             200      #>   Mean (SD)   34.1 (6.8)    35.8 (7.1)      36.1 (7.4)      35.3 (7.1)  #>   Median         32.8          35.4            36.2            34.8     #>   Min - Max   22.4 - 48.0   23.3 - 57.5    23.0 - 58.3     22.4 - 58.3  #> Sex                                                                     #>   n               38            40              32             110      #>   F            38 (100%)     40 (100%)      32 (100%)       110 (100%)  #>   M                0             0              0               0 adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\")  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) result #>                     A: Drug X    B: Placebo    C: Combination   All Patients #>                      (N=69)        (N=73)          (N=58)         (N=200)    #> ———————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                     #>   n                    69            73              58             200      #>   Mean (SD)        34.1 (6.8)    35.8 (7.1)      36.1 (7.4)      35.3 (7.1)  #>   Median              32.8          35.4            36.2            34.8     #>   Min - Max        22.4 - 48.0   23.3 - 57.5    23.0 - 58.3     22.4 - 58.3  #> Sex                                                                          #>   n                    69            73              58             200      #>   F                38 (55.1%)    40 (54.8%)      32 (55.2%)      110 (55%)   #>   M                     0             0              0               0       #>   Missing Values   31 (44.9%)    33 (45.2%)      26 (44.8%)       90 (45%)"},{"path":"https://insightsengineering.github.io/tern/articles/missing_values.html","id":"missing-values-in-numeric-variables","dir":"Articles","previous_headings":"","what":"Missing Values in Numeric Variables","title":"Missing Values in Tern","text":"Numeric variables missing values altered. means NA value numeric variable included summary statistics, included denominator value calculating percent values. make value less 30 missing AGE variable valued greater 30 included table .","code":"adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl)  vars <- c(\"AGE\", \"SEX\") var_labels <- c(   \"Age (yr)\",   \"Sex\" )  result <- basic_table(show_colcounts = TRUE) %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) result #>                A: Drug X    B: Placebo    C: Combination   All Patients #>                 (N=69)        (N=73)          (N=58)         (N=200)    #> ——————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                #>   n               46            56              44             146      #>   Mean (SD)   37.8 (5.2)    38.3 (6.3)      39.1 (5.9)      38.3 (5.8)  #>   Median         37.2          37.3            37.5            37.5     #>   Min - Max   30.3 - 48.0   30.0 - 57.5    30.5 - 58.3     30.0 - 58.3  #> Sex                                                                     #>   n               69            73              58             200      #>   F           38 (55.1%)    40 (54.8%)      32 (55.2%)      110 (55%)   #>   M           31 (44.9%)    33 (45.2%)      26 (44.8%)       90 (45%)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"tern-tabulation","dir":"Articles","previous_headings":"","what":"tern Tabulation","title":"Tabulation","text":"tern R package provides functions create common analyses clinical trials R. core functionality tabulation built general purpose rtables package. New users first begin reading “Introduction tern” “Introduction rtables” vignettes. packages used vignette : datasets used vignette :","code":"library(rtables) library(tern) library(dplyr) adsl <- ex_adsl adae <- ex_adae adrs <- ex_adrs"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"tern-analyze-functions","dir":"Articles","previous_headings":"","what":"tern Analyze Functions","title":"Tabulation","text":"Analyze functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analyze functions wrappers around rtables::analyze function, offer various methods useful perspective clinical trials statistical projects. Examples tern analyze functions tern::count_occurrences, tern::summarize_ancova tern::analyze_vars. one prefix identify tern analyze functions recommended use tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"internals-of-tern-analyze-functions","dir":"Articles","previous_headings":"tern Analyze Functions","what":"Internals of tern Analyze Functions","title":"Tabulation","text":"Please skip subsection interested internals tern analyze functions. Internally tern analyze functions like tern::summarize_ancova mainly built 4 elements chain: descriptions function type: analysis helper functions h_*. functions useful help define analysis. statistics function s_*. Statistics functions computation numbers tabulated later. order separate computation formatting, take care rcell type formatting . formatted analysis functions a_*. arguments corresponding statistics functions, can customized calling rtables::make_afun() . used afun rtables::analyze(). analyze functions rtables::analyze(..., afun = make_afun(tern::a_*)). Analyze functions used combination rtables layout functions, pipeline creates table. last element chain. use native rtables::analyze function tern formatted analysis functions afun parameter. rtables::make_afun function helpful somebody wants attach format formatted analysis function.","code":"h_ancova() -> tern:::s_ancova() -> tern:::a_ancova() -> summarize_ancova() l <- basic_table() %>%     split_cols_by(var = \"ARM\") %>%     split_rows_by(var = \"AVISIT\") %>%     analyze(vars = \"AVAL\", afun = a_summary)  build_table(l, df = adrs) afun <- make_afun(     a_summary,     .stats = NULL,     .formats = c(median = \"xx.\"),     .labels = c(median = \"My median\"),     .indent_mods = c(median = 1L) )  l2 <- basic_table() %>%     split_cols_by(var = \"ARM\") %>%     split_rows_by(var = \"AVISIT\") %>%     analyze(vars = \"AVAL\", afun = afun)  build_table(l2, df = adrs)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"tabulation-examples","dir":"Articles","previous_headings":"","what":"Tabulation Examples","title":"Tabulation","text":"going create 3 different tables using tern analyze functions rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"demographic-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Demographic Table","title":"Tabulation","text":"Demographic tables provide summary characteristics patients enrolled clinical trial. Typically table columns represent treatment arms variables summarized table demographic properties age, sex, race, etc. example function tern analyze_vars() remaining layout functions rtables. change display order categorical variables table use factor variables explicitly set order levels. case display order columns rows. Note forcats package many useful functions help types data processing steps (used ). tern package includes many functions similar analyze_vars(). functions called layout creating functions used combination rtables layout functions just like examples . Layout creating functions wrapping calls rtables analyze(), analyze_colvars() summarize_row_groups() provide options easy formatting analysis modifications. customize display demographics table, can via arguments analyze_vars(). layout creating functions tern include standard arguments .stats, .formats, .labels .indent_mods control statistics displayed numbers formatted. Refer package help help(\"analyze_vars\") ?analyze_vars see full set options. example change default summary numeric variables include number records, mean standard deviation (single statistic, .e. within single cell). categorical variables modify summary include number records counts categories. also modify display format mean standard deviation print two decimal places instead just one. One feature layout can used different datasets create different summaries. example, can easily create summary demographics Brazil China subgroups, respectively:","code":"# Select variables to include in table. vars <- c(\"AGE\", \"SEX\") var_labels <- c(\"Age (yr)\", \"Sex\")  basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) #>                       A: Drug X    B: Placebo    C: Combination   All Patients #>                        (N=134)       (N=134)        (N=132)         (N=400)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                      134           134            132             400      #>   Mean (SD)          33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4)  #>   Median                33.0          35.0            35.0            34.0     #>   Min - Max          21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0  #> Sex                                                                            #>   n                      134           134            132             400      #>   F                   79 (59%)     77 (57.5%)       66 (50%)      222 (55.5%)  #>   M                  51 (38.1%)     55 (41%)       60 (45.5%)     166 (41.5%)  #>   U                   3 (2.2%)      2 (1.5%)         4 (3%)         9 (2.2%)   #>   UNDIFFERENTIATED    1 (0.7%)          0           2 (1.5%)        3 (0.8%) # Reorder the levels in the ARM variable. adsl$ARM <- factor(adsl$ARM, levels = c(\"B: Placebo\", \"A: Drug X\", \"C: Combination\")) # nolint  # Reorder the levels in the SEX variable. adsl$SEX <- factor(adsl$SEX, levels = c(\"M\", \"F\", \"U\", \"UNDIFFERENTIATED\")) # nolint  basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels   ) %>%   build_table(adsl) #>                      B: Placebo     A: Drug X    C: Combination   All Patients #>                        (N=134)       (N=134)        (N=132)         (N=400)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                      134           134            132             400      #>   Mean (SD)          35.4 (7.9)    33.8 (6.6)      35.4 (7.7)      34.9 (7.4)  #>   Median                35.0          33.0            35.0            34.0     #>   Min - Max          21.0 - 62.0   21.0 - 50.0    20.0 - 69.0     20.0 - 69.0  #> Sex                                                                            #>   n                      134           134            132             400      #>   M                   55 (41%)     51 (38.1%)      60 (45.5%)     166 (41.5%)  #>   F                  77 (57.5%)     79 (59%)        66 (50%)      222 (55.5%)  #>   U                   2 (1.5%)      3 (2.2%)         4 (3%)         9 (2.2%)   #>   UNDIFFERENTIATED        0         1 (0.7%)        2 (1.5%)        3 (0.8%) # Select statistics and modify default formats. basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels,     .stats = c(\"n\", \"mean_sd\", \"count\"),     .formats = c(mean_sd = \"xx.xx (xx.xx)\")   ) %>%   build_table(adsl) #>                       B: Placebo     A: Drug X     C: Combination   All Patients #>                        (N=134)        (N=134)         (N=132)         (N=400)    #> ———————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                         #>   n                      134            134             132             400      #>   Mean (SD)          35.43 (7.90)   33.77 (6.55)    35.43 (7.72)    34.88 (7.44) #> Sex                                                                              #>   n                      134            134             132             400      #>   M                       55             51              60             166      #>   F                       77             79              66             222      #>   U                       2              3               4               9       #>   UNDIFFERENTIATED        0              1               2               3 lyt <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_overall_col(\"All Patients\") %>%   add_colcounts() %>%   analyze_vars(     vars = vars,     var_labels = var_labels   )  build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"BRA\")) #>                      B: Placebo     A: Drug X    C: Combination   All Patients #>                         (N=7)        (N=13)          (N=10)          (N=30)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                       7            13              10              30      #>   Mean (SD)          32.0 (6.1)    36.7 (6.4)     38.3 (10.6)      36.1 (8.1)  #>   Median                32.0          37.0            35.0            35.5     #>   Min - Max          25.0 - 42.0   24.0 - 47.0    25.0 - 64.0     24.0 - 64.0  #> Sex                                                                            #>   n                       7            13              10              30      #>   M                   4 (57.1%)     8 (61.5%)       5 (50%)        17 (56.7%)  #>   F                   3 (42.9%)     5 (38.5%)       5 (50%)        13 (43.3%)  #>   U                       0             0              0               0       #>   UNDIFFERENTIATED        0             0              0               0  build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"CHN\")) #>                      B: Placebo     A: Drug X    C: Combination   All Patients #>                        (N=81)        (N=74)          (N=64)         (N=219)    #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr)                                                                       #>   n                      81            74              64             219      #>   Mean (SD)          35.7 (7.3)    33.0 (6.4)      35.2 (6.4)      34.6 (6.8)  #>   Median                36.0          32.0            35.0            34.0     #>   Min - Max          21.0 - 58.0   23.0 - 48.0    21.0 - 49.0     21.0 - 58.0  #> Sex                                                                            #>   n                      81            74              64             219      #>   M                  35 (43.2%)    27 (36.5%)      30 (46.9%)       92 (42%)   #>   F                  45 (55.6%)    44 (59.5%)      29 (45.3%)     118 (53.9%)  #>   U                   1 (1.2%)      2 (2.7%)        3 (4.7%)        6 (2.7%)   #>   UNDIFFERENTIATED        0         1 (1.4%)        2 (3.1%)        3 (1.4%)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"adverse-event-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Adverse Event Table","title":"Tabulation","text":"standard table adverse events summary system organ class preferred term. frequency counts preferred term, multiple occurrences AE individual count . create table need use combination several layout creating functions tabulation pipeline. start creating high-level summary. layout creating function tern can summarize_num_patients(): Note table, denominator used percentages shown header table (N = xx) defined based subject-level dataset adsl. done using alt_df_counts argument build_table(), provides alternative data set deriving counts header. often required work data sets include multiple records per patient df, adae .","code":"basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                  A: Drug X    B: Placebo    C: Combination   All Patients #>                                                   (N=134)       (N=134)        (N=132)         (N=400)    #> ————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE   122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%)  #> Overall total number of events                      609           622            703             1934"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"statistics-functions","dir":"Articles","previous_headings":"Tabulation Examples > Adverse Event Table","what":"Statistics Functions","title":"Tabulation","text":"building rest AE table helpful introduce tern package design conventions. layout creating function tern wrapper Statistics function. Statistics functions ones actual computation numbers table. functions always return named lists whose elements statistics available include layout via .stats argument layout creating function level. Statistics functions follow naming convention always begin s_* ease use documented page layout creating function counterpart. helpful review Statistic function understand logic used calculate numbers table see options may available modify analysis. example, Statistics function calculating numbers summarize_num_patients() s_num_patients(). results Statistics function list elements unique, nonunique unique_count: results can see unique nonunique statistics displayed “Patients” column initial AE table output . Also can see raw numbers formatted way. formatting functionality handled layout creating function level .formats argument. Now know types statistics can derived s_num_patients(), can try modifying default layout returned summarize_num_patients(). Instead reporting unique nonqunie statistics, specify analysis include unique_count statistic. result show counts unique patients. Note make update .stats .labels argument summarize_num_patients(). Let’s now continue building layout adverse event table. top-level summary, can repeat summary system organ class level. split analysis data split_rows_by() calling summarize_num_patients(). table looks almost ready. final step, need layout creating function can produce count table event frequencies. layout creating function count_occurrences(). Let’s first try using function simpler layout without row splits: Putting everything together, final AE table looks like :","code":"s_num_patients(x = adae$USUBJID, labelstr = \"\", .N_col = nrow(adae)) #> $unique #> [1] 365.000000   0.188728 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 1934 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 365 #> attr(,\"label\") #> [1] \"(n)\" basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = \"unique_count\",     .labels = c(unique_count = \"Total number of patients with at least one AE\")   ) %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                 A: Drug X   B: Placebo   C: Combination   All Patients #>                                                  (N=134)     (N=134)        (N=132)         (N=400)    #> —————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE      122         123            120             365 basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   split_rows_by(     \"AEBODSYS\",     child_labels = \"visible\",     nested = FALSE,     indent_mod = -1L,     split_fun = drop_split_levels   ) %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                    A: Drug X    B: Placebo    C: Combination   All Patients #>                                                     (N=134)       (N=134)        (N=132)         (N=400)    #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE     122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%)  #> Overall total number of events                        609           622            703             1934     #> cl A.1                                                                                                      #>   Total number of patients with at least one AE   78 (58.2%)    75 (56.0%)      89 (67.4%)     242 (60.5%)  #>   Overall total number of events                      132           130            160             422      #> cl B.1                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%)  #>   Overall total number of events                      56            60              62             178      #> cl B.2                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    74 (55.2%)      85 (64.4%)     238 (59.5%)  #>   Overall total number of events                      129           138            143             410      #> cl C.1                                                                                                      #>   Total number of patients with at least one AE   43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%)  #>   Overall total number of events                      55            63              64             182      #> cl C.2                                                                                                      #>   Total number of patients with at least one AE   35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%)  #>   Overall total number of events                      48            53              65             166      #> cl D.1                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    67 (50.0%)      80 (60.6%)     226 (56.5%)  #>   Overall total number of events                      127           106            135             368      #> cl D.2                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%)  #>   Overall total number of events                      62            72              74             208 basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   count_occurrences(vars = \"AEDECOD\") %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                 A: Drug X    B: Placebo   C: Combination   All Patients #>                  (N=134)      (N=134)        (N=132)         (N=400)    #> ——————————————————————————————————————————————————————————————————————— #> dcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%)  #> dcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%)  #> dcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%)  #> dcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%)  #> dcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%)  #> dcd C.1.1.1.3   43 (32.1%)   46 (34.3%)     43 (32.6%)     132 (33.0%)  #> dcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%)  #> dcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%)  #> dcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%)  #> dcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) basic_table() %>%   split_cols_by(var = \"ACTARM\") %>%   add_colcounts() %>%   add_overall_col(label = \"All Patients\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   split_rows_by(     \"AEBODSYS\",     child_labels = \"visible\",     nested = FALSE,     indent_mod = -1L,     split_fun = drop_split_levels   ) %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       unique = \"Total number of patients with at least one AE\",       nonunique = \"Overall total number of events\"     )   ) %>%   count_occurrences(vars = \"AEDECOD\") %>%   build_table(     df = adae,     alt_counts_df = adsl   ) #>                                                    A: Drug X    B: Placebo    C: Combination   All Patients #>                                                     (N=134)       (N=134)        (N=132)         (N=400)    #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE     122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%)  #> Overall total number of events                        609           622            703             1934     #> cl A.1                                                                                                      #>   Total number of patients with at least one AE   78 (58.2%)    75 (56.0%)      89 (67.4%)     242 (60.5%)  #>   Overall total number of events                      132           130            160             422      #>     dcd A.1.1.1.1                                 50 (37.3%)    45 (33.6%)      63 (47.7%)     158 (39.5%)  #>     dcd A.1.1.1.2                                 48 (35.8%)    48 (35.8%)      50 (37.9%)     146 (36.5%)  #> cl B.1                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%)  #>   Overall total number of events                      56            60              62             178      #>     dcd B.1.1.1.1                                 47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%)  #> cl B.2                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    74 (55.2%)      85 (64.4%)     238 (59.5%)  #>   Overall total number of events                      129           138            143             410      #>     dcd B.2.1.2.1                                 49 (36.6%)    44 (32.8%)      52 (39.4%)     145 (36.2%)  #>     dcd B.2.2.3.1                                 48 (35.8%)    54 (40.3%)      51 (38.6%)     153 (38.2%)  #> cl C.1                                                                                                      #>   Total number of patients with at least one AE   43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%)  #>   Overall total number of events                      55            63              64             182      #>     dcd C.1.1.1.3                                 43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%)  #> cl C.2                                                                                                      #>   Total number of patients with at least one AE   35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%)  #>   Overall total number of events                      48            53              65             166      #>     dcd C.2.1.2.1                                 35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%)  #> cl D.1                                                                                                      #>   Total number of patients with at least one AE   79 (59.0%)    67 (50.0%)      80 (60.6%)     226 (56.5%)  #>   Overall total number of events                      127           106            135             368      #>     dcd D.1.1.1.1                                 50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%)  #>     dcd D.1.1.4.2                                 48 (35.8%)    42 (31.3%)      50 (37.9%)     140 (35.0%)  #> cl D.2                                                                                                      #>   Total number of patients with at least one AE   47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%)  #>   Overall total number of events                      62            72              74             208      #>     dcd D.2.1.5.3                                 47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%)"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"response-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Response Table","title":"Tabulation","text":"typical response table binary clinical trial endpoint may composed several different analyses: Proportion responders treatment group Difference proportion responders comparison groups vs. control group Chi-Square test difference response rates comparison groups vs. control group can build table layout like following approach used AE table: table section produced using different layout creating function tern. First start data preparation steps set analysis dataset. select endpoint analyze PARAMCD define logical variable is_rsp indicates whether patient classified responder . create summary proportion responders treatment group, use estimate_proportion() layout creating function: specify arm table used reference, use argument ref_group split_cols_by(). change reference arm “B: Placebo” arm displayed first column: customize analysis, can use method conf_level arguments modify type confidence interval calculated: next table section needed summarize difference response rates reference arm comparison arm. Use estimate_proportion_diff() layout creating function : final section needed complete table includes statistical test difference response rates. Use test_proportion_diff() layout creating function : customize output, use method argument select Chi-Squared test Schouten correction. Now can put table sections together one layout pipeline. Note one small change needed. Since primary analysis variable table sections (is_rsp), need give sub-table unique name. done adding table_names argument providing unique names :","code":"# Preprocessing to select an analysis endpoint. anl <- adrs %>%   dplyr::filter(PARAMCD == \"BESRSPI\") %>%   dplyr::mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) basic_table() %>%   split_cols_by(var = \"ARM\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\",     table_names = \"est_prop\"   ) %>%   build_table(anl) #>                                   A: Drug X      B: Placebo    C: Combination #>                                    (N=134)        (N=134)         (N=132)     #> ————————————————————————————————————————————————————————————————————————————— #> Responders                       114 (85.1%)     90 (67.2%)     120 (90.9%)   #> 95% CI (Wald, with correction)   (78.7, 91.5)   (58.8, 75.5)    (85.6, 96.2) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\"   ) %>%   build_table(anl) #>                                   B: Placebo     A: Drug X     C: Combination #>                                    (N=134)        (N=134)         (N=132)     #> ————————————————————————————————————————————————————————————————————————————— #> Responders                        90 (67.2%)    114 (85.1%)     120 (90.9%)   #> 95% CI (Wald, with correction)   (58.8, 75.5)   (78.7, 91.5)    (85.6, 96.2) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\",     method = \"clopper-pearson\",     conf_level = 0.9   ) %>%   build_table(anl) #>                             B: Placebo     A: Drug X     C: Combination #>                              (N=134)        (N=134)         (N=132)     #> ——————————————————————————————————————————————————————————————————————— #> Responders                  90 (67.2%)    114 (85.1%)     120 (90.9%)   #> 90% CI (Clopper-Pearson)   (59.9, 73.9)   (79.1, 89.9)    (85.7, 94.7) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion_diff(     vars = \"is_rsp\",     show_labels = \"visible\",     var_labels = \"Unstratified Analysis\"   ) %>%   build_table(anl) #>                                      B: Placebo    A: Drug X    C: Combination #>                                       (N=134)       (N=134)        (N=132)     #> —————————————————————————————————————————————————————————————————————————————— #> Unstratified Analysis                                                          #>   Difference in Response rate (%)                    17.9            23.7      #>     95% CI (Wald, with correction)                (7.2, 28.6)    (13.7, 33.8) basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   test_proportion_diff(vars = \"is_rsp\") %>%   build_table(anl) #>                                B: Placebo   A: Drug X   C: Combination #>                                 (N=134)      (N=134)       (N=132)     #> —————————————————————————————————————————————————————————————————————— #>   p-value (Chi-Squared Test)                 0.0006        <0.0001 basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   test_proportion_diff(     vars = \"is_rsp\",     method = \"schouten\"   ) %>%   build_table(anl) #>                                                         B: Placebo   A: Drug X   C: Combination #>                                                          (N=134)      (N=134)       (N=132)     #> ——————————————————————————————————————————————————————————————————————————————————————————————— #>   p-value (Chi-Squared Test with Schouten Correction)                 0.0008        <0.0001 basic_table() %>%   split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   estimate_proportion(     vars = \"is_rsp\",     method = \"clopper-pearson\",     conf_level = 0.9,     table_names = \"est_prop\"   ) %>%   estimate_proportion_diff(     vars = \"is_rsp\",     show_labels = \"visible\",     var_labels = \"Unstratified Analysis\",     table_names = \"est_prop_diff\"   ) %>%   test_proportion_diff(     vars = \"is_rsp\",     method = \"schouten\",     table_names = \"test_prop_diff\"   ) %>%   build_table(anl) #>                                                          B: Placebo     A: Drug X     C: Combination #>                                                           (N=134)        (N=134)         (N=132)     #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> Responders                                               90 (67.2%)    114 (85.1%)     120 (90.9%)   #> 90% CI (Clopper-Pearson)                                (59.9, 73.9)   (79.1, 89.9)    (85.7, 94.7)  #> Unstratified Analysis                                                                                #>   Difference in Response rate (%)                                          17.9            23.7      #>     95% CI (Wald, with correction)                                     (7.2, 28.6)     (13.7, 33.8)  #>   p-value (Chi-Squared Test with Schouten Correction)                     0.0008         <0.0001"},{"path":"https://insightsengineering.github.io/tern/articles/tables.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Tabulation","text":"Tabulation tern builds top layout tabulation framework rtables. Complex tables built step step pipeline combining layout creating functions perform specific type analysis. tern analyze functions introduced vignette : analyze_vars() summarize_num_patients() count_occurrences() estimate_proportion() estimate_proportion_diff() test_proportion_diff() Layout creating functions build formatted layout controlling features labels, numerical display formats indentation. functions wrappers Statistics functions calculate raw summaries analysis. can easily spot Statistics functions documentation always begin prefix s_. can helpful inspect run Statistics functions understand ways analysis can customized.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"introduction-to-tern","dir":"Articles","previous_headings":"","what":"Introduction to tern","title":"Introduction to tern","text":"vignette shows general purpose syntax tern R package. tern R package contains analytical functions creating tables graphs useful clinical trials statistical analysis. main focus clinical trial reporting tables graphs related clinical trials also valuable. core functionality tabulation built top general purpose rtables package. strongly recommended start reading “Introduction rtables” vignette get familiar concept rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"common-clinical-trials-analyses","dir":"Articles","previous_headings":"","what":"Common Clinical Trials Analyses","title":"Introduction to tern","text":"package provides large range functionality create tables graphs used clinical trial statistical analysis. rtables tabulation extended clinical trials specific functions: demographics unique patients exposure across patients change baseline parameters statistical model fits: MMRM, logistic regression, Cox regression, … … rtables tabulation helper functions: pre-processing conversions transformations … data visualizations connected clinical trials: Kaplan-Meier plots forest plots line plots … data visualizations helper functions: arrange/stack multiple graphs embellishing graphs/tables metadata details, adding titles, footnotes, page number, etc. … reference tern functions available tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"analytical-functions-for-rtables","dir":"Articles","previous_headings":"","what":"Analytical Functions for rtables","title":"Introduction to tern","text":"Analytical functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analytical functions wrappers around rtables::analyze function; offer various methods useful perspective clinical trials statistical projects. Examples tern analytical functions tern::count_occurrences, tern::summarize_ancova tern::analyze_vars. one prefix identify tern analytical functions recommended use reference subsection tern website. rtables code first describe two tables assign descriptions variables lyt lyt2. built tables using actual data rtables::build_table. description table called table layout. analyze instruction adds layout ARM variable analyzed mean analysis function result rounded 1 decimal place. Hence, layout “pre-data”; , ’s description build table get data. Defining table layout pure rtables code. tern function analyze_vars replaces rtables::analyze function . see tern offers advanced analysis extending rtables function calls one additional function call. examples tabulation analyze functions presented Tabulation vignette.","code":"library(tern) library(dplyr) # Create table layout pure rtables lyt <- rtables::basic_table() %>%   rtables::split_cols_by(var = \"ARM\") %>%   rtables::split_rows_by(var = \"AVISIT\") %>%   rtables::analyze(vars = \"AVAL\", mean, format = \"xx.x\") # Create table layout with tern analyze_vars analyze function lyt2 <- rtables::basic_table() %>%   rtables::split_cols_by(var = \"ARM\") %>%   rtables::split_rows_by(var = \"AVISIT\") %>%   tern::analyze_vars(vars = \"AVAL\", .formats = c(\"mean_sd\" = \"(xx.xx, xx.xx)\")) # Apply table layout to data and produce `rtables` object  adrs <- formatters::ex_adrs  rtables::build_table(lyt, df = adrs) #>                    A: Drug X   B: Placebo   C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING                                                  #>   mean                3.0         3.0            3.0       #> BASELINE                                                   #>   mean                2.5         2.8            2.5       #> END OF INDUCTION                                           #>   mean                1.7         2.1            1.6       #> FOLLOW UP                                                  #>   mean                2.2         2.9            2.0 rtables::build_table(lyt2, df = adrs) #>                     A: Drug X      B: Placebo    C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING                                                       #>   n                    154            178             144       #>   Mean (SD)        (3.00, 0.00)   (3.00, 0.00)    (3.00, 0.00)  #>   Median               3.0            3.0             3.0       #>   Min - Max         3.0 - 3.0      3.0 - 3.0       3.0 - 3.0    #> BASELINE                                                        #>   n                    136            146             124       #>   Mean (SD)        (2.46, 0.88)   (2.77, 1.00)    (2.46, 1.08)  #>   Median               3.0            3.0             3.0       #>   Min - Max         1.0 - 4.0      1.0 - 5.0       1.0 - 5.0    #> END OF INDUCTION                                                #>   n                    218            205             217       #>   Mean (SD)        (1.75, 0.90)   (2.14, 1.28)    (1.65, 1.06)  #>   Median               2.0            2.0             1.0       #>   Min - Max         1.0 - 4.0      1.0 - 5.0       1.0 - 5.0    #> FOLLOW UP                                                       #>   n                    164            153             167       #>   Mean (SD)        (2.23, 1.26)   (2.89, 1.29)    (1.97, 1.01)  #>   Median               2.0            4.0             2.0       #>   Min - Max         1.0 - 4.0      1.0 - 4.0       1.0 - 4.0"},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"clinical-trials-visualizations","dir":"Articles","previous_headings":"","what":"Clinical Trials Visualizations","title":"Introduction to tern","text":"Clinical trial related plots complement rich palette tern tabulation analysis functions. Thus tern package delivers full-featured tool clinical trial reporting. tern plot functions return ggplot2 gTree objects, latter returned table attached plot. optional nestcolor package can loaded apply standardized NEST color palette tern plots. Line plot without table generated tern::g_lineplot function.  Line plot table generated tern::g_lineplot function.  first plot ggplot2 object second plot gTree object, latter contains table. second plot properly resized get clear readable table content. tern functions used plot generation mostly g_ prefixed. tern plot functions listed tern website functions reference.","code":"adsl <- formatters::ex_adsl adlb <- formatters::ex_adlb adlb <- dplyr::filter(adlb, PARAMCD == \"ALT\", AVISIT != \"SCREENING\") library(nestcolor) # Mean with CI tern::g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\") # Mean with CI, table and customized confidence level tern::g_lineplot(   adlb,   adsl,   table = c(\"n\", \"mean\", \"mean_ci\"),   title = \"Plot of Mean and 80% Confidence Limits by Visit\" )"},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"interactive-apps","dir":"Articles","previous_headings":"","what":"Interactive Apps","title":"Introduction to tern","text":"tern outputs easily accommodated shiny apps. recommend applying tern outputs teal apps. teal package shiny-based interactive exploration framework analyzing data. teal shiny apps tern outputs available teal.modules.clinical package.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to tern","text":"summary, tern contains many additional functions creating tables, listing graphs used clinical trials statistical analyses. design package gives users lot flexibility meet analysis needs regulatory exploratory reporting context. information please explore tern website.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"tern-formatting-functions-overview","dir":"Articles","previous_headings":"","what":"tern Formatting Functions Overview","title":"Formatting Functions","text":"tern R package provides functions create common analyses clinical trials R functions default formatting arguments displaying values output specific way. tern formatting differs compared formatting available formatters package tern formats capable handling logical statements, allowing fine-tuning output displayed. Depending type value displayed, value , format output change. Whereas using formatters package, specified format applied regardless value. see available formatting functions available tern see ?formatting_functions. see available format strings available formatters see formatters::list_valid_format_labels().","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"comparing-tern-formatters-formats","dir":"Articles","previous_headings":"","what":"Comparing tern & formatters Formats","title":"Formatting Functions","text":"packages used vignette : example demonstrates use tern formatting count_abnormal() function. example “low” category non-zero numerator value fraction percentage value displayed, “high” value numerator value zero fraction value displayed without also displaying redundant zero percentage value. following example count_abnormal() function utilized . time “low” values “high” values non-zero numerator show percentage. following example demonstrates difference formatters used instead format output. choose use \"xx / xx\" value format. “high” value zero numerator value “low” value non-zero numerator, yet displayed format. concept occurs using available formats formatters package. following example displays result using \"xx.x / xx.x\" format instead. Use formatters::list_valid_format_labels() see full list available formats formatters.","code":"library(rtables) library(formatters) library(tern) library(dplyr) df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = format_fraction)   ) %>%   build_table(df2) #>         all obs   #> ————————————————— #> low    2/2 (100%) #> high      0/2 df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = format_fraction)   ) %>%   build_table(df2) #>         all obs  #> ———————————————— #> low    1/2 (50%) #> high   1/2 (50%) df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE ) df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = \"xx / xx\")   ) %>%   build_table(df2) #>        all obs #> —————————————— #> low     2 / 2  #> high    0 / 2 df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE ) df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = \"xx.x / xx.x\")   ) %>%   build_table(df2) #>         all obs  #> ———————————————— #> low    2.0 / 2.0 #> high   0.0 / 2.0"},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"formatting-function-basics","dir":"Articles","previous_headings":"","what":"Formatting Function Basics","title":"Formatting Functions","text":"Current tern formatting functions consider following aspects setting custom behaviors: Missing values - custom value string can set display missing values instead NA. 0’s - cell value zero, tern fraction formatting functions exclude accompanying percentage value. Number decimal places display - number decimal places can fixed needed. Value thresholds - different format value can displayed depending whether value within certain threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"number-of-decimal-places-to-display","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Number of Decimal Places to Display","title":"Formatting Functions","text":"Two functions set fixed number decimal places (specifically 1) format_fraction_fixed_dp() format_count_fraction_fixed_dp(). default, formatting functions remove trailing zeros, two functions always one decimal place percentage, even digit zero. See following example:","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 3L)) #> [1] \"1/3 (33.3%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\"  format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.25)) #> [1] \"2 (25.0%)\""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"value-thresholds","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Value Thresholds","title":"Formatting Functions","text":"Functions set custom values according certain threshold include format_extreme_values(), format_extreme_values_ci(), format_fraction_threshold(). extreme value formats work similarly allow user specify maximum number digits include, large small values given special string value. example: format_fraction_threshold() function allows user specify lower percentage threshold, values instead assigned special string value. example: See documentation function specific details behavior customize .","code":"extreme_format <- format_extreme_values(digits = 2) extreme_format(0.235) #> [1] \"0.23\" extreme_format(0.001) #> [1] \"<0.01\" extreme_format(Inf) #> [1] \">999.99\" fraction_format <- format_fraction_threshold(0.05) fraction_format(x = c(20, 0.1)) #> [1] 10 fraction_format(x = c(2, 0.01)) #> [1] \"<5\""},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"creating-custom-formatting-functions","dir":"Articles","previous_headings":"","what":"Creating Custom Formatting Functions","title":"Formatting Functions","text":"table requires customized output displayed using one pre-existing tern formatting functions, may want consider creating new formatting function. creating formatting function important consider aspects listed Formatting Function Customization section . section create custom formatting function derived format_fraction_fixed_dp() function. First take look function detail customize . see numerator value greater 0, fraction percentage displayed. numerator 0, fraction shown. Percent values always display 1 decimal place. create dummy dataset observe output value behavior formatting function applied. Now modify function make custom formatting function, custom_format. want display 3 decimal places percent value, numerator value 0 want display 0 value (without denominator).","code":"# First we will see how the format_fraction_fixed_dp code works and displays the outputs format_fraction_fixed_dp <- function(x, ...) {   attr(x, \"label\") <- NULL   checkmate::assert_vector(x)   checkmate::assert_count(x[\"num\"])   checkmate::assert_count(x[\"denom\"])    result <- if (x[\"num\"] == 0) {     paste0(x[\"num\"], \"/\", x[\"denom\"])   } else {     paste0(       x[\"num\"], \"/\", x[\"denom\"],       \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\"     )   }   return(result) } df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE ) %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = format_fraction_fixed_dp)   ) %>%   build_table(df2) #>          all obs    #> ——————————————————— #> low    2/2 (100.0%) #> high       0/2 custom_format <- function(x, ...) {   attr(x, \"label\") <- NULL   checkmate::assert_vector(x)   checkmate::assert_count(x[\"num\"])   checkmate::assert_count(x[\"denom\"])    result <- if (x[\"num\"] == 0) {     paste0(x[\"num\"]) # We remove the denominator on this line so that only a 0 is displayed   } else {     paste0(       x[\"num\"], \"/\", x[\"denom\"],       \" (\", sprintf(\"%.3f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" # We include 3 decimal places with %.3f     )   }   return(result) }  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\"),     exclude_base_abn = FALSE,     .formats = list(fraction = custom_format) # Here we implement our new custom_format function   ) %>%   build_table(df2) #>           all obs     #> ————————————————————— #> low    2/2 (100.000%) #> high         0"},{"path":"https://insightsengineering.github.io/tern/articles/tern_formats.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Formatting Functions","text":"tern analysis function pre-specified default format functions implement generating output, taken formatters package custom formatting functions stored tern. tern functions differ compared formatters logical statements can used set value-dependent customized formats. like create custom formatting function use tern, sure carefully consider rules want implement handle different input values.","code":""},{"path":"https://insightsengineering.github.io/tern/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Joe Zhu. Author, maintainer. Daniel Sabanés Bové. Author. Jana Stoilova. Author. Heng Wang. Author. Francois Collin. Author. Adrian Waddell. Author. Pawel Rucki. Author. Chendi Liao. Author. Jennifer Li. Author. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/tern/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Zhu J, Sabanés Bové D, Stoilova J, Wang H, Collin F, Waddell , Rucki P, Liao C, Li J (2023). tern: Create Common TLGs Used Clinical Trials. R package version 0.9.2, https://github.com/insightsengineering/tern.","code":"@Manual{,   title = {tern: Create Common TLGs Used in Clinical Trials},   author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li},   year = {2023},   note = {R package version 0.9.2},   url = {https://github.com/insightsengineering/tern}, }"},{"path":"https://insightsengineering.github.io/tern/index.html","id":"tern-","dir":"","previous_headings":"","what":"Create Common TLGs Used in Clinical Trials","title":"Create Common TLGs Used in Clinical Trials","text":"tern R package contains analysis functions create tables graphs used clinical trial reporting. package provides large range functionality, example: forest plots line plots Kaplan-Meier plots … logistic regression Cox regression … unique patients exposure across patients change baseline parameters … Many outputs available added teal applications interactive exploration data. teal modules available teal.modules.clinical package.","code":""},{"path":"https://insightsengineering.github.io/tern/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Create Common TLGs Used in Clinical Trials","text":"tern available CRAN can install latest released version : can install latest development version directly GitHub running following: Note recommended create use GITHUB_PAT installing GitHub. See package vignettes browseVignettes(package = \"tern\") usage package.","code":"install.packages(\"tern\") if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_github(\"insightsengineering/tern\")"},{"path":"https://insightsengineering.github.io/tern/index.html","id":"related","dir":"","previous_headings":"","what":"Related","title":"Create Common TLGs Used in Clinical Trials","text":"rtables - table engine used TLG Catalog - website showcasing many examples clinical trial tables, listings, graphs teal.modules.clinical - teal modules interactive data analysis","code":""},{"path":"https://insightsengineering.github.io/tern/index.html","id":"acknowledgment","dir":"","previous_headings":"","what":"Acknowledgment","title":"Create Common TLGs Used in Clinical Trials","text":"package result joint efforts many developers stakeholders. like thank everyone contributed far!","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/pull_request_template.html","id":null,"dir":"","previous_headings":"","what":"Pull Request","title":"Pull Request","text":"Fixes #nnn","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts with Abnormal Range Values — abnormal","title":"Patient Counts with Abnormal Range Values — abnormal","text":"Primary analysis variable .var indicates abnormal range result (character factor) additional analysis variables id (character factor) baseline (character factor). direction specified abnormal (e.g. high low) count patients numerator denominator follows: num : number patients abnormality recorded treatment. denom: number patients least one post-baseline assessment.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts with Abnormal Range Values — abnormal","text":"","code":"s_count_abnormal(   df,   .var,   abnormal = list(Low = \"LOW\", High = \"HIGH\"),   variables = list(id = \"USUBJID\", baseline = \"BNRIND\"),   exclude_base_abn = FALSE )  a_count_abnormal(   df,   .var,   abnormal = list(Low = \"LOW\", High = \"HIGH\"),   variables = list(id = \"USUBJID\", baseline = \"BNRIND\"),   exclude_base_abn = FALSE )  count_abnormal(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = var,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts with Abnormal Range Values — abnormal","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. abnormal (named list) list identifying abnormal range level(s) var. Defaults list(Low = \"LOW\", High = \"HIGH\") can also group different levels named list, example, abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")). variables (named list string) list additional analysis variables. exclude_base_abn (flag) whether exclude subjects baseline abnormality numerator denominator. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts with Abnormal Range Values — abnormal","text":"s_count_abnormal() returns statistic fraction vector num denom counts patients. a_count_abnormal() returns corresponding list formatted rtables::CellValue(). count_abnormal() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts with Abnormal Range Values — abnormal","text":"s_count_abnormal(): Statistics function counts patients abnormal range values single abnormal level. a_count_abnormal(): Formatted analysis function used afun count_abnormal(). count_abnormal(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Patient Counts with Abnormal Range Values — abnormal","text":"count_abnormal() works single variable containing multiple abnormal levels. df filtered include post-baseline records. denominator includes patients might abnormal levels baseline, patients missing baseline. Patients abnormalities baseline can optionally excluded numerator denominator.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts with Abnormal Range Values — abnormal","text":"","code":"library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:testthat’: #>  #>     matches #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union  df <- data.frame(   USUBJID = as.character(c(1, 1, 2, 2)),   ANRIND = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BNRIND = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  # Select only post-baseline records. df <- df %>%   filter(ONTRTFL == \"Y\") # Layout creating function. basic_table() %>%   count_abnormal(var = \"ANRIND\", abnormal = list(high = \"HIGH\", low = \"LOW\")) %>%   build_table(df) #>         all obs  #> ———————————————— #> high   1/2 (50%) #> low    1/2 (50%)  # Passing of statistics function and formatting arguments. df2 <- data.frame(   ID = as.character(c(1, 1, 2, 2)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")),   ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"),   stringsAsFactors = FALSE )  # Select only post-baseline records. df2 <- df2 %>%   filter(ONTRTFL == \"Y\")  basic_table() %>%   count_abnormal(     var = \"RANGE\",     abnormal = list(low = \"LOW\", high = \"HIGH\"),     variables = list(id = \"ID\", baseline = \"BL_RANGE\")   ) %>%   build_table(df2) #>         all obs  #> ———————————————— #> low    1/2 (50%) #> high   1/2 (50%)"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"Primary analysis variable .var indicates abnormal range result (character factor), additional analysis variables id (character factor) baseline (character factor). direction specified abnormal (e.g. high low) condition baseline range result count patients numerator denominator follows: <Abnormal> denom: number patients without abnormality baseline (excluding missing baseline) num:  number patients denom also least one abnormality post-baseline <Abnormal> denom: number patients abnormality baseline num: number patients denom also least one abnormality post-baseline Total denom: number patients least one valid measurement post-baseline num: number patients denom also least one abnormality post-baseline","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"","code":"s_count_abnormal_by_baseline(   df,   .var,   abnormal,   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\",   variables = list(id = \"USUBJID\", baseline = \"BNRIND\") )  a_count_abnormal_by_baseline(   df,   .var,   abnormal,   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\",   variables = list(id = \"USUBJID\", baseline = \"BNRIND\") )  count_abnormal_by_baseline(   lyt,   var,   abnormal,   na_str = \"<Missing>\",   nested = TRUE,   ...,   table_names = abnormal,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. abnormal (character) identifying abnormal range level(s) .var. na_level Please use na_str argument instead. na_str (string) explicit na_level argument used pre-processing steps (maybe df_explicit_na()). default \"<Missing>\". variables (named list string) list additional analysis variables. lyt (layout) input layout analyses added . nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"s_count_abnormal_by_baseline() returns statistic fraction named list 3 labeled elements: not_abnormal, abnormal, total. element contains vector num denom patient counts. a_count_abnormal_by_baseline() returns corresponding list formatted rtables::CellValue(). count_abnormal_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_baseline() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"s_count_abnormal_by_baseline(): Statistics function single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function used afun count_abnormal_by_baseline(). count_abnormal_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"df filtered include post-baseline records. baseline variable analysis variable contains NA, expected NA conveyed na_level appropriately beforehand df_explicit_na() explicit_na().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts with Abnormal Range Values by Baseline Status — abnormal_by_baseline","text":"","code":"df <- data.frame(   USUBJID = as.character(c(1:6)),   ANRIND = factor(c(rep(\"LOW\", 4), \"NORMAL\", \"HIGH\")),   BNRIND = factor(c(\"LOW\", \"NORMAL\", \"HIGH\", NA, \"LOW\", \"NORMAL\")) ) df <- df_explicit_na(df)  # Layout creating function. basic_table() %>%   count_abnormal_by_baseline(var = \"ANRIND\", abnormal = c(High = \"HIGH\")) %>%   build_table(df) #>                all obs   #> ———————————————————————— #> High                     #>   Not high    1/4 (25%)  #>   High           0/1     #>   Total      1/6 (16.7%)  # Passing of statistics function and formatting arguments. df2 <- data.frame(   ID = as.character(c(1, 2, 3, 4)),   RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")),   BLRANGE = factor(c(\"LOW\", \"HIGH\", \"HIGH\", \"NORMAL\")) )  basic_table() %>%   count_abnormal_by_baseline(     var = \"RANGE\",     abnormal = c(Low = \"LOW\"),     variables = list(id = \"ID\", baseline = \"BLRANGE\"),     .formats = c(fraction = \"xx / xx\"),     .indent_mods = c(fraction = 2L)   ) %>%   build_table(df2) #>                 all obs #> ——————————————————————— #> Low                     #>       Not low    1 / 3  #>       Low        0 / 1  #>       Total      1 / 4"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"Primary analysis variable .var indicates whether single, replicated last marked laboratory abnormality observed (factor). Additional analysis variables id (character factor) direction (factor) indicating direction abnormality. Denominator number patients least one valid measurement analysis. Single, last Last replicated: Numerator number patients Single, last Last replicated levels, respectively. : Numerator number patients either single replicated marked abnormalities.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"","code":"s_count_abnormal_by_marked(   df,   .var = \"AVALCAT1\",   .spl_context,   category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")),   variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") )  a_count_abnormal_by_marked(   df,   .var = \"AVALCAT1\",   .spl_context,   category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")),   variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") )  count_abnormal_by_marked(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables. category (list) different marked category names single last replicated. variables (named list string) list additional analysis variables. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"s_count_abnormal_by_marked() returns statistic count_fraction Single, last, Last replicated, results. a_count_abnormal_by_marked() returns corresponding list formatted rtables::CellValue(). count_abnormal_by_marked() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_marked() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"s_count_abnormal_by_marked(): Statistics function patients marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function used afun count_abnormal_by_marked(). count_abnormal_by_marked(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"Single, last Last replicated levels mutually exclusive. patient abnormalities meet Single, last Last replicated criteria, patient counted Last replicated category.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_marked.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Patients with Marked Laboratory Abnormalities — abnormal_by_marked","text":"","code":"library(dplyr)  df <- data.frame(   USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))),   ARMCD = factor(c(rep(\"ARM A\", 5), rep(\"ARM B\", 5), rep(\"ARM A\", 5), rep(\"ARM B\", 5))),   ANRIND = factor(c(     \"NORMAL\", \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"HIGH\",     \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"NORMAL\", \"HIGH HIGH\", \"NORMAL\", \"LOW\", \"LOW\", \"LOW LOW\", \"LOW\",     \"LOW\", \"LOW\", \"LOW LOW\", \"NORMAL\", \"LOW LOW\"   )),   ONTRTFL = rep(c(\"\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\"), 2),   PARAMCD = factor(c(rep(\"CRP\", 10), rep(\"ALT\", 10))),   AVALCAT1 = factor(rep(c(\"\", \"\", \"\", \"SINGLE\", \"REPLICATED\", \"\", \"\", \"LAST\", \"\", \"SINGLE\"), 2)),   stringsAsFactors = FALSE )  df <- df %>%   mutate(abn_dir = factor(     case_when(       ANRIND == \"LOW LOW\" ~ \"Low\",       ANRIND == \"HIGH HIGH\" ~ \"High\",       TRUE ~ \"\"     ),     levels = c(\"Low\", \"High\")   ))  # Select only post-baseline records. df <- df %>% filter(ONTRTFL == \"Y\") df_crp <- df %>%   filter(PARAMCD == \"CRP\") %>%   droplevels() full_parent_df <- list(df_crp, \"not_needed\") cur_col_subset <- list(rep(TRUE, nrow(df_crp)), \"not_needed\") spl_context <- data.frame(   split = c(\"PARAMCD\", \"GRADE_DIR\"),   full_parent_df = I(full_parent_df),   cur_col_subset = I(cur_col_subset) )  map <- unique(   df[df$abn_dir %in% c(\"Low\", \"High\") & df$AVALCAT1 != \"\", c(\"PARAMCD\", \"abn_dir\")] ) %>%   lapply(as.character) %>%   as.data.frame() %>%   arrange(PARAMCD, abn_dir)  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   split_rows_by(\"PARAMCD\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = \"unique_count\"   ) %>%   split_rows_by(     \"abn_dir\",     split_fun = trim_levels_to_map(map)   ) %>%   count_abnormal_by_marked(     var = \"AVALCAT1\",     variables = list(       id = \"USUBJID\",       param = \"PARAMCD\",       direction = \"abn_dir\"     )   ) %>%   build_table(df = df) #>                           ARM A      ARM B   #> ———————————————————————————————————————————— #> ALT (n)                     1          1     #>   Low                                        #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%) #> CRP (n)                     1          1     #>   High                                       #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%)  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   split_rows_by(\"PARAMCD\") %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = \"unique_count\"   ) %>%   split_rows_by(     \"abn_dir\",     split_fun = trim_levels_in_group(\"abn_dir\")   ) %>%   count_abnormal_by_marked(     var = \"AVALCAT1\",     variables = list(       id = \"USUBJID\",       param = \"PARAMCD\",       direction = \"abn_dir\"     )   ) %>%   build_table(df = df) #>                           ARM A      ARM B   #> ———————————————————————————————————————————— #> ALT (n)                     1          1     #>   Low                                        #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%) #> CRP (n)                     1          1     #>   High                                       #>     Single, not last     1 (100%)      0     #>     Last or replicated      0       1 (100%) #>     Any Abnormality      1 (100%)   1 (100%)"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"Primary analysis variable .var indicates toxicity grade (factor), additional analysis variables id (character factor), param (factor) grade_dir (factor). pre-processing steps crucial using function. certain direction (e.g. high low) function counts patients denominator number patients least one valid measurement treatment, patients numerator follows: 1 4: Numerator number patients worst grades 1-4 respectively; : Numerator number patients least one abnormality, means grade different 0.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"","code":"s_count_abnormal_by_worst_grade(   df,   .var = \"GRADE_ANL\",   .spl_context,   variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") )  a_count_abnormal_by_worst_grade(   df,   .var = \"GRADE_ANL\",   .spl_context,   variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") )  count_abnormal_by_worst_grade(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables. variables (named list string) list additional analysis variables. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"s_count_abnormal_by_worst_grade() returns single statistic count_fraction grades 1 4 \"\" results. a_count_abnormal_by_worst_grade() returns corresponding list formatted rtables::CellValue(). count_abnormal_by_worst_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_worst_grade() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"pre-processing steps crucial using function. standard lab grade variable ATOXGR, derive following two variables: grade direction variable (e.g. GRADE_DIR) required order obtain correct denominators building layout used define row splitting. toxicity grade variable (e.g. GRADE_ANL) negative values ATOXGR replaced absolute values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"s_count_abnormal_by_worst_grade(): Statistics function counts patients worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function used afun count_abnormal_by_worst_grade(). count_abnormal_by_worst_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"Prior tabulation, df must filtered include post-baseline records worst grade flags.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality — abnormal_by_worst_grade","text":"","code":"library(dplyr) library(forcats) adlb <- tern_ex_adlb  # Data is modified in order to have some parameters with grades only in one direction # and simulate the real data. adlb$ATOXGR[adlb$PARAMCD == \"ALT\" & adlb$ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\")] <- \"-1\" adlb$ANRIND[adlb$PARAMCD == \"ALT\" & adlb$ANRIND == \"HIGH\"] <- \"LOW\" adlb$WGRHIFL[adlb$PARAMCD == \"ALT\"] <- \"\"  adlb$ATOXGR[adlb$PARAMCD == \"IGA\" & adlb$ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\")] <- \"1\" adlb$ANRIND[adlb$PARAMCD == \"IGA\" & adlb$ANRIND == \"LOW\"] <- \"HIGH\" adlb$WGRLOFL[adlb$PARAMCD == \"IGA\"] <- \"\"  # Here starts the real pre-processing. adlb_f <- adlb %>%   filter(!AVISIT %in% c(\"SCREENING\", \"BASELINE\")) %>%   mutate(     GRADE_DIR = factor(       case_when(         ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\") ~ \"LOW\",         ATOXGR == \"0\" ~ \"ZERO\",         ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\") ~ \"HIGH\"       ),       levels = c(\"LOW\", \"ZERO\", \"HIGH\")     ),     GRADE_ANL = fct_relevel(       fct_recode(ATOXGR, `1` = \"-1\", `2` = \"-2\", `3` = \"-3\", `4` = \"-4\"),       c(\"0\", \"1\", \"2\", \"3\", \"4\")     )   ) %>%   filter(WGRLOFL == \"Y\" | WGRHIFL == \"Y\") %>%   droplevels()  adlb_f_alt <- adlb_f %>%   filter(PARAMCD == \"ALT\") %>%   droplevels() full_parent_df <- list(adlb_f_alt, \"not_needed\") cur_col_subset <- list(rep(TRUE, nrow(adlb_f_alt)), \"not_needed\")  # This mimics a split structure on PARAM and GRADE_DIR for a total column spl_context <- data.frame(   split = c(\"PARAM\", \"GRADE_DIR\"),   full_parent_df = I(full_parent_df),   cur_col_subset = I(cur_col_subset) )  # Map excludes records without abnormal grade since they should not be displayed # in the table. map <- unique(adlb_f[adlb_f$GRADE_DIR != \"ZERO\", c(\"PARAM\", \"GRADE_DIR\", \"GRADE_ANL\")]) %>%   lapply(as.character) %>%   as.data.frame() %>%   arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   split_rows_by(\"PARAM\") %>%   split_rows_by(\"GRADE_DIR\", split_fun = trim_levels_to_map(map)) %>%   count_abnormal_by_worst_grade(     var = \"GRADE_ANL\",     variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\")   ) %>%   build_table(df = adlb_f) #>                                          ARM A        ARM B        ARM C    #> ——————————————————————————————————————————————————————————————————————————— #> Alanine Aminotransferase Measurement                                        #>   LOW                                                                       #>     1                                  12 (17.4%)    5 (6.8%)    8 (13.8%)  #>     2                                   9 (13%)     13 (17.8%)   6 (10.3%)  #>     3                                   6 (8.7%)     4 (5.5%)    6 (10.3%)  #>     4                                  7 (10.1%)     7 (9.6%)    6 (10.3%)  #>     Any                                34 (49.3%)   29 (39.7%)   26 (44.8%) #> C-Reactive Protein Measurement                                              #>   LOW                                                                       #>     1                                  11 (15.9%)   12 (16.4%)   7 (12.1%)  #>     2                                  8 (11.6%)     2 (2.7%)    6 (10.3%)  #>     3                                   4 (5.8%)    9 (12.3%)    6 (10.3%)  #>     4                                  7 (10.1%)     6 (8.2%)     4 (6.9%)  #>     Any                                30 (43.5%)   29 (39.7%)   23 (39.7%) #>   HIGH                                                                      #>     1                                  8 (11.6%)    11 (15.1%)    2 (3.4%)  #>     2                                   9 (13%)     11 (15.1%)    11 (19%)  #>     3                                  14 (20.3%)   10 (13.7%)    5 (8.6%)  #>     4                                   2 (2.9%)     4 (5.5%)    6 (10.3%)  #>     Any                                33 (47.8%)   36 (49.3%)   24 (41.4%) #> Immunoglobulin A Measurement                                                #>   HIGH                                                                      #>     1                                  7 (10.1%)     7 (9.6%)    6 (10.3%)  #>     2                                  8 (11.6%)     6 (8.2%)    8 (13.8%)  #>     3                                  7 (10.1%)     5 (6.8%)    9 (15.5%)  #>     4                                   6 (8.7%)     2 (2.7%)     3 (5.2%)  #>     Any                                28 (40.6%)   20 (27.4%)   26 (44.8%)"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"Patient count fraction laboratory events (worsen baseline) shift table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"","code":"s_count_abnormal_lab_worsen_by_baseline(   df,   .var = \"ATOXGR\",   variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") )  a_count_abnormal_lab_worsen_by_baseline(   df,   .var = \"ATOXGR\",   variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") )  count_abnormal_lab_worsen_by_baseline(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = NULL,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. variables (named list string) list additional analysis variables including: id (string) subject variable name. baseline_var (string) name data column containing baseline toxicity variable. direction_var (string) see direction_var details. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"s_count_abnormal_lab_worsen_by_baseline() returns counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\". a_count_abnormal_lab_worsen_by_baseline() returns corresponding list formatted rtables::CellValue(). count_abnormal_lab_worsen_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_lab_worsen_by_baseline() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"s_count_abnormal_lab_worsen_by_baseline(): Statistics function patients whose worst post-baseline lab grades worse baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function used afun count_abnormal_lab_worsen_by_baseline(). count_abnormal_lab_worsen_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/abnormal_by_worst_grade_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline — abnormal_by_worst_grade_worsen","text":"","code":"library(dplyr)  # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>%   mutate(     GRADDR = case_when(       PARAMCD == \"ALT\" ~ \"B\",       PARAMCD == \"CRP\" ~ \"L\",       PARAMCD == \"IGA\" ~ \"H\"     )   ) %>%   filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")  df <- h_adlb_worsen(   adlb,   worst_flag_low = c(\"WGRLOFL\" = \"Y\"),   worst_flag_high = c(\"WGRHIFL\" = \"Y\"),   direction_var = \"GRADDR\" )  basic_table() %>%   split_cols_by(\"ARMCD\") %>%   add_colcounts() %>%   split_rows_by(\"PARAMCD\") %>%   split_rows_by(\"GRADDR\") %>%   count_abnormal_lab_worsen_by_baseline(     var = \"ATOXGR\",     variables = list(       id = \"USUBJID\",       baseline_var = \"BTOXGR\",       direction_var = \"GRADDR\"     )   ) %>%   append_topleft(\"Direction of Abnormality\") %>%   build_table(df = df, alt_counts_df = tern_ex_adsl) #>                                ARM A           ARM B           ARM C     #> Direction of Abnormality      (N=69)          (N=73)          (N=58)     #> ———————————————————————————————————————————————————————————————————————— #> IGA                                                                      #>   High                                                                   #>     1                       6/63 (9.5%)     6/64 (9.4%)      4/50 (8%)   #>     2                      8/64 (12.5%)     5/67 (7.5%)    8/53 (15.1%)  #>     3                      7/66 (10.6%)     5/68 (7.4%)    9/57 (15.8%)  #>     4                       6/68 (8.8%)     2/72 (2.8%)     3/58 (5.2%)  #>     Any                    27/68 (39.7%)    18/72 (25%)    24/58 (41.4%) #> ALT                                                                      #>   High                                                                   #>     1                      7/63 (11.1%)     6/62 (9.7%)     2/48 (4.2%)  #>     2                       12/63 (19%)      4/67 (6%)      11/50 (22%)  #>     3                       4/65 (6.2%)    11/71 (15.5%)   7/56 (12.5%)  #>     4                       1/67 (1.5%)    8/71 (11.3%)      4/57 (7%)   #>     Any                    24/67 (35.8%)   29/71 (40.8%)   24/57 (42.1%) #>   Low                                                                    #>     1                      12/67 (17.9%)    4/66 (6.1%)    7/52 (13.5%)  #>     2                      9/68 (13.2%)    12/69 (17.4%)   6/55 (10.9%)  #>     3                       6/69 (8.7%)     4/71 (5.6%)     5/56 (8.9%)  #>     4                      7/69 (10.1%)     7/73 (9.6%)    6/58 (10.3%)  #>     Any                    34/69 (49.3%)    27/73 (37%)    24/58 (41.4%) #> CRP                                                                      #>   Low                                                                    #>     1                      11/66 (16.7%)   10/67 (14.9%)    4/47 (8.5%)  #>     2                      8/66 (12.1%)     1/70 (1.4%)     6/50 (12%)   #>     3                       4/68 (5.9%)    9/70 (12.9%)     5/53 (9.4%)  #>     4                      7/69 (10.1%)     6/72 (8.3%)     4/55 (7.3%)  #>     Any                    30/69 (43.5%)   26/72 (36.1%)   19/55 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Function to Configure Risk Difference Column — add_riskdiff","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"Wrapper function rtables::add_combo_levels() configures settings risk difference column added rtables object. add risk difference column table, function used split_fun calls rtables::split_cols_by(), followed setting argument riskdiff TRUE following analyze function calls.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"","code":"add_riskdiff(   arm_x,   arm_y,   col_label = \"Risk Difference (%) (95% CI)\",   pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"arm_x (character) Name reference arm use risk difference calculations. arm_y (character) Name arm compare reference arm risk difference calculations. col_label (character) Label use rendering risk difference column within table. pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"closure suitable use split function (split_fun) within rtables::split_cols_by() creating table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/add_riskdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Function to Configure Risk Difference Column — add_riskdiff","text":"","code":"adae <- tern_ex_adae adae$AESEV <- factor(adae$AESEV)  lyt <- basic_table() %>%   split_cols_by(\"ARMCD\", split_fun = add_riskdiff(arm_x = \"ARM A\", arm_y = \"ARM B\")) %>%   count_occurrences_by_grade(     var = \"AESEV\",     riskdiff = TRUE   )  tbl <- build_table(lyt, df = adae) tbl #>              ARM A        ARM B        ARM C      Risk Difference (%) (95% CI) #> —————————————————————————————————————————————————————————————————————————————— #> MILD        6 (3.0%)     4 (2.3%)     2 (1.2%)          0.7 (-2.5 - 3.9)       #> MODERATE   19 (9.4%)    15 (8.5%)    14 (8.6%)          0.9 (-4.8 - 6.7)       #> SEVERE     34 (16.8%)   38 (21.5%)   32 (19.8%)        -4.6 (-12.6 - 3.3)"},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Layout Creating Function to Add Row Total Counts — add_rowcounts","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"works analogously rtables::add_colcounts() rows. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"","code":"add_rowcounts(lyt, alt_counts = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"lyt (layout) input layout analyses added . alt_counts (flag) whether row counts taken alt_counts_df (TRUE) df (FALSE). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"modified layout latest row split labels now row-wise total counts (.e. without column-based subsetting) attached parentheses.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"Row count values contained row count rows displayed considered zero rows default pruning.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/add_rowcounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layout Creating Function to Add Row Total Counts — add_rowcounts","text":"","code":"basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   split_rows_by(\"RACE\", split_fun = drop_split_levels) %>%   add_rowcounts() %>%   analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") %>%   build_table(DM) #>                                    A: Drug X   B: Placebo   C: Combination #>                                     (N=121)     (N=106)        (N=129)     #> —————————————————————————————————————————————————————————————————————————— #> ASIAN (N=231)                                                              #>   Min.                               20.00       21.00          22.00      #>   1st Qu.                            29.00       28.00          30.00      #>   Median                             33.00       32.50          33.50      #>   Mean                               34.20       32.68          34.63      #>   3rd Qu.                            38.50       36.00          38.00      #>   Max.                               58.00       55.00          53.00      #> BLACK OR AFRICAN AMERICAN (N=79)                                           #>   Min.                               23.00       21.00          24.00      #>   1st Qu.                            29.00       28.75          29.00      #>   Median                             33.00       30.00          32.00      #>   Mean                               34.68       31.71          34.00      #>   3rd Qu.                            37.25       36.25          39.00      #>   Max.                               60.00       42.00          51.00      #> WHITE (N=46)                                                               #>   Min.                               30.00       25.00          28.00      #>   1st Qu.                            38.00       31.00          30.25      #>   Median                             40.50       37.50          35.00      #>   Mean                               39.36       36.93          35.11      #>   3rd Qu.                            43.50       40.00          37.50      #>   Max.                               47.00       55.00          47.00"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Adverse Event Baskets — aesi_label","text":"","code":"aesi_label(aesi, scope = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Adverse Event Baskets — aesi_label","text":"aesi (character) standardized MedDRA query name (e.g. SMQzzNAM) customized query name (e.g. CQzzNAM). scope (character) scope query (e.g. SMQzzSC).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Adverse Event Baskets — aesi_label","text":"string standard label AE basket.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/aesi_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Labels for Adverse Event Baskets — aesi_label","text":"","code":"adae <- tern_ex_adae  # Standardized query label includes scope. aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC) #> [1] \"C.1.1.1.3/B.2.2.3.1 aesi (BROAD)\"  # Customized query label. aesi_label(adae$CQ01NAM) #> [1] \"D.2.1.5.3/A.1.1.1.1 aesi\""},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"risk difference column, function uses statistics function associated afun calculates risk difference values arm X (reference group) arm Y. arms specified configuring risk difference column done using add_riskdiff() split function previous call rtables::split_cols_by(). columns, applies afun usual. function utilizes stat_propdiff_ci() function perform risk difference calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"","code":"afun_riskdiff(   df,   labelstr = \"\",   .var,   .N_col,   .N_row,   .df_row,   .spl_context,   .all_col_counts,   .stats,   .indent_mods,   afun,   s_args = list() )"},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .var (string) single variable name passed rtables requested statistics function. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .df_row (data.frame) data frame across columns given row split. .spl_context (data.frame) gives information ancestor split states passed rtables. .all_col_counts (vector integer) value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .stats (character) statistics select table. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. afun (named list) named list containing one name-value pair name corresponds name statistics function used calculations value corresponding analysis function. s_args (named list) Additional arguments passed statistics function analysis function supplied afun.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis Function to Calculate Risk Difference Column Values — afun_riskdiff","text":"list formatted rtables::CellValue().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Selected Statistics Names — afun_selected_stats","title":"Get Selected Statistics Names — afun_selected_stats","text":"Helper function used creating afun.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Selected Statistics Names — afun_selected_stats","text":"","code":"afun_selected_stats(.stats, all_stats)"},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Selected Statistics Names — afun_selected_stats","text":".stats (vector NULL) input layout creating function. Note NULL means context default statistics used. all_stats (character) statistics can selected potentially.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/afun_selected_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Selected Statistics Names — afun_selected_stats","text":"character vector selected statistics.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_colvars_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze Functions on Columns — analyze_colvars_functions","title":"Analyze Functions on Columns — analyze_colvars_functions","text":"functions wrappers rtables::analyze_colvars() apply corresponding tern statistics functions add analysis given table layout. particular, functions designed analysis methods split different columns. analyze_vars_in_cols(): fundamental tabulation analysis methods onto columns. words, analysis methods defined column space, .e. become column labels. changing variable vector, list functions can applied different variables, caveat number statistical functions. tabulate_rsp_subgroups(): similarly analyze_vars_in_cols, function combines analyze_colvars summarize_row_groups compact way produce standard tables show analysis methods columns. tabulate_survival_subgroups(): function similar , used tables. analyze_patients_exposure_in_cols(): based analyze_colvars. needs summarize_patients_exposure_in_cols() leverage nesting label rows analysis rtables::summarize_row_groups(). summarize_coxreg(): generally based rtables::summarize_row_groups(), behaves similarly tabulate_* functions described designed provide specific standard tables may contain nested structure combination summarize_row_groups() rtables::analyze_colvars().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/analyze_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze Functions — analyze_functions","title":"Analyze Functions — analyze_functions","text":"functions wrappers rtables::analyze() apply corresponding tern statistics functions add analysis given table layout: analyze_num_patients() compare_vars() count_abnormal() count_abnormal_by_baseline() count_abnormal_by_marked() count_abnormal_by_worst_grade() count_cumulative() count_missed_doses() count_occurrences() count_occurrences_by_grade() count_patients_events_in_cols() count_patients_with_event() count_patients_with_flags() count_values() coxph_pairwise() estimate_incidence_rate() estimate_multinomial_rsp() estimate_odds_ratio() estimate_proportion() estimate_proportion_diff() summarize_ancova() summarize_colvars(): even function uses rtables::analyze_colvars(), applies analysis methods different rows one variables split different columns. comparison, analyze_colvars_functions leverage analyze_colvars context split rows analysis methods columns. summarize_change() analyze_vars(): formerly known summarize_vars(), renamed reflect core function rtables::analyze(). surv_time() surv_timepoint() test_proportion_diff()","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze Variables — analyze_variables","title":"Analyze Variables — analyze_variables","text":"use S3 generic function s_summary() implement summaries different x objects. used statistics function combination analyze function analyze_vars(). Deprecation cycle started summarize_vars going renamed analyze_vars. Intention reflect better core underlying rtables functions; case rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyze Variables — analyze_variables","text":"","code":"s_summary(x, na.rm = TRUE, denom, .N_row, .N_col, .var, ...)  # S3 method for numeric s_summary(   x,   na.rm = TRUE,   denom,   .N_row,   .N_col,   .var,   control = control_analyze_vars(),   ... )  # S3 method for factor s_summary(   x,   na.rm = TRUE,   denom = c(\"n\", \"N_row\", \"N_col\"),   .N_row,   .N_col,   ... )  # S3 method for character s_summary(   x,   na.rm = TRUE,   denom = c(\"n\", \"N_row\", \"N_col\"),   .N_row,   .N_col,   .var,   verbose = TRUE,   ... )  # S3 method for logical s_summary(   x,   na.rm = TRUE,   denom = c(\"n\", \"N_row\", \"N_col\"),   .N_row,   .N_col,   ... )  a_summary(   x,   .N_col,   .N_row,   .var = NULL,   .df_row = NULL,   .ref_group = NULL,   .in_ref_col = FALSE,   compare = FALSE,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL,   na.rm = TRUE,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   ... )  analyze_vars(   lyt,   vars,   var_labels = vars,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   nested = TRUE,   ...,   na.rm = TRUE,   show_labels = \"default\",   table_names = vars,   section_div = NA_character_,   .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )  summarize_vars(...)"},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyze Variables — analyze_variables","text":"x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .var (string) single variable name passed rtables requested statistics function. ... arguments passed s_summary(). control (list) parameters descriptive statistics details, specified using helper function control_analyze_vars(). possible parameter options : conf_level (proportion) confidence level interval mean median. quantiles (numeric) vector length two specify quantiles. quantile_type (numeric) 1 9 selecting quantile algorithms used. See type stats::quantile(). test_mean (numeric) value test mean null hypothesis calculating p-value. verbose (logical) Defaults TRUE, prints warnings messages. mainly used print information factor casting. .df_row (data.frame) data frame across columns given row split. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. compare (logical) Whether comparison statistics analyzed instead summary statistics (compare = TRUE adds pval statistic comparing reference group). .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analyze Variables — analyze_variables","text":"s_summary() returns different statistics depending class x. x class numeric, returns list following named numeric items: n: length() x. sum: sum() x. mean: mean() x. sd: stats::sd() x. se: standard error x mean, .e.: (sd(x) / sqrt(length(x))). mean_sd: mean() stats::sd() x. mean_se: mean() x standard error (see ). mean_ci: CI mean x (stat_mean_ci()). mean_sei: SE interval mean x, .e.: (mean() -/+ stats::sd() / sqrt()). mean_sdi: SD interval mean x, .e.: (mean() -/+ stats::sd()). mean_pval: two-sided p-value mean x (stat_mean_pval()). median: stats::median() x. mad: median absolute deviation x, .e.: (stats::median() xc, xc = x - stats::median()). median_ci: CI median x (stat_median_ci()). quantiles: Two sample quantiles x (stats::quantile()). iqr: stats::IQR() x. range: range_noinf() x. min: max() x. max: min() x. median_range: median() range_noinf() x. cv: coefficient variation x, .e.: (stats::sd() / mean() * 100). geom_mean: geometric mean x, .e.: (exp(mean(log(x)))). geom_cv: geometric coefficient variation x, .e.: (sqrt(exp(sd(log(x)) ^ 2) - 1) * 100). x class factor converted character, returns list named numeric items: n: length() x. count: list number cases level factor x. count_fraction: Similar count also includes proportion cases level factor x relative denominator, NA denominator zero. x class logical, returns list named numeric items: n: length() x (possibly removing NAs). count: Count TRUE x. count_fraction: Count proportion TRUE x relative denominator, NA denominator zero. Note NAs x never counted leading NA . a_summary() returns corresponding list formatted rtables::CellValue(). analyze_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_summary() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyze Variables — analyze_variables","text":"possible use \"auto\" analyze_vars subset methods. uses format_auto() determine automatically number digits analyzed variable (.vars), current row data (.df_row[[.var]], see ?rtables::additional_fun_params), whole data. Also column split considered.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Analyze Variables — analyze_variables","text":"s_summary(): S3 generic function produces variable summary. s_summary(numeric): Method numeric class. s_summary(factor): Method factor class. s_summary(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_summary(logical): Method logical class. a_summary(): Formatted analysis function used afun analyze_vars() compare_vars() cfun summarize_colvars(). analyze_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_vars():  Use analyze_vars instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Analyze Variables — analyze_variables","text":"x empty vector, NA returned. expected feature return rcell content rtables intersection column row delimits empty data selection. mean function applied empty vector, NA returned instead NaN, latter standard behavior R. x empty factor, list still returned counts one element per factor level. levels x, function fails. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"<Missing>\") also excluded na.rm set TRUE. Automatic conversion character factor guarantee table can generated correctly. particular sparse tables likely can fail. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table(). use comparison (additional p-value statistic), parameter compare must set TRUE. Ensure either NA values converted explicit NA level NA values left .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyze Variables — analyze_variables","text":"","code":"# `s_summary.numeric`  ## Basic usage: empty numeric returns NA-filled items. s_summary(numeric()) #> $n #> n  #> 0  #>  #> $sum #> sum  #>  NA  #>  #> $mean #> mean  #>   NA  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>   NA   NA  #>  #> $mean_se #> mean   se  #>   NA   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>     NA  #>  #> $mad #> mad  #>  NA  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>  NA  #>  #> $range #> min max  #>  NA  NA  #>  #> $min #> min  #>  NA  #>  #> $max #> max  #>  NA  #>  #> $median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>       NaN  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>   ## Management of NA values. x <- c(NA_real_, 1) s_summary(x, na.rm = TRUE) #> $n #> n  #> 1  #>  #> $sum #> sum  #>   1  #>  #> $mean #> mean  #>    1  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>    1   NA  #>  #> $mean_se #> mean   se  #>    1   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>      1  #>  #> $mad #> mad  #>   0  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>             1             1  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>   0  #>  #> $range #> min max  #>   1   1  #>  #> $min #> min  #>   1  #>  #> $max #> max  #>   1  #>  #> $median_range #> median    min    max  #>      1      1      1  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>         1  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>  s_summary(x, na.rm = FALSE) #> $n #> n  #> 2  #>  #> $sum #> sum  #>  NA  #>  #> $mean #> mean  #>   NA  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>   NA   NA  #>  #> $mean_se #> mean   se  #>   NA   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>     NA  #>  #> $mad #> mad  #>  NA  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>  NA  #>  #> $range #> min max  #>  NA  NA  #>  #> $min #> min  #>  NA  #>  #> $max #> max  #>  NA  #>  #> $median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>        NA  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>   x <- c(NA_real_, 1, 2) s_summary(x, stats = NULL) #> $n #> n  #> 2  #>  #> $sum #> sum  #>   3  #>  #> $mean #> mean  #>  1.5  #>  #> $sd #>        sd  #> 0.7071068  #>  #> $se #>  se  #> 0.5  #>  #> $mean_sd #>      mean        sd  #> 1.5000000 0.7071068  #>  #> $mean_se #> mean   se  #>  1.5  0.5  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>   -4.853102    7.853102  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>            1            2  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>    0.7928932    2.2071068  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #>   p_value  #> 0.2048328  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>    1.5  #>  #> $mad #> mad  #>   0  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>             1             2  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>   1  #>  #> $range #> min max  #>   1   2  #>  #> $min #> min  #>   1  #>  #> $max #> max  #>   2  #>  #> $median_range #> median    min    max  #>    1.5    1.0    2.0  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #>       cv  #> 47.14045  #>  #> $geom_mean #> geom_mean  #>  1.414214  #>  #> $geom_mean_ci #>  mean_ci_lwr  mean_ci_upr  #>   0.01729978 115.60839614  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #>  geom_cv  #> 52.10922  #>   ## Benefits in `rtables` contructions: require(rtables) dta_test <- data.frame(   Group = rep(LETTERS[1:3], each = 2),   sub_group = rep(letters[1:2], each = 3),   x = 1:6 )  ## The summary obtained in with `rtables`: basic_table() %>%   split_cols_by(var = \"Group\") %>%   split_rows_by(var = \"sub_group\") %>%   analyze(vars = \"x\", afun = s_summary) %>%   build_table(df = dta_test) #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #> Warning: number of items to replace is not a multiple of replacement length #>                                                  A                        B                       C                   #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> a                                                                                                                     #>   n                                              2                        1                       0                   #>   sum                                            3                        3                       NA                  #>   mean                                          1.5                       3                       NA                  #>   sd                                     0.707106781186548               NA                       NA                  #>   se                                            0.5                      NA                       NA                  #>   mean_sd                              1.5, 0.707106781186548           3, NA                     NA                  #>   mean_se                                     1.5, 0.5                  3, NA                     NA                  #>   Mean 95% CI                   -4.85310236808735, 7.85310236808735      NA                       NA                  #>   Mean -/+ 1xSE                                 1, 2                     NA                       NA                  #>   Mean -/+ 1xSD                 0.792893218813452, 2.20710678118655      NA                       NA                  #>   Mean p-value (H0: mean = 0)            0.204832764699133               NA                       NA                  #>   median                                        1.5                       3                       NA                  #>   mad                                            0                        0                       NA                  #>   Median 95% CI                                  NA                      NA                       NA                  #>   25% and 75%-ile                               1, 2                    3, 3                      NA                  #>   iqr                                            1                        0                       NA                  #>   range                                         1, 2                    3, 3                      NA                  #>   min                                            1                        3                       NA                  #>   max                                            2                        3                       NA                  #>   Median (Min - Max)                         1.5, 1, 2                 3, 3, 3                    NA                  #>   cv                                      47.1404520791032               NA                       NA                  #>   geom_mean                               1.41421356237309                3                       NA                  #>   Geometric Mean 95% CI         0.0172997815631007, 115.608396135236     NA                       NA                  #>   geom_cv                                 52.1092246837487               NA                       NA                  #> b                                                                                                                     #>   n                                              0                        1                       2                   #>   sum                                            NA                       4                       11                  #>   mean                                           NA                       4                      5.5                  #>   sd                                             NA                      NA               0.707106781186548           #>   se                                             NA                      NA                      0.5                  #>   mean_sd                                        NA                     4, NA           5.5, 0.707106781186548        #>   mean_se                                        NA                     4, NA                  5.5, 0.5               #>   Mean 95% CI                                    NA                      NA      -0.853102368087347, 11.8531023680873 #>   Mean -/+ 1xSE                                  NA                      NA                      5, 6                 #>   Mean -/+ 1xSD                                  NA                      NA       4.79289321881345, 6.20710678118655  #>   Mean p-value (H0: mean = 0)                    NA                      NA               0.0577158767526089          #>   median                                         NA                       4                      5.5                  #>   mad                                            NA                       0                       0                   #>   Median 95% CI                                  NA                      NA                       NA                  #>   25% and 75%-ile                                NA                     4, 4                     5, 6                 #>   iqr                                            NA                       0                       1                   #>   range                                          NA                     4, 4                     5, 6                 #>   min                                            NA                       4                       5                   #>   max                                            NA                       4                       6                   #>   Median (Min - Max)                             NA                    4, 4, 4                5.5, 5, 6               #>   cv                                             NA                      NA                12.8564869306645           #>   geom_mean                                      NA                       4                5.47722557505166           #>   Geometric Mean 95% CI                          NA                      NA       1.71994304449266, 17.4424380482025  #>   geom_cv                                        NA                      NA                12.945835316564             ## By comparison with `lapply`: X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group))) lapply(X, function(x) s_summary(x$x)) #> $A.a #> $A.a$n #> n  #> 2  #>  #> $A.a$sum #> sum  #>   3  #>  #> $A.a$mean #> mean  #>  1.5  #>  #> $A.a$sd #>        sd  #> 0.7071068  #>  #> $A.a$se #>  se  #> 0.5  #>  #> $A.a$mean_sd #>      mean        sd  #> 1.5000000 0.7071068  #>  #> $A.a$mean_se #> mean   se  #>  1.5  0.5  #>  #> $A.a$mean_ci #> mean_ci_lwr mean_ci_upr  #>   -4.853102    7.853102  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $A.a$mean_sei #> mean_sei_lwr mean_sei_upr  #>            1            2  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $A.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>    0.7928932    2.2071068  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $A.a$mean_pval #>   p_value  #> 0.2048328  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $A.a$median #> median  #>    1.5  #>  #> $A.a$mad #> mad  #>   0  #>  #> $A.a$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $A.a$quantiles #> quantile_0.25 quantile_0.75  #>             1             2  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $A.a$iqr #> iqr  #>   1  #>  #> $A.a$range #> min max  #>   1   2  #>  #> $A.a$min #> min  #>   1  #>  #> $A.a$max #> max  #>   2  #>  #> $A.a$median_range #> median    min    max  #>    1.5    1.0    2.0  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $A.a$cv #>       cv  #> 47.14045  #>  #> $A.a$geom_mean #> geom_mean  #>  1.414214  #>  #> $A.a$geom_mean_ci #>  mean_ci_lwr  mean_ci_upr  #>   0.01729978 115.60839614  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $A.a$geom_cv #>  geom_cv  #> 52.10922  #>  #>  #> $B.a #> $B.a$n #> n  #> 1  #>  #> $B.a$sum #> sum  #>   3  #>  #> $B.a$mean #> mean  #>    3  #>  #> $B.a$sd #> sd  #> NA  #>  #> $B.a$se #> se  #> NA  #>  #> $B.a$mean_sd #> mean   sd  #>    3   NA  #>  #> $B.a$mean_se #> mean   se  #>    3   NA  #>  #> $B.a$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $B.a$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $B.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $B.a$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $B.a$median #> median  #>      3  #>  #> $B.a$mad #> mad  #>   0  #>  #> $B.a$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $B.a$quantiles #> quantile_0.25 quantile_0.75  #>             3             3  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $B.a$iqr #> iqr  #>   0  #>  #> $B.a$range #> min max  #>   3   3  #>  #> $B.a$min #> min  #>   3  #>  #> $B.a$max #> max  #>   3  #>  #> $B.a$median_range #> median    min    max  #>      3      3      3  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $B.a$cv #> cv  #> NA  #>  #> $B.a$geom_mean #> geom_mean  #>         3  #>  #> $B.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $B.a$geom_cv #> geom_cv  #>      NA  #>  #>  #> $C.a #> $C.a$n #> n  #> 0  #>  #> $C.a$sum #> sum  #>  NA  #>  #> $C.a$mean #> mean  #>   NA  #>  #> $C.a$sd #> sd  #> NA  #>  #> $C.a$se #> se  #> NA  #>  #> $C.a$mean_sd #> mean   sd  #>   NA   NA  #>  #> $C.a$mean_se #> mean   se  #>   NA   NA  #>  #> $C.a$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $C.a$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $C.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $C.a$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $C.a$median #> median  #>     NA  #>  #> $C.a$mad #> mad  #>  NA  #>  #> $C.a$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $C.a$quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $C.a$iqr #> iqr  #>  NA  #>  #> $C.a$range #> min max  #>  NA  NA  #>  #> $C.a$min #> min  #>  NA  #>  #> $C.a$max #> max  #>  NA  #>  #> $C.a$median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $C.a$cv #> cv  #> NA  #>  #> $C.a$geom_mean #> geom_mean  #>       NaN  #>  #> $C.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $C.a$geom_cv #> geom_cv  #>      NA  #>  #>  #> $A.b #> $A.b$n #> n  #> 0  #>  #> $A.b$sum #> sum  #>  NA  #>  #> $A.b$mean #> mean  #>   NA  #>  #> $A.b$sd #> sd  #> NA  #>  #> $A.b$se #> se  #> NA  #>  #> $A.b$mean_sd #> mean   sd  #>   NA   NA  #>  #> $A.b$mean_se #> mean   se  #>   NA   NA  #>  #> $A.b$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $A.b$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $A.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $A.b$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $A.b$median #> median  #>     NA  #>  #> $A.b$mad #> mad  #>  NA  #>  #> $A.b$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $A.b$quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $A.b$iqr #> iqr  #>  NA  #>  #> $A.b$range #> min max  #>  NA  NA  #>  #> $A.b$min #> min  #>  NA  #>  #> $A.b$max #> max  #>  NA  #>  #> $A.b$median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $A.b$cv #> cv  #> NA  #>  #> $A.b$geom_mean #> geom_mean  #>       NaN  #>  #> $A.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $A.b$geom_cv #> geom_cv  #>      NA  #>  #>  #> $B.b #> $B.b$n #> n  #> 1  #>  #> $B.b$sum #> sum  #>   4  #>  #> $B.b$mean #> mean  #>    4  #>  #> $B.b$sd #> sd  #> NA  #>  #> $B.b$se #> se  #> NA  #>  #> $B.b$mean_sd #> mean   sd  #>    4   NA  #>  #> $B.b$mean_se #> mean   se  #>    4   NA  #>  #> $B.b$mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $B.b$mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $B.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $B.b$mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $B.b$median #> median  #>      4  #>  #> $B.b$mad #> mad  #>   0  #>  #> $B.b$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $B.b$quantiles #> quantile_0.25 quantile_0.75  #>             4             4  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $B.b$iqr #> iqr  #>   0  #>  #> $B.b$range #> min max  #>   4   4  #>  #> $B.b$min #> min  #>   4  #>  #> $B.b$max #> max  #>   4  #>  #> $B.b$median_range #> median    min    max  #>      4      4      4  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $B.b$cv #> cv  #> NA  #>  #> $B.b$geom_mean #> geom_mean  #>         4  #>  #> $B.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $B.b$geom_cv #> geom_cv  #>      NA  #>  #>  #> $C.b #> $C.b$n #> n  #> 2  #>  #> $C.b$sum #> sum  #>  11  #>  #> $C.b$mean #> mean  #>  5.5  #>  #> $C.b$sd #>        sd  #> 0.7071068  #>  #> $C.b$se #>  se  #> 0.5  #>  #> $C.b$mean_sd #>      mean        sd  #> 5.5000000 0.7071068  #>  #> $C.b$mean_se #> mean   se  #>  5.5  0.5  #>  #> $C.b$mean_ci #> mean_ci_lwr mean_ci_upr  #>  -0.8531024  11.8531024  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $C.b$mean_sei #> mean_sei_lwr mean_sei_upr  #>            5            6  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $C.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>     4.792893     6.207107  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $C.b$mean_pval #>    p_value  #> 0.05771588  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $C.b$median #> median  #>    5.5  #>  #> $C.b$mad #> mad  #>   0  #>  #> $C.b$median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $C.b$quantiles #> quantile_0.25 quantile_0.75  #>             5             6  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $C.b$iqr #> iqr  #>   1  #>  #> $C.b$range #> min max  #>   5   6  #>  #> $C.b$min #> min  #>   5  #>  #> $C.b$max #> max  #>   6  #>  #> $C.b$median_range #> median    min    max  #>    5.5    5.0    6.0  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $C.b$cv #>       cv  #> 12.85649  #>  #> $C.b$geom_mean #> geom_mean  #>  5.477226  #>  #> $C.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>    1.719943   17.442438  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $C.b$geom_cv #>  geom_cv  #> 12.94584  #>  #>   # `s_summary.factor`  ## Basic usage: s_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\"))) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>   # Empty factor returns zero-filled items. s_summary(factor(levels = c(\"a\", \"b\", \"c\"))) #> $n #> [1] 0 #>  #> $count #> $count$a #> [1] 0 #>  #> $count$b #> [1] 0 #>  #> $count$c #> [1] 0 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 0 0 #>  #> $count_fraction$b #> [1] 0 0 #>  #> $count_fraction$c #> [1] 0 0 #>  #>  #> $n_blq #> [1] 0 #>   ## Management of NA values. x <- factor(c(NA, \"Female\")) x <- explicit_na(x) s_summary(x, na.rm = TRUE) #> $n #> [1] 1 #>  #> $count #> $count$Female #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$Female #> [1] 1 1 #>  #>  #> $n_blq #> [1] 0 #>  s_summary(x, na.rm = FALSE) #> $n #> [1] 2 #>  #> $count #> $count$Female #> [1] 1 #>  #> $count$`<Missing>` #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$Female #> [1] 1.0 0.5 #>  #> $count_fraction$`<Missing>` #> [1] 1.0 0.5 #>  #>  #> $n_blq #> [1] 0 #>   ## Different denominators. x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.3 #>  #> $count_fraction$b #> [1] 1.0 0.1 #>  #> $count_fraction$c #> [1] 1.0 0.1 #>  #>  #> $n_blq #> [1] 0 #>  s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.00 0.15 #>  #> $count_fraction$b #> [1] 1.00 0.05 #>  #> $count_fraction$c #> [1] 1.00 0.05 #>  #>  #> $n_blq #> [1] 0 #>   # `s_summary.character`  ## Basic usage: s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\"), .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\", \"\"), .var = \"x\", na.rm = FALSE, verbose = FALSE) #> $n #> [1] 6 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #> $count$`NA` #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #>  #> $count_fraction$b #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$c #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$`NA` #> [1] 1.0000000 0.1666667 #>  #>  #> $n_blq #> [1] 0 #>   # `s_summary.logical`  ## Basic usage: s_summary(c(TRUE, FALSE, TRUE, TRUE)) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.00 0.75 #>  #> $n_blq #> [1] 0 #>   # Empty factor returns zero-filled items. s_summary(as.logical(c())) #> $n #> [1] 0 #>  #> $count #> [1] 0 #>  #> $count_fraction #> [1] 0 0 #>  #> $n_blq #> [1] 0 #>   ## Management of NA values. x <- c(NA, TRUE, FALSE) s_summary(x, na.rm = TRUE) #> $n #> [1] 2 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0 0.5 #>  #> $n_blq #> [1] 0 #>  s_summary(x, na.rm = FALSE) #> $n #> [1] 3 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0000000 0.3333333 #>  #> $n_blq #> [1] 0 #>   ## Different denominators. x <- c(TRUE, FALSE, TRUE, TRUE) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.0 0.3 #>  #> $n_blq #> [1] 0 #>  s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.00 0.15 #>  #> $n_blq #> [1] 0 #>   a_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1        n              5          0         n #> 2        a              3          0         a #> 3        b              1          0         b #> 4        c              1          0         c #> 5        a        3 (60%)          0         a #> 6        b        1 (20%)          0         b #> 7        c        1 (20%)          0         c #> 8    n_blq              0          0     n_blq a_summary(   factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")),   .ref_group = factor(c(\"a\", \"a\", \"b\", \"c\")), compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                     row_name formatted_cell indent_mod #> 1                          n              5          0 #> 2                          a              3          0 #> 3                          b              1          0 #> 4                          c              1          0 #> 5                          a        3 (60%)          0 #> 6                          b        1 (20%)          0 #> 7                          c        1 (20%)          0 #> 8                      n_blq              0          0 #> 9 p-value (chi-squared test)         0.9560          0 #>                    row_label #> 1                          n #> 2                          a #> 3                          b #> 4                          c #> 5                          a #> 6                          b #> 7                          c #> 8                      n_blq #> 9 p-value (chi-squared test)  a_summary(c(\"A\", \"B\", \"A\", \"C\"), .var = \"x\", .N_col = 10, .N_row = 10, verbose = FALSE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1        n              4          0         n #> 2        A              2          0         A #> 3        B              1          0         B #> 4        C              1          0         C #> 5        A        2 (50%)          0         A #> 6        B        1 (25%)          0         B #> 7        C        1 (25%)          0         C #> 8    n_blq              0          0     n_blq a_summary(   c(\"A\", \"B\", \"A\", \"C\"),   .ref_group = c(\"B\", \"A\", \"C\"), .var = \"x\", compare = TRUE, verbose = FALSE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                     row_name formatted_cell indent_mod #> 1                          n              4          0 #> 2                          A              2          0 #> 3                          B              1          0 #> 4                          C              1          0 #> 5                          A        2 (50%)          0 #> 6                          B        1 (25%)          0 #> 7                          C        1 (25%)          0 #> 8                      n_blq              0          0 #> 9 p-value (chi-squared test)         0.9074          0 #>                    row_label #> 1                          n #> 2                          A #> 3                          B #> 4                          C #> 5                          A #> 6                          B #> 7                          C #> 8                      n_blq #> 9 p-value (chi-squared test)  a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>         row_name formatted_cell indent_mod      row_label #> 1              n              5          0              n #> 2          count              3          0          count #> 3 count_fraction        3 (60%)          0 count_fraction #> 4          n_blq              0          0          n_blq a_summary(   c(TRUE, FALSE, FALSE, TRUE, TRUE),   .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                     row_name formatted_cell indent_mod #> 1                          n              5          0 #> 2                      count              3          0 #> 3             count_fraction        3 (60%)          0 #> 4                      n_blq              0          0 #> 5 p-value (chi-squared test)                         0 #>                    row_label #> 1                          n #> 2                      count #> 3             count_fraction #> 4                      n_blq #> 5 p-value (chi-squared test)  a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = \"bla\") #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                       row_name    formatted_cell indent_mod #> 1                            n                10          0 #> 2                          Sum              -4.5          0 #> 3                         Mean              -0.4          0 #> 4                           SD               1.0          0 #> 5                           SE               0.3          0 #> 6                    Mean (SD)        -0.4 (1.0)          0 #> 7                    Mean (SE)        -0.4 (0.3)          0 #> 8                  Mean 95% CI     (-1.14, 0.25)          0 #> 9                Mean -/+ 1xSE    (-0.75, -0.14)          0 #> 10               Mean -/+ 1xSD     (-1.42, 0.53)          0 #> 11 Mean p-value (H0: mean = 0)              0.18          0 #> 12                      Median              -0.2          0 #> 13   Median Absolute Deviation              -0.0          0 #> 14               Median 95% CI     (-1.50, 0.49)          0 #> 15             25% and 75%-ile        -1.2 - 0.3          0 #> 16                         IQR               1.5          0 #> 17                   Min - Max        -2.1 - 1.0          0 #> 18                     Minimum              -2.1          0 #> 19                     Maximum               1.0          0 #> 20          Median (Min - Max) -0.2 (-2.1 - 1.0)          0 #> 21                      CV (%)            -219.8          0 #> 22              Geometric Mean                NA          0 #> 23       Geometric Mean 95% CI                NA          0 #> 24         CV % Geometric Mean                NA          0 #>                      row_label #> 1                            n #> 2                          Sum #> 3                         Mean #> 4                           SD #> 5                           SE #> 6                    Mean (SD) #> 7                    Mean (SE) #> 8                  Mean 95% CI #> 9                Mean -/+ 1xSE #> 10               Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12                      Median #> 13   Median Absolute Deviation #> 14               Median 95% CI #> 15             25% and 75%-ile #> 16                         IQR #> 17                   Min - Max #> 18                     Minimum #> 19                     Maximum #> 20          Median (Min - Max) #> 21                      CV (%) #> 22              Geometric Mean #> 23       Geometric Mean 95% CI #> 24         CV % Geometric Mean a_summary(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .var = \"bla\", compare = TRUE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                       row_name  formatted_cell indent_mod #> 1                            n              10          0 #> 2                          Sum            50.1          0 #> 3                         Mean             5.0          0 #> 4                           SD             1.2          0 #> 5                           SE             0.4          0 #> 6                    Mean (SD)       5.0 (1.2)          0 #> 7                    Mean (SE)       5.0 (0.4)          0 #> 8                  Mean 95% CI    (4.13, 5.88)          0 #> 9                Mean -/+ 1xSE    (4.62, 5.39)          0 #> 10               Mean -/+ 1xSD    (3.78, 6.23)          0 #> 11 Mean p-value (H0: mean = 0)            0.00          0 #> 12                      Median             5.3          0 #> 13   Median Absolute Deviation             0.0          0 #> 14               Median 95% CI    (3.48, 6.48)          0 #> 15             25% and 75%-ile       3.8 - 6.1          0 #> 16                         IQR             2.3          0 #> 17                   Min - Max       3.3 - 6.6          0 #> 18                     Minimum             3.3          0 #> 19                     Maximum             6.6          0 #> 20          Median (Min - Max) 5.3 (3.3 - 6.6)          0 #> 21                      CV (%)            24.5          0 #> 22              Geometric Mean             4.9          0 #> 23       Geometric Mean 95% CI    (4.05, 5.84)          0 #> 24         CV % Geometric Mean            26.1          0 #> 25            p-value (t-test)         <0.0001          0 #>                      row_label #> 1                            n #> 2                          Sum #> 3                         Mean #> 4                           SD #> 5                           SE #> 6                    Mean (SD) #> 7                    Mean (SE) #> 8                  Mean 95% CI #> 9                Mean -/+ 1xSE #> 10               Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12                      Median #> 13   Median Absolute Deviation #> 14               Median 95% CI #> 15             25% and 75%-ile #> 16                         IQR #> 17                   Min - Max #> 18                     Minimum #> 19                     Maximum #> 20          Median (Min - Max) #> 21                      CV (%) #> 22              Geometric Mean #> 23       Geometric Mean 95% CI #> 24         CV % Geometric Mean #> 25            p-value (t-test)  ## Fabricated dataset. dta_test <- data.frame(   USUBJID = rep(1:6, each = 3),   PARAMCD = rep(\"lab\", 6 * 3),   AVISIT  = rep(paste0(\"V\", 1:3), 6),   ARM     = rep(LETTERS[1:3], rep(6, 3)),   AVAL    = c(9:1, rep(NA, 9)) )  # `analyze_vars()` in `rtables` pipelines ## Default output within a `rtables` pipeline. l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   split_rows_by(var = \"AVISIT\") %>%   analyze_vars(vars = \"AVAL\")  build_table(l, df = dta_test) #>                   A           B       C  #> ———————————————————————————————————————— #> V1                                       #>   n               2           1       0  #>   Mean (SD)   7.5 (2.1)   3.0 (NA)    NA #>   Median         7.5         3.0      NA #>   Min - Max   6.0 - 9.0   3.0 - 3.0   NA #> V2                                       #>   n               2           1       0  #>   Mean (SD)   6.5 (2.1)   2.0 (NA)    NA #>   Median         6.5         2.0      NA #>   Min - Max   5.0 - 8.0   2.0 - 2.0   NA #> V3                                       #>   n               2           1       0  #>   Mean (SD)   5.5 (2.1)   1.0 (NA)    NA #>   Median         5.5         1.0      NA #>   Min - Max   4.0 - 7.0   1.0 - 1.0   NA  ## Select and format statistics output. l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   split_rows_by(var = \"AVISIT\") %>%   analyze_vars(     vars = \"AVAL\",     .stats = c(\"n\", \"mean_sd\", \"quantiles\"),     .formats = c(\"mean_sd\" = \"xx.x, xx.x\"),     .labels = c(n = \"n\", mean_sd = \"Mean, SD\", quantiles = c(\"Q1 - Q3\"))   )  build_table(l, df = dta_test) #>                  A           B       C  #> ——————————————————————————————————————— #> V1                                      #>   n              2           1       0  #>   Mean, SD   7.5, 2.1     3.0, NA    NA #>   Q1 - Q3    6.0 - 9.0   3.0 - 3.0   NA #> V2                                      #>   n              2           1       0  #>   Mean, SD   6.5, 2.1     2.0, NA    NA #>   Q1 - Q3    5.0 - 8.0   2.0 - 2.0   NA #> V3                                      #>   n              2           1       0  #>   Mean, SD   5.5, 2.1     1.0, NA    NA #>   Q1 - Q3    4.0 - 7.0   1.0 - 1.0   NA  ## Use arguments interpreted by `s_summary`. l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   split_rows_by(var = \"AVISIT\") %>%   analyze_vars(vars = \"AVAL\", na.rm = FALSE)  build_table(l, df = dta_test) #>                   A       B    C  #> ————————————————————————————————— #> V1                                #>   n               2       2    2  #>   Mean (SD)   7.5 (2.1)   NA   NA #>   Median         7.5      NA   NA #>   Min - Max   6.0 - 9.0   NA   NA #> V2                                #>   n               2       2    2  #>   Mean (SD)   6.5 (2.1)   NA   NA #>   Median         6.5      NA   NA #>   Min - Max   5.0 - 8.0   NA   NA #> V3                                #>   n               2       2    2  #>   Mean (SD)   5.5 (2.1)   NA   NA #>   Median         5.5      NA   NA #>   Min - Max   4.0 - 7.0   NA   NA  ## Handle `NA` levels first when summarizing factors. dta_test$AVISIT <- NA_character_ dta_test <- df_explicit_na(dta_test) l <- basic_table() %>%   split_cols_by(var = \"ARM\") %>%   analyze_vars(vars = \"AVISIT\", na.rm = FALSE)  build_table(l, df = dta_test) #>                A          B          C     #> —————————————————————————————————————————— #> n              6          6          6     #> <Missing>   6 (100%)   6 (100%)   6 (100%)  # auto format dt <- data.frame(\"VAR\" = c(0.001, 0.2, 0.0011000, 3, 4)) basic_table() %>%   analyze_vars(     vars = \"VAR\",     .stats = c(\"n\", \"mean\", \"mean_sd\", \"range\"),     .formats = c(\"mean_sd\" = \"auto\", \"range\" = \"auto\")   ) %>%   build_table(dt) #>                  all obs      #> ————————————————————————————— #> n                   5         #> Mean               1.4        #> Mean (SD)   1.44042 (1.91481) #> Min - Max    0.0010 - 4.0000"},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary numeric variables in columns — analyze_vars_in_cols","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"Layout-creating function can used creating column-wise summary tables. function sets analysis methods column labels wrapper rtables::analyze_colvars(). designed principally PK tables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"","code":"analyze_vars_in_cols(   lyt,   vars,   ...,   .stats = c(\"n\", \"mean\", \"sd\", \"se\", \"cv\", \"geom_cv\"),   .labels = c(n = \"n\", mean = \"Mean\", sd = \"SD\", se = \"SE\", cv = \"CV (%)\", geom_cv =     \"CV % Geometric Mean\"),   row_labels = NULL,   do_summarize_row_groups = FALSE,   split_col_vars = TRUE,   imp_rule = NULL,   avalcat_var = \"AVALCAT1\",   cache = FALSE,   .indent_mods = NULL,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   nested = TRUE,   .formats = NULL,   .aligns = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . ... additional arguments lower level functions. .stats (character) statistics select table. .labels (named character) labels statistics (without indent). row_labels (character) function works columns space, usual .labels character vector applies column space. can change row labels defining parameter named character vector names corresponding split values. defaults NULL contains one string, duplicate row label. do_summarize_row_groups (flag) defaults FALSE applies analysis current label rows. wrapper rtables::summarize_row_groups() can accept labelstr define row labels. behavior supported never need overload row labels. split_col_vars (flag) defaults TRUE puts analysis results onto columns. option allows add multiple instances functions, also nested fashion, without adding splits. split must happen one time single layout. imp_rule (character) imputation rule setting. Defaults NULL imputation rule. Can also \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. order use imputation rule, avalcat_var argument must specified. See imputation_rule() details imputation. avalcat_var (character) imp_rule NULL, name variable indicates whether row data corresponds analysis value category \"BLQ\", \"LTR\", \"<PCLLOQ\", none (defaults \"AVALCAT1\"). Variable must present data match variable used calculate n_blq statistic (included .stats). cache (flag) whether store computed values temporary caching environment. speed calculations large tables, set FALSE rtable layout used multiple tables different data. Defaults FALSE. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .aligns (character) alignment table contents (including labels). NULL, \"center\" applied. See formatters::list_valid_aligns() list currently supported alignments.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout summarize given variables, arrange output columns, add table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"experimental implementation rtables::summarize_row_groups() rtables::analyze_colvars() may subjected changes rtables extends support complex analysis pipelines column space. reasons, encourage read examples carefully file issues cases differ . labelstr behaves differently usual. defined (default NULL), row labels assigned automatically split values case rtables::analyze_colvars (do_summarize_row_groups = FALSE, default), group label do_summarize_row_groups = TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/analyze_vars_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary numeric variables in columns — analyze_vars_in_cols","text":"","code":"library(dplyr)  # Data preparation adpp <- tern_ex_adpp %>% h_pkparam_sort()  lyt <- basic_table() %>%   split_rows_by(var = \"STRATA1\", label_pos = \"topleft\") %>%   split_rows_by(     var = \"SEX\",     label_pos = \"topleft\",     child_label = \"hidden\"   ) %>% # Removes duplicated labels   analyze_vars_in_cols(vars = \"AGE\") result <- build_table(lyt = lyt, df = adpp) result #> STRATA1                                                          #>   SEX      n    Mean    SD    SE    CV (%)   CV % Geometric Mean #> ———————————————————————————————————————————————————————————————— #> A                                                                #>   F       81    38.8   5.4    0.6    13.8           14.3         #>   M       81    38.9   5.9    0.7    15.1           14.9         #> B                                                                #>   F       90    36.0   6.4    0.7    17.7           17.9         #>   M       81    36.4   6.5    0.7    17.9           17.8         #> C                                                                #>   F       117   34.1   6.2    0.6    18.2           18.3         #>   M       72    33.2   11.5   1.4    34.6           31.6          # By selecting just some statistics and ad-hoc labels lyt <- basic_table() %>%   split_rows_by(var = \"ARM\", label_pos = \"topleft\") %>%   split_rows_by(     var = \"SEX\",     label_pos = \"topleft\",     child_labels = \"hidden\",     split_fun = drop_split_levels   ) %>%   analyze_vars_in_cols(     vars = \"AGE\",     .stats = c(\"n\", \"cv\", \"geom_mean\"),     .labels = c(       n = \"aN\",       cv = \"aCV\",       geom_mean = \"aGeomMean\"     )   ) result <- build_table(lyt = lyt, df = adpp) result #> ARM                                     #>   SEX            aN    aCV    aGeomMean #> ——————————————————————————————————————— #> A: Drug X                               #> B: Placebo                              #> C: Combination                          #>   F              288   17.6     35.5    #>   M              234   23.4     35.3     # Changing row labels lyt <- basic_table() %>%   analyze_vars_in_cols(     vars = \"AGE\",     row_labels = \"some custom label\"   ) result <- build_table(lyt, df = adpp) result #>                      n    Mean   SD    SE    CV (%)   CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————————— #> some custom label   522   36.1   7.4   0.3    20.4           20.6          # Pharmacokinetic parameters lyt <- basic_table() %>%   split_rows_by(     var = \"TLG_DISPLAY\",     split_label = \"PK Parameter\",     label_pos = \"topleft\",     child_label = \"hidden\"   ) %>%   analyze_vars_in_cols(     vars = \"AVAL\"   ) result <- build_table(lyt, df = adpp) result #> PK Parameter   n    Mean     SD    SE    CV (%)   CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax           58   29.7    5.6    0.7    19.0           19.3         #> AUCinf obs     58   207.5   34.9   4.6    16.8           17.3         #> CL obs         58    5.1    1.0    0.1    20.6           22.7         #> Ae             58    1.5    0.3    0.0    21.3           24.1         #> Fe             58   15.7    3.6    0.5    22.7           24.0         #> CLR            58    0.0    0.0    0.0    19.9           22.2         #> Rmax           58    9.6    2.0    0.3    21.1           21.6         #> Tonset         58    3.0    0.7    0.1    22.4           23.3         #> RENALCLD       58    0.0    0.0    0.0    19.0           19.4          # Multiple calls (summarize label and analyze underneath) lyt <- basic_table() %>%   split_rows_by(     var = \"TLG_DISPLAY\",     split_label = \"PK Parameter\",     label_pos = \"topleft\"   ) %>%   analyze_vars_in_cols(     vars = \"AVAL\",     do_summarize_row_groups = TRUE # does a summarize level   ) %>%   split_rows_by(\"SEX\",     child_label = \"hidden\",     label_pos = \"topleft\"   ) %>%   analyze_vars_in_cols(     vars = \"AVAL\",     split_col_vars = FALSE # avoids re-splitting the columns   ) result <- build_table(lyt, df = adpp) result #> PK Parameter                                                          #>   SEX          n    Mean     SD    SE    CV (%)   CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax           58   29.7    5.6    0.7    19.0           19.3         #>   F            32   30.1    6.1    1.1    20.4           21.0         #>   M            26   29.2    5.0    1.0    17.2           17.2         #> AUCinf obs     58   207.5   34.9   4.6    16.8           17.3         #>   F            32   209.5   43.3   7.7    20.7           21.6         #>   M            26   205.0   20.9   4.1    10.2           10.4         #> CL obs         58    5.1    1.0    0.1    20.6           22.7         #>   F            32    5.1    1.1    0.2    20.8           23.2         #>   M            26    5.1    1.1    0.2    20.7           22.5         #> Ae             58    1.5    0.3    0.0    21.3           24.1         #>   F            32    1.5    0.3    0.1    19.3           20.8         #>   M            26    1.5    0.4    0.1    23.9           27.9         #> Fe             58   15.7    3.6    0.5    22.7           24.0         #>   F            32   16.3    3.4    0.6    20.9           22.3         #>   M            26   15.0    3.7    0.7    24.7           25.6         #> CLR            58    0.0    0.0    0.0    19.9           22.2         #>   F            32    0.0    0.0    0.0    20.5           24.8         #>   M            26    0.0    0.0    0.0    19.5           18.9         #> Rmax           58    9.6    2.0    0.3    21.1           21.6         #>   F            32    9.6    2.2    0.4    22.4           23.5         #>   M            26    9.6    1.9    0.4    19.7           19.5         #> Tonset         58    3.0    0.7    0.1    22.4           23.3         #>   F            32    3.0    0.7    0.1    24.7           25.9         #>   M            26    3.0    0.6    0.1    19.8           20.0         #> RENALCLD       58    0.0    0.0    0.0    19.0           19.4         #>   F            32    0.0    0.0    0.0    19.6           20.4         #>   M            26    0.0    0.0    0.0    18.0           17.6"},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Variable Labels to Top Left Corner in Table — append_varlabels","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"Helper layout creating function just append variable labels given variables vector given dataset top left corner. variable label found variable name used instead. Multiple variable labels concatenated slashes.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"","code":"append_varlabels(lyt, df, vars, indent = 0L)"},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"lyt (layout) input layout analyses added . df (data.frame) data set containing analysis variables. vars (character) variable names labels looked df. indent (integer) non-negative number nested indent space, default 0L means indent. 1L means two spaces indent, 2L means four spaces indent .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"modified layout new variable label(s) added top-left material.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"optimal implementation course, since using data set layout creation. mature rtables implementation also improved necessary anymore.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/append_varlabels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Variable Labels to Top Left Corner in Table — append_varlabels","text":"","code":"lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   split_rows_by(\"SEX\") %>%   append_varlabels(DM, \"SEX\") %>%   analyze(\"AGE\", afun = mean) %>%   append_varlabels(DM, \"AGE\", indent = 1) build_table(lyt, DM) #> SEX                   A: Drug X          B: Placebo       C: Combination  #>   Age                  (N=121)            (N=106)            (N=129)      #> ————————————————————————————————————————————————————————————————————————— #> F                                                                         #>   mean             33.7142857142857   33.8392857142857   34.8852459016393 #> M                                                                         #>   mean             36.5490196078431         32.1         34.2794117647059 #> U                                                                         #>   mean                    NA                 NA                 NA        #> UNDIFFERENTIATED                                                          #>   mean                    NA                 NA                 NA         lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"SEX\") %>%   analyze(\"AGE\", afun = mean) %>%   append_varlabels(DM, c(\"SEX\", \"AGE\")) build_table(lyt, DM) #> SEX / Age             A: Drug X          B: Placebo       C: Combination  #> ————————————————————————————————————————————————————————————————————————— #> F                                                                         #>   mean             33.7142857142857   33.8392857142857   34.8852459016393 #> M                                                                         #>   mean             36.5490196078431         32.1         34.2794117647059 #> U                                                                         #>   mean                    NA                 NA                 NA        #> UNDIFFERENTIATED                                                          #>   mean                    NA                 NA                 NA"},{"path":"https://insightsengineering.github.io/tern/reference/argument_convention.html","id":null,"dir":"Reference","previous_headings":"","what":"Standard Arguments — argument_convention","title":"Standard Arguments — argument_convention","text":"documentation function lists arguments tern used repeatedly express analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/argument_convention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standard Arguments — argument_convention","text":"... additional arguments lower level functions. .aligns (character) alignment table contents (including labels). NULL, \"center\" applied. See formatters::list_valid_aligns() list currently supported alignments. .all_col_counts (vector integer) value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .df_row (data.frame) data frame across columns given row split. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent). .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .ref_group (data.frame vector) data corresponding reference group. .spl_context (data.frame) gives information ancestor split states passed rtables. .stats (character) statistics select table. .var (string) single variable name passed rtables requested statistics function. add_total_level (flag) adds \"total\" level others includes levels constitute split. custom label can set level via custom_label argument. col_by (factor) defining column groups. conf_level (proportion) confidence level interval. data (data.frame) dataset containing variables summarize. df (data.frame) data set containing analysis variables. draw (flag) whether plot drawn. drop (flag) non appearing occurrence levels dropped resulting table. Note case remaining occurrence levels table sorted alphabetically. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. id (string) subject variable name. is_event (logical)TRUE event, FALSE time event censored. indent_mod Please use .indent_mods argument instead. label_all (string) label total population analysis. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. lyt (layout) input layout analyses added . method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed. na.rm (flag) whether NA values removed x prior analysis. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. newpage (flag) whether plot drawn new page. considered draw = TRUE used. prune_zero_rows (flag) whether prune zero rows. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. rsp (logical) whether subject responder . show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. tte (numeric) contains time--event duration values. var_labels (character) character label. variables (named list string) list additional analysis variables. vars (character) variable names primary analysis variable iterated . var (string) single variable name primary analysis variable. x (numeric) vector numbers want analyze.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/argument_convention.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Standard Arguments — argument_convention","text":"Although function just returns NULL two uses, tern users provides documentation arguments commonly consistently used framework. developer adds single reference point import roxygen argument description : @inheritParams argument_convention","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange Multiple Grobs — arrange_grobs","title":"Arrange Multiple Grobs — arrange_grobs","text":"Arrange grobs new grob n*m (rows*cols) layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange Multiple Grobs — arrange_grobs","text":"","code":"arrange_grobs(   ...,   grobs = list(...),   ncol = NULL,   nrow = NULL,   padding_ht = grid::unit(2, \"line\"),   padding_wt = grid::unit(2, \"line\"),   vp = NULL,   gp = NULL,   name = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange Multiple Grobs — arrange_grobs","text":"... grobs. grobs list grobs. ncol number columns layout. nrow number rows layout. padding_ht unit length 1, vertical space grob. padding_wt unit length 1, horizontal space grob. vp viewport() object (NULL). gp gpar() object. name character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange Multiple Grobs — arrange_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/arrange_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange Multiple Grobs — arrange_grobs","text":"","code":"library(grid)  # \\donttest{ num <- lapply(1:9, textGrob) grid::grid.newpage() grid.draw(arrange_grobs(grobs = num, ncol = 2))  showViewport()   g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, nrow = 2))  showViewport()   grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, ncol = 3))   grid::grid.newpage() grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2))) vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1))  showViewport()  # }"},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to rtable — as.rtable","title":"Convert to rtable — as.rtable","text":"new generic function convert objects rtable tables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to rtable — as.rtable","text":"","code":"as.rtable(x, ...)  # S3 method for data.frame as.rtable(x, format = \"xx.xx\", ...)"},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to rtable — as.rtable","text":"x object converted rtable. ... additional arguments methods. format format used columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to rtable — as.rtable","text":"rtables table object. Note concrete class depend method used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Convert to rtable — as.rtable","text":".rtable(data.frame): method converting data.frame contain numeric columns rtable.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as.rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to rtable — as.rtable","text":"","code":"x <- data.frame(   a = 1:10,   b = rnorm(10) ) as.rtable(x) #>        a       b   #> —————————————————— #> 1    1.00    -0.55 #> 2    2.00    0.97  #> 3    3.00    0.45  #> 4    4.00    0.83  #> 5    5.00    -0.63 #> 6    6.00    -0.12 #> 7    7.00    -0.70 #> 8    8.00    -0.06 #> 9    9.00    0.20  #> 10   10.00   0.42"},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of a Vector to a Factor — as_factor_keep_attributes","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"Converts x factor keeps attributes. Warns appropriately user can decide whether prefer converting factor manually (e.g. full control factor levels).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"","code":"as_factor_keep_attributes(   x,   x_name = deparse(substitute(x)),   na_level = \"<Missing>\",   verbose = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"x (atomic) object convert. x_name (string) name x. na_level (string) explicit missing level used converting character vector. verbose defaults TRUE. prints warnings messages.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/as_factor_keep_attributes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of a Vector to a Factor — as_factor_keep_attributes","text":"factor attributes (except class) x. modify x already factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional Assertions for checkmate — assertions","title":"Additional Assertions for checkmate — assertions","text":"Additional assertion functions can used together checkmate package.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional Assertions for checkmate — assertions","text":"","code":"assert_list_of_variables(x, .var.name = checkmate::vname(x), add = NULL)  assert_df_with_variables(   df,   variables,   na_level = NULL,   .var.name = checkmate::vname(df),   add = NULL )  assert_valid_factor(   x,   min.levels = 1,   max.levels = NULL,   null.ok = TRUE,   any.missing = TRUE,   n.levels = NULL,   len = NULL,   .var.name = checkmate::vname(x),   add = NULL )  assert_df_with_factors(   df,   variables,   min.levels = 1,   max.levels = NULL,   any.missing = TRUE,   na_level = NULL,   .var.name = checkmate::vname(df),   add = NULL )  assert_proportion_value(x, include_boundaries = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional Assertions for checkmate — assertions","text":"x () object test. .var.name [character(1)] Name checked object print assertions. Defaults heuristic implemented vname. add [AssertCollection] Collection store assertion messages. See AssertCollection. df (data.frame) data set test. variables (named list character) list variables test. na_level (character) string using represent NA missing data. NA values please consider using directly .na() similar approaches. min.levels [integer(1)] Minimum number factor levels. Default NULL (check). max.levels [integer(1)] Maximum number factor levels. Default NULL (check). null.ok [logical(1)] set TRUE, x may also NULL. case type check x performed, additional checks disabled. .missing [logical(1)] vectors missing values allowed? Default TRUE. n.levels [integer(1)] Exact number factor levels. Default NULL (check). len [integer(1)] Exact expected length x. include_boundaries (logical) whether include boundaries testing proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional Assertions for checkmate — assertions","text":"Nothing assertion passes, otherwise prints error message.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/assertions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Additional Assertions for checkmate — assertions","text":"assert_list_of_variables(): Checks whether x valid list variable names. NULL elements list x dropped Filter(Negate(.null), x). assert_df_with_variables(): Check whether df data frame analysis variables. Please notice produces error variables present data.frame opposite required. assert_valid_factor(): Check whether x valid factor (.e. levels empty string levels). Note NULL NA elements allowed. assert_df_with_factors(): Check whether df data frame analysis variables factors. Note creation NA direct call factor() trim NA levels vector list . assert_proportion_value(): Check whether x proportion: number 0 1.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for Bins in Percent — bins_percent_labels","title":"Labels for Bins in Percent — bins_percent_labels","text":"creates labels quantile based bins percent. assumes right-closed intervals produced cut_quantile_bins().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Bins in Percent — bins_percent_labels","text":"","code":"bins_percent_labels(probs, digits = 0)"},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Bins in Percent — bins_percent_labels","text":"probs (proportion vector) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. digits (integer) number decimal places round percent numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/bins_percent_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Bins in Percent — bins_percent_labels","text":"character vector labels format [0%,20%], (20%,50%], etc.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Content Row Function to Add Row Total to Labels — c_label_n","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"takes label latest row split level adds row total df parentheses. function differs c_label_n_alt() taking row counts df rather alt_counts_df, used add_rowcounts() alt_counts set FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"","code":"c_label_n(df, labelstr, .N_row)"},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Content Row Function to Add Row Total to Labels — c_label_n","text":"important use df rather .N_row implementation, former already split columns refer first column data .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":null,"dir":"Reference","previous_headings":"","what":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"takes label latest row split level adds row total alt_counts_df parentheses. function differs c_label_n() taking row counts alt_counts_df rather df, used add_rowcounts() alt_counts set TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"","code":"c_label_n_alt(df, labelstr, .alt_df_row)"},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/c_label_n_alt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content Row Function to Add alt_counts_df Row Total to Labels — c_label_n_alt","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"can useful tabulating model results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"","code":"cfun_by_flag(analysis_var, flag_var, format = \"xx\", .indent_mods = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"analysis_var (string) variable name column containing values returned content function. flag_var (string) variable name logical column identifying row returned. format (string)rtables format use.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cfun_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for Content Functions given Data Frame with Flag Input — cfun_by_flag","text":"content function gives df$analysis_var row identified .df_row$flag given format.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_diff_prop_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Check: Proportion Difference Arguments — check_diff_prop_ci","title":"Check: Proportion Difference Arguments — check_diff_prop_ci","text":"Verifies /convert arguments valid values used estimation difference responder proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_diff_prop_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check: Proportion Difference Arguments — check_diff_prop_ci","text":"","code":"check_diff_prop_ci(rsp, grp, strata = NULL, conf_level, correct = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/check_diff_prop_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check: Proportion Difference Arguments — check_diff_prop_ci","text":"rsp (logical) whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). strata (factor) variable one level per stratum length rsp. conf_level (proportion) confidence level interval. correct (logical) whether include continuity correction. information, see stats::prop.test().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Element Dimension — check_same_n","title":"Check Element Dimension — check_same_n","text":"Checks elements ... dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Element Dimension — check_same_n","text":"","code":"check_same_n(..., omit_null = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Element Dimension — check_same_n","text":"... (data.frames vectors) data frames/vectors. omit_null (logical) whether NULL elements ... omitted check.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/check_same_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Element Dimension — check_same_n","text":"logical value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Combination Functions Class — combination_function","title":"Combination Functions Class — combination_function","text":"CombinationFunction S4 class extends standard functions. special functions can combined negated logical operators.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combination Functions Class — combination_function","text":"","code":"# S4 method for CombinationFunction,CombinationFunction &amp;(e1, e2)  # S4 method for CombinationFunction,CombinationFunction |(e1, e2)  # S4 method for CombinationFunction !(x)"},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combination Functions Class — combination_function","text":"e1 (CombinationFunction) left hand side logical operator. e2 (CombinationFunction) right hand side logical operator. x (CombinationFunction) function negated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combination Functions Class — combination_function","text":"Returns logical value indicating whether left hand side equation equals right hand side.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Combination Functions Class — combination_function","text":"e1 & e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. e1 | e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. `!`(CombinationFunction): Logical negation CombinationFunction functions. resulting object class, evaluates original function. result opposite results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combination_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combination Functions Class — combination_function","text":"","code":"higher <- function(a) {   force(a)   CombinationFunction(     function(x) {       x > a     }   ) }  lower <- function(b) {   force(b)   CombinationFunction(     function(x) {       x < b     }   ) }  c1 <- higher(5) c2 <- lower(10) c3 <- higher(5) & lower(10) c3(7) #> [1] TRUE"},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Counts — combine_counts","title":"Combine Counts — combine_counts","text":"Simplifies estimation column counts, especially group combination required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Counts — combine_counts","text":"","code":"combine_counts(fct, groups_list = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Counts — combine_counts","text":"fct (factor) variable levels needs grouped. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Counts — combine_counts","text":"vector column counts.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/combine_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Counts — combine_counts","text":"","code":"ref <- c(\"A: Drug X\", \"B: Placebo\") groups <- combine_groups(fct = DM$ARM, ref = ref)  col_counts <- combine_counts(   fct = DM$ARM,   groups_list = groups )  basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze_vars(\"AGE\") %>%   build_table(DM, col_counts = col_counts) #>             A: Drug X/B: Placebo   C: Combination #>                   (N=227)             (N=129)     #> ————————————————————————————————————————————————— #> n                   227                 129       #> Mean (SD)        34.0 (7.2)          34.6 (6.5)   #> Median              33.0                33.0      #> Min - Max       20.0 - 60.0         22.0 - 53.0    ref <- \"A: Drug X\" groups <- combine_groups(fct = DM$ARM, ref = ref) col_counts <- combine_counts(   fct = DM$ARM,   groups_list = groups )  basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze_vars(\"AGE\") %>%   build_table(DM, col_counts = col_counts) #>              A: Drug X    B: Placebo/C: Combination #>               (N=121)              (N=235)          #> ——————————————————————————————————————————————————— #> n               121                  235            #> Mean (SD)   34.9 (7.8)           33.9 (6.5)         #> Median         33.0                 33.0            #> Min - Max   20.0 - 60.0          21.0 - 55.0"},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Reference and Treatment Group Combination — combine_groups","title":"Reference and Treatment Group Combination — combine_groups","text":"Facilitate re-combination groups divided reference treatment groups; helps arranging groups columns rtables framework teal modules.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reference and Treatment Group Combination — combine_groups","text":"","code":"combine_groups(fct, ref = NULL, collapse = \"/\")"},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reference and Treatment Group Combination — combine_groups","text":"fct (factor) variable levels needs grouped. ref (string) reference level(s). collapse (string) character string separate fct ref.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reference and Treatment Group Combination — combine_groups","text":"list first item ref (reference) second item trt (treatment).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reference and Treatment Group Combination — combine_groups","text":"","code":"groups <- combine_groups(   fct = DM$ARM,   ref = c(\"B: Placebo\") )  basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze_vars(\"AGE\") %>%   build_table(DM) #>             B: Placebo    A: Drug X/C: Combination #>               (N=106)             (N=250)          #> —————————————————————————————————————————————————— #> n               106                 250            #> Mean (SD)   33.0 (6.3)           34.7 (7.1)        #> Median         32.0                 33.0           #> Min - Max   21.0 - 55.0         20.0 - 60.0"},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Factor Levels — combine_levels","title":"Combine Factor Levels — combine_levels","text":"Combine specified old factor Levels single new level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Factor Levels — combine_levels","text":"","code":"combine_levels(x, levels, new_level = paste(levels, collapse = \"/\"))"},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Factor Levels — combine_levels","text":"x factor levels level names combined new_level name new level","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Factor Levels — combine_levels","text":"factor new levels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Factor Levels — combine_levels","text":"","code":"x <- factor(letters[1:5], levels = letters[5:1]) combine_levels(x, levels = c(\"a\", \"b\")) #> [1] a/b a/b c   d   e   #> Levels: e d c a/b  combine_levels(x, c(\"e\", \"b\")) #> [1] a   e/b c   d   e/b #> Levels: e/b d c a"},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Two Vectors Element Wise — combine_vectors","title":"Combine Two Vectors Element Wise — combine_vectors","text":"Combine Two Vectors Element Wise","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Two Vectors Element Wise — combine_vectors","text":"","code":"combine_vectors(x, y)"},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Two Vectors Element Wise — combine_vectors","text":"x (vector) first vector combine. y (vector) second vector combine.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Two Vectors Element Wise — combine_vectors","text":"list element combines corresponding elements x y.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/combine_vectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Two Vectors Element Wise — combine_vectors","text":"","code":"combine_vectors(1:3, 4:6) #> [[1]] #> [1] 1 4 #>  #> [[2]] #> [1] 2 5 #>  #> [[3]] #> [1] 3 6 #>"},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare Variables Between Groups — compare_variables","title":"Compare Variables Between Groups — compare_variables","text":"Comparison reference group different x objects.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare Variables Between Groups — compare_variables","text":"","code":"s_compare(x, .ref_group, .in_ref_col, ...)  # S3 method for numeric s_compare(x, .ref_group, .in_ref_col, ...)  # S3 method for factor s_compare(x, .ref_group, .in_ref_col, denom = \"n\", na.rm = TRUE, ...)  # S3 method for character s_compare(   x,   .ref_group,   .in_ref_col,   denom = \"n\",   na.rm = TRUE,   .var,   verbose = TRUE,   ... )  # S3 method for logical s_compare(x, .ref_group, .in_ref_col, na.rm = TRUE, denom = \"n\", ...)  a_compare(   x,   .N_col,   .N_row,   .var = NULL,   .df_row = NULL,   .ref_group = NULL,   .in_ref_col = FALSE,   ... )  compare_vars(   lyt,   vars,   var_labels = vars,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   nested = TRUE,   ...,   na.rm = TRUE,   show_labels = \"default\",   table_names = vars,   section_div = NA_character_,   .stats = c(\"n\", \"mean_sd\", \"count_fraction\", \"pval\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare Variables Between Groups — compare_variables","text":"x (numeric) vector numbers want analyze. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. ... arguments passed s_compare(). denom (string) choice denominator factor proportions, can n (number values row column intersection). na.rm (flag) whether NA values removed x prior analysis. .var (string) single variable name passed rtables requested statistics function. verbose (logical) Whether warnings messages printed. Mainly used print information factor casting. Defaults TRUE. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .df_row (data.frame) data frame across columns given row split. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare Variables Between Groups — compare_variables","text":"s_compare() returns output s_summary() comparisons versus reference group form p-values. a_compare() returns corresponding list formatted rtables::CellValue(). compare_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_compare() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Compare Variables Between Groups — compare_variables","text":"s_compare(): S3 generic function produce comparison summary. s_compare(numeric): Method numeric class. uses standard t-test calculate p-value. s_compare(factor): Method factor class. uses chi-squared test calculate p-value. s_compare(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_compare(logical): Method logical class. chi-squared test used. missing values removed, counted FALSE. a_compare(): Formatted analysis function used afun compare_vars(). compare_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compare Variables Between Groups — compare_variables","text":"factor variables, denom factor proportions can n since purpose compare proportions columns, therefore row-based proportion make sense. Proportion based N_col difficult since use counts chi-squared test statistic, therefore missing values accounted explicit factor levels. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"<Missing>\") also excluded na.rm set TRUE. character variables, automatic conversion factor guarantee table generated correctly. particular sparse tables likely can fail. Therefore always better manually convert character variables factors pre-processing. compare_vars(), column split must define reference group via ref_group comparison well defined. a_compare() deprecated favor a_summary() argument compare set TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/compare_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare Variables Between Groups — compare_variables","text":"","code":"# `s_compare.numeric`  ## Usual case where both this and the reference group vector have more than 1 value. s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE) #> $n #>  n  #> 10  #>  #> $sum #>      sum  #> 55.16893  #>  #> $mean #>     mean  #> 5.516893  #>  #> $sd #>        sd  #> 0.8702667  #>  #> $se #>        se  #> 0.2752025  #>  #> $mean_sd #>      mean        sd  #> 5.5168927 0.8702667  #>  #> $mean_se #>      mean        se  #> 5.5168927 0.2752025  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.894341    6.139444  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>     5.241690     5.792095  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>     4.646626     6.387159  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #>      p_value  #> 8.894777e-09  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #>   median  #> 5.570577  #>  #> $mad #>          mad  #> 4.440892e-16  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>      4.652630      6.115083  #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>      4.788385      6.102656  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #>      iqr  #> 1.314271  #>  #> $range #>      min      max  #> 3.940419 6.973096  #>  #> $min #>      min  #> 3.940419  #>  #> $max #>      max  #> 6.973096  #>  #> $median_range #>   median      min      max  #> 5.570577 3.940419 6.973096  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #>       cv  #> 15.77458  #>  #> $geom_mean #> geom_mean  #>  5.452097  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.846722    6.133086  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #>  geom_cv  #> 16.56498  #>  #> $pval #> [1] 2.084023e-09 #>   ## If one group has not more than 1 value, then p-value is not calculated. s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE) #> $n #>  n  #> 10  #>  #> $sum #>      sum  #> 49.73612  #>  #> $mean #>     mean  #> 4.973612  #>  #> $sd #>      sd  #> 1.08076  #>  #> $se #>        se  #> 0.3417662  #>  #> $mean_sd #>     mean       sd  #> 4.973612 1.080760  #>  #> $mean_se #>      mean        se  #> 4.9736118 0.3417662  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.200483    5.746741  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>     4.631846     5.315378  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>     3.892852     6.054372  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #>      p_value  #> 1.466974e-07  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #>   median  #> 4.932608  #>  #> $mad #>           mad  #> -4.440892e-16  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>      3.994767      5.825929  #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>      4.664539      5.807297  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #>      iqr  #> 1.142758  #>  #> $range #>      min      max  #> 2.786148 6.663672  #>  #> $min #>      min  #> 2.786148  #>  #> $max #>      max  #> 6.663672  #>  #> $median_range #>   median      min      max  #> 4.932608 2.786148 6.663672  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #>       cv  #> 21.72988  #>  #> $geom_mean #> geom_mean  #>  4.853244  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>    4.078462    5.775211  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #>  geom_cv  #> 24.67715  #>  #> $pval #> character(0) #>   ## Empty numeric does not fail, it returns NA-filled items and no p-value. s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE) #> $n #> n  #> 0  #>  #> $sum #> sum  #>  NA  #>  #> $mean #> mean  #>   NA  #>  #> $sd #> sd  #> NA  #>  #> $se #> se  #> NA  #>  #> $mean_sd #> mean   sd  #>   NA   NA  #>  #> $mean_se #> mean   se  #>   NA   NA  #>  #> $mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Mean 95% CI\" #>  #> $mean_sei #> mean_sei_lwr mean_sei_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #>  #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr  #>           NA           NA  #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #>  #> $mean_pval #> p_value  #>      NA  #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #>  #> $median #> median  #>     NA  #>  #> $mad #> mad  #>  NA  #>  #> $median_ci #> median_ci_lwr median_ci_upr  #>            NA            NA  #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #>  #> $quantiles #> quantile_0.25 quantile_0.75  #>            NA            NA  #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #>  #> $iqr #> iqr  #>  NA  #>  #> $range #> min max  #>  NA  NA  #>  #> $min #> min  #>  NA  #>  #> $max #> max  #>  NA  #>  #> $median_range #> median    min    max  #>     NA     NA     NA  #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #>  #> $cv #> cv  #> NA  #>  #> $geom_mean #> geom_mean  #>       NaN  #>  #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr  #>          NA          NA  #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #>  #> $geom_cv #> geom_cv  #>      NA  #>  #> $pval #> character(0) #>   # `s_compare.factor`  ## Basic usage: x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) y <- factor(c(\"a\", \"b\", \"c\")) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>   ## Management of NA values. x <- explicit_na(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\", NA, NA))) y <- explicit_na(factor(c(\"a\", \"b\", \"c\", NA))) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>  s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 7 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #> $count$`<Missing>` #> [1] 2 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0000000 0.4285714 #>  #> $count_fraction$b #> [1] 1.0000000 0.1428571 #>  #> $count_fraction$c #> [1] 1.0000000 0.1428571 #>  #> $count_fraction$`<Missing>` #> [1] 2.0000000 0.2857143 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.9063036 #>   # `s_compare.character`  ## Basic usage: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\") y <- c(\"a\", \"b\", \"c\") s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>   ## Note that missing values handling can make a large difference: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\", NA) y <- c(\"a\", \"b\", \"c\", rep(NA, 20)) s_compare(x,   .ref_group = y, .in_ref_col = FALSE,   .var = \"x\", verbose = FALSE ) #> $n #> [1] 5 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #>  #> $count_fraction$b #> [1] 1.0 0.2 #>  #> $count_fraction$c #> [1] 1.0 0.2 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.7659283 #>  s_compare(x,   .ref_group = y, .in_ref_col = FALSE, .var = \"x\",   na.rm = FALSE, verbose = FALSE ) #> $n #> [1] 6 #>  #> $count #> $count$a #> [1] 3 #>  #> $count$b #> [1] 1 #>  #> $count$c #> [1] 1 #>  #> $count$`<Missing>` #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #>  #> $count_fraction$b #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$c #> [1] 1.0000000 0.1666667 #>  #> $count_fraction$`<Missing>` #> [1] 1.0000000 0.1666667 #>  #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.005768471 #>   # `s_compare.logical`  ## Basic usage: x <- c(TRUE, FALSE, TRUE, TRUE) y <- c(FALSE, FALSE, TRUE) s_compare(x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 4 #>  #> $count #> [1] 3 #>  #> $count_fraction #> [1] 3.00 0.75 #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.2702894 #>   ## Management of NA values. x <- c(NA, TRUE, FALSE) y <- c(NA, NA, NA, NA, FALSE) s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 2 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0 0.5 #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.3864762 #>  s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 3 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0000000 0.3333333 #>  #> $n_blq #> [1] 0 #>  #> $pval_counts #> [1] 0.1675463 #>   # `a_compare` deprecated - use `a_summary()` instead a_compare(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .stats = c(\"n\", \"pval\")) #> Warning: `a_compare()` was deprecated in tern 0.8.3. #> ℹ Please use a_summary() with argument `compare` set to TRUE instead. #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>           row_name formatted_cell indent_mod        row_label #> 1                n             10          0                n #> 2 p-value (t-test)        <0.0001          0 p-value (t-test)  # `compare_vars()` in `rtables` pipelines  ## Default output within a `rtables` pipeline. lyt <- basic_table() %>%   split_cols_by(\"ARMCD\", ref_group = \"ARM B\") %>%   compare_vars(c(\"AGE\", \"SEX\")) build_table(lyt, tern_ex_adsl) #>                                  ARM B        ARM A        ARM C    #> ——————————————————————————————————————————————————————————————————— #> AGE                                                                 #>   n                                73           69           58     #>   Mean (SD)                    35.8 (7.1)   34.1 (6.8)   36.1 (7.4) #>   p-value (t-test)                            0.1446       0.8212   #> SEX                                                                 #>   n                                73           69           58     #>   F                            40 (54.8%)   38 (55.1%)   32 (55.2%) #>   M                            33 (45.2%)   31 (44.9%)   26 (44.8%) #>   p-value (chi-squared test)                  1.0000       1.0000    ## Select and format statistics output. lyt <- basic_table() %>%   split_cols_by(\"ARMCD\", ref_group = \"ARM C\") %>%   compare_vars(     vars = \"AGE\",     .stats = c(\"mean_sd\", \"pval\"),     .formats = c(mean_sd = \"xx.x, xx.x\"),     .labels = c(mean_sd = \"Mean, SD\")   ) build_table(lyt, df = tern_ex_adsl) #>                      ARM C       ARM A       ARM B   #> ———————————————————————————————————————————————————— #> Mean, SD           36.1, 7.4   34.1, 6.8   35.8, 7.1 #> p-value (t-test)                0.1176      0.8212"},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for Descriptive Statistics — control_analyze_vars","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"Sets list parameters summaries descriptive statistics. Typically used internally specify details s_summary(). function family mainly used analyze_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"","code":"control_analyze_vars(   conf_level = 0.95,   quantiles = c(0.25, 0.75),   quantile_type = 2,   test_mean = 0 )"},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"conf_level (proportion) confidence level interval. quantiles (numeric) length two specify quantiles calculate. quantile_type (numeric) 1 9 selecting quantile algorithms used. Default set 2 matches default quantile algorithm SAS proc univariate set QNTLDEF=5. differs R's default. See type stats::quantile(). test_mean (numeric) test mean null hypothesis calculating p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_analyze_vars.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Control Function for Descriptive Statistics — control_analyze_vars","text":"Deprecation cycle started control_summarize_vars going renamed control_analyze_vars. Intention reflect better core underlying rtables functions; case analyze_vars() wraps rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for CoxPH Model — control_coxph","title":"Control Function for CoxPH Model — control_coxph","text":"auxiliary function controlling arguments CoxPH model, typically used internally specify details CoxPH model s_coxph_pairwise(). conf_level refers Hazard Ratio estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for CoxPH Model — control_coxph","text":"","code":"control_coxph(   pval_method = c(\"log-rank\", \"wald\", \"likelihood\"),   ties = c(\"efron\", \"breslow\", \"exact\"),   conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for CoxPH Model — control_coxph","text":"pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for CoxPH Model — control_coxph","text":"list components names arguments","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Controls for Cox Regression — control_coxreg","title":"Controls for Cox Regression — control_coxreg","text":"Sets list parameters Cox regression fit. Used internally.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Controls for Cox Regression — control_coxreg","text":"","code":"control_coxreg(   pval_method = c(\"wald\", \"likelihood\"),   ties = c(\"exact\", \"efron\", \"breslow\"),   conf_level = 0.95,   interaction = FALSE )"},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Controls for Cox Regression — control_coxreg","text":"pval_method (string) method used estimation p.values; wald (default) likelihood. ties (string) among exact (equivalent DISCRETE SAS), efron breslow, see survival::coxph(). Note: equivalent SAS EXACT method R. conf_level (proportion) confidence level interval. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Controls for Cox Regression — control_coxreg","text":"list items names corresponding arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/control_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Controls for Cox Regression — control_coxreg","text":"","code":"control_coxreg() #> $pval_method #> [1] \"wald\" #>  #> $ties #> [1] \"exact\" #>  #> $conf_level #> [1] 0.95 #>  #> $interaction #> [1] FALSE #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for incidence rate — control_incidence_rate","title":"Control function for incidence rate — control_incidence_rate","text":"auxiliary function controlling arguments incidence rate, used internally specify details s_incidence_rate().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate(   conf_level = 0.95,   conf_type = c(\"normal\", \"normal_log\", \"exact\", \"byar\"),   input_time_unit = c(\"year\", \"day\", \"week\", \"month\"),   num_pt_year = 100,   time_unit_input = lifecycle::deprecated(),   time_unit_output = lifecycle::deprecated() )"},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for incidence rate — control_incidence_rate","text":"conf_level (proportion) confidence level interval. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric) number patient-years use calculating adverse event rates. time_unit_input Please use input_time_unit argument instead. time_unit_output Please use num_pt_year argument instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for incidence rate — control_incidence_rate","text":"list components names arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/control_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate(0.9, \"exact\", \"month\", 100) #> $conf_level #> [1] 0.9 #>  #> $conf_type #> [1] \"exact\" #>  #> $input_time_unit #> [1] \"month\" #>  #> $num_pt_year #> [1] 100 #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for g_lineplot Function — control_lineplot_vars","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"Default values variables parameter g_lineplot function. variable's default value can overwritten variable.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"","code":"control_lineplot_vars(   x = \"AVISIT\",   y = \"AVAL\",   strata = \"ARM\",   paramcd = \"PARAMCD\",   y_unit = \"AVALU\",   cohort_id = \"USUBJID\" )"},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"x (character) x variable name. y (character) y variable name. strata (character NA) strata variable name. paramcd (character NA)paramcd variable name. y_unit (character NA)y_unit variable name. cohort_id (character NA) variable identify subjects cohorts.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"named character vector variable names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_lineplot_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control Function for g_lineplot Function — control_lineplot_vars","text":"","code":"control_lineplot_vars() #>         x         y    strata   paramcd    y_unit cohort_id  #>  \"AVISIT\"    \"AVAL\"     \"ARM\" \"PARAMCD\"   \"AVALU\" \"USUBJID\"  control_lineplot_vars(strata = NA) #>         x         y    strata   paramcd    y_unit cohort_id  #>  \"AVISIT\"    \"AVAL\"        NA \"PARAMCD\"   \"AVALU\" \"USUBJID\""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for Logistic Regression Model Fitting — control_logistic","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"auxiliary function controlling arguments logistic regression models. conf_level refers confidence level used Odds Ratio CIs.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"","code":"control_logistic(response_definition = \"response\", conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"response_definition (string) definition event terms response. used fitting logistic regression model left hand side formula. Note evaluated expression result either logical vector factor 2 levels. default just \"response\" original response variable used modified . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control Function for Logistic Regression Model Fitting — control_logistic","text":"","code":"# Standard options. control_logistic() #> $response_definition #> [1] \"response\" #>  #> $conf_level #> [1] 0.95 #>   # Modify confidence level. control_logistic(conf_level = 0.9) #> $response_definition #> [1] \"response\" #>  #> $conf_level #> [1] 0.9 #>   # Use a different response definition. control_logistic(response_definition = \"I(response %in% c('CR', 'PR'))\") #> $response_definition #> [1] \"I(response %in% c('CR', 'PR'))\" #>  #> $conf_level #> [1] 0.95 #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"auxiliary function controlling arguments STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"","code":"control_step(   biomarker = NULL,   use_percentile = TRUE,   bandwidth,   degree = 0L,   num_points = 39L )"},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"biomarker (numeric NULL) optional provision numeric biomarker variable, used infer bandwidth, see . use_percentile (flag) TRUE, running windows created according quantiles rather actual values, .e. bandwidth refers percentage data covered window. Suggest TRUE biomarker variable uniformly distributed. bandwidth (number NULL) indicating bandwidth window. Depending argument use_percentile, can either length actual-value windows real biomarker scale, percentage windows. use_percentile = TRUE, number 0 1. NULL, treat bandwidth infinity, means one global model fitted. default, 0.25 used percentage windows one quarter range biomarker variable actual-value windows. degree (count) degree polynomial function biomarker interaction term treatment arm fitted window. 0 (default), biomarker variable included model fitted biomarker window. num_points (count) number points hazard ratios estimated. smallest number 2.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"list components names arguments, except biomarker just used calculate bandwidth case actual biomarker windows requested.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations — control_step","text":"","code":"# Provide biomarker values and request actual values to be used, # so that bandwidth is chosen from range. control_step(biomarker = 1:10, use_percentile = FALSE) #> $use_percentile #> [1] FALSE #>  #> $bandwidth #> [1] 2.25 #>  #> $degree #> [1] 0 #>  #> $num_points #> [1] 39 #>   # Use a global model with quadratic biomarker interaction term. control_step(bandwidth = NULL, degree = 2) #> $use_percentile #> [1] TRUE #>  #> $bandwidth #> NULL #>  #> $degree #> [1] 2 #>  #> $num_points #> [1] 39 #>   # Reduce number of points to be used. control_step(num_points = 10) #> $use_percentile #> [1] TRUE #>  #> $bandwidth #> [1] 0.25 #>  #> $degree #> [1] 0 #>  #> $num_points #> [1] 10 #>"},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for survfit Model for Survival Time — control_surv_time","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_time(). conf_level refers survival time estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"","code":"control_surv_time(   conf_level = 0.95,   conf_type = c(\"plain\", \"log\", \"log-log\"),   quantiles = c(0.25, 0.75) )"},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported. quantiles (numeric) length two specify quantiles survival time.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for survfit Model for Survival Time — control_surv_time","text":"list components names arguments","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_timepoint(). conf_level refers patient risk estimation time point.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"","code":"control_surv_timepoint(   conf_level = 0.95,   conf_type = c(\"plain\", \"log\", \"log-log\") )"},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/control_surv_timepoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control Function for survfit Model for Patient's Survival Rate at time point — control_surv_timepoint","text":"list components names arguments","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Counts with Thresholds — count_cumulative","title":"Cumulative Counts with Thresholds — count_cumulative","text":"Summarize cumulative counts (numeric) vector less , less equal , greater , greater equal user-specific thresholds.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Counts with Thresholds — count_cumulative","text":"","code":"s_count_cumulative(   x,   thresholds,   lower_tail = TRUE,   include_eq = TRUE,   .N_col,   ... )  a_count_cumulative(   x,   thresholds,   lower_tail = TRUE,   include_eq = TRUE,   .N_col,   ... )  count_cumulative(   lyt,   vars,   var_labels = vars,   show_labels = \"visible\",   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Counts with Thresholds — count_cumulative","text":"x (numeric) vector numbers want analyze. thresholds (numeric) vector cutoff value counts. lower_tail (logical) whether count lower tail, default TRUE. include_eq (logical) whether include value equal threshold count, default TRUE. .N_col (count) denominator fraction calculation. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Counts with Thresholds — count_cumulative","text":"s_count_cumulative() returns named list count_fractions: list thresholds value component, component containing vector count fraction. a_count_cumulative() returns corresponding list formatted rtables::CellValue(). count_cumulative() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_cumulative() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cumulative Counts with Thresholds — count_cumulative","text":"s_count_cumulative(): Statistics function produces named list given numeric vector thresholds. a_count_cumulative(): Formatted analysis function used afun count_cumulative(). count_cumulative(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Counts with Thresholds — count_cumulative","text":"","code":"basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_cumulative(     vars = \"AGE\",     thresholds = c(40, 60)   ) %>%   build_table(tern_ex_adsl) #>           A: Drug X    B: Placebo   C: Combination #>             (N=69)       (N=73)         (N=58)     #> —————————————————————————————————————————————————— #> AGE                                                #>   <= 40   52 (75.4%)   58 (79.5%)     41 (70.7%)   #>   <= 60   69 (100%)    73 (100%)      58 (100%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Missed Doses — count_missed_doses","title":"Counting Missed Doses — count_missed_doses","text":"specific functions count patients missed doses. difference count_cumulative() mainly special labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Missed Doses — count_missed_doses","text":"","code":"s_count_nonmissing(x)  s_count_missed_doses(x, thresholds, .N_col)  a_count_missed_doses(x, thresholds, .N_col)  count_missed_doses(   lyt,   vars,   var_labels = vars,   show_labels = \"visible\",   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Missed Doses — count_missed_doses","text":"x (numeric) vector numbers want analyze. thresholds (vector count) number missed doses patients least . .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Missed Doses — count_missed_doses","text":"s_count_nonmissing() returns statistic n count non-missing values x. s_count_missed_doses() returns statistics n count_fraction one element threshold. a_count_missed_doses() returns corresponding list formatted rtables::CellValue(). count_missed_doses() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_missed_doses() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Missed Doses — count_missed_doses","text":"s_count_nonmissing(): Statistics function count non-missing values. s_count_missed_doses(): Statistics function count patients missed doses. a_count_missed_doses(): Formatted analysis function used afun count_missed_doses(). count_missed_doses(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_missed_doses.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Missed Doses — count_missed_doses","text":"","code":"set.seed(1) x <- c(sample(1:10, 10), NA)  library(dplyr)  anl <- tern_ex_adsl %>%   distinct(STUDYID, USUBJID, ARM) %>%   mutate(     PARAMCD = \"TNDOSMIS\",     PARAM = \"Total number of missed doses during study\",     AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE),     AVALC = \"\"   )  basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_missed_doses(\"AVAL\", thresholds = c(1, 5, 10, 15), var_labels = \"Missed Doses\") %>%   build_table(anl, alt_counts_df = tern_ex_adsl) #>                              A: Drug X    B: Placebo   C: Combination #>                                (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————— #> Missed Doses                                                          #>   n                              69           73             58       #>   At least 1 missed dose     65 (94.2%)   67 (91.8%)     58 (100%)    #>   At least 5 missed doses    54 (78.3%)   51 (69.9%)     54 (93.1%)   #>   At least 10 missed doses   31 (44.9%)   40 (54.8%)     31 (53.4%)   #>   At least 15 missed doses   17 (24.6%)   23 (31.5%)     20 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Counts — count_occurrences","title":"Occurrence Counts — count_occurrences","text":"Functions analyzing frequencies fractions occurrences patients occurrence data. Primary analysis variables dictionary terms. occurrences counted total counts. Multiple occurrences within patient lowest term level displayed table counted .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Counts — count_occurrences","text":"","code":"s_count_occurrences(   df,   denom = c(\"N_col\", \"n\"),   .N_col,   .df_row,   drop = TRUE,   .var = \"MHDECOD\",   id = \"USUBJID\" )  a_count_occurrences(   df,   denom = c(\"N_col\", \"n\"),   .N_col,   .df_row,   drop = TRUE,   .var = \"MHDECOD\",   id = \"USUBJID\" )  count_occurrences(   lyt,   vars,   var_labels = vars,   show_labels = \"hidden\",   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = \"count_fraction\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Counts — count_occurrences","text":"df (data.frame) data set containing analysis variables. denom (string) choice denominator patient proportions. Can : N_col: total number patients column across rows n: number patients occurrences .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .df_row (data.frame) data frame across columns given row split. drop (flag) non appearing occurrence levels dropped resulting table. Note case remaining occurrence levels table sorted alphabetically. .var (string) single variable name passed rtables requested statistics function. id (string) subject variable name. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Counts — count_occurrences","text":"s_count_occurrences() returns list : count: list counts one element per occurrence. count_fraction: list counts fractions one element per occurrence. fraction: list numerators denominators one element per occurrence. a_count_occurrences() returns corresponding list formatted rtables::CellValue(). count_occurrences() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Counts — count_occurrences","text":"s_count_occurrences(): Statistics function counts number patients report occurrence. a_count_occurrences(): Formatted analysis function used afun count_occurrences(). count_occurrences(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Occurrence Counts — count_occurrences","text":"default, occurrences appear given row split dropped table occurrences table sorted alphabetically per row split. Therefore, corresponding layout needs use split_fun = drop_split_levels split_rows_by calls. Use drop = FALSE like show occurrences.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Counts — count_occurrences","text":"","code":"df <- data.frame(   USUBJID = as.character(c(1, 1, 2, 4, 4, 4)),   MHDECOD = c(\"MH1\", \"MH2\", \"MH1\", \"MH1\", \"MH1\", \"MH3\") )  N_per_col <- 4L  # Count unique occurrences per subject. s_count_occurrences(   df,   .N_col = N_per_col,   .df_row = df,   .var = \"MHDECOD\",   id = \"USUBJID\" ) #> $count #> $count$MH1 #> [1] 3 #>  #> $count$MH2 #> [1] 1 #>  #> $count$MH3 #> [1] 1 #>  #>  #> $count_fraction #> $count_fraction$MH1 #> [1] 3.00 0.75 #>  #> $count_fraction$MH2 #> [1] 1.00 0.25 #>  #> $count_fraction$MH3 #> [1] 1.00 0.25 #>  #>  #> $fraction #> $fraction$MH1 #>   num denom  #>     3     4  #>  #> $fraction$MH2 #>   num denom  #>     1     4  #>  #> $fraction$MH3 #>   num denom  #>     1     4  #>  #>   #  We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. afun <- make_afun(a_count_occurrences, .ungroup_stats = c(\"count\", \"count_fraction\", \"fraction\")) afun(   df,   .N_col = N_per_col,   .df_row = df,   .var = \"MHDECOD\",   id = \"USUBJID\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1      MH1              3          0       MH1 #> 2      MH2              1          0       MH2 #> 3      MH3              1          0       MH3 #> 4      MH1      3 (75.0%)          0       MH1 #> 5      MH2      1 (25.0%)          0       MH2 #> 6      MH3      1 (25.0%)          0       MH3 #> 7      MH1    3/4 (75.0%)          0       MH1 #> 8      MH2    1/4 (25.0%)          0       MH2 #> 9      MH3    1/4 (25.0%)          0       MH3  library(dplyr) df <- data.frame(   USUBJID = as.character(c(     1, 1, 2, 4, 4, 4,     6, 6, 6, 7, 7, 8   )),   MHDECOD = c(     \"MH1\", \"MH2\", \"MH1\", \"MH1\", \"MH1\", \"MH3\",     \"MH2\", \"MH2\", \"MH3\", \"MH1\", \"MH2\", \"MH4\"   ),   ARM = rep(c(\"A\", \"B\"), each = 6) ) df_adsl <- df %>%   select(USUBJID, ARM) %>%   unique()  # Create table layout lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_occurrences(vars = \"MHDECOD\", .stats = c(\"count_fraction\"))  # Apply table layout to data and produce `rtable` object lyt %>%   build_table(df, alt_counts_df = df_adsl) %>%   prune_table() #>           A           B     #>         (N=3)       (N=3)   #> ——————————————————————————— #> MH1   3 (100%)    1 (33.3%) #> MH2   1 (33.3%)   2 (66.7%) #> MH3   1 (33.3%)   1 (33.3%) #> MH4       0       1 (33.3%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Counts by Grade — count_occurrences_by_grade","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"Functions analyzing frequencies fractions occurrences grade patients occurrence data. Multiple occurrences within one individual counted greatest intensity/highest grade level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"","code":"s_count_occurrences_by_grade(   df,   .var,   .N_col,   id = \"USUBJID\",   grade_groups = list(),   remove_single = TRUE,   labelstr = \"\" )  a_count_occurrences_by_grade(   df,   .var,   .N_col,   id = \"USUBJID\",   grade_groups = list(),   remove_single = TRUE,   labelstr = \"\" )  count_occurrences_by_grade(   lyt,   var,   var_labels = var,   show_labels = \"default\",   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = var,   .stats = NULL,   .formats = NULL,   .indent_mods = NULL,   .labels = NULL )  summarize_occurrences_by_grade(   lyt,   var,   na_str = NA_character_,   ...,   .stats = NULL,   .formats = NULL,   .indent_mods = NULL,   .labels = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. id (string) subject variable name. grade_groups (named list character) containing groupings grades. remove_single (logical)TRUE include elements one-element grade groups output list; case grade groups names included output. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. lyt (layout) input layout analyses added . var_labels (character) labels show result table. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"s_count_occurrences_by_grade() returns list counts fractions one element per grade level grade level grouping. a_count_occurrences_by_grade() returns corresponding list formatted rtables::CellValue(). count_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences_by_grade() table layout. summarize_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_occurrences_by_grade() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"s_count_occurrences_by_grade(): Statistics function counts number patients highest grade. a_count_occurrences_by_grade(): Formatted analysis function used afun count_occurrences_by_grade(). count_occurrences_by_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function can take content function arguments additional format arguments. function wrapper rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_occurrences_by_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Counts by Grade — count_occurrences_by_grade","text":"","code":"library(dplyr) df <- data.frame(   USUBJID = as.character(c(1:6, 1)),   ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"A\"), levels = c(\"A\", \"B\")),   AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)),   AESEV = factor(     x = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"MILD\", \"MILD\", \"MODERATE\", \"SEVERE\"),     levels = c(\"MILD\", \"MODERATE\", \"SEVERE\")   ),   stringsAsFactors = FALSE ) df_adsl <- df %>%   select(USUBJID, ARM) %>%   unique()  s_count_occurrences_by_grade(   df,   .N_col = 10L,   .var = \"AETOXGR\",   id = \"USUBJID\",   grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> $count_fraction #> $count_fraction$ANY #> [1] 6.0 0.6 #>  #> $count_fraction$`1` #> [1] 1.0 0.1 #>  #> $count_fraction$`2` #> [1] 2.0 0.2 #>  #> $count_fraction$`3` #> [1] 2.0 0.2 #>  #> $count_fraction$`4` #> [1] 1.0 0.1 #>  #> $count_fraction$`5` #> [1] 0 0 #>  #>   #  We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. afun <- make_afun(a_count_occurrences_by_grade, .ungroup_stats = \"count_fraction\") afun(   df,   .N_col = 10L,   .var = \"AETOXGR\",   id = \"USUBJID\",   grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1      ANY      6 (60.0%)          0       ANY #> 2        1      1 (10.0%)          0         1 #> 3        2      2 (20.0%)          0         2 #> 4        3      2 (20.0%)          0         3 #> 5        4      1 (10.0%)          0         4 #> 6        5              0          0         5  # Layout creating function with custom format. basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_occurrences_by_grade(     var = \"AESEV\",     .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\")   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                  A               B       #>                (N=3)           (N=3)     #> ———————————————————————————————————————— #> MILD       0.00 (0.00%)    2.00 (66.67%) #> MODERATE   1.00 (33.33%)   1.00 (33.33%) #> SEVERE     2.00 (66.67%)   0.00 (0.00%)   # Define additional grade groupings. grade_groups <- list(   \"-Any-\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),   \"Grade 1-2\" = c(\"1\", \"2\"),   \"Grade 3-5\" = c(\"3\", \"4\", \"5\") )  basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_occurrences_by_grade(     var = \"AETOXGR\",     grade_groups = grade_groups   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                 A           B     #>               (N=3)       (N=3)   #> ————————————————————————————————— #> -Any-       3 (100%)    3 (100%)  #> Grade 1-2   1 (33.3%)   2 (66.7%) #> 1               0       1 (33.3%) #> 2           1 (33.3%)   1 (33.3%) #> Grade 3-5   2 (66.7%)   1 (33.3%) #> 3           2 (66.7%)       0     #> 4               0       1 (33.3%) #> 5               0           0      # Layout creating function with custom format. basic_table() %>%   add_colcounts() %>%   split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>%   summarize_occurrences_by_grade(     var = \"AESEV\",     .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\")   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                 all obs    #>                  (N=6)     #> —————————————————————————— #> A                          #>   MILD       0.00 (0.00%)  #>   MODERATE   1.00 (16.67%) #>   SEVERE     2.00 (33.33%) #> B                          #>   MILD       2.00 (33.33%) #>   MODERATE   1.00 (16.67%) #>   SEVERE     0.00 (0.00%)   basic_table() %>%   add_colcounts() %>%   split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>%   summarize_occurrences_by_grade(     var = \"AETOXGR\",     grade_groups = grade_groups   ) %>%   build_table(df, alt_counts_df = df_adsl) #>                all obs  #>                 (N=6)   #> ——————————————————————— #> A                       #>   -Any-       3 (50.0%) #>   Grade 1-2   1 (16.7%) #>   1               0     #>   2           1 (16.7%) #>   Grade 3-5   2 (33.3%) #>   3           2 (33.3%) #>   4               0     #>   5               0     #> B                       #>   -Any-       3 (50.0%) #>   Grade 1-2   2 (33.3%) #>   1           1 (16.7%) #>   2           1 (16.7%) #>   Grade 3-5   1 (16.7%) #>   3               0     #>   4           1 (16.7%) #>   5               0"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Patients and Events in Columns — count_patients_events_in_cols","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"Counting number unique patients total number specific events column table layout required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"","code":"s_count_patients_and_multiple_events(   df,   id,   filters_list,   empty_stats = character(),   labelstr = \"\",   custom_label = NULL )  summarize_patients_events_in_cols(   lyt,   id = \"USUBJID\",   filters_list = list(),   na_str = NA_character_,   ...,   .stats = c(\"unique\", \"all\", names(filters_list)),   .labels = c(unique = \"Patients (All)\", all = \"Events (All)\",     labels_or_names(filters_list)),   col_split = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"df (data.frame) data set containing analysis variables. id (string) subject variable name. filters_list (named list character) element list describes one type event describe filters, format s_count_patients_with_event(). label, used column title. empty_stats (character) optional names statistics returned empty corresponding table cells stay blank. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. custom_label (string NULL) provided labelstr empty used label. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. ... additional arguments lower level functions. .stats (character) statistics select table. .labels (named character) labels statistics (without indent). col_split (flag) whether columns split. Set FALSE required column split done already earlier layout pipe.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"s_count_patients_and_multiple_events() returns list statistics: unique: number unique patients df. : number rows df. one element name filters_list: number rows df, .e. events, fulfilling filter condition. summarize_patients_events_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_patients_and_multiple_events() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"s_count_patients_and_multiple_events(): Statistics function counts numbers patients multiple events defined filters. Used analysis function afun summarize_patients_events_in_cols(). summarize_patients_events_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_events_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Patients and Events in Columns — count_patients_events_in_cols","text":"","code":"# `s_count_patients_and_multiple_events()` df <- data.frame(   USUBJID = rep(c(\"id1\", \"id2\", \"id3\", \"id4\"), c(2, 3, 1, 1)),   ARM = c(\"A\", \"A\", \"B\", \"B\", \"B\", \"B\", \"A\"),   AESER = rep(\"Y\", 7),   AESDTH = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"N\"),   AEREL = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"Y\"),   AEDECOD = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"C\", \"D\"),   AEBODSYS = rep(c(\"SOC1\", \"SOC2\", \"SOC3\"), c(3, 3, 1)) )  # `summarize_patients_events_in_cols()` basic_table() %>%   summarize_patients_events_in_cols(     filters_list = list(       related = formatters::with_label(c(AEREL = \"Y\"), \"Events (Related)\"),       fatal = c(AESDTH = \"Y\"),       fatal_related = c(AEREL = \"Y\", AESDTH = \"Y\")     ),     custom_label = \"%s Total number of patients and events\"   ) %>%   build_table(df) #>                                          Patients (All)   Events (All)   Events (Related)   fatal   fatal_related #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> %s Total number of patients and events         4               7                5             4           4"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the Number of Patients with a Particular Event — count_patients_with_event","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"primary analysis variable .var denotes unique patient identifier.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"","code":"s_count_patients_with_event(   df,   .var,   filters,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  a_count_patients_with_event(   df,   .var,   filters,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  count_patients_with_event(   lyt,   vars,   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = \"count_fraction\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"df (data.frame) data set containing analysis variables. .var (character) name column contains unique identifier. filters (character) character vector specifying column names flag variables used counting number unique identifiers satisfying conditions. Multiple column names flags accepted format c(\"column_name1\" = \"flag1\", \"column_name2\" = \"flag2\"). Note equality accepted condition. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"s_count_patients_with_event() returns count fraction unique identifiers defined event. a_count_patients_with_event() returns corresponding list formatted rtables::CellValue(). count_patients_with_event() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_event() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"s_count_patients_with_event(): Statistics function counts number patients defined event occurred. a_count_patients_with_event(): Formatted analysis function used afun count_patients_with_event(). count_patients_with_event(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the Number of Patients with a Particular Event — count_patients_with_event","text":"","code":"library(dplyr)  # `s_count_patients_with_event()`  s_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\") ) #> $n #> [1] 164 #>  #> $count #> [1] 164 #>  #> $count_fraction #> [1] 164   1 #>  #> $n_blq #> [1] 0 #>  s_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\") ) #> $n #> [1] 164 #>  #> $count #> [1] 79 #>  #> $count_fraction #> [1] 79.0000000  0.4817073 #>  #> $n_blq #> [1] 0 #>  s_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"),   denom = \"N_col\",   .N_col = 456 ) #> $n #> [1] 164 #>  #> $count #> [1] 79 #>  #> $count_fraction #> [1] 79.0000000  0.1732456 #>  #> $n_blq #> [1] 0 #>   # `a_count_patients_with_event()`  a_count_patients_with_event(   tern_ex_adae,   .var = \"SUBJID\",   filters = c(\"TRTEMFL\" = \"Y\"),   .N_col = 100,   .N_row = 100 ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>         row_name formatted_cell indent_mod      row_label #> 1              n            164          0              n #> 2          count            164          0          count #> 3 count_fraction     164 (100%)          0 count_fraction #> 4          n_blq              0          0          n_blq  # `count_patients_with_event()`  lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_values(     \"STUDYID\",     values = \"AB12345\",     .stats = \"count\",     .labels = c(count = \"Total AEs\")   ) %>%   count_patients_with_event(     \"SUBJID\",     filters = c(\"TRTEMFL\" = \"Y\"),     .labels = c(count_fraction = \"Total number of patients with at least one adverse event\"),     table_names = \"tbl_all\"   ) %>%   count_patients_with_event(     \"SUBJID\",     filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"),     .labels = c(count_fraction = \"Total number of patients with fatal AEs\"),     table_names = \"tbl_fatal\"   ) %>%   count_patients_with_event(     \"SUBJID\",     filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\", \"AEREL\" = \"Y\"),     .labels = c(count_fraction = \"Total number of patients with related fatal AEs\"),     .indent_mods = c(count_fraction = 2L),     table_names = \"tbl_rel_fatal\"   ) build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) #>                                                            A: Drug X    B: Placebo   C: Combination #>                                                              (N=69)       (N=73)         (N=58)     #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs                                                     202          177            162       #> Total number of patients with at least one adverse event   59 (100%)    57 (100%)      48 (100%)    #> Total number of patients with fatal AEs                    28 (47.5%)   31 (54.4%)     20 (41.7%)   #>     Total number of patients with related fatal AEs        28 (47.5%)   31 (54.4%)     20 (41.7%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the Number of Patients with Particular Flags — count_patients_with_flags","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"primary analysis variable .var denotes unique patient identifier.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"","code":"s_count_patients_with_flags(   df,   .var,   flag_variables,   flag_labels = NULL,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  a_count_patients_with_flags(   df,   .var,   flag_variables,   flag_labels = NULL,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  count_patients_with_flags(   lyt,   var,   var_labels = var,   show_labels = \"hidden\",   riskdiff = FALSE,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = paste0(\"tbl_flags_\", var),   .stats = \"count_fraction\",   .formats = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"df (data.frame) data set containing analysis variables. .var (character) name column contains unique identifier. flag_variables (character) character vector specifying names logical variables analysis dataset used counting number unique identifiers. flag_labels (character) vector labels use flag variables. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. lyt (layout) input layout analyses added . var (string) single variable name passed rtables requested statistics function. var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"s_count_patients_with_flags() returns count fraction unique identifiers particular flag list statistics n, count, count_fraction, n_blq, one element per flag. a_count_patients_with_flags() returns corresponding list formatted rtables::CellValue(). count_patients_with_flags() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_flags() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"s_count_patients_with_flags(): Statistics function counts number patients particular flag variable TRUE. a_count_patients_with_flags(): Formatted analysis function used afun count_patients_with_flags(). count_patients_with_flags(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"flag_labels specified, variables labels extracted df. variables labeled, variable names used instead. Alternatively, named vector can supplied flag_variables within name-value pair name corresponds variable name value label use variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/count_patients_with_flags.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the Number of Patients with Particular Flags — count_patients_with_flags","text":"","code":"library(dplyr)  # `s_count_patients_with_flags()`  # Add labelled flag variables to analysis dataset. adae <- tern_ex_adae %>%   mutate(     fl1 = TRUE,     fl2 = TRTEMFL == \"Y\",     fl3 = TRTEMFL == \"Y\" & AEOUT == \"FATAL\",     fl4 = TRTEMFL == \"Y\" & AEOUT == \"FATAL\" & AEREL == \"Y\"   ) labels <- c(   \"fl1\" = \"Total AEs\",   \"fl2\" = \"Total number of patients with at least one adverse event\",   \"fl3\" = \"Total number of patients with fatal AEs\",   \"fl4\" = \"Total number of patients with related fatal AEs\" ) formatters::var_labels(adae)[names(labels)] <- labels  s_count_patients_with_flags(   adae,   \"SUBJID\",   flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"),   denom = \"N_col\",   .N_col = 1000 ) #> $n #> $n$`Total AEs` #> [1] 164 #>  #> $n$`Total number of patients with at least one adverse event` #> [1] 164 #>  #> $n$`Total number of patients with fatal AEs` #> [1] 164 #>  #> $n$`Total number of patients with related fatal AEs` #> [1] 164 #>  #>  #> $count #> $count$`Total AEs` #> [1] 164 #>  #> $count$`Total number of patients with at least one adverse event` #> [1] 164 #>  #> $count$`Total number of patients with fatal AEs` #> [1] 79 #>  #> $count$`Total number of patients with related fatal AEs` #> [1] 79 #>  #>  #> $count_fraction #> $count_fraction$`Total AEs` #> [1] 164.000   0.164 #>  #> $count_fraction$`Total number of patients with at least one adverse event` #> [1] 164.000   0.164 #>  #> $count_fraction$`Total number of patients with fatal AEs` #> [1] 79.000  0.079 #>  #> $count_fraction$`Total number of patients with related fatal AEs` #> [1] 79.000  0.079 #>  #>  #> $n_blq #> $n_blq$`Total AEs` #> [1] 0 #>  #> $n_blq$`Total number of patients with at least one adverse event` #> [1] 0 #>  #> $n_blq$`Total number of patients with fatal AEs` #> [1] 0 #>  #> $n_blq$`Total number of patients with related fatal AEs` #> [1] 0 #>  #>   #  We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly.  # `a_count_patients_with_flags()`  afun <- make_afun(a_count_patients_with_flags,   .stats = \"count_fraction\",   .ungroup_stats = \"count_fraction\" ) afun(   adae,   .N_col = 10L,   .N_row = 10L,   .var = \"USUBJID\",   flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\") ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>                                                   row_name formatted_cell #> 1                                                Total AEs     164 (100%) #> 2 Total number of patients with at least one adverse event     164 (100%) #> 3                  Total number of patients with fatal AEs     79 (48.2%) #> 4          Total number of patients with related fatal AEs     79 (48.2%) #>   indent_mod                                                row_label #> 1          0                                                Total AEs #> 2          0 Total number of patients with at least one adverse event #> 3          0                  Total number of patients with fatal AEs #> 4          0          Total number of patients with related fatal AEs  # `count_patients_with_flags()`  lyt2 <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   count_patients_with_flags(     \"SUBJID\",     flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"),     denom = \"N_col\"   ) build_table(lyt2, adae, alt_counts_df = tern_ex_adsl) #>                                                            A: Drug X    B: Placebo   C: Combination #>                                                              (N=69)       (N=73)         (N=58)     #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs                                                  59 (85.5%)   57 (78.1%)     48 (82.8%)   #> Total number of patients with at least one adverse event   59 (85.5%)   57 (78.1%)     48 (82.8%)   #> Total number of patients with fatal AEs                    28 (40.6%)   31 (42.5%)     20 (34.5%)   #> Total number of patients with related fatal AEs            28 (40.6%)   31 (42.5%)     20 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Specific Values — count_values_funs","title":"Counting Specific Values — count_values_funs","text":"can count occurrence specific values variable interest.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Specific Values — count_values_funs","text":"","code":"s_count_values(   x,   values,   na.rm = TRUE,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  # S3 method for character s_count_values(x, values = \"Y\", na.rm = TRUE, ...)  # S3 method for factor s_count_values(x, values = \"Y\", ...)  # S3 method for logical s_count_values(x, values = TRUE, ...)  a_count_values(   x,   values,   na.rm = TRUE,   .N_col,   .N_row,   denom = c(\"n\", \"N_row\", \"N_col\") )  count_values(   lyt,   vars,   values,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = \"count_fraction\",   .formats = NULL,   .labels = c(count_fraction = paste(values, collapse = \", \")),   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Specific Values — count_values_funs","text":"x (numeric) vector numbers want analyze. values (character) specific values counted. na.rm (flag) whether NA values removed x prior analysis. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Specific Values — count_values_funs","text":"s_count_values() returns output s_summary() specified values non-numeric variable. a_count_values() returns corresponding list formatted rtables::CellValue(). count_values() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_values() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Specific Values — count_values_funs","text":"s_count_values(): S3 generic function count values. s_count_values(character): Method character class. s_count_values(factor): Method factor class. makes automatic conversion character forwards method characters. s_count_values(logical): Method logical class. a_count_values(): Formatted analysis function used afun count_values(). count_values(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Counting Specific Values — count_values_funs","text":"factor variables, s_count_values checks whether values included levels x fails otherwise. count_values(), variable labels shown one element vars, otherwise hidden.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/count_values_funs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Specific Values — count_values_funs","text":"","code":"# `s_count_values.character` s_count_values(x = c(\"a\", \"b\", \"a\"), values = \"a\") #> $n #> [1] 3 #>  #> $count #> [1] 2 #>  #> $count_fraction #> [1] 2.0000000 0.6666667 #>  #> $n_blq #> [1] 0 #>  s_count_values(x = c(\"a\", \"b\", \"a\", NA, NA), values = \"b\", na.rm = FALSE) #> $n #> [1] 5 #>  #> $count #> [1] 1 #>  #> $count_fraction #> [1] 1.0 0.2 #>  #> $n_blq #> [1] 0 #>   # `s_count_values.factor` s_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\") #> $n #> [1] 3 #>  #> $count #> [1] 2 #>  #> $count_fraction #> [1] 2.0000000 0.6666667 #>  #> $n_blq #> [1] 0 #>   # `s_count_values.logical` s_count_values(x = c(TRUE, FALSE, TRUE)) #> $n #> [1] 3 #>  #> $count #> [1] 2 #>  #> $count_fraction #> [1] 2.0000000 0.6666667 #>  #> $n_blq #> [1] 0 #>   # `a_count_values` a_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\", .N_col = 10, .N_row = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>         row_name formatted_cell indent_mod      row_label #> 1              n              3          0              n #> 2          count              2          0          count #> 3 count_fraction     2 (66.67%)          0 count_fraction #> 4          n_blq              0          0          n_blq  # `count_values` basic_table() %>%   count_values(\"Species\", values = \"setosa\") %>%   build_table(iris) #>            all obs   #> ———————————————————— #> setosa   50 (33.33%)"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox Proportional Hazards Regression — cox_regression","title":"Cox Proportional Hazards Regression — cox_regression","text":"Fits Cox regression model estimates hazard ratio describe effect size survival analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox Proportional Hazards Regression — cox_regression","text":"","code":"s_coxreg(model_df, .stats, .which_vars = \"all\", .var_nms = NULL)  a_coxreg(   df,   labelstr,   eff = FALSE,   var_main = FALSE,   multivar = FALSE,   variables,   at = list(),   control = control_coxreg(),   .spl_context,   .stats,   .formats,   .indent_mods = NULL,   na_level = lifecycle::deprecated(),   na_str = \"\",   cache_env = NULL )  summarize_coxreg(   lyt,   variables,   control = control_coxreg(),   at = list(),   multivar = FALSE,   common_var = \"STUDYID\",   .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\"),   .formats = c(n = \"xx\", hr = \"xx.xx\", ci = \"(xx.xx, xx.xx)\", pval =     \"x.xxxx | (<0.0001)\", pval_inter = \"x.xxxx | (<0.0001)\"),   varlabels = NULL,   .indent_mods = NULL,   na_level = lifecycle::deprecated(),   na_str = \"\",   .section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox Proportional Hazards Regression — cox_regression","text":"model_df (data.frame) contains resulting model fit fit_coxreg function tidying applied via broom::tidy(). .stats (character) name statistics reported among: n: number observations (univariate ) hr: hazard ratio ci: confidence interval pval: p-value treatment effect pval_inter: p-value interaction effect treatment covariate (univariate ) .which_vars (character) rows statistics returned given model. Defaults \"\". options include \"var_main\" main effects, \"inter\" interaction effects, \"multi_lvl\" multivariate model covariate level rows. .which_vars \"\" specific variables can selected specifying .var_nms. .var_nms (character) term value rows df .stats returned. Typically name variable. using variable labels, var vector desired variable name variable label order see .stats related variable. .which_vars \"var_main\" .var_nms variable name. df (data.frame) data set containing analysis variables. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. eff (flag) whether treatment effect calculated. Defaults FALSE. var_main (flag) whether main effects calculated. Defaults FALSE. multivar (flag) Defaults FALSE. TRUE multivariate Cox regression run, otherwise univariate Cox regression run. variables (named list string) list additional analysis variables. (list numeric) candidate covariate numeric, use specify value covariate effect estimated. control (list) list parameters returned helper function control_coxreg(). .spl_context (data.frame) gives information ancestor split states passed rtables. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_level Please use na_str argument instead. na_str (string) custom string replace NA values . Defaults \"\". cache_env (environment) environment object used cache regression model order avoid repeatedly fitting model every row table. Defaults NULL (caching). lyt (layout) input layout analyses added . common_var (character) name factor variable dataset takes value rows. created pre-processing variable currently exists. varlabels (list) named list corresponds names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. .section_div (character) string repeated section divider sections. Defaults NA section divider. vector two strings given, first used treatment covariate sections second different covariates.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox Proportional Hazards Regression — cox_regression","text":"s_coxreg() returns selected statistic Cox regression model selected variable(s). a_coxreg() returns formatted rtables::CellValue(). summarize_coxreg() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add Cox regression table containing chosen statistics table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox Proportional Hazards Regression — cox_regression","text":"Cox models commonly used methods estimate magnitude effect survival analysis. assumes proportional hazards: ratio hazards groups (e.g., two arms) constant time. ratio referred \"hazard ratio\" (HR) one commonly reported metrics describe effect size survival analysis (NEST Team, 2020).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox Proportional Hazards Regression — cox_regression","text":"s_coxreg(): Statistics function transforms results tabulated fit_coxreg_univar() fit_coxreg_multivar() list. a_coxreg(): Analysis function used afun rtables::analyze() cfun rtables::summarize_row_groups() within summarize_coxreg(). summarize_coxreg(): Layout-creating function creates Cox regression summary table layout. function wrapper several rtables layouting functions. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox Proportional Hazards Regression — cox_regression","text":"","code":"library(survival)  # Testing dataset [survival::bladder]. set.seed(1, kind = \"Mersenne-Twister\") dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   tibble::tibble(     TIME = stop,     STATUS = event,     ARM = as.factor(rx),     COVAR1 = as.factor(enum) %>% formatters::with_label(\"A Covariate Label\"),     COVAR2 = factor(       sample(as.factor(enum)),       levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\")     ) %>% formatters::with_label(\"Sex (F/M)\")   ) ) dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) dta_bladder$STUDYID <- factor(\"X\")  plot(   survfit(Surv(TIME, STATUS) ~ ARM + COVAR1, data = dta_bladder),   lty = 2:4,   xlab = \"Months\",   col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") )   # s_coxreg  # Univariate u1_variables <- list(   time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder) df1 <- broom::tidy(univar_model) s_coxreg(model_df = df1, .stats = \"hr\") #> $hr #> $hr$`2 vs control (1)` #> [1] 0.6386426 #>  #>  #> $hr #> $hr$`A Covariate Label` #> [1] 0.607037 #>  #>  #> $hr #> $hr$`Sex (F/M)` #> [1] 0.6242738 #>  #>   # Univariate with interactions univar_model_inter <- fit_coxreg_univar(   variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder ) df1_inter <- broom::tidy(univar_model_inter) s_coxreg(model_df = df1_inter, .stats = \"hr\", .which_vars = \"inter\", .var_nms = \"COVAR1\") #> $hr #> $hr$`  1` #> [1] 0.6284569 #>  #> $hr$`  2` #> [1] 0.5806499 #>  #> $hr$`  3` #> [1] 0.5486103 #>  #> $hr$`  4` #> [1] 0.6910725 #>  #>   # Univariate without treatment arm - only \"COVAR2\" covariate effects u2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder) df1_covs <- broom::tidy(univar_covs_model) s_coxreg(model_df = df1_covs, .stats = \"hr\", .var_nms = c(\"COVAR2\", \"Sex (F/M)\")) #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #>  #>  #> $hr #> $hr$M #> [1] 1.3271 #>  #>   # Multivariate. m1_variables <- list(   time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder) df2 <- broom::tidy(multivar_model) s_coxreg(model_df = df2, .stats = \"pval\", .which_vars = \"var_main\", .var_nms = \"COVAR1\") #> $pval #> $pval$`A Covariate Label (reference = 1)` #> [1] 7.209956e-09 #>  #>  s_coxreg(   model_df = df2, .stats = \"pval\", .which_vars = \"multi_lvl\",   .var_nms = c(\"COVAR1\", \"A Covariate Label\") ) #> $pval #> $pval$`2` #> [1] 0.001120332 #>  #> $pval$`3` #> [1] 6.293725e-06 #>  #> $pval$`4` #> [1] 3.013875e-08 #>  #>   # Multivariate without treatment arm - only \"COVAR1\" main effect m2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder) df2_covs <- broom::tidy(multivar_covs_model) s_coxreg(model_df = df2_covs, .stats = \"hr\") #> $hr #> $hr$`2` #> [1] 0.4600728 #>  #> $hr$`3` #> [1] 0.3100455 #>  #> $hr$`4` #> [1] 0.1854177 #>  #>  #> $hr #> $hr$`A Covariate Label (reference = 1)` #> numeric(0) #>  #>  #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #>  #>  #> $hr #> $hr$M #> [1] 1.285406 #>  #>   a_coxreg(   df = dta_bladder,   labelstr = \"Label 1\",   variables = u1_variables,   .spl_context = list(value = \"COVAR1\"),   .stats = \"n\",   .formats = \"xx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1  Label 1            340          0   Label 1  a_coxreg(   df = dta_bladder,   labelstr = \"\",   variables = u1_variables,   .spl_context = list(value = \"COVAR2\"),   .stats = \"pval\",   .formats = \"xx.xxxx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>    row_name formatted_cell indent_mod row_label #> 1 Sex (F/M)         0.0182          0 Sex (F/M)  # summarize_coxreg  result_univar <- basic_table() %>%   summarize_coxreg(variables = u1_variables) %>%   build_table(dta_bladder) result_univar #>                        n    Hazard Ratio      95% CI      p-value #> ————————————————————————————————————————————————————————————————— #> Treatment:                                                        #>   2 vs control (1)    340       0.64       (0.43, 0.94)   0.0242  #> Covariate:                                                        #>   A Covariate Label   340       0.61       (0.41, 0.90)   0.0126  #>   Sex (F/M)           340       0.62       (0.42, 0.92)   0.0182   result_multivar <- basic_table() %>%   summarize_coxreg(     variables = m1_variables,     multivar = TRUE,   ) %>%   build_table(dta_bladder) result_multivar #>                                       Hazard Ratio      95% CI      p-value #> ——————————————————————————————————————————————————————————————————————————— #> Treatment:                                                                  #>   ARM (reference = 1)                                                       #>     2                                     0.61       (0.41, 0.90)   0.0123  #> Covariate:                                                                  #>   A Covariate Label (reference = 1)                                 <0.0001 #>     2                                     0.46       (0.28, 0.73)   0.0011  #>     3                                     0.31       (0.18, 0.51)   <0.0001 #>     4                                     0.18       (0.10, 0.33)   <0.0001 #>   Sex (F/M) (reference = F)                                                 #>     M                                     1.29       (0.88, 1.89)   0.1911   result_univar_covs <- basic_table() %>%   summarize_coxreg(     variables = u2_variables,   ) %>%   build_table(dta_bladder) result_univar_covs #>                                       Hazard Ratio      95% CI      p-value #> ——————————————————————————————————————————————————————————————————————————— #> Covariate:                                                                  #>   A Covariate Label (reference = 1)                                 <0.0001 #>     2                                     0.45       (0.28, 0.71)   0.0007  #>     3                                     0.31       (0.19, 0.52)   <0.0001 #>     4                                     0.18       (0.10, 0.33)   <0.0001 #>   Sex (F/M) (reference = F)                                                 #>     M                                     1.33       (0.91, 1.94)   0.1414   result_multivar_covs <- basic_table() %>%   summarize_coxreg(     variables = m2_variables,     multivar = TRUE,     varlabels = c(\"Covariate 1\", \"Covariate 2\") # custom labels   ) %>%   build_table(dta_bladder) result_multivar_covs #>                                 Hazard Ratio      95% CI      p-value #> ————————————————————————————————————————————————————————————————————— #> Covariate:                                                            #>   Covariate 1 (reference = 1)                                 <0.0001 #>     2                               0.46       (0.29, 0.74)   0.0012  #>     3                               0.31       (0.19, 0.52)   <0.0001 #>     4                               0.19       (0.10, 0.34)   <0.0001 #>   Covariate 2 (reference = F)                                         #>     M                               1.29       (0.88, 1.88)   0.1958"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox Regression Helper: Interactions — cox_regression_inter","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"Test estimate effect treatment interaction covariate. effect estimated HR tested treatment given level covariate, comparison treatment control.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"","code":"h_coxreg_inter_effect(x, effect, covar, mod, label, control, ...)  # S3 method for numeric h_coxreg_inter_effect(x, effect, covar, mod, label, control, at, ...)  # S3 method for factor h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...)  # S3 method for character h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...)  h_coxreg_extract_interaction(effect, covar, mod, data, at, control)  h_coxreg_inter_estimations(   variable,   given,   lvl_var,   lvl_given,   mod,   conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"x (numeric factor) values covariate tested. effect (string) name effect tested estimated. covar (string) name covariate model. mod (coxph) fitted Cox regression model (see survival::coxph()). label (string) label returned term_label. control (list) list controls returned control_coxreg(). ... see methods. (list) list items named covariate, every item vector levels interaction estimated. data (data.frame) data frame model fit. variable, given (string) name variables interaction. seek estimation levels variable given levels given. lvl_var, lvl_given (character) corresponding levels given levels(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"h_coxreg_inter_effect() returns data.frame covariate interaction effects consisting following variables: effect, term, term_label, level, n, hr, lcl, ucl, pval, pval_inter. h_coxreg_extract_interaction() returns result interaction test estimated values. interaction, h_coxreg_univar_extract() applied instead. h_coxreg_inter_estimations() returns list matrices (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female) model abbreviated: y ~ Arm + Sex + Arm:Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) estimation Hazard Ratio arm C/sex M given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5). interaction coefficient deduced b2 + b5 standard error obtained $sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"h_coxreg_inter_effect(): S3 generic helper function determine interaction effect. h_coxreg_inter_effect(numeric): Method numeric class. Estimates interaction numeric covariate. h_coxreg_inter_effect(factor): Method factor class. Estimate interaction factor covariate. h_coxreg_inter_effect(character): Method character class. Estimate interaction character covariate. makes automatic conversion factor forwards method factors. h_coxreg_extract_interaction(): higher level function get results interaction test estimated values. h_coxreg_inter_estimations(): Hazard ratio estimation interactions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"Automatic conversion character factor guarantee results can generated correctly. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cox_regression_inter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox Regression Helper: Interactions — cox_regression_inter","text":"","code":"library(survival)  set.seed(1, kind = \"Mersenne-Twister\")  # Testing dataset [survival::bladder]. dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   data.frame(     time = stop,     status = event,     armcd = as.factor(rx),     covar1 = as.factor(enum),     covar2 = factor(       sample(as.factor(enum)),       levels = 1:4,       labels = c(\"F\", \"F\", \"M\", \"M\")     )   ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)  plot(   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),   lty = 2:4,   xlab = \"Months\",   col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") )   mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) h_coxreg_extract_interaction(   mod = mod, effect = \"armcd\", covar = \"covar1\", data = dta_bladder,   control = control_coxreg() ) #>                    effect   term        term_label level   n        hr #> 1              Covariate: covar1 A Covariate Label       340        NA #> armcd2/covar11 Covariate: covar1                 1     1  NA 0.6341111 #> armcd2/covar12 Covariate: covar1                 2     2  NA 0.5845305 #> armcd2/covar13 Covariate: covar1                 3     3  NA 0.5507703 #> armcd2/covar14 Covariate: covar1                 4     4  NA 0.6910643 #>                      lcl      ucl      pval pval_inter #> 1                     NA       NA 0.1302825   0.988245 #> armcd2/covar11 0.3514676 1.144051        NA         NA #> armcd2/covar12 0.2716689 1.257692        NA         NA #> armcd2/covar13 0.2244668 1.351415        NA         NA #> armcd2/covar14 0.2315248 2.062715        NA         NA  mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) result <- h_coxreg_inter_estimations(   variable = \"armcd\", given = \"covar1\",   lvl_var = levels(dta_bladder$armcd),   lvl_given = levels(dta_bladder$covar1),   mod = mod, conf_level = .95 ) result #> $armcd2 #>                      coef  se(coef)        hr       lcl      ucl #> armcd2/covar11 -0.4555312 0.3010803 0.6341111 0.3514676 1.144051 #> armcd2/covar12 -0.5369464 0.3909383 0.5845305 0.2716689 1.257692 #> armcd2/covar13 -0.5964375 0.4579624 0.5507703 0.2244668 1.351415 #> armcd2/covar14 -0.3695225 0.5579418 0.6910643 0.2315248 2.062715 #>  #> attr(,\"details\") #> [1] \"Estimations of armcd hazard ratio given the level of covar1 compared to armcd level 1.\""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor Function for compare_vars() — create_afun_compare","title":"Constructor Function for compare_vars() — create_afun_compare","text":"Constructor function creates combined formatted analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor Function for compare_vars() — create_afun_compare","text":"","code":"create_afun_compare(   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor Function for compare_vars() — create_afun_compare","text":".stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor Function for compare_vars() — create_afun_compare","text":"Combined formatted analysis function use compare_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_compare.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Constructor Function for compare_vars() — create_afun_compare","text":"function deprecated favor direct implementation a_summary() argument compare set TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"Constructor function creates combined formatted analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"","code":"create_afun_summary(.stats, .formats, .labels, .indent_mods)"},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":".stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"Combined formatted analysis function use analyze_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/create_afun_summary.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Constructor Function for analyze_vars() and summarize_colvars() — create_afun_summary","text":"function deprecated favor direct implementation a_summary().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":null,"dir":"Reference","previous_headings":"","what":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"cuts numeric vector sample quantile bins.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"","code":"cut_quantile_bins(   x,   probs = c(0.25, 0.5, 0.75),   labels = NULL,   type = 7,   ordered = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"x (numeric) continuous variable values cut quantile bins. may contain NA values, used quantile calculations, included return vector. probs (proportion vector) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. labels (character) unique labels quantile bins. n probabilities probs, must n + 1 long. type (integer) type quantiles use, see stats::quantile() details. ordered (flag) result ordered factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"factor variable appropriately-labeled bins levels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"Intervals closed right side. , first bin interval [-Inf, q1] q1 first quantile, second bin (q1, q2], etc., last bin (qn, +Inf] qn last quantile.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/cut_quantile_bins.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cutting Numeric Vector into Empirical Quantile Bins — cut_quantile_bins","text":"","code":"# Default is to cut into quartile bins. cut_quantile_bins(cars$speed) #>  [1] [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   #>  [7] [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   #> [13] [0%,25%]   [0%,25%]   [0%,25%]   (25%,50%]  (25%,50%]  (25%,50%]  #> [19] (25%,50%]  (25%,50%]  (25%,50%]  (25%,50%]  (25%,50%]  (25%,50%]  #> [25] (25%,50%]  (25%,50%]  (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  #> [31] (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  (50%,75%]  #> [37] (50%,75%]  (50%,75%]  (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [43] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [49] (75%,100%] (75%,100%] #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%]  # Use custom quantiles. cut_quantile_bins(cars$speed, probs = c(0.1, 0.2, 0.6, 0.88)) #>  [1] [0%,10%]   [0%,10%]   [0%,10%]   [0%,10%]   [0%,10%]   (10%,20%]  #>  [7] (10%,20%]  (10%,20%]  (10%,20%]  (10%,20%]  (10%,20%]  (20%,60%]  #> [13] (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  #> [19] (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  #> [25] (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  (20%,60%]  #> [31] (20%,60%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  #> [37] (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  (60%,88%]  #> [43] (60%,88%]  (60%,88%]  (88%,100%] (88%,100%] (88%,100%] (88%,100%] #> [49] (88%,100%] (88%,100%] #> Levels: [0%,10%] < (10%,20%] < (20%,60%] < (60%,88%] < (88%,100%]  # Use custom labels. cut_quantile_bins(cars$speed, labels = paste0(\"Q\", 1:4)) #>  [1] Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 #> [26] Q2 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 #> Levels: Q1 < Q2 < Q3 < Q4  # NAs are preserved in result factor. ozone_binned <- cut_quantile_bins(airquality$Ozone) which(is.na(ozone_binned)) #>  [1]   5  10  25  26  27  32  33  34  35  36  37  39  42  43  45  46  52  53  54 #> [20]  55  56  57  58  59  60  61  65  72  75  83  84 102 103 107 115 119 150 # So you might want to make these explicit. explicit_na(ozone_binned) #>   [1] (50%,75%]  (50%,75%]  [0%,25%]   [0%,25%]   <Missing>  (25%,50%]  #>   [7] (25%,50%]  (25%,50%]  [0%,25%]   <Missing>  [0%,25%]   [0%,25%]   #>  [13] [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   (50%,75%]  [0%,25%]   #>  [19] (25%,50%]  [0%,25%]   [0%,25%]   [0%,25%]   [0%,25%]   (50%,75%]  #>  [25] <Missing>  <Missing>  <Missing>  (25%,50%]  (50%,75%]  (75%,100%] #>  [31] (50%,75%]  <Missing>  <Missing>  <Missing>  <Missing>  <Missing>  #>  [37] <Missing>  (25%,50%]  <Missing>  (75%,100%] (50%,75%]  <Missing>  #>  [43] <Missing>  (25%,50%]  <Missing>  <Missing>  (25%,50%]  (50%,75%]  #>  [49] (25%,50%]  [0%,25%]   [0%,25%]   <Missing>  <Missing>  <Missing>  #>  [55] <Missing>  <Missing>  <Missing>  <Missing>  <Missing>  <Missing>  #>  [61] <Missing>  (75%,100%] (50%,75%]  (50%,75%]  <Missing>  (75%,100%] #>  [67] (50%,75%]  (75%,100%] (75%,100%] (75%,100%] (75%,100%] <Missing>  #>  [73] [0%,25%]   (25%,50%]  <Missing>  [0%,25%]   (50%,75%]  (50%,75%]  #>  [79] (50%,75%]  (75%,100%] (50%,75%]  [0%,25%]   <Missing>  <Missing>  #>  [85] (75%,100%] (75%,100%] (25%,50%]  (50%,75%]  (75%,100%] (50%,75%]  #>  [91] (75%,100%] (50%,75%]  (50%,75%]  [0%,25%]   [0%,25%]   (75%,100%] #>  [97] (50%,75%]  (75%,100%] (75%,100%] (75%,100%] (75%,100%] <Missing>  #> [103] <Missing>  (50%,75%]  (25%,50%]  (75%,100%] <Missing>  (25%,50%]  #> [109] (50%,75%]  (25%,50%]  (25%,50%]  (50%,75%]  (25%,50%]  [0%,25%]   #> [115] <Missing>  (50%,75%]  (75%,100%] (75%,100%] <Missing>  (75%,100%] #> [121] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [127] (75%,100%] (50%,75%]  (50%,75%]  (25%,50%]  (25%,50%]  (25%,50%]  #> [133] (25%,50%]  (50%,75%]  (25%,50%]  (25%,50%]  [0%,25%]   [0%,25%]   #> [139] (50%,75%]  [0%,25%]   [0%,25%]   (25%,50%]  [0%,25%]   [0%,25%]   #> [145] (25%,50%]  (50%,75%]  [0%,25%]   [0%,25%]   (25%,50%]  <Missing>  #> [151] [0%,25%]   [0%,25%]   (25%,50%]  #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%] < <Missing>"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Description function produces labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(abnormal)"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"abnormal (character) identifying abnormal range level(s) .var.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Abnormal category labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description Function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(\"LOW\") #> $not_abnormal #> [1] \"Not low\" #>  #> $abnormal #> [1] \"Low\" #>  #> $total #> [1] \"Total\" #>"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of Cumulative Count — d_count_cumulative","title":"Description of Cumulative Count — d_count_cumulative","text":"helper function describes analysis s_count_cumulative().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of Cumulative Count — d_count_cumulative","text":"","code":"d_count_cumulative(threshold, lower_tail, include_eq)"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of Cumulative Count — d_count_cumulative","text":"threshold (number) cutoff value threshold count values x. lower_tail (logical) whether count lower tail, default TRUE. include_eq (logical) whether include value equal threshold count, default TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of Cumulative Count — d_count_cumulative","text":"Labels s_count_cumulative().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description Function that Calculates Labels for s_count_missed_doses(). — d_count_missed_doses","text":"","code":"d_count_missed_doses(thresholds)"},{"path":"https://insightsengineering.github.io/tern/reference/d_count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description Function that Calculates Labels for s_count_missed_doses(). — d_count_missed_doses","text":"thresholds (vector count) number missed doses patients least .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description Function that Calculates Labels for s_count_missed_doses(). — d_count_missed_doses","text":"d_count_missed_doses() returns named character vector labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of Standard Oncology Response — d_onco_rsp_label","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"Describe oncology response standard way.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label(x)"},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"x (character) standard oncology code described.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"Response labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_onco_rsp_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description of Standard Oncology Response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label(   c(\"CR\", \"PR\", \"SD\", \"NON CR/PD\", \"PD\", \"NE\", \"Missing\", \"<Missing>\", \"NE/Missing\") ) #>                           CR                           PR  #>       Complete Response (CR)        Partial Response (PR)  #>                           SD                    NON CR/PD  #>          Stable Disease (SD) Non-CR or Non-PD (NON CR/PD)  #>                           PD                           NE  #>     Progressive Disease (PD)           Not Evaluable (NE)  #>                      Missing                    <Missing>  #>                      Missing                    <Missing>  #>                   NE/Missing  #>       Missing or unevaluable  #> 9 Levels: Complete Response (CR) Partial Response (PR) ... <Missing>  # Adding some values not considered in d_onco_rsp_label  d_onco_rsp_label(   c(\"CR\", \"PR\", \"hello\", \"hi\") ) #>                     CR                     PR                  hello  #> Complete Response (CR)  Partial Response (PR)                  hello  #>                     hi  #>                     hi  #> Levels: Complete Response (CR) Partial Response (PR) hello hi"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_pkparam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate PK reference dataset — d_pkparam","text":"","code":"d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/reference/d_pkparam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate PK reference dataset — d_pkparam","text":"data.frame PK parameters","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_pkparam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate PK reference dataset — d_pkparam","text":"","code":"pk_reference_dataset <- d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the Proportion Summary — d_proportion","title":"Description of the Proportion Summary — d_proportion","text":"helper function describes analysis s_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the Proportion Summary — d_proportion","text":"","code":"d_proportion(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the Proportion Summary — d_proportion","text":"conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. long (flag) whether long short (default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the Proportion Summary — d_proportion","text":"String describing analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of Method Used for Proportion Comparison — d_proportion_diff","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"auxiliary function describes analysis s_proportion_diff.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"","code":"d_proportion_diff(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. long (logical) Whether long short (default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of Method Used for Proportion Comparison — d_proportion_diff","text":"string describing analysis.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"Internal function check variables included tabulate_rsp_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"","code":"d_rsp_subgroups_colvars(vars, conf_level = NULL, method = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"vars (character) variable names primary analysis variable iterated . conf_level (proportion) confidence level interval. method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_rsp_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Column Variables in Binary Response by Subgroup Table — d_rsp_subgroups_colvars","text":"list variables tabulate labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"Internal function check variables included tabulate_survival_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"","code":"d_survival_subgroups_colvars(vars, conf_level, method, time_unit = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"vars (character) name statistics reported among: n_tot_events: Total number events per group. n_events: Number events per group. n_tot: Total number observations per group. n: Number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. conf_level (proportion) confidence level interval. method (character) p-value method testing hazard ratio = 1. time_unit (string) label unit median survival time. Default NULL skips displaying unit.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"list variables labels tabulate.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_survival_subgroups_colvars.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Labels for Column Variables in Survival Duration by Subgroup Table — d_survival_subgroups_colvars","text":"least one n_tot n_tot_events must provided vars.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"auxiliary function describes analysis s_test_proportion_diff.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"","code":"d_test_proportion_diff(method)"},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/d_test_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the Difference Test Between Two Proportions — d_test_proportion_diff","text":"string describing test p-value derived.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of Days to Months — day2month","title":"Conversion of Days to Months — day2month","text":"Conversion Days Months","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of Days to Months — day2month","text":"","code":"day2month(x)"},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of Days to Months — day2month","text":"x (numeric) time days.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of Days to Months — day2month","text":"numeric vector time months.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/day2month.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of Days to Months — day2month","text":"","code":"x <- c(403, 248, 30, 86) day2month(x) #> [1] 13.2402464  8.1478439  0.9856263  2.8254620"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"function useful label grid grobs (also ggplot2, lattice plots) title, footnote, page numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"","code":"decorate_grob(   grob,   titles,   footnotes,   page = \"\",   width_titles = grid::unit(1, \"npc\") - grid::stringWidth(page),   width_footnotes = grid::unit(1, \"npc\") - grid::stringWidth(page),   border = TRUE,   margins = grid::unit(c(1, 0, 1, 0), \"lines\"),   padding = grid::unit(rep(1, 4), \"lines\"),   outer_margins = grid::unit(c(2, 1.5, 3, 1.5), \"cm\"),   gp_titles = grid::gpar(),   gp_footnotes = grid::gpar(fontsize = 8),   name = NULL,   gp = grid::gpar(),   vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"grob grid grob object, optionally NULL grob decoration shown. titles vector character strings. Vector elements separated newline strings wrapped according page width. footnotes vector character string. rules titles. page string page numeration, NULL page number displayed. width_titles unit object width_footnotes unit object border boolean, whether border drawn around plot . margins unit object length 4 padding unit object length 4 outer_margins unit object length 4 gp_titles gpar object gp_footnotes gpar object name character identifier grob.  Used find     grob display list /child another grob. gp \"gpar\" object, typically output     call function gpar.  basically     list graphical parameter settings. vp viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"grid grob (gTree).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"titles footnotes ragged, .e. title wrapped individually.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob — decorate_grob","text":"","code":"library(grid)  titles <- c(   \"Edgar Anderson's Iris Data\",   paste(     \"This famous (Fisher's or Anderson's) iris data set gives the measurements\",     \"in centimeters of the variables sepal length and width and petal length\",     \"and width, respectively, for 50 flowers from each of 3 species of iris.\"   ) )  footnotes <- c(   \"The species are Iris setosa, versicolor, and virginica.\",   paste(     \"iris is a data frame with 150 cases (rows) and 5 variables (columns) named\",     \"Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.\"   ) )  ## empty plot grid.newpage()  grid.draw(   decorate_grob(     NULL,     titles = titles,     footnotes = footnotes,     page = \"Page 4 of 10\"   ) )   # grid p <- gTree(   children = gList(     rectGrob(),     xaxisGrob(),     yaxisGrob(),     textGrob(\"Sepal.Length\", y = unit(-4, \"lines\")),     textGrob(\"Petal.Length\", x = unit(-3.5, \"lines\"), rot = 90),     pointsGrob(iris$Sepal.Length, iris$Petal.Length, gp = gpar(col = iris$Species), pch = 16)   ),   vp = vpStack(plotViewport(), dataViewport(xData = iris$Sepal.Length, yData = iris$Petal.Length)) ) grid.newpage() grid.draw(p)   grid.newpage() grid.draw(   decorate_grob(     grob = p,     titles = titles,     footnotes = footnotes,     page = \"Page 6 of 129\"   ) )   ## with ggplot2 library(ggplot2)  p_gg <- ggplot2::ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) +   ggplot2::geom_point() p_gg  p <- ggplotGrob(p_gg) grid.newpage() grid.draw(   decorate_grob(     grob = p,     titles = titles,     footnotes = footnotes,     page = \"Page 6 of 129\"   ) )   ## with lattice library(lattice)  xyplot(Sepal.Length ~ Petal.Length, data = iris, col = iris$Species)  p <- grid.grab()  grid.newpage() grid.draw(   decorate_grob(     grob = p,     titles = titles,     footnotes = footnotes,     page = \"Page 6 of 129\"   ) )   # with gridExtra - no borders library(gridExtra) #>  #> Attaching package: ‘gridExtra’ #> The following object is masked from ‘package:dplyr’: #>  #>     combine grid.newpage() grid.draw(   decorate_grob(     tableGrob(       head(mtcars)     ),     titles = \"title\",     footnotes = \"footnote\",     border = FALSE   ) )"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Update Page Number — decorate_grob_factory","title":"Update Page Number — decorate_grob_factory","text":"Automatically updates page number.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update Page Number — decorate_grob_factory","text":"","code":"decorate_grob_factory(npages, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update Page Number — decorate_grob_factory","text":"npages number pages total ... passed decorate_grob()","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update Page Number — decorate_grob_factory","text":"Closure increments page number.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"Note uses decorate_grob_factory() function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"","code":"decorate_grob_set(grobs, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"grobs list grid grobs ... arguments passed decorate_grob().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"decorated grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/decorate_grob_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Decorate Set of grobs and Add Page Numbering — decorate_grob_set","text":"","code":"library(ggplot2) library(grid) g <- with(data = iris, {   list(     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Length, Sepal.Width, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Length, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Width, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Length, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Width, col = Species)) +         ggplot2::geom_point()     ),     ggplot2::ggplotGrob(       ggplot2::ggplot(mapping = aes(Petal.Length, Petal.Width, col = Species)) +         ggplot2::geom_point()     )   ) }) lg <- decorate_grob_set(grobs = g, titles = \"Hello\\nOne\\nTwo\\nThree\", footnotes = \"\")  draw_grob(lg[[1]])  draw_grob(lg[[2]])  draw_grob(lg[[6]])"},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"Utility functions get valid statistic methods different method groups (.stats) associated formats (.formats) labels (.labels). utility used across tern, working principles can seen analyze_vars(). See notes understand experimental.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"","code":"get_stats(   method_groups = \"analyze_vars_numeric\",   stats_in = NULL,   add_pval = FALSE )  get_formats_from_stats(stats, formats_in = NULL)  get_labels_from_stats(stats, labels_in = NULL)  tern_default_formats  tern_default_labels  summary_formats(type = \"numeric\", include_pval = FALSE)  summary_labels(type = \"numeric\", include_pval = FALSE)  summary_custom(   type = \"numeric\",   include_pval = FALSE,   stats_custom = NULL,   formats_custom = NULL,   labels_custom = NULL,   indent_mods_custom = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"tern_default_formats list available formats, named relevant statistic. tern_default_labels character vector available labels, named relevant statistic.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"method_groups (character) indicates group statistical methods need defaults . character vector can used collect one group statistical methods. stats_in (character) desired stats picked selected method group. add_pval (flag) \"pval\" \"pval_counts\" (method_groups contains \"analyze_vars_counts\") added statistical methods? stats (character) statistical methods get defaults formats labels . formats_in (named vector)  inserted formats replace defaults. can character vector formatters::list_valid_format_labels() custom format function. labels_in (named vector)  inserted labels replace defaults. type (flag) going \"numeric\" \"counts\"? include_pval (flag) deprecated parameter. add_pval. stats_custom (named vector character) vector statistics include defaults. argument overrides include_pval custom value arguments settings statistics returned. formats_custom (named vector character) vector custom statistics formats use place defaults defined summary_formats(). Names subset statistics defined stats_custom (default statistics NULL). labels_custom (named vector character) vector custom statistics labels use place defaults defined summary_labels(). Names subset statistics defined stats_custom (default statistics NULL). indent_mods_custom (integer named vector integer) vector custom indentation modifiers statistics use instead default 0L statistics. Names subset statistics defined stats_custom (default statistics NULL). Alternatively, indentation modifier can applied statistics setting indent_mods_custom single integer value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"get_stats() returns character vector default statistical methods. get_formats_from_stats() returns named list formats, value formatters::list_valid_format_labels() custom function (e.g. formatting_functions). get_labels_from_stats() returns named character vector default labels (present otherwise NULL). summary_formats() returns named vector default statistic formats given data type. summary_labels returns named vector default statistic labels given data type. summary_custom returns list 4 named elements: stats, formats, labels, indent_mods.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"Current choices type counts numeric analyze_vars() affect get_stats().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"get_stats(): Get defaults statistical methods different groups methods. get_formats_from_stats(): Get formats vector statistical methods. present NULL returned. get_labels_from_stats(): Get labels vector statistical methods. tern_default_formats: Named list default formats tern. tern_default_labels: character vector contains default labels tern. summary_formats(): Quick function retrieve default formats summary statistics: analyze_vars() analyze_vars_in_cols() principally. summary_labels(): Quick function retrieve default labels summary statistics. Returns labels descriptive statistics understood rtables. Similar summary_formats summary_custom():  Function configure settings default custom summary statistics given data type. addition selecting custom subset statistics, user can also set custom formats, labels, indent modifiers statistics.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"defaults experimental use names functions retrieve default statistics. generalized groups methods according reasonable groupings. Formats tern rtables can functions take table cell value return string. well documented vignette(\"custom_appearance\", package = \"rtables\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/default_stats_formats_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Defaults for statistical method names and their associated formats & labels — default_stats_formats_labels","text":"","code":"# analyze_vars is numeric num_stats <- get_stats(\"analyze_vars_numeric\") # also the default  # Other type cnt_stats <- get_stats(\"analyze_vars_counts\")  # Weirdly taking the pval from count_occurrences only_pval <- get_stats(\"count_occurrences\", add_pval = TRUE, stats_in = \"pval\")  # All count_occurrences all_cnt_occ <- get_stats(\"count_occurrences\")  # Multiple get_stats(c(\"count_occurrences\", \"analyze_vars_counts\")) #> [1] \"count\"                   \"count_fraction_fixed_dp\" #> [3] \"fraction\"                \"n\"                       #> [5] \"count_fraction\"          \"n_blq\"                    # Defaults formats get_formats_from_stats(num_stats) #> $n #> [1] \"xx.\" #>  #> $sum #> [1] \"xx.x\" #>  #> $mean #> [1] \"xx.x\" #>  #> $sd #> [1] \"xx.x\" #>  #> $se #> [1] \"xx.x\" #>  #> $mean_sd #> [1] \"xx.x (xx.x)\" #>  #> $mean_se #> [1] \"xx.x (xx.x)\" #>  #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_pval #> [1] \"xx.xx\" #>  #> $median #> [1] \"xx.x\" #>  #> $mad #> [1] \"xx.x\" #>  #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $quantiles #> [1] \"xx.x - xx.x\" #>  #> $iqr #> [1] \"xx.x\" #>  #> $range #> [1] \"xx.x - xx.x\" #>  #> $min #> [1] \"xx.x\" #>  #> $max #> [1] \"xx.x\" #>  #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #>  #> $cv #> [1] \"xx.x\" #>  #> $geom_mean #> [1] \"xx.x\" #>  #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $geom_cv #> [1] \"xx.x\" #>  get_formats_from_stats(cnt_stats) #> $n #> [1] \"xx.\" #>  #> $count #> [1] \"xx.\" #>  #> $count_fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else { #>     paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $n_blq #> [1] \"xx.\" #>  get_formats_from_stats(only_pval) #> $pval #> [1] \"x.xxxx | (<0.0001)\" #>  get_formats_from_stats(all_cnt_occ) #> $count #> [1] \"xx.\" #>  #> $count_fraction_fixed_dp #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else if (x[2] == 1) { #>     sprintf(\"%d (100%%)\", x[1]) #>   } else { #>     sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>   checkmate::assert_vector(x) #>   checkmate::assert_count(x[\"num\"]) #>   checkmate::assert_count(x[\"denom\"]) #>  #>   result <- if (x[\"num\"] == 0) { #>     paste0(x[\"num\"], \"/\", x[\"denom\"]) #>   } else { #>     paste0( #>       x[\"num\"], \"/\", x[\"denom\"], #>       \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #>     ) #>   } #>   return(result) #> } #> <environment: namespace:tern> #>   # Addition of customs get_formats_from_stats(all_cnt_occ, formats_in = c(\"fraction\" = c(\"xx\"))) #> $count #> [1] \"xx.\" #>  #> $count_fraction_fixed_dp #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else if (x[2] == 1) { #>     sprintf(\"%d (100%%)\", x[1]) #>   } else { #>     sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $fraction #> [1] \"xx\" #>  get_formats_from_stats(all_cnt_occ, formats_in = list(\"fraction\" = c(\"xx.xx\", \"xx\"))) #> $count #> [1] \"xx.\" #>  #> $count_fraction_fixed_dp #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else if (x[2] == 1) { #>     sprintf(\"%d (100%%)\", x[1]) #>   } else { #>     sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $fraction #> [1] \"xx.xx\" \"xx\"    #>   # Defaults labels get_labels_from_stats(num_stats) #>                             n                           sum  #>                           \"n\"                         \"Sum\"  #>                          mean                            sd  #>                        \"Mean\"                          \"SD\"  #>                            se                       mean_sd  #>                          \"SE\"                   \"Mean (SD)\"  #>                       mean_se                       mean_ci  #>                   \"Mean (SE)\"                 \"Mean 95% CI\"  #>                      mean_sei                      mean_sdi  #>               \"Mean -/+ 1xSE\"               \"Mean -/+ 1xSD\"  #>                     mean_pval                        median  #> \"Mean p-value (H0: mean = 0)\"                      \"Median\"  #>                           mad                     median_ci  #>   \"Median Absolute Deviation\"               \"Median 95% CI\"  #>                     quantiles                           iqr  #>             \"25% and 75%-ile\"                         \"IQR\"  #>                         range                           min  #>                   \"Min - Max\"                     \"Minimum\"  #>                           max                  median_range  #>                     \"Maximum\"          \"Median (Min - Max)\"  #>                            cv                     geom_mean  #>                      \"CV (%)\"              \"Geometric Mean\"  #>                  geom_mean_ci                       geom_cv  #>       \"Geometric Mean 95% CI\"         \"CV % Geometric Mean\"  get_labels_from_stats(cnt_stats) #>                n            count   count_fraction            n_blq  #>              \"n\"          \"count\" \"count_fraction\"          \"n_blq\"  get_labels_from_stats(only_pval) #>               pval  #> \"p-value (t-test)\"  get_labels_from_stats(all_cnt_occ) #>                   count count_fraction_fixed_dp                fraction  #>                 \"count\"                      \"\"                      \"\"   # Addition of customs get_labels_from_stats(all_cnt_occ, labels_in = c(\"fraction\" = \"Fraction\")) #>                   count count_fraction_fixed_dp                fraction  #>                 \"count\"                      \"\"              \"Fraction\"  get_labels_from_stats(all_cnt_occ, labels_in = list(\"fraction\" = c(\"Some more fractions\"))) #> $count #> [1] \"count\" #>  #> $count_fraction_fixed_dp #> [1] \"\" #>  #> $fraction #> [1] \"Some more fractions\" #>   summary_formats() #> $n #> [1] \"xx.\" #>  #> $sum #> [1] \"xx.x\" #>  #> $mean #> [1] \"xx.x\" #>  #> $sd #> [1] \"xx.x\" #>  #> $se #> [1] \"xx.x\" #>  #> $mean_sd #> [1] \"xx.x (xx.x)\" #>  #> $mean_se #> [1] \"xx.x (xx.x)\" #>  #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #>  #> $mean_pval #> [1] \"xx.xx\" #>  #> $median #> [1] \"xx.x\" #>  #> $mad #> [1] \"xx.x\" #>  #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $quantiles #> [1] \"xx.x - xx.x\" #>  #> $iqr #> [1] \"xx.x\" #>  #> $range #> [1] \"xx.x - xx.x\" #>  #> $min #> [1] \"xx.x\" #>  #> $max #> [1] \"xx.x\" #>  #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #>  #> $cv #> [1] \"xx.x\" #>  #> $geom_mean #> [1] \"xx.x\" #>  #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $geom_cv #> [1] \"xx.x\" #>  summary_formats(type = \"counts\", include_pval = TRUE) #> $n #> [1] \"xx.\" #>  #> $count #> [1] \"xx.\" #>  #> $count_fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else { #>     paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $n_blq #> [1] \"xx.\" #>  #> $pval_counts #> [1] \"x.xxxx | (<0.0001)\" #>   summary_labels() #>                             n                           sum  #>                           \"n\"                         \"Sum\"  #>                          mean                            sd  #>                        \"Mean\"                          \"SD\"  #>                            se                       mean_sd  #>                          \"SE\"                   \"Mean (SD)\"  #>                       mean_se                       mean_ci  #>                   \"Mean (SE)\"                 \"Mean 95% CI\"  #>                      mean_sei                      mean_sdi  #>               \"Mean -/+ 1xSE\"               \"Mean -/+ 1xSD\"  #>                     mean_pval                        median  #> \"Mean p-value (H0: mean = 0)\"                      \"Median\"  #>                           mad                     median_ci  #>   \"Median Absolute Deviation\"               \"Median 95% CI\"  #>                     quantiles                           iqr  #>             \"25% and 75%-ile\"                         \"IQR\"  #>                         range                           min  #>                   \"Min - Max\"                     \"Minimum\"  #>                           max                  median_range  #>                     \"Maximum\"          \"Median (Min - Max)\"  #>                            cv                     geom_mean  #>                      \"CV (%)\"              \"Geometric Mean\"  #>                  geom_mean_ci                       geom_cv  #>       \"Geometric Mean 95% CI\"         \"CV % Geometric Mean\"  summary_labels(type = \"counts\", include_pval = TRUE) #>                            n                        count  #>                          \"n\"                      \"count\"  #>               count_fraction                        n_blq  #>             \"count_fraction\"                      \"n_blq\"  #>                  pval_counts  #> \"p-value (chi-squared test)\"   summary_custom() #> Warning: `summary_custom()` was deprecated in tern 0.9.0.9001. #> ℹ Please use `get_stats`, `get_formats_from_stats`, and `get_labels_from_stats` #>   directly instead. #> $stats #>  [1] \"n\"            \"sum\"          \"mean\"         \"sd\"           \"se\"           #>  [6] \"mean_sd\"      \"mean_se\"      \"mean_ci\"      \"mean_sei\"     \"mean_sdi\"     #> [11] \"mean_pval\"    \"median\"       \"mad\"          \"median_ci\"    \"quantiles\"    #> [16] \"iqr\"          \"range\"        \"min\"          \"max\"          \"median_range\" #> [21] \"cv\"           \"geom_mean\"    \"geom_mean_ci\" \"geom_cv\"      #>  #> $formats #> $formats$n #> [1] \"xx.\" #>  #> $formats$sum #> [1] \"xx.x\" #>  #> $formats$mean #> [1] \"xx.x\" #>  #> $formats$sd #> [1] \"xx.x\" #>  #> $formats$se #> [1] \"xx.x\" #>  #> $formats$mean_sd #> [1] \"xx.x (xx.x)\" #>  #> $formats$mean_se #> [1] \"xx.x (xx.x)\" #>  #> $formats$mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$mean_sei #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$mean_sdi #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$mean_pval #> [1] \"xx.xx\" #>  #> $formats$median #> [1] \"xx.x\" #>  #> $formats$mad #> [1] \"xx.x\" #>  #> $formats$median_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$quantiles #> [1] \"xx.x - xx.x\" #>  #> $formats$iqr #> [1] \"xx.x\" #>  #> $formats$range #> [1] \"xx.x - xx.x\" #>  #> $formats$min #> [1] \"xx.x\" #>  #> $formats$max #> [1] \"xx.x\" #>  #> $formats$median_range #> [1] \"xx.x (xx.x - xx.x)\" #>  #> $formats$cv #> [1] \"xx.x\" #>  #> $formats$geom_mean #> [1] \"xx.x\" #>  #> $formats$geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #>  #> $formats$geom_cv #> [1] \"xx.x\" #>  #>  #> $labels #>                             n                           sum  #>                           \"n\"                         \"Sum\"  #>                          mean                            sd  #>                        \"Mean\"                          \"SD\"  #>                            se                       mean_sd  #>                          \"SE\"                   \"Mean (SD)\"  #>                       mean_se                       mean_ci  #>                   \"Mean (SE)\"                 \"Mean 95% CI\"  #>                      mean_sei                      mean_sdi  #>               \"Mean -/+ 1xSE\"               \"Mean -/+ 1xSD\"  #>                     mean_pval                        median  #> \"Mean p-value (H0: mean = 0)\"                      \"Median\"  #>                           mad                     median_ci  #>   \"Median Absolute Deviation\"               \"Median 95% CI\"  #>                     quantiles                           iqr  #>             \"25% and 75%-ile\"                         \"IQR\"  #>                         range                           min  #>                   \"Min - Max\"                     \"Minimum\"  #>                           max                  median_range  #>                     \"Maximum\"          \"Median (Min - Max)\"  #>                            cv                     geom_mean  #>                      \"CV (%)\"              \"Geometric Mean\"  #>                  geom_mean_ci                       geom_cv  #>       \"Geometric Mean 95% CI\"         \"CV % Geometric Mean\"  #>  #> $indent_mods #>            n          sum         mean           sd           se      mean_sd  #>            0            0            0            0            0            0  #>      mean_se      mean_ci     mean_sei     mean_sdi    mean_pval       median  #>            0            0            0            0            0            0  #>          mad    median_ci    quantiles          iqr        range          min  #>            0            0            0            0            0            0  #>          max median_range           cv    geom_mean geom_mean_ci      geom_cv  #>            0            0            0            0            0            0  #>  summary_custom(type = \"counts\", include_pval = TRUE) #> $stats #> [1] \"n\"              \"count\"          \"count_fraction\" \"n_blq\"          #> [5] \"pval_counts\"    #>  #> $formats #> $formats$n #> [1] \"xx.\" #>  #> $formats$count #> [1] \"xx.\" #>  #> $formats$count_fraction #> function(x, ...) { #>   attr(x, \"label\") <- NULL #>  #>   if (any(is.na(x))) { #>     return(\"NA\") #>   } #>  #>   checkmate::assert_vector(x) #>   checkmate::assert_integerish(x[1]) #>   assert_proportion_value(x[2], include_boundaries = TRUE) #>  #>   result <- if (x[1] == 0) { #>     \"0\" #>   } else { #>     paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #>   } #>  #>   return(result) #> } #> <environment: namespace:tern> #>  #> $formats$n_blq #> [1] \"xx.\" #>  #> $formats$pval_counts #> [1] \"x.xxxx | (<0.0001)\" #>  #>  #> $labels #>                            n                        count  #>                          \"n\"                      \"count\"  #>               count_fraction                        n_blq  #>             \"count_fraction\"                      \"n_blq\"  #>                  pval_counts  #> \"p-value (chi-squared test)\"  #>  #> $indent_mods #>              n          count count_fraction          n_blq    pval_counts  #>              0              0              0              0              0  #>  summary_custom(   include_pval = TRUE, stats_custom = c(\"n\", \"mean\", \"sd\", \"pval\"),   labels_custom = c(sd = \"Std. Dev.\"), indent_mods_custom = 3L ) #> $stats #> [1] \"n\"    \"mean\" \"sd\"   \"pval\" #>  #> $formats #> $formats$n #> [1] \"xx.\" #>  #> $formats$mean #> [1] \"xx.x\" #>  #> $formats$sd #> [1] \"xx.x\" #>  #> $formats$pval #> [1] \"x.xxxx | (<0.0001)\" #>  #>  #> $labels #>                  n               mean                 sd               pval  #>                \"n\"             \"Mean\"        \"Std. Dev.\" \"p-value (t-test)\"  #>  #> $indent_mods #>    n mean   sd pval  #>    3    3    3    3  #>"},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Intervals for a Difference of Binomials — desctools_binom","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"Several confidence intervals difference proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"","code":"desctools_binom(   x1,   n1,   x2,   n2,   conf.level = 0.95,   sides = c(\"two.sided\", \"left\", \"right\"),   method = c(\"ac\", \"wald\", \"waldcc\", \"score\", \"scorecc\", \"mn\", \"mee\", \"blj\", \"ha\", \"hal\",     \"jp\") )  desctools_binomci(   x,   n,   conf.level = 0.95,   sides = c(\"two.sided\", \"left\", \"right\"),   method = c(\"wilson\", \"wald\", \"waldcc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\",     \"wilsoncc\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\",     \"pratt\", \"midp\", \"lik\", \"blaker\"),   rand = 123,   tol = 1e-05 )"},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"conf.level (proportion) confidence level, defaults 0.95. sides (character) side confidence interval compute. Must one \"two-sided\" (default), \"left\", \"right\". method (character) method use. Can one : \"wald\", \"wilson\", \"wilsoncc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\", \"pratt\", \"midp\", \"lik\", \"blaker\". x (count) number successes n (count) number trials","code":""},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"matrix 3 values: est: estimate proportion difference. lwr.ci: estimate lower end confidence interval. upr.ci: estimate upper end confidence interval. matrix 3 columns containing: est: estimate proportion difference. lwr.ci: lower end confidence interval. upr.ci: upper end confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/desctools_binom.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Confidence Intervals for a Difference of Binomials — desctools_binom","text":"desctools_binom(): Several confidence intervals difference proportions. desctools_binomci(): Compute confidence intervals binomial proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"helper function encode missing entries across groups categorical variables data frame.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"","code":"df_explicit_na(   data,   omit_columns = NULL,   char_as_factor = TRUE,   logical_as_factor = FALSE,   na_level = \"<Missing>\" )"},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"data (data.frame) data set. omit_columns (character) names variables data modified function. char_as_factor (flag) whether convert character variables data factors. logical_as_factor (flag) whether convert logical variables data factors. na_level (string) used replace NA empty values inside non-omit_columns columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"data.frame chosen modifications applied.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"Missing entries NA empty strings replaced specified value. factor variables include missing values, missing value inserted last level. Similarly, case character logical variables converted factors char_as_factor logical_as_factor options, missing values set last level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/df_explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encode Categorical Missing Values in a Data Frame — df_explicit_na","text":"","code":"my_data <- data.frame(   u = c(TRUE, FALSE, NA, TRUE),   v = factor(c(\"A\", NA, NA, NA), levels = c(\"Z\", \"A\")),   w = c(\"A\", \"B\", NA, \"C\"),   x = c(\"D\", \"E\", \"F\", NA),   y = c(\"G\", \"H\", \"I\", \"\"),   z = c(1, 2, 3, 4),   stringsAsFactors = FALSE )  # Example 1 # Encode missing values in all character or factor columns. df_explicit_na(my_data) #>       u         v         w         x         y z #> 1  TRUE         A         A         D         G 1 #> 2 FALSE <Missing>         B         E         H 2 #> 3    NA <Missing> <Missing>         F         I 3 #> 4  TRUE <Missing>         C <Missing> <Missing> 4 # Also convert logical columns to factor columns. df_explicit_na(my_data, logical_as_factor = TRUE) #>           u         v         w         x         y z #> 1      TRUE         A         A         D         G 1 #> 2     FALSE <Missing>         B         E         H 2 #> 3 <Missing> <Missing> <Missing>         F         I 3 #> 4      TRUE <Missing>         C <Missing> <Missing> 4 # Encode missing values in a subset of columns. df_explicit_na(my_data, omit_columns = c(\"x\", \"y\")) #>       u         v         w    x y z #> 1  TRUE         A         A    D G 1 #> 2 FALSE <Missing>         B    E H 2 #> 3    NA <Missing> <Missing>    F I 3 #> 4  TRUE <Missing>         C <NA>   4  # Example 2 # Here we purposefully convert all `M` values to `NA` in the `SEX` variable. # After running `df_explicit_na` the `NA` values are encoded as `<Missing>` but they are not # included when generating `rtables`. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl)  # If you want the `Na` values to be displayed in the table use the `na_level` argument. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\")  # Example 3 # Numeric variables that have missing values are not altered. This means that any `NA` value in # a numeric variable will not be included in the summary statistics, nor will they be included # in the denominator value for calculating the percent values. adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl)"},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw grob — draw_grob","title":"Draw grob — draw_grob","text":"Draw grob device page.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw grob — draw_grob","text":"","code":"draw_grob(grob, newpage = TRUE, vp = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw grob — draw_grob","text":"grob grid object newpage draw new page vp viewport() object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw grob — draw_grob","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/draw_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw grob — draw_grob","text":"","code":"library(dplyr) library(grid)  # \\donttest{ rect <- rectGrob(width = grid::unit(0.5, \"npc\"), height = grid::unit(0.5, \"npc\")) rect %>% draw_grob(vp = grid::viewport(angle = 45))   num <- lapply(1:10, textGrob) num %>%   arrange_grobs(grobs = .) %>%   draw_grob() showViewport()  # }"},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Return an empty numeric if all elements are NA. — empty_vector_if_na","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"Return empty numeric elements NA.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"empty_vector_if_na(x)"},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"x (numeric) vector.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"empty numeric elements x NA, otherwise x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/empty_vector_if_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"x <- c(NA, NA, NA) # Internal function - empty_vector_if_na"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Hazard Ratio Estimation in Interactions — estimate_coef","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"function estimates hazard ratios arms interaction variable given specific values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"","code":"estimate_coef(   variable,   given,   lvl_var,   lvl_given,   coef,   mmat,   vcov,   conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"variable, given Names two variable interaction. seek estimation levels variable given levels given. lvl_var, lvl_given corresponding levels given levels. coef Numeric estimated coefficients. mmat name numeric filled 0 used template obtain design matrix. vcov Variance-covariance matrix underlying model. conf_level Single numeric confidence level estimate intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"list matrix (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female). model abbreviated: y ~ Arm + Sex + Arm x Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) Given want estimation Hazard Ratio arm C/sex M, estimation given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5), therefore interaction coefficient given b2 + b5 standard error obtained $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ confidence level 0.95.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/estimate_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hazard Ratio Estimation in Interactions — estimate_coef","text":"","code":"library(dplyr) library(survival)  ADSL <- tern_ex_adsl %>%   filter(SEX %in% c(\"F\", \"M\"))  adtte <- tern_ex_adtte %>% filter(PARAMCD == \"PFS\") adtte$ARMCD <- droplevels(adtte$ARMCD) adtte$SEX <- droplevels(adtte$SEX)  mod <- coxph(   formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2,   data = adtte )  mmat <- stats::model.matrix(mod)[1, ] mmat[!mmat == 0] <- 0"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"Estimate proportion along confidence interval proportion regarding level factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"","code":"s_length_proportion(x, .N_col, ...)  a_length_proportion(x, .N_col, ...)  estimate_multinomial_response(   lyt,   var,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = var,   .stats = \"prop_ci\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"x (numeric) vector numbers want analyze. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. ... additional arguments lower level functions. lyt (layout) input layout analyses added . var (string) single variable name passed rtables requested statistics function. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"s_length_proportion() returns statistics s_proportion(). a_length_proportion() returns corresponding list formatted rtables::CellValue(). estimate_multinomial_response() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_length_proportion() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"s_length_proportion(): Statistics function feeds length x number successes, .N_col total number successes failures s_proportion(). a_length_proportion(): Formatted analysis function used afun estimate_multinomial_response(). estimate_multinomial_response(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/estimate_multinomial_rsp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimation of Proportions per Level of Factor — estimate_multinomial_rsp","text":"","code":"s_length_proportion(rep(\"CR\", 10), .N_col = 100) #> $n_prop #> [1] 10.0  0.1 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #> [1]  3.620108 16.379892 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #>  s_length_proportion(factor(character(0)), .N_col = 100) #> $n_prop #> [1] 0 0 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #> [1] 0.0 0.5 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #>   a_length_proportion(rep(\"CR\", 10), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod                      row_label #> 1   n_prop     10 (10.0%)          0                     Responders #> 2  prop_ci  (3.62, 16.38)          0 95% CI (Wald, with correction) a_length_proportion(factor(character(0)), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod                      row_label #> 1   n_prop       0 (0.0%)          0                     Responders #> 2  prop_ci   (0.00, 0.50)          0 95% CI (Wald, with correction)  library(dplyr)  # Use of the layout creating function. dta_test <- data.frame(   USUBJID = paste0(\"S\", 1:12),   ARM     = factor(rep(LETTERS[1:3], each = 4)),   AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0)) ) %>% mutate(   AVALC = factor(AVAL,     levels = c(0, 1),     labels = c(\"Complete Response (CR)\", \"Partial Response (PR)\")   ) )  lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   estimate_multinomial_response(var = \"AVALC\")  tbl <- build_table(lyt, dta_test)  html <- as_html(tbl) html #> <div class=\"rtables-all-parts-block rtables-container\"> #>   <table class=\"table table-condensed table-hover\"> #>     <tr> #>       <th style=\"white-space:pre;\"><\/th> #>       <th class=\"text-center\">A<\/th> #>       <th class=\"text-center\">B<\/th> #>       <th class=\"text-center\">C<\/th> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\">Complete Response (CR)<\/td> #>       <td class=\"text-center\">0 (0.0%)<\/td> #>       <td class=\"text-center\">2 (50.0%)<\/td> #>       <td class=\"text-center\">4 (100.0%)<\/td> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\" style=\"padding-left: 3ch\">95% CI (Wald, with correction)<\/td> #>       <td class=\"text-center\">(0.00, 12.50)<\/td> #>       <td class=\"text-center\">(0.00, 100.00)<\/td> #>       <td class=\"text-center\">(87.50, 100.00)<\/td> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\">Partial Response (PR)<\/td> #>       <td class=\"text-center\">4 (100.0%)<\/td> #>       <td class=\"text-center\">2 (50.0%)<\/td> #>       <td class=\"text-center\">0 (0.0%)<\/td> #>     <\/tr> #>     <tr> #>       <td class=\"text-left\" style=\"padding-left: 3ch\">95% CI (Wald, with correction)<\/td> #>       <td class=\"text-center\">(87.50, 100.00)<\/td> #>       <td class=\"text-center\">(0.00, 100.00)<\/td> #>       <td class=\"text-center\">(0.00, 12.50)<\/td> #>     <\/tr> #>     <caption style=\"caption-side:top;\"><div class=\"rtables-titles-block rtables-container\"> #>         <div class=\"rtables-main-titles-block rtables-container\"> #>           <p class=\"rtables-main-title\"><\/p> #>         <\/div> #>         <div class=\"rtables-subtitles-block rtables-container\"><\/div> #>       <\/div> #>     <\/caption> #>   <\/table> #>   <div class=\"rtables-footers-block rtables-container\"><\/div> #> <\/div> # \\donttest{ Viewer(html) # }"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimation of Proportions — estimate_proportions","title":"Estimation of Proportions — estimate_proportions","text":"Estimate proportion responders within studied population.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimation of Proportions — estimate_proportions","text":"","code":"s_proportion(   df,   .var,   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\",     \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"),   weights = NULL,   max_iterations = 50,   variables = list(strata = NULL),   long = FALSE )  a_proportion(   df,   .var,   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\",     \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"),   weights = NULL,   max_iterations = 50,   variables = list(strata = NULL),   long = FALSE )  estimate_proportion(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimation of Proportions — estimate_proportions","text":"df (logical data.frame) logical vector used, indicates whether subject responder . TRUE represents successful outcome. data.frame provided, also strata variable names must provided variables list element strata strings. case data.frame, logical vector responses must indicated variable name .var. .var (string) single variable name passed rtables requested statistics function. conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010)  minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights. variables (named list string) list additional analysis variables. long (flag) long description required. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments ultimately conveyed s_proportion(). show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimation of Proportions — estimate_proportions","text":"s_proportion() returns statistics n_prop (n proportion) prop_ci (proportion CI) given variable. a_proportion() returns corresponding list formatted rtables::CellValue(). estimate_proportion() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Estimation of Proportions — estimate_proportions","text":"s_proportion(): Statistics function estimating proportion along confidence interval. a_proportion(): Formatted analysis function used afun estimate_proportion(). estimate_proportion(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/estimate_proportions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimation of Proportions — estimate_proportions","text":"","code":"# Case with only logical vector. rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0) s_proportion(rsp_v) #> $n_prop #> [1] 4.0 0.5 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #> [1]  9.102404 90.897596 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #>   # Example for Stratified Wilson CI nex <- 100 # Number of example rows dta <- data.frame(   \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE),   \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE),   \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE),   stringsAsFactors = TRUE )  s_proportion(   df = dta,   .var = \"rsp\",   variables = list(strata = c(\"f1\", \"f2\")),   conf_level = 0.90,   method = \"strat_wilson\" ) #> $n_prop #> [1] 49.00  0.49 #> attr(,\"label\") #> [1] \"Responders\" #>  #> $prop_ci #>    lower    upper  #> 40.80675 56.65017  #> attr(,\"label\") #> [1] \"90% CI (Stratified Wilson, without correction)\" #>   dta_test <- data.frame(   USUBJID = paste0(\"S\", 1:12),   ARM     = rep(LETTERS[1:3], each = 4),   AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0)) )  basic_table() %>%   split_cols_by(\"ARM\") %>%   estimate_proportion(vars = \"AVAL\") %>%   build_table(df = dta_test) #>                                        A              B              C      #> ——————————————————————————————————————————————————————————————————————————— #> Responders                        4 (100.0%)      2 (50.0%)      0 (0.0%)   #> 95% CI (Wald, with correction)   (87.5, 100.0)   (0.0, 100.0)   (0.0, 12.5)"},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated CDISC Data for Examples — ex_data","title":"Simulated CDISC Data for Examples — ex_data","text":"Simulated CDISC Data Examples","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated CDISC Data for Examples — ex_data","text":"","code":"tern_ex_adsl  tern_ex_adae  tern_ex_adlb  tern_ex_adpp  tern_ex_adrs  tern_ex_adtte"},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated CDISC Data for Examples — ex_data","text":"rds (data.frame) object class tbl_df (inherits tbl, data.frame) 200 rows 21 columns. object class tbl_df (inherits tbl, data.frame) 541 rows 42 columns. object class tbl_df (inherits tbl, data.frame) 4200 rows 50 columns. object class tbl_df (inherits tbl, data.frame) 522 rows 25 columns. object class tbl_df (inherits tbl, data.frame) 1600 rows 29 columns. object class tbl_df (inherits tbl, data.frame) 1000 rows 28 columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ex_data.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Simulated CDISC Data for Examples — ex_data","text":"tern_ex_adsl: ADSL data tern_ex_adae: ADAE data tern_ex_adlb: ADLB data tern_ex_adpp: ADPP data tern_ex_adrs: ADRS data tern_ex_adtte: ADTTE data","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing Data — explicit_na","title":"Missing Data — explicit_na","text":"Substitute missing data string factor level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing Data — explicit_na","text":"","code":"explicit_na(x, label = \"<Missing>\")"},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing Data — explicit_na","text":"x (factor character vector) values missing values substituted. label (character) string missing data replaced .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing Data — explicit_na","text":"x NA values substituted label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing Data — explicit_na","text":"","code":"explicit_na(c(NA, \"a\", \"b\")) #> [1] \"<Missing>\" \"a\"         \"b\"         is.na(explicit_na(c(NA, \"a\", \"b\"))) #> [1] FALSE FALSE FALSE  explicit_na(factor(c(NA, \"a\", \"b\"))) #> [1] <Missing> a         b         #> Levels: a b <Missing> is.na(explicit_na(factor(c(NA, \"a\", \"b\")))) #> [1] FALSE FALSE FALSE  explicit_na(sas_na(c(\"a\", \"\"))) #> [1] \"a\"         \"<Missing>\""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Elements by Name — extract_by_name","title":"Extract Elements by Name — extract_by_name","text":"utility function extracts elements vector x names. Differences standard [ function :","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Elements by Name — extract_by_name","text":"","code":"extract_by_name(x, names)"},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Elements by Name — extract_by_name","text":"x (named vector) extract named elements . names (character) vector names extract.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Elements by Name — extract_by_name","text":"NULL x NULL, otherwise extracted elements x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_by_name.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Elements by Name — extract_by_name","text":"x NULL, still always NULL returned (base function). x NULL, intersection names made names elements returned. , names appear x returned NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"Prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates, subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"","code":"extract_rsp_biomarkers(   variables,   data,   groups_lists = list(),   control = control_logistic(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (named list) controls response definition confidence level produced control_logistic(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_rsp, prop, , lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"can also specify continuous variable rsp use response_definition control convert internally logical variable reflecting binary response.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_biomarkers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_rsp_biomarkers","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   mutate(rsp = AVALC == \"CR\")  # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`, # in logistic regression models with one covariate `RACE`. The subgroups # are defined by the levels of `BMRKR2`. df <- extract_rsp_biomarkers(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adrs_f ) df #>   biomarker              biomarker_label n_tot n_rsp      prop        or #> 1    BMRKR1 Continuous Level Biomarker 1   200   164 0.8200000 0.9755036 #> 2       AGE                          Age   200   164 0.8200000 0.9952416 #> 3    BMRKR1 Continuous Level Biomarker 1    70    53 0.7571429 1.1524547 #> 4       AGE                          Age    70    53 0.7571429 0.9261012 #> 5    BMRKR1 Continuous Level Biomarker 1    68    58 0.8529412 0.8773122 #> 6       AGE                          Age    68    58 0.8529412 0.9867104 #> 7    BMRKR1 Continuous Level Biomarker 1    62    53 0.8548387 0.8792921 #> 8       AGE                          Age    62    53 0.8548387 1.0630262 #>         lcl      ucl conf_level      pval     pval_label     subgroup    var #> 1 0.8804862 1.080775       0.95 0.6352602 p-value (Wald) All Patients    ALL #> 2 0.9462617 1.046757       0.95 0.8530389 p-value (Wald) All Patients    ALL #> 3 0.9462127 1.403650       0.95 0.1584187 p-value (Wald)          LOW BMRKR2 #> 4 0.8487519 1.010500       0.95 0.0844837 p-value (Wald)          LOW BMRKR2 #> 5 0.7277189 1.057657       0.95 0.1699778 p-value (Wald)       MEDIUM BMRKR2 #> 6 0.8798911 1.106498       0.95 0.8189816 p-value (Wald)       MEDIUM BMRKR2 #> 7 0.7189748 1.075357       0.95 0.2103709 p-value (Wald)         HIGH BMRKR2 #> 8 0.9595973 1.177603       0.95 0.2418840 p-value (Wald)         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis  # Here we group the levels of `BMRKR2` manually, and we add a stratification # variable `STRATA1`. We also here use a continuous variable `EOSDY` # which is then binarized internally (response is defined as this variable # being larger than 500). df_grouped <- extract_rsp_biomarkers(   variables = list(     rsp = \"EOSDY\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     subgroups = \"BMRKR2\",     strat = \"STRATA1\"   ),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ),   control = control_logistic(     response_definition = \"I(response > 500)\"   ) ) #> Warning: Ran out of iterations and did not converge #> Warning: Ran out of iterations and did not converge #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: NaNs produced #> Warning: Ran out of iterations and did not converge #> Warning: Ran out of iterations and did not converge df_grouped #>   biomarker              biomarker_label n_tot n_rsp prop or lcl ucl conf_level #> 1    BMRKR1 Continuous Level Biomarker 1   200   200    1  1   0 Inf       0.95 #> 2       AGE                          Age   200   200    1  1   0 Inf       0.95 #> 3    BMRKR1 Continuous Level Biomarker 1    70    70    1  1 NaN NaN       0.95 #> 4       AGE                          Age    70    70    1  1 NaN NaN       0.95 #> 5    BMRKR1 Continuous Level Biomarker 1   138   138    1  1 NaN NaN       0.95 #> 6       AGE                          Age   138   138    1  1 NaN NaN       0.95 #> 7    BMRKR1 Continuous Level Biomarker 1   200   200    1  1   0 Inf       0.95 #> 8       AGE                          Age   200   200    1  1   0 Inf       0.95 #>   pval     pval_label        subgroup    var                    var_label #> 1    1 p-value (Wald)    All Patients    ALL                 All Patients #> 2    1 p-value (Wald)    All Patients    ALL                 All Patients #> 3  NaN p-value (Wald)             low BMRKR2 Continuous Level Biomarker 2 #> 4  NaN p-value (Wald)             low BMRKR2 Continuous Level Biomarker 2 #> 5  NaN p-value (Wald)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 6  NaN p-value (Wald)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 7    1 p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> 8    1 p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #>   row_type #> 1  content #> 2  content #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis #> 7 analysis #> 8 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"Prepares response rates odds ratios population subgroups data frames. Simple wrapper h_odds_ratio_subgroups_df() h_proportion_subgroups_df(). Result list two data.frames: prop . variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"","code":"extract_rsp_subgroups(   variables,   data,   groups_lists = list(),   conf_level = 0.95,   method = NULL,   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. conf_level (proportion) confidence level interval. method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"named list two elements: prop: data.frame containing columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. : data.frame containing columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_rsp_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Response Data for Population Subgroups in Data Frames — extract_rsp_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  # Unstratified analysis. df <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) df #> $prop #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142  2.714000 1.1804488  6.239827       0.95 All Patients    ALL #> 2        78 10.800000 2.2669576 51.452218       0.95            F    SEX #> 3        64  0.920000 0.2966470  2.853223       0.95            M    SEX #> 4        50  3.553846 1.0047370 12.570277       0.95          LOW BMRKR2 #> 5        49  2.705882 0.5911718 12.385232       0.95       MEDIUM BMRKR2 #> 6        43  2.250000 0.3970298 12.750933       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>   # Stratified analysis. df_strat <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strat = \"STRATA1\"),   data = adrs_f ) df_strat #> $prop #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142 2.6343899 1.1537821  6.015009       0.95 All Patients    ALL #> 2        78 9.5946605 2.0379337 45.171985       0.95            F    SEX #> 3        64 0.8947158 0.2936803  2.725809       0.95            M    SEX #> 4        50 3.5976656 1.0101319 12.813374       0.95          LOW BMRKR2 #> 5        49 2.6242168 0.5162572 13.339308       0.95       MEDIUM BMRKR2 #> 6        43 2.2816865 0.4116391 12.647228       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>   # Grouping of the BMRKR2 levels. df_grouped <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) df_grouped #> $prop #>           arm  n n_rsp      prop        subgroup    var #> 1  B: Placebo 73    50 0.6849315    All Patients    ALL #> 2   A: Drug X 69    59 0.8550725    All Patients    ALL #> 3  B: Placebo 40    25 0.6250000               F    SEX #> 4   A: Drug X 38    36 0.9473684               F    SEX #> 5  B: Placebo 33    25 0.7575758               M    SEX #> 6   A: Drug X 31    23 0.7419355               M    SEX #> 7  B: Placebo 24    13 0.5416667             low BMRKR2 #> 8   A: Drug X 26    21 0.8076923             low BMRKR2 #> 9  B: Placebo 47    30 0.6382979      low/medium BMRKR2 #> 10  A: Drug X 52    44 0.8461538      low/medium BMRKR2 #> 11 B: Placebo 73    50 0.6849315 low/medium/high BMRKR2 #> 12  A: Drug X 69    59 0.8550725 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or      lcl       ucl conf_level        subgroup    var #> 1       142  2.714000 1.180449  6.239827       0.95    All Patients    ALL #> 2        78 10.800000 2.266958 51.452218       0.95               F    SEX #> 3        64  0.920000 0.296647  2.853223       0.95               M    SEX #> 4        50  3.553846 1.004737 12.570277       0.95             low BMRKR2 #> 5        99  3.116667 1.193409  8.139385       0.95      low/medium BMRKR2 #> 6       142  2.714000 1.180449  6.239827       0.95 low/medium/high BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"Prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables), optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"","code":"extract_survival_biomarkers(   variables,   data,   groups_lists = list(),   control = control_coxreg(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) list parameters returned helper function control_coxreg(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_tot_events, median, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_biomarkers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame — extract_survival_biomarkers","text":"","code":"# Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`, # in multiple regression models containing one covariate `RACE`, # as well as one stratification variable `STRATA1`. The subgroups # are defined by the levels of `BMRKR2`.  library(dplyr)  adtte <- tern_ex_adtte adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_biomarkers(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     strata = \"STRATA1\",     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adtte_f ) df #>   biomarker              biomarker_label n_tot n_tot_events   median        hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 2       AGE                          Age   200          141 753.5176 1.0106406 #> 3    BMRKR1 Continuous Level Biomarker 1    70           52 735.4722 0.9905065 #> 4       AGE                          Age    70           52 735.4722 1.0106279 #> 5    BMRKR1 Continuous Level Biomarker 1    68           42 858.9952 0.9623210 #> 6       AGE                          Age    68           42 858.9952 1.0360765 #> 7    BMRKR1 Continuous Level Biomarker 1    62           47 727.8043 1.0770946 #> 8       AGE                          Age    62           47 727.8043 1.0009890 #>         lcl      ucl conf_level      pval     pval_label     subgroup    var #> 1 0.9538978 1.050625       0.95 0.9646086 p-value (Wald) All Patients    ALL #> 2 0.9871004 1.034742       0.95 0.3787395 p-value (Wald) All Patients    ALL #> 3 0.9142220 1.073156       0.95 0.8155443 p-value (Wald)          LOW BMRKR2 #> 4 0.9621192 1.061582       0.95 0.6735773 p-value (Wald)          LOW BMRKR2 #> 5 0.8708694 1.063376       0.95 0.4509368 p-value (Wald)       MEDIUM BMRKR2 #> 6 0.9727439 1.103532       0.95 0.2707796 p-value (Wald)       MEDIUM BMRKR2 #> 7 0.9756250 1.189118       0.95 0.1412524 p-value (Wald)         HIGH BMRKR2 #> 8 0.9678535 1.035259       0.95 0.9541048 p-value (Wald)         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis  # Here we group the levels of `BMRKR2` manually. df_grouped <- extract_survival_biomarkers(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     strata = \"STRATA1\",     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) df_grouped #>   biomarker              biomarker_label n_tot n_tot_events   median        hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 2       AGE                          Age   200          141 753.5176 1.0106406 #> 3    BMRKR1 Continuous Level Biomarker 1    70           52 735.4722 0.9905065 #> 4       AGE                          Age    70           52 735.4722 1.0106279 #> 5    BMRKR1 Continuous Level Biomarker 1   138           94 777.8929 0.9801709 #> 6       AGE                          Age   138           94 777.8929 1.0236283 #> 7    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 8       AGE                          Age   200          141 753.5176 1.0106406 #>         lcl      ucl conf_level      pval     pval_label        subgroup    var #> 1 0.9538978 1.050625       0.95 0.9646086 p-value (Wald)    All Patients    ALL #> 2 0.9871004 1.034742       0.95 0.3787395 p-value (Wald)    All Patients    ALL #> 3 0.9142220 1.073156       0.95 0.8155443 p-value (Wald)             low BMRKR2 #> 4 0.9621192 1.061582       0.95 0.6735773 p-value (Wald)             low BMRKR2 #> 5 0.9235465 1.040267       0.95 0.5094582 p-value (Wald)      low/medium BMRKR2 #> 6 0.9859367 1.062761       0.95 0.2224475 p-value (Wald)      low/medium BMRKR2 #> 7 0.9538978 1.050625       0.95 0.9646086 p-value (Wald) low/medium/high BMRKR2 #> 8 0.9871004 1.034742       0.95 0.3787395 p-value (Wald) low/medium/high BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"Prepares estimates median survival times treatment hazard ratios population subgroups data frames. Simple wrapper h_survtime_subgroups_df() h_coxph_subgroups_df(). Result list two data.frames: survtime hr. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"","code":"extract_survival_subgroups(   variables,   data,   groups_lists = list(),   control = control_coxph(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"named list two elements: survtime: data.frame containing columns arm, n, n_events, median, subgroup, var, var_label, row_type. hr: data.frame containing columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extract_survival_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepares Survival Data for Population Subgroups in Data Frames — extract_survival_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(   \"ARM\" = adtte_labels[[\"ARM\"]],   \"SEX\" = adtte_labels[[\"SEX\"]],   \"AVALU\" = adtte_labels[[\"AVALU\"]],   \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) df #> $survtime #>           arm  n n_events    median     subgroup    var #> 1  B: Placebo 73       57  727.8043 All Patients    ALL #> 2   A: Drug X 69       44  974.6402 All Patients    ALL #> 3  B: Placebo 40       31  599.1772            F    SEX #> 4   A: Drug X 38       24 1016.2982            F    SEX #> 5  B: Placebo 33       26  888.4916            M    SEX #> 6   A: Drug X 31       20  974.6402            M    SEX #> 7  B: Placebo 24       21  735.4722          LOW BMRKR2 #> 8   A: Drug X 26       15  974.6402          LOW BMRKR2 #> 9  B: Placebo 23       14  731.8352       MEDIUM BMRKR2 #> 10  A: Drug X 26       17  964.2197       MEDIUM BMRKR2 #> 11 B: Placebo 26       22  654.8245         HIGH BMRKR2 #> 12  A: Drug X 17       12 1016.2982         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $hr #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        49           31 0.7651261 0.3641277 1.6077269       0.95 0.47860004 #> 6        43           34 0.6662356 0.3257413 1.3626456       0.95 0.26285846 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis #>   df_grouped <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) df_grouped #> $survtime #>           arm  n n_events    median        subgroup    var #> 1  B: Placebo 73       57  727.8043    All Patients    ALL #> 2   A: Drug X 69       44  974.6402    All Patients    ALL #> 3  B: Placebo 40       31  599.1772               F    SEX #> 4   A: Drug X 38       24 1016.2982               F    SEX #> 5  B: Placebo 33       26  888.4916               M    SEX #> 6   A: Drug X 31       20  974.6402               M    SEX #> 7  B: Placebo 24       21  735.4722             low BMRKR2 #> 8   A: Drug X 26       15  974.6402             low BMRKR2 #> 9  B: Placebo 47       35  735.4722      low/medium BMRKR2 #> 10  A: Drug X 52       32  964.2197      low/medium BMRKR2 #> 11 B: Placebo 73       57  727.8043 low/medium/high BMRKR2 #> 12  A: Drug X 69       44  974.6402 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $hr #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        99           67 0.7472958 0.4600419 1.2139136       0.95 0.23764314 #> 6       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #>           pval_label        subgroup    var                    var_label #> 1 p-value (log-rank)    All Patients    ALL                 All Patients #> 2 p-value (log-rank)               F    SEX                          Sex #> 3 p-value (log-rank)               M    SEX                          Sex #> 4 p-value (log-rank)             low BMRKR2 Continuous Level Biomarker 2 #> 5 p-value (log-rank)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 p-value (log-rank) low/medium/high BMRKR2 Continuous Level Biomarker 2 #>   row_type #> 1  content #> 2 analysis #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis #>"},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Extreme Values — extreme_format","title":"Formatting Extreme Values — extreme_format","text":"rtables formatting functions handle extreme values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Extreme Values — extreme_format","text":"","code":"h_get_format_threshold(digits = 2L)  h_format_threshold(x, digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Extreme Values — extreme_format","text":"digits (integer) number decimal places display. x (number) value format.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Extreme Values — extreme_format","text":"h_get_format_threshold() returns list 2 elements: threshold, low high thresholds, format_string, thresholds formatted strings. h_format_threshold() returns given value, value within digit threshold relation given value digit threshold, formatted string.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Formatting Extreme Values — extreme_format","text":"input, apply format specified number digits. value threshold, returns \"<0.01\" e.g. number digits 2. value threshold, returns \">999.99\" e.g. number digits 2. zero, returns \"0.00\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Formatting Extreme Values — extreme_format","text":"h_get_format_threshold(): Internal helper function calculate threshold create formatted strings used Formatting Functions. Returns list elements threshold format_string. h_format_threshold(): Internal helper function apply threshold format value. Creates formatted string used Formatting Functions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/extreme_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Extreme Values — extreme_format","text":"","code":"h_get_format_threshold(2L) #> $threshold #>    low   high  #>   0.01 999.99  #>  #> $format_string #>       low      high  #>   \"<0.01\" \">999.99\"  #>   h_format_threshold(0.001) #> [1] \"<0.01\" h_format_threshold(1000) #> [1] \">999.99\""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/f_conf_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for confidence interval — f_conf_level","text":"","code":"f_conf_level(conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/f_conf_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for confidence interval — f_conf_level","text":"conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/f_conf_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for confidence interval — f_conf_level","text":"string.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/f_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for p-value — f_pval","text":"","code":"f_pval(test_mean)"},{"path":"https://insightsengineering.github.io/tern/reference/f_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for p-value — f_pval","text":"test_mean (number) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/f_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for p-value — f_pval","text":"string.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"collapses levels keeps new group levels, order provided. returned factor levels order given, possible missing level last (included missing values).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"","code":"fct_collapse_only(.f, ..., .na_level = \"<Missing>\")"},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":".f (factor character) original vector. ... (named character vectors) levels vector provided collapsed new level given respective name. .na_level (string) level use levels, missing new factor. Note level must contained new levels specified ....","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"modified factor collapsed levels. Values levels included given character vector input set missing level .na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"existing NAs input vector replaced missing level. needed, explicit_na() can called separately result.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fct_collapse_only.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapsing of Factor Levels and Keeping Only Those New Group Levels — fct_collapse_only","text":"","code":"fct_collapse_only(factor(c(\"a\", \"b\", \"c\", \"d\")), TRT = \"b\", CTRL = c(\"c\", \"d\")) #> [1] <Missing> TRT       CTRL      CTRL      #> Levels: TRT CTRL <Missing>"},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Certain Levels from a Factor — fct_discard","title":"Discard Certain Levels from a Factor — fct_discard","text":"discards observations well levels specified factor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Certain Levels from a Factor — fct_discard","text":"","code":"fct_discard(x, discard)"},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Certain Levels from a Factor — fct_discard","text":"x (factor) original factor. discard (character) levels discard.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Certain Levels from a Factor — fct_discard","text":"modified factor observations well levels discard dropped.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_discard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discard Certain Levels from a Factor — fct_discard","text":"","code":"fct_discard(factor(c(\"a\", \"b\", \"c\")), \"c\") #> [1] a b #> Levels: a b"},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":null,"dir":"Reference","previous_headings":"","what":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"inserts explicit missings factor based condition. Additionally, existing NA values explicitly converted given na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(x, condition, na_level = \"<Missing>\")"},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"x (factor) original factor. condition (logical) insert missings. na_level (string) level use missings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"modified factor inserted existing NA converted na_level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fct_explicit_na_if.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insertion of Explicit Missings in a Factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(factor(c(\"a\", \"b\", NA)), c(TRUE, FALSE, FALSE)) #> [1] <Missing> b         <Missing> #> Levels: a b <Missing>"},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Fits for Cox Proportional Hazards Regression — fit_coxreg","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"Fitting functions univariate multivariate Cox regression models.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"","code":"fit_coxreg_univar(variables, data, at = list(), control = control_coxreg())  fit_coxreg_multivar(variables, data, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"variables (list) named list corresponds names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. data (data.frame) dataset containing variables fit models. (list numeric) candidate covariate numeric, use specify value covariate effect estimated. control (list) list parameters returned helper function control_coxreg().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"fit_coxreg_univar() returns coxreg.univar class object named list 5 elements: mod: Cox regression models fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model. : Value covariate effect estimated. fit_coxreg_multivar() returns coxreg.multivar class object named list 4 elements: mod: Cox regression model fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"fit_coxreg_univar(): Fit series univariate Cox regression models given inputs. fit_coxreg_multivar(): Fit multivariate Cox regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"using fit_coxreg_univar two study arms.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fit_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fits for Cox Proportional Hazards Regression — fit_coxreg","text":"","code":"library(survival)  set.seed(1, kind = \"Mersenne-Twister\")  # Testing dataset [survival::bladder]. dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   data.frame(     time = stop,     status = event,     armcd = as.factor(rx),     covar1 = as.factor(enum),     covar2 = factor(       sample(as.factor(enum)),       levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\")     )   ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)  plot(   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),   lty = 2:4,   xlab = \"Months\",   col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") )   # fit_coxreg_univar  ## Cox regression: arm + 1 covariate. mod1 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = \"covar1\"   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91) )  ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates. mod2 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91, interaction = TRUE) )  ## Cox regression: arm + 1 covariate, stratified analysis. mod3 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", strata = \"covar2\",     covariates = c(\"covar1\")   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91) )  ## Cox regression: no arm, only covariates. mod4 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder )  # fit_coxreg_multivar  ## Cox regression: multivariate Cox regression. multivar_model <- fit_coxreg_multivar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder )  # Example without treatment arm. multivar_covs_model <- fit_coxreg_multivar(   variables = list(     time = \"time\", event = \"status\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder )"},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit for Logistic Regression — fit_logistic","title":"Fit for Logistic Regression — fit_logistic","text":"Fit (conditional) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit for Logistic Regression — fit_logistic","text":"","code":"fit_logistic(   data,   variables = list(response = \"Response\", arm = \"ARMCD\", covariates = NULL, interaction =     NULL, strata = NULL),   response_definition = \"response\" )"},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit for Logistic Regression — fit_logistic","text":"data (data.frame) data frame model fit. variables (named list string) list additional analysis variables. response_definition (string) definition event terms response. used fitting (conditional) logistic regression model left hand side formula.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit for Logistic Regression — fit_logistic","text":"fitted logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"model-specification","dir":"Reference","previous_headings":"","what":"Model Specification","title":"Fit for Logistic Regression — fit_logistic","text":"variables list needs include following elements: arm: Treatment arm variable name. response: response arm variable name. Usually 0/1 variable. covariates: either NULL (covariates) character vector covariate variable names. interaction: either NULL (interaction) string single covariate variable name already included covariates. interaction treatment arm included model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit for Logistic Regression — fit_logistic","text":"","code":"library(dplyr)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred"},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"fits Subgroup Treatment Effect Pattern logistic regression models binary (response) outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated odds ratios comparison second level vs. first one. (conditional) logistic regression model fit : response ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"","code":"fit_rsp_step(variables, data, control = c(control_step(), control_logistic()))"},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"variables (named list character) list analysis variables: needs response, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_logistic().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fit_rsp_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome — fit_rsp_step","text":"","code":"# Testing dataset with just two treatment arms. library(survival) library(dplyr)  adrs_f <- tern_ex_adrs %>%   filter(     PARAMCD == \"BESRSPI\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\")   ) %>%   mutate(     # Reorder levels of ARM to have Placebo as reference arm for Odds Ratio calculations.     ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")),     RSP = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     SEX = factor(SEX)   )  variables <- list(   arm = \"ARM\",   biomarker = \"BMRKR1\",   covariates = \"AGE\",   response = \"RSP\" )  # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. # We use a large enough bandwidth to avoid too small subgroups and linear separation in those. step_matrix <- fit_rsp_step(   variables = variables,   data = adrs_f,   control = c(control_logistic(), control_step(bandwidth = 0.5)) ) #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings dim(step_matrix) #> [1] 39 11 head(step_matrix) #>      Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,]             0.025                0            0.525        1.472755 #> [2,]             0.050                0            0.550        2.028546 #> [3,]             0.075                0            0.575        2.204086 #> [4,]             0.100                0            0.600        2.799776 #> [5,]             0.125                0            0.625        2.969998 #> [6,]             0.150                0            0.650        3.149068 #>      Interval Lower Interval Upper  n    logor       se   ci_lower ci_upper #> [1,]      0.4459546       6.057928 75 1.230538 1.185904 -1.0937914 3.554868 #> [2,]      0.4459546       6.371236 78 1.244538 1.183795 -1.0756577 3.564734 #> [3,]      0.4459546       6.701030 82 1.302066 1.182851 -1.0162794 3.620411 #> [4,]      0.4459546       6.886642 85 1.361186 1.182169 -0.9558222 3.678194 #> [5,]      0.4459546       7.026219 89 1.231372 1.179518 -1.0804407 3.543184 #> [6,]      0.4459546       7.144031 92 1.242238 1.180350 -1.0712045 3.555681  # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different logistic regression options, including confidence level. step_matrix2 <- fit_rsp_step(   variables = variables,   data = adrs_f,   control = c(control_logistic(conf_level = 0.9), control_step(bandwidth = 0.6, degree = 1)) ) #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings  # Use a global constant model. This is helpful as a reference for the subgroup models. step_matrix3 <- fit_rsp_step(   variables = variables,   data = adrs_f,   control = c(control_logistic(), control_step(bandwidth = NULL, num_points = 2L)) )  # It is also possible to use strata, i.e. use conditional logistic regression models. variables2 <- list(   arm = \"ARM\",   biomarker = \"BMRKR1\",   covariates = \"AGE\",   response = \"RSP\",   strata = c(\"STRATA1\", \"STRATA2\") )  step_matrix4 <- fit_rsp_step(   variables = variables2,   data = adrs_f,   control = c(control_logistic(), control_step(bandwidth = 0.6)) ) #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings #> Warning: Fit warnings occurred, please consider using a simpler model, or larger `bandwidth`, less `num_points` in `control_step()` settings"},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"fits Subgroup Treatment Effect Pattern models survival outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated hazard ratios comparison second level vs. first one. model fit : Surv(time, event) ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"","code":"fit_survival_step(   variables,   data,   control = c(control_step(), control_coxph()) )"},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"variables (named list character) list analysis variables: needs time, event, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_coxph().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/fit_survival_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome — fit_survival_step","text":"","code":"# Testing dataset with just two treatment arms. library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")),     is_event = CNSR == 0   ) labels <- c(\"ARM\" = \"Treatment Arm\", \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  variables <- list(   arm = \"ARM\",   biomarker = \"BMRKR1\",   covariates = c(\"AGE\", \"BMRKR2\"),   event = \"is_event\",   time = \"AVAL\" )  # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. step_matrix <- fit_survival_step(   variables = variables,   data = adtte_f ) dim(step_matrix) #> [1] 39 12 head(step_matrix) #>      Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,]             0.025                0            0.275        1.472755 #> [2,]             0.050                0            0.300        2.028546 #> [3,]             0.075                0            0.325        2.204086 #> [4,]             0.100                0            0.350        2.799776 #> [5,]             0.125                0            0.375        2.969998 #> [6,]             0.150                0            0.400        3.149068 #>      Interval Lower Interval Upper  n events      loghr        se   ci_lower #> [1,]      0.4459546       4.182444 39     29 0.03729601 0.4243013 -0.7943192 #> [2,]      0.4459546       4.349471 43     31 0.23246622 0.3881347 -0.5282639 #> [3,]      0.4459546       4.626913 46     32 0.07952492 0.3813113 -0.6678314 #> [4,]      0.4459546       4.805767 50     35 0.09020455 0.3638368 -0.6229026 #> [5,]      0.4459546       4.929816 53     37 0.15522702 0.3477999 -0.5264483 #> [6,]      0.4459546       5.020539 57     41 0.11135760 0.3332843 -0.5418676 #>       ci_upper #> [1,] 0.8689112 #> [2,] 0.9931963 #> [3,] 0.8268813 #> [4,] 0.8033117 #> [5,] 0.8369023 #> [6,] 0.7645828  # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different Cox regression options. step_matrix2 <- fit_survival_step(   variables = variables,   data = adtte_f,   control = c(control_coxph(conf_level = 0.9), control_step(degree = 2)) )  # Use a global model with cubic interaction and only 5 points. step_matrix3 <- fit_survival_step(   variables = variables,   data = adtte_f,   control = c(control_coxph(), control_step(bandwidth = NULL, degree = 3, num_points = 5L)) )"},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Viewport Tree for the Forest Plot — forest_viewport","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"Create Viewport Tree Forest Plot","code":""},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"","code":"forest_viewport(   tbl,   width_row_names = NULL,   width_columns = NULL,   width_forest = grid::unit(1, \"null\"),   gap_column = grid::unit(1, \"lines\"),   gap_header = grid::unit(1, \"lines\"),   mat_form = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"tbl (rtable) width_row_names (grid::unit) Width row names width_columns (grid::unit) Width column spans width_forest (grid::unit) Width forest plot gap_column (grid::unit) Gap width columns gap_header (grid::unit) Gap width header mat_form matrix print form table","code":""},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"viewport tree.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/forest_viewport.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Viewport Tree for the Forest Plot — forest_viewport","text":"","code":"library(grid)  tbl <- rtable(   header = rheader(     rrow(\"\", \"E\", rcell(\"CI\", colspan = 2)),     rrow(\"\", \"A\", \"B\", \"C\")   ),   rrow(\"row 1\", 1, 0.8, 1.1),   rrow(\"row 2\", 1.4, 0.8, 1.6),   rrow(\"row 3\", 1.2, 0.8, 1.2) )  # \\donttest{ v <- forest_viewport(tbl)  grid::grid.newpage() showViewport(v)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic formats from data significant digits — format_auto","title":"Automatic formats from data significant digits — format_auto","text":"Formatting function majority default methods used analyze_vars(). non-derived values, significant digits data used (e.g. range), derived values one digits (measure location dispersion like mean, standard deviation). function can called internally \"auto\" like, example, .formats = c(\"mean\" = \"auto\"). See details see works inner function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic formats from data significant digits — format_auto","text":"","code":"format_auto(dt_var, x_stat)"},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic formats from data significant digits — format_auto","text":"dt_var (numeric)  data statistics created upon. Used find significant digits. analyze_vars comes .df_row (see rtables::additional_fun_params), row data row splits. column split considered. x_stat (string)  string indicating current statistical method used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic formats from data significant digits — format_auto","text":"string rtables prints table cell.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic formats from data significant digits — format_auto","text":"internal function needed work rtables default structure format functions, .e. function(x, ...), x results statistical evaluation. can one element (e.g. .stats = \"mean_sd\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_auto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic formats from data significant digits — format_auto","text":"","code":"x_todo <- c(0.001, 0.2, 0.0011000, 3, 4) res <- c(mean(x_todo[1:3]), sd(x_todo[1:3]))  # x is the result coming into the formatting function -> res!! format_auto(dt_var = x_todo, x_stat = \"mean_sd\")(x = res) #> [1] \"0.06737 (0.11486)\" format_auto(x_todo, \"range\")(x = range(x_todo)) #> [1] \"0.0010 - 4.0000\" no_sc_x <- c(0.0000001, 1) format_auto(no_sc_x, \"range\")(x = no_sc_x) #> [1] \"0.0000001 - 1.0000000\""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Count and Fraction — format_count_fraction","title":"Formatting Count and Fraction — format_count_fraction","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Count and Fraction — format_count_fraction","text":"","code":"format_count_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Count and Fraction — format_count_fraction","text":"x (integer) vector length 2, count fraction. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Count and Fraction — format_count_fraction","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Count and Fraction — format_count_fraction","text":"","code":"format_count_fraction(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"x (integer) vector length 2, count fraction. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Count and Percentage with Fixed Single Decimal Place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.5)) #> [1] \"2 (50.0%)\" format_count_fraction_fixed_dp(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"Formats count together fraction special consideration count less 10.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"x (integer) vector length 2, count fraction. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"string format count (fraction %). count less 10, count printed.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_count_fraction_lt10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Count and Fraction with Special Case for Count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x = c(275, 0.9673)) #> [1] \"275 (96.7%)\" format_count_fraction_lt10(x = c(2, 0.6667)) #> [1] \"2\" format_count_fraction_lt10(x = c(9, 1)) #> [1] \"9\""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting a Single Extreme Value — format_extreme_values","title":"Formatting a Single Extreme Value — format_extreme_values","text":"Create Formatting Function single extreme value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting a Single Extreme Value — format_extreme_values","text":"","code":"format_extreme_values(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting a Single Extreme Value — format_extreme_values","text":"digits (integer) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting a Single Extreme Value — format_extreme_values","text":"rtables formatting function uses threshold digits return formatted extreme value.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting a Single Extreme Value — format_extreme_values","text":"","code":"format_fun <- format_extreme_values(2L) format_fun(x = 0.127) #> [1] \"0.13\" format_fun(x = Inf) #> [1] \">999.99\" format_fun(x = 0) #> [1] \"0.00\" format_fun(x = 0.009) #> [1] \"<0.01\""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"Formatting Function extreme values part confidence interval. Values formatted e.g. \"(xx.xx, xx.xx)\" number digits 2.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"","code":"format_extreme_values_ci(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"digits (integer) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"rtables formatting function uses threshold digits return formatted extreme values confidence interval.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_extreme_values_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Extreme Values Part of a Confidence Interval — format_extreme_values_ci","text":"","code":"format_fun <- format_extreme_values_ci(2L) format_fun(x = c(0.127, Inf)) #> [1] \"(0.13, >999.99)\" format_fun(x = c(0, 0.009)) #> [1] \"(0.00, <0.01)\""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Fraction and Percentage — format_fraction","title":"Formatting Fraction and Percentage — format_fraction","text":"Formats fraction together ratio percent.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Fraction and Percentage — format_fraction","text":"","code":"format_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Fraction and Percentage — format_fraction","text":"x (integer) elements num denom. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Fraction and Percentage — format_fraction","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Fraction and Percentage — format_fraction","text":"","code":"format_fraction(x = c(num = 2L, denom = 3L)) #> [1] \"2/3 (66.7%)\" format_fraction(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"Formats fraction together ratio percent fixed single decimal place. Includes trailing zero case whole number percentages always keep one decimal place.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"x (integer) elements num denom. ... required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Fraction and Percentage with Fixed Single Decimal Place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 4L)) #> [1] \"1/4 (25.0%)\" format_fraction_fixed_dp(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Fraction with Lower Threshold — format_fraction_threshold","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"Formats fraction second element input x fraction. applies lower threshold, just stated fraction smaller .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"","code":"format_fraction_threshold(threshold)"},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"threshold (proportion) lower threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"rtables formatting function takes numeric input x second element fraction formatted. fraction equal threshold, displayed percentage. positive threshold, returns, e.g. \"<1\" threshold 0.01. zero, just \"0\" returned.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_fraction_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Fraction with Lower Threshold — format_fraction_threshold","text":"","code":"format_fun <- format_fraction_threshold(0.05) format_fun(x = c(20, 0.1)) #> [1] 10 format_fun(x = c(2, 0.01)) #> [1] \"<5\" format_fun(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Numeric Values By Significant Figures — format_sigfig","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"Format numeric values print specified number significant figures.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"","code":"format_sigfig(sigfig, format = \"xx\", num_fmt = \"fg\")"},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"sigfig (integer) number significant figures display. format (character) format label (string) apply printing value. Decimal places string ignored favor formatting significant figures. Formats options : \"xx\", \"xx / xx\", \"(xx, xx)\", \"xx - xx\", \"xx (xx)\". num_fmt (character) numeric format modifiers apply value. Defaults \"fg\" standard significant figures formatting - fixed (non-scientific notation) format (\"f\") sigfig equal number significant figures instead decimal places (\"g\"). See formatC() format argument options.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_sigfig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting Numeric Values By Significant Figures — format_sigfig","text":"","code":"fmt_3sf <- format_sigfig(3) fmt_3sf(1.658) #> [1] \"1.66\" fmt_3sf(1e1) #> [1] \"10.0\"  fmt_5sf <- format_sigfig(5) fmt_5sf(0.57) #> [1] \"0.57000\" fmt_5sf(0.000025645) #> [1] \"0.000025645\""},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting: XX as Formatting Function — format_xx","title":"Formatting: XX as Formatting Function — format_xx","text":"Translate string x dots interpreted number place holders, others formatting elements.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatting: XX as Formatting Function — format_xx","text":"","code":"format_xx(str)"},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatting: XX as Formatting Function — format_xx","text":"str (string) template.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatting: XX as Formatting Function — format_xx","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/format_xx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Formatting: XX as Formatting Function — format_xx","text":"","code":"test <- list(c(1.658, 0.5761), c(1e1, 785.6))  z <- format_xx(\"xx (xx.x)\") sapply(test, z) #> [1] \"2 (0.6)\"    \"10 (785.6)\"  z <- format_xx(\"xx.x - xx.x\") sapply(test, z) #> [1] \"1.7 - 0.6\"  \"10 - 785.6\"  z <- format_xx(\"xx.x, incl. xx.x% NE\") sapply(test, z) #> [1] \"1.7, incl. 0.6% NE\"  \"10, incl. 785.6% NE\""},{"path":"https://insightsengineering.github.io/tern/reference/formatting_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting Functions — formatting_functions","title":"Formatting Functions — formatting_functions","text":"See list formatting functions created tern work rtables. available formats can listed via formatters::list_valid_format_labels(). Additional custom formats can created via formatters::sprintf_format() function.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Forest Plot based on a Table — g_forest","text":"","code":"g_forest(   tbl,   col_x = attr(tbl, \"col_x\"),   col_ci = attr(tbl, \"col_ci\"),   vline = 1,   forest_header = attr(tbl, \"forest_header\"),   xlim = c(0.1, 10),   logx = TRUE,   x_at = c(0.1, 1, 10),   width_row_names = NULL,   width_columns = NULL,   width_forest = grid::unit(1, \"null\"),   col_symbol_size = attr(tbl, \"col_symbol_size\"),   col = getOption(\"ggplot2.discrete.colour\")[1],   gp = NULL,   draw = TRUE,   newpage = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Forest Plot based on a Table — g_forest","text":"tbl (rtable) col_x (integer) column index estimator. default tries get tbl attribute col_x, otherwise needs manually specified. col_ci (integer) column index confidence intervals. default tries get tbl attribute col_ci, otherwise needs manually specified. vline (number) x coordinate vertical line, NULL line omitted. forest_header (character, length 2) text displayed left right vline, respectively. vline = NULL forest_header needs NULL . default tries get tbl attribute forest_header. xlim (numeric) limits x axis. logx (flag) show x-values logarithm scale. x_at (numeric) x-tick locations, NULL get automatically chosen. width_row_names (unit) width row names. NULL widths get automatically calculated. See grid::unit(). width_columns (unit) widths table columns. NULL widths get automatically calculated. See grid::unit(). width_forest (unit) width forest column. NULL widths get automatically calculated. See grid::unit(). col_symbol_size (integer) column index tbl containing data used determine relative size estimator plot symbol. Typically, symbol size proportional sample size used calculate estimator. NULL, symbol size used subgroups. default tries get tbl attribute col_symbol_size, otherwise needs manually specified. col (character) color(s). gp \"gpar\" object, typically output     call function gpar.  basically     list graphical parameter settings. draw (flag) whether plot drawn. newpage (flag) whether plot drawn new page. considered draw = TRUE used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Forest Plot based on a Table — g_forest","text":"gTree object containing forest plot table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Forest Plot based on a Table — g_forest","text":"Create forest plot rtables::rtable() object column single value column 2 values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_forest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Forest Plot based on a Table — g_forest","text":"","code":"# \\donttest{ library(dplyr) library(forcats) library(nestcolor)  adrs <- tern_ex_adrs n_records <- 20 adrs_labels <- formatters::var_labels(adrs, fill = TRUE) adrs <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   slice(seq_len(n_records)) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs) <- c(adrs_labels, \"Response\") df <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA2\")),   data = adrs ) # Full commonly used response table.  tbl <- basic_table() %>%   tabulate_rsp_subgroups(df) p <- g_forest(tbl, gp = grid::gpar(fontsize = 10))  draw_grob(p)   # Odds ratio only table.  tbl_or <- basic_table() %>%   tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"or\", \"ci\")) tbl_or #>                                                                  #> Baseline Risk Factors     Total n   Odds Ratio       95% CI      #> ———————————————————————————————————————————————————————————————— #> All Patients                20         0.76       (0.10, 5.96)   #> Sex                                                              #>   F                         11       >999.99     (0.00, >999.99) #>   M                          9         0.22       (0.01, 3.98)   #> Stratification Factor 2                                          #>   S1                        10         1.00       (0.05, 22.18)  #>   S2                        10         0.67       (0.04, 11.29)  p <- g_forest(   tbl_or,   forest_header = c(\"Comparison\\nBetter\", \"Treatment\\nBetter\") )  draw_grob(p)   # Survival forest plot example. adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = TRUE) adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- list(   \"ARM\" = adtte_labels[\"ARM\"],   \"SEX\" = adtte_labels[\"SEX\"],   \"AVALU\" = adtte_labels[\"AVALU\"],   \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- as.character(labels) df <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) table_hr <- basic_table() %>%   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1]) g_forest(table_hr)  # Works with any `rtable`. tbl <- rtable(   header = c(\"E\", \"CI\", \"N\"),   rrow(\"\", 1, c(.8, 1.2), 200),   rrow(\"\", 1.2, c(1.1, 1.4), 50) ) g_forest(   tbl = tbl,   col_x = 1,   col_ci = 2,   xlim = c(0.5, 2),   x_at = c(0.5, 1, 2),   col_symbol_size = 3 )  tbl <- rtable(   header = rheader(     rrow(\"\", rcell(\"A\", colspan = 2)),     rrow(\"\", \"c1\", \"c2\")   ),   rrow(\"row 1\", 1, c(.8, 1.2)),   rrow(\"row 2\", 1.2, c(1.1, 1.4)) ) g_forest(   tbl = tbl,   col_x = 1,   col_ci = 2,   xlim = c(0.5, 2),   x_at = c(0.5, 1, 2),   vline = 1,   forest_header = c(\"Hello\", \"World\") )  # }"},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier Plot — g_km","title":"Kaplan-Meier Plot — g_km","text":"survival model, graphic rendered along tabulated annotation including number patient risk given time median survival per group.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier Plot — g_km","text":"","code":"g_km(   df,   variables,   control_surv = control_surv_timepoint(),   col = NULL,   lty = NULL,   lwd = 0.5,   censor_show = TRUE,   pch = 3,   size = 2,   max_time = NULL,   xticks = NULL,   xlab = \"Days\",   yval = c(\"Survival\", \"Failure\"),   ylab = paste(yval, \"Probability\"),   ylim = NULL,   title = NULL,   footnotes = NULL,   draw = TRUE,   newpage = TRUE,   gp = NULL,   vp = NULL,   name = NULL,   font_size = 12,   ci_ribbon = FALSE,   ggtheme = nestcolor::theme_nest(),   annot_at_risk = TRUE,   annot_at_risk_title = TRUE,   annot_surv_med = TRUE,   annot_coxph = FALSE,   annot_stats = NULL,   annot_stats_vlines = FALSE,   control_coxph_pw = control_coxph(),   position_coxph = c(-0.03, -0.02),   position_surv_med = c(0.95, 0.9),   width_annots = list(surv_med = grid::unit(0.3, \"npc\"), coxph = grid::unit(0.4, \"npc\")) )"},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier Plot — g_km","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strat (character NULL) variable names indicating stratification factors. control_surv (list) parameters comparison details, specified using helper function control_surv_timepoint(). possible parameter options : conf_level (proportion) confidence level interval survival rate. conf_type (string)\"plain\" (default), \"log\", \"log-log\" confidence interval type, see survival::survfit(). Note option \"none\" longer supported. col (character) lines colors. Length vector equal number strata survival::survfit(). lty (numeric) line type. Length vector equal number strata survival::survfit(). lwd (numeric) line width. Length vector equal number strata survival::survfit(). censor_show (flag) whether show censored. pch (numeric, string) value character points symbol indicate censored cases. size (numeric) size censored point, class unit. max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL). xticks (numeric, number, NULL) numeric vector ticks single number spacing ticks x axis. NULL (default), labeling::extended() used determine optimal tick position x axis. xlab (string) label x-axis. yval (string) value y-axis. Options Survival (default) Failure probability. ylab (string) label y-axis. ylim (vector numeric) vector length 2 containing lower upper limits y-axis. NULL (default), minimum maximum y-values displayed used limits. title (string) title plot. footnotes (string) footnotes plot. draw (flag) whether plot drawn. newpage (flag) whether plot drawn new page. considered draw = TRUE used. gp \"gpar\" object, typically output     call function gpar.  basically     list graphical parameter settings. vp viewport object (NULL). name character identifier grob.  Used find     grob display list /child another grob. font_size (number) font size used. ci_ribbon (flag) draw confidence interval around Kaplan-Meier curve. ggtheme (theme) graphical theme provided ggplot2 control outlook Kaplan-Meier curve. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE. annot_surv_med (flag) compute add annotation table Kaplan-Meier curve estimating median survival time per group. annot_coxph (flag) add annotation table survival::coxph() model. annot_stats (string) statistics annotations add plot. Options median (median survival follow-time) min (minimum survival follow-time). annot_stats_vlines (flag) add vertical lines corresponding statistics specified annot_stats. annot_stats NULL lines added. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. position_coxph (numeric) x y positions plotting survival::coxph() model. position_surv_med (numeric) x y positions plotting annotation table estimating median survival time per group. width_annots (named list units) named list widths annotation tables names surv_med (median survival time table) coxph (survival::coxph() model table), value width (units) implement printing annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier Plot — g_km","text":"grob class gTree.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_km.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier Plot — g_km","text":"","code":"# \\donttest{ library(dplyr) library(ggplot2) library(survival) library(grid) library(nestcolor)  df <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0) variables <- list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\")  # 1. Example - basic option  res <- g_km(df = df, variables = variables)  res <- g_km(df = df, variables = variables, yval = \"Failure\")  res <- g_km(   df = df,   variables = variables,   control_surv = control_surv_timepoint(conf_level = 0.9),   col = c(\"grey25\", \"grey50\", \"grey75\"),   annot_at_risk_title = FALSE )  res <- g_km(df = df, variables = variables, ggtheme = theme_minimal())  res <- g_km(df = df, variables = variables, ggtheme = theme_minimal(), lty = 1:3)  res <- g_km(df = df, variables = variables, max = 2000)  res <- g_km(   df = df,   variables = variables,   annot_stats = c(\"min\", \"median\"),   annot_stats_vlines = TRUE )   # 2. Example - Arrange several KM curve on a single graph device  # 2.1 Use case: A general graph on the top, a zoom on the bottom. grid.newpage() lyt <- grid.layout(nrow = 2, ncol = 1) %>%   viewport(layout = .) %>%   pushViewport()  res <- g_km(   df = df, variables = variables, newpage = FALSE, annot_surv_med = FALSE,   vp = viewport(layout.pos.row = 1, layout.pos.col = 1) ) res <- g_km(   df = df, variables = variables, max = 1000, newpage = FALSE, annot_surv_med = FALSE,   ggtheme = theme_dark(),   vp = viewport(layout.pos.row = 2, layout.pos.col = 1) )   # 2.1 Use case: No annotations on top, annotated graph on bottom grid.newpage() lyt <- grid.layout(nrow = 2, ncol = 1) %>%   viewport(layout = .) %>%   pushViewport()  res <- g_km(   df = df, variables = variables, newpage = FALSE,   annot_surv_med = FALSE, annot_at_risk = FALSE,   vp = viewport(layout.pos.row = 1, layout.pos.col = 1) ) res <- g_km(   df = df, variables = variables, max = 2000, newpage = FALSE, annot_surv_med = FALSE,   annot_at_risk = TRUE,   ggtheme = theme_dark(),   vp = viewport(layout.pos.row = 2, layout.pos.col = 1) )   # Add annotation from a pairwise coxph analysis g_km(   df = df, variables = variables,   annot_coxph = TRUE )   # Change widths/sizes of surv_med and coxph annotation tables. g_km(   df = df, variables = c(variables, list(strat = \"SEX\")),   annot_coxph = TRUE,   width_annots = list(surv_med = grid::unit(2, \"in\"), coxph = grid::unit(3, \"in\")) )   g_km(   df = df, variables = c(variables, list(strat = \"SEX\")),   font_size = 15,   annot_coxph = TRUE,   control_coxph = control_coxph(pval_method = \"wald\", ties = \"exact\", conf_level = 0.99),   position_coxph = c(0.5, 0.5) )   # Change position of the treatment group annotation table. g_km(   df = df, variables = c(variables, list(strat = \"SEX\")),   font_size = 15,   annot_coxph = TRUE,   control_coxph = control_coxph(pval_method = \"wald\", ties = \"exact\", conf_level = 0.99),   position_surv_med = c(1, 0.7) )  # }"},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Line plot with the optional table — g_lineplot","title":"Line plot with the optional table — g_lineplot","text":"Line plot optional table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Line plot with the optional table — g_lineplot","text":"","code":"g_lineplot(   df,   alt_counts_df = NULL,   variables = control_lineplot_vars(),   mid = \"mean\",   interval = \"mean_ci\",   whiskers = c(\"mean_ci_lwr\", \"mean_ci_upr\"),   table = NULL,   sfun = tern::s_summary,   ...,   mid_type = \"pl\",   mid_point_size = 2,   position = ggplot2::position_dodge(width = 0.4),   legend_title = NULL,   legend_position = \"bottom\",   ggtheme = nestcolor::theme_nest(),   y_lab = NULL,   y_lab_add_paramcd = TRUE,   y_lab_add_unit = TRUE,   title = \"Plot of Mean and 95% Confidence Limits by Visit\",   subtitle = \"\",   subtitle_add_paramcd = TRUE,   subtitle_add_unit = TRUE,   caption = NULL,   table_format = summary_formats(),   table_labels = summary_labels(),   table_font_size = 3,   newpage = TRUE,   col = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Line plot with the optional table — g_lineplot","text":"df (data.frame) data set containing analysis variables. alt_counts_df (data.frame NULL) data set used () counts objects strata. variables (named character vector) variable names df data set. Details : x (character) name x-axis variable. y (character) name y-axis variable. strata (character) name grouping variable, .e. treatment arm. Can NA indicate lack groups. cohort_id (character) name variable identifies group belonging. applies strata NULL. paramcd (character) name variable parameter's code. Used y-axis label plot's subtitle. Can NA paramcd added y-axis label subtitle. y_unit (character) name variable units y. Used y-axis label plot's subtitle. Can NA y unit added y-axis label subtitle. mid (character NULL) names statistics plotted midpoints. statistics indicated mid variable must present object returned sfun, double numeric type vector length one. interval (character NULL) names statistics plotted intervals. statistics indicated interval variable must present object returned sfun, double numeric type vector length two. whiskers (character) names interval whiskers plotted. Must match names attribute interval element list returned sfun. possible specify one whisker , lower upper. table (character NULL) names statistics displayed table plot. statistics indicated table variable must present object returned sfun. sfun (closure) function compute values required statistics. must return named list atomic vectors. names list elements refer names statistics used mid, interval, table. must able accept input vector data statistics computed. ... optional arguments sfun. mid_type (character) controls type mid plot, can point (p), line (l), point line (pl). mid_point_size (integer double) controls font size point mid plot. position (character call) geom element position adjustment, either string, result call position adjustment function. legend_title (character string) legend title. legend_position (character) position plot legend (none, left, right, bottom, top, two-element numeric vector). ggtheme (theme) graphical theme provided ggplot2 control styling plot. y_lab (character) y-axis label. equal NULL, label added. y_lab_add_paramcd (logical) paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added y-axis label y_lab? y_lab_add_unit (logical) y unit, .e. unique(df[[variables[\"y_unit\"]]]) added y-axis label y_lab? title (character) plot title. subtitle (character) plot subtitle. subtitle_add_paramcd (logical) paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added plot's subtitle subtitle? subtitle_add_unit (logical) y unit, .e. unique(df[[variables[\"y_unit\"]]]) added plot's subtitle subtitle? caption (character) optional caption plot. table_format (named character NULL) format patterns descriptive statistics used (optional) table appended plot. passed directly h_format_row function format parameter. Names table_format must match names statistics returned sfun function. table_labels (named character NULL) labels descriptive statistics used (optional) table appended plot. Names table_labels must match names statistics returned sfun function. table_font_size (integer double) controls font size values table. newpage (logical) plot drawn new page? col (character) colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Line plot with the optional table — g_lineplot","text":"ggplot line plot (statistics table applicable).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_lineplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Line plot with the optional table — g_lineplot","text":"","code":"library(nestcolor)  adsl <- tern_ex_adsl adlb <- tern_ex_adlb %>% dplyr::filter(ANL01FL == \"Y\", PARAMCD == \"ALT\", AVISIT != \"SCREENING\") adlb$AVISIT <- droplevels(adlb$AVISIT) adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min))  # Mean with CI g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\")   # Mean with CI, no stratification g_lineplot(adlb, variables = control_lineplot_vars(strata = NA))   # Mean, upper whisker of CI, no strata counts N g_lineplot(   adlb,   whiskers = \"mean_ci_upr\",   title = \"Plot of Mean and Upper 95% Confidence Limit by Visit\" )   # Median with CI g_lineplot(   adlb,   adsl,   mid = \"median\",   interval = \"median_ci\",   whiskers = c(\"median_ci_lwr\", \"median_ci_upr\"),   title = \"Plot of Median and 95% Confidence Limits by Visit\" )   # Mean, +/- SD g_lineplot(adlb, adsl,   interval = \"mean_sdi\",   whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"),   title = \"Plot of Median +/- SD by Visit\" )   # Mean with CI plot with stats table g_lineplot(adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\"))   # Mean with CI, table and customized confidence level g_lineplot(   adlb,   adsl,   table = c(\"n\", \"mean\", \"mean_ci\"),   control = control_analyze_vars(conf_level = 0.80),   title = \"Plot of Mean and 80% Confidence Limits by Visit\" )   # Mean with CI, table, filtered data adlb_f <- dplyr::filter(adlb, ARMCD != \"ARM A\" | AVISIT == \"BASELINE\") g_lineplot(adlb_f, table = c(\"n\", \"mean\"))"},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a STEP Graph — g_step","title":"Create a STEP Graph — g_step","text":"Based STEP results, creates ggplot graph showing estimated HR along continuous biomarker value subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a STEP Graph — g_step","text":"","code":"g_step(   df,   use_percentile = \"Percentile Center\" %in% names(df),   est = list(col = \"blue\", lty = 1),   ci_ribbon = list(fill = getOption(\"ggplot2.discrete.colour\")[1], alpha = 0.5),   col = getOption(\"ggplot2.discrete.colour\") )"},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a STEP Graph — g_step","text":"df (tibble) result tidy.step(). use_percentile (flag) whether use percentiles x axis actual biomarker values. est (named list)col lty settings estimate line. ci_ribbon (named list NULL)fill alpha settings confidence interval ribbon area, NULL plot CI ribbon. col (character) colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a STEP Graph — g_step","text":"ggplot STEP graph.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/g_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a STEP Graph — g_step","text":"","code":"library(nestcolor) library(survival) lung$sex <- factor(lung$sex)  # Survival example. vars <- list(   time = \"time\",   event = \"status\",   arm = \"sex\",   biomarker = \"age\" )  step_matrix <- fit_survival_step(   variables = vars,   data = lung,   control = c(control_coxph(), control_step(num_points = 10, degree = 2)) ) step_data <- broom::tidy(step_matrix)  # Default plot. g_step(step_data)   # Add the reference 1 horizontal line. library(ggplot2) g_step(step_data) +   ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2)   # Use actual values instead of percentiles, different color for estimate and no CI, # use log scale for y axis. g_step(   step_data,   use_percentile = FALSE,   est = list(col = \"blue\", lty = 1),   ci_ribbon = NULL ) + scale_y_log10()   # Adding another curve based on additional column. step_data$extra <- exp(step_data$`Percentile Center`) g_step(step_data) +   ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = \"green\")   # Response example. vars <- list(   response = \"status\",   arm = \"sex\",   biomarker = \"age\" )  step_matrix <- fit_rsp_step(   variables = vars,   data = lung,   control = c(     control_logistic(response_definition = \"I(response == 2)\"),     control_step()   ) ) step_data <- broom::tidy(step_matrix) g_step(step_data)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Horizontal Waterfall Plot — g_waterfall","text":"","code":"g_waterfall(   height,   id,   col_var = NULL,   col = getOption(\"ggplot2.discrete.colour\"),   xlab = NULL,   ylab = NULL,   col_legend_title = NULL,   title = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Horizontal Waterfall Plot — g_waterfall","text":"height (`numeric``) vector containing values plotted waterfall bars. id (character) vector containing IDs use x-axis label waterfall bars. col_var (factor, character NULL) categorical variable bar coloring. NULL default. col (character) colors. xlab (character) x label. Default \"ID\". ylab (character) y label. Default \"Value\". col_legend_title (character) text displayed legend title. title (character) text displayed plot title.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Horizontal Waterfall Plot — g_waterfall","text":"ggplot waterfall plot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Horizontal Waterfall Plot — g_waterfall","text":"basic waterfall plot visualizes quantity height ordered value markup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/g_waterfall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Horizontal Waterfall Plot — g_waterfall","text":"","code":"library(dplyr) library(nestcolor)  g_waterfall(height = c(3, 5, -1), id = letters[1:3])   g_waterfall(   height = c(3, 5, -1),   id = letters[1:3],   col_var = letters[1:3] )   adsl_f <- tern_ex_adsl %>%   select(USUBJID, STUDYID, ARM, ARMCD, SEX)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"OVRINV\") %>%   mutate(pchg = rnorm(n(), 10, 50))  adrs_f <- head(adrs_f, 30) adrs_f <- adrs_f[!duplicated(adrs_f$USUBJID), ] head(adrs_f) #> # A tibble: 5 × 30 #>   STUDYID COUNTRY SITEID SUBJID   AGE SEX   ARMCD ARM      ACTARMCD ACTARM RACE  #>   <chr>   <fct>   <chr>  <chr>  <dbl> <fct> <fct> <fct>    <fct>    <fct>  <fct> #> 1 AB12345 BRA     BRA-1  id-105  37.8 F     ARM A A: Drug… ARM A    A: Dr… ASIAN #> 2 AB12345 BRA     BRA-1  id-171  29.8 F     ARM B B: Plac… ARM B    B: Pl… ASIAN #> 3 AB12345 BRA     BRA-1  id-177  38.9 F     ARM B B: Plac… ARM B    B: Pl… ASIAN #> 4 AB12345 BRA     BRA-1  id-23   41.3 F     ARM A A: Drug… ARM A    A: Dr… AMER… #> 5 AB12345 BRA     BRA-1  id-59   26.7 F     ARM A A: Drug… ARM A    A: Dr… ASIAN #> # ℹ 19 more variables: TRTSDTM <dttm>, TRTEDTM <dttm>, EOSDY <dbl>, #> #   STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>, BMRKR2 <fct>, REGION1 <fct>, #> #   SAFFL <fct>, USUBJID <chr>, AVISIT <fct>, PARAMCD <fct>, PARAM <fct>, #> #   AVALC <fct>, AVAL <int>, ADTM <date>, AVISITN <dbl>, DTHFL <fct>, #> #   pchg <dbl>  g_waterfall(   height = adrs_f$pchg,   id = adrs_f$USUBJID,   col_var = adrs_f$AVALC )   g_waterfall(   height = adrs_f$pchg,   id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID),   col_var = adrs_f$SEX )   g_waterfall(   height = adrs_f$pchg,   id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID),   xlab = \"ID\",   ylab = \"Percentage Change\",   title = \"Waterfall plot\" )"},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility function to return a named list of covariate names. — get_covariates","title":"Utility function to return a named list of covariate names. — get_covariates","text":"Utility function return named list covariate names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to return a named list of covariate names. — get_covariates","text":"","code":"get_covariates(covariates)"},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to return a named list of covariate names. — get_covariates","text":"covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_covariates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to return a named list of covariate names. — get_covariates","text":"named list character vector.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth Function with Optional Grouping — get_smooths","title":"Smooth Function with Optional Grouping — get_smooths","text":"produces loess smoothed estimates y Student confidence intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth Function with Optional Grouping — get_smooths","text":"","code":"get_smooths(df, x, y, groups = NULL, level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth Function with Optional Grouping — get_smooths","text":"df (data.frame) data set containing analysis variables. x (character) value x column name. y (character) value y column name. groups (character) vector optional grouping variables names. level (numeric) level confidence interval use (0.95 default).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/get_smooths.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Smooth Function with Optional Grouping — get_smooths","text":"data.frame original x, smoothed y, ylow, yhigh, optional groups variables formatted factor type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert List of Groups to Data Frame — groups_list_to_df","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"converts list group levels data frame format expected rtables::add_combo_levels().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"","code":"groups_list_to_df(groups_list)"},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"tibble::tibble() required format.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/groups_list_to_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert List of Groups to Data Frame — groups_list_to_df","text":"","code":"grade_groups <- list(   \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"),   \"Grade 3-4 (%)\" = c(\"3\", \"4\"),   \"Grade 5 (%)\" = \"5\" ) groups_list_to_df(grade_groups) #> # A tibble: 3 × 4 #>   valname  label         levelcombo exargs     #>   <chr>    <chr>         <list>     <list>     #> 1 AnyGrade Any Grade (%) <chr [5]>  <list [0]> #> 2 Grade34  Grade 3-4 (%) <chr [2]>  <list [0]> #> 3 Grade5   Grade 5 (%)   <chr [1]>  <list [0]>"},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"Helper function prepare df generate patient count shift table","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"","code":"h_adlb_worsen(   adlb,   worst_flag_low = NULL,   worst_flag_high = NULL,   direction_var )"},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"adlb (data.frame)ADLB dataframe worst_flag_low (named vector) Worst low post-baseline lab grade flag variable worst_flag_high (named vector) Worst high post-baseline lab grade flag variable direction_var (string) Direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"h_adlb_worsen() returns adlb  data.frame containing worst labs specified according worst_flag_low worst_flag_high direction specified according direction_var. instance, lab needed low direction , records flagged worst_flag_low selected. lab needed low high directions, worst low records selected low direction, worst high record selected high direction.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_adlb_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to Prepare ADLB with Worst Labs — h_adlb_worsen","text":"","code":"library(dplyr)  # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>%   mutate(     GRADDR = case_when(       PARAMCD == \"ALT\" ~ \"B\",       PARAMCD == \"CRP\" ~ \"L\",       PARAMCD == \"IGA\" ~ \"H\"     )   ) %>%   filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")  df <- h_adlb_worsen(   adlb,   worst_flag_low = c(\"WGRLOFL\" = \"Y\"),   worst_flag_high = c(\"WGRHIFL\" = \"Y\"),   direction_var = \"GRADDR\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"Helper function merges ADSL ADLB datasets missing lab test records inserted output dataset. Remember na_level must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"","code":"h_adsl_adlb_merge_using_worst_flag(   adsl,   adlb,   worst_flag = c(WGRHIFL = \"Y\"),   by_visit = FALSE,   no_fillin_visits = c(\"SCREENING\", \"BASELINE\") )"},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"adsl (data.frame)ADSL dataframe. adlb (data.frame)ADLB dataframe. worst_flag (named vector) Worst post-baseline lab flag variable. by_visit (logical) defaults FALSE generate worst grade per patient. worst grade per patient per visit specified worst_flag, by_visit TRUE generate worst grade patient per visit. no_fillin_visits (named character) Visits considered post-baseline worst toxicity grade. Defaults c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"df containing variables shared adlb adsl along variables PARAM, PARAMCD, ATOXGR, BTOXGR relevant analysis. Optionally, AVISIT AVISITN included by_visit = TRUE no_fillin_visits = c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"result data missing records created following situations: Patients present adsl lab data adlb (baseline post-baseline). Patients post-baseline lab values. Patients without post-baseline values flagged worst.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for Deriving Analysis Datasets for LBT13 and LBT14 — h_adsl_adlb_merge_using_worst_flag","text":"","code":"# `h_adsl_adlb_merge_using_worst_flag` adlb_out <- h_adsl_adlb_merge_using_worst_flag(   tern_ex_adsl,   tern_ex_adlb,   worst_flag = c(\"WGRHIFL\" = \"Y\") )  # `h_adsl_adlb_merge_using_worst_flag` by visit example adlb_out_by_visit <- h_adsl_adlb_merge_using_worst_flag(   tern_ex_adsl,   tern_ex_adlb,   worst_flag = c(\"WGRLOVFL\" = \"Y\"),   by_visit = TRUE )"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":"","code":"h_ancova(.var, .df_row, variables, interaction_item = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. variables (named list strings) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". interaction_item (character) name variable interactions arm. interaction needed, default option NULL.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":"summary linear model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to Return Results of a Linear Model — h_ancova","text":"","code":"h_ancova(   .var = \"Sepal.Length\",   .df_row = iris,   variables = list(arm = \"Species\", covariates = c(\"Petal.Length * Petal.Width\", \"Sepal.Width\")) ) #>  Species    emmean     SE  df lower.CL upper.CL #>  setosa       6.15 0.3371 143     5.49     6.82 #>  versicolor   5.72 0.0668 143     5.59     5.85 #>  virginica    5.41 0.1488 143     5.11     5.70 #>  #> Confidence level used: 0.95"},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Helper function s_count_occurrences_by_grade() insert grade groupings list individual grade frequencies. order final result follows order grade_groups. elements -grade group (), .e. grade group equal refs moved end. Grade groups names must unique.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups(grade_groups, refs, remove_single = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"grade_groups (named list character) containing groupings grades. refs (named list numeric) name corresponds reference grade level entry represents count. remove_single (logical)TRUE include elements one-element grade groups output list; case grade groups names included output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Formatted list grade groupings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_append_grade_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups(   list(     \"Any Grade\" = as.character(1:5),     \"Grade 1-2\" = c(\"1\", \"2\"),     \"Grade 3-4\" = c(\"3\", \"4\")   ),   list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #>  #> $`Grade 1-2` #> [1] 30 #>  #> $`1` #> [1] 10 #>  #> $`2` #> [1] 20 #>  #> $`Grade 3-4` #> [1] 70 #>  #> $`3` #> [1] 30 #>  #> $`4` #> [1] 40 #>  #> $`5` #> [1] 50 #>   h_append_grade_groups(   list(     \"Any Grade\" = as.character(5:1),     \"Grade A\" = \"5\",     \"Grade B\" = c(\"4\", \"3\")   ),   list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #>  #> $`Grade A` #> [1] 50 #>  #> $`Grade B` #> [1] 70 #>  #> $`4` #> [1] 40 #>  #> $`3` #> [1] 30 #>  #> $`2` #> [1] 20 #>  #> $`1` #> [1] 10 #>   h_append_grade_groups(   list(     \"Any Grade\" = as.character(1:5),     \"Grade 1-2\" = c(\"1\", \"2\"),     \"Grade 3-4\" = c(\"3\", \"4\")   ),   list(\"1\" = 10, \"2\" = 5, \"3\" = 0) ) #> $`Any Grade` #> [1] 15 #>  #> $`Grade 1-2` #> [1] 15 #>  #> $`1` #> [1] 10 #>  #> $`2` #> [1] 5 #>  #> $`Grade 3-4` #> [1] 0 #>  #> $`3` #> [1] 0 #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Obtain Column Indices — h_col_indices","title":"Obtain Column Indices — h_col_indices","text":"Helper function extract column indices VTableTree given vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Obtain Column Indices — h_col_indices","text":"","code":"h_col_indices(table_tree, col_names)"},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Obtain Column Indices — h_col_indices","text":"table_tree (VTableTree) table extract indices . col_names (character) vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_col_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Obtain Column Indices — h_col_indices","text":"vector column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for s_count_cumulative() — h_count_cumulative","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"Helper function calculate count fraction x values lower upper tail given threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"","code":"h_count_cumulative(   x,   threshold,   lower_tail = TRUE,   include_eq = TRUE,   na.rm = TRUE,   .N_col )"},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"x (numeric) vector numbers want analyze. threshold (number) cutoff value threshold count values x. lower_tail (logical) whether count lower tail, default TRUE. include_eq (logical) whether include value equal threshold count, default TRUE. na.rm (flag) whether NA values removed x prior analysis. .N_col (count) denominator fraction calculation.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"named vector items: count: count values less , less equal , greater , greater equal threshold user specification. fraction: fraction count.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for s_count_cumulative() — h_count_cumulative","text":"","code":"set.seed(1, kind = \"Mersenne-Twister\") x <- c(sample(1:10, 10), NA) .N_col <- length(x) h_count_cumulative(x, 5, .N_col = .N_col) #>     count  fraction  #> 5.0000000 0.4545455  h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col) #>     count  fraction  #> 6.0000000 0.5454545  h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col) #>      count   fraction  #> 10.0000000  0.9090909  h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col) #>    count fraction  #>        0        0"},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"Helper functions used fit_coxreg_univar() fit_coxreg_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"","code":"h_coxreg_univar_formulas(variables, interaction = FALSE)  h_coxreg_multivar_formula(variables)  h_coxreg_univar_extract(effect, covar, data, mod, control = control_coxreg())  h_coxreg_multivar_extract(var, data, mod, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"variables (named list string) list additional analysis variables. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE. effect (string) treatment variable. covar (string) name covariate model. data (data.frame) dataset containing variables summarize. mod (coxph) Cox regression model fitted survival::coxph(). control (list) list controls returned control_coxreg(). var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"h_coxreg_univar_formulas() returns character vector coercible formulas (e.g stats::.formula()). h_coxreg_multivar_formula() returns string coercible formula (e.g stats::.formula()). h_coxreg_univar_extract() returns data.frame variables effect, term, term_label, level, n, hr, lcl, ucl, pval. h_coxreg_multivar_extract() returns data.frame variables pval, hr, lcl, ucl, level, n, term, term_label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"h_coxreg_univar_formulas(): Helper Cox regression formula. Creates list formulas. used internally fit_coxreg_univar() comparison univariate Cox regression models. h_coxreg_multivar_formula(): Helper multivariate Cox regression formula. Creates formulas string. used internally fit_coxreg_multivar() comparison multivariate Cox regression models. Interactions included multivariate Cox regression model. h_coxreg_univar_extract(): Utility function help tabulate result univariate Cox regression model. h_coxreg_multivar_extract(): Tabulation multivariate Cox regressions. Utility function help tabulate result multivariate Cox regression model treatment/covariate variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Cox Proportional Hazards Regression — h_cox_regression","text":"","code":"# `h_coxreg_univar_formulas`  ## Simple formulas. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\")   ) ) #>                                        ref  #>     \"survival::Surv(time, status) ~ armcd\"  #>                                          X  #> \"survival::Surv(time, status) ~ armcd + X\"  #>                                          y  #> \"survival::Surv(time, status) ~ armcd + y\"   ## Addition of an optional strata. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"),     strata = \"SITE\"   ) ) #>                                                       ref  #>     \"survival::Surv(time, status) ~ armcd + strata(SITE)\"  #>                                                         X  #> \"survival::Surv(time, status) ~ armcd + X + strata(SITE)\"  #>                                                         y  #> \"survival::Surv(time, status) ~ armcd + y + strata(SITE)\"   ## Inclusion of the interaction term. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"),     strata = \"SITE\"   ),   interaction = TRUE ) #>                                                       ref  #>     \"survival::Surv(time, status) ~ armcd + strata(SITE)\"  #>                                                         X  #> \"survival::Surv(time, status) ~ armcd * X + strata(SITE)\"  #>                                                         y  #> \"survival::Surv(time, status) ~ armcd * y + strata(SITE)\"   ## Only covariates fitted in separate models. h_coxreg_univar_formulas(   variables = list(     time = \"time\", event = \"status\", covariates = c(\"X\", \"y\")   ) ) #>                                      X                                      y  #> \"survival::Surv(time, status) ~ 1 + X\" \"survival::Surv(time, status) ~ 1 + y\"   # `h_coxreg_multivar_formula`  h_coxreg_multivar_formula(   variables = list(     time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\")   ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE\"  # Addition of an optional strata. h_coxreg_multivar_formula(   variables = list(     time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\"),     strata = \"SITE\"   ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE + strata(SITE)\"  # Example without treatment arm. h_coxreg_multivar_formula(   variables = list(     time = \"AVAL\", event = \"event\", covariates = c(\"RACE\", \"AGE\"),     strata = \"SITE\"   ) ) #> [1] \"survival::Surv(AVAL, event) ~ 1 + RACE + AGE + strata(SITE)\"  library(survival)  dta_simple <- data.frame(   time = c(5, 5, 10, 10, 5, 5, 10, 10),   status = c(0, 0, 1, 0, 0, 1, 1, 1),   armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c(\"A\", \"B\")),   var1 = c(45, 55, 65, 75, 55, 65, 85, 75),   var2 = c(\"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"U\") ) mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_univar_extract(   effect = \"armcd\", covar = \"armcd\", mod = mod, data = dta_simple ) result #>       effect  term       term_label level n       hr       lcl      ucl #> 1 Treatment: armcd B vs control (A)     B 8 6.551448 0.4606904 93.16769 #>       pval #> 1 0.165209  mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_multivar_extract(   var = \"var1\", mod = mod, data = dta_simple ) result #>        pval        hr      lcl      ucl level n term term_label #> 2 0.4456195 0.9423284 0.808931 1.097724  var1 8 var1       var1"},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function: tidy survival fit — h_data_plot","title":"Helper function: tidy survival fit — h_data_plot","text":"Convert survival fit data data frame designed plotting within g_km. starts broom::tidy() result, : Post-processes strata column factor. Extends stratum additional first row time 0 probability 1 downstream plot lines start coordinates. Adds censor column. Filters rows max_time.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function: tidy survival fit — h_data_plot","text":"","code":"h_data_plot(fit_km, armval = \"All\", max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function: tidy survival fit — h_data_plot","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function: tidy survival fit — h_data_plot","text":"tibble columns time, n.risk, n.event, n.censor, estimate, std.error, conf.high, conf.low, strata, censor.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_data_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function: tidy survival fit — h_data_plot","text":"","code":"# \\donttest{ library(dplyr) library(survival)  # Test with multiple arms tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_data_plot() #> # A tibble: 203 × 10 #>     time n.risk n.event n.censor estimate std.error conf.high conf.low strata #>    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <fct>  #>  1   0       70       0        0    1        0          1        1     ARM A  #>  2  10.4     69       1        0    0.986    0.0146     1        0.958 ARM A  #>  3  20.5     68       1        0    0.971    0.0208     1        0.932 ARM A  #>  4  21.5     67       1        0    0.957    0.0257     1        0.910 ARM A  #>  5  25.3     66       0        1    0.957    0.0257     1        0.910 ARM A  #>  6  48.1     65       1        0    0.942    0.0300     0.999    0.888 ARM A  #>  7  66.6     64       0        1    0.942    0.0300     0.999    0.888 ARM A  #>  8  73.9     63       0        1    0.942    0.0300     0.999    0.888 ARM A  #>  9  78.9     62       0        1    0.942    0.0300     0.999    0.888 ARM A  #> 10  90.3     61       1        0    0.926    0.0342     0.991    0.866 ARM A  #> # ℹ 193 more rows #> # ℹ 1 more variable: censor <dbl>  # Test with single arm tern_ex_adtte %>%   filter(PARAMCD == \"OS\", ARMCD == \"ARM B\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_data_plot(armval = \"ARM B\") #> # A tibble: 74 × 10 #>      time n.risk n.event n.censor estimate std.error conf.high conf.low strata #>     <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <chr>  #>  1   0        74       0        0    1        0          1        1     ARM B  #>  2   2.17     73       1        0    0.986    0.0138     1        0.960 ARM B  #>  3  16.9      72       1        0    0.973    0.0196     1        0.936 ARM B  #>  4  22.6      71       1        0    0.959    0.0242     1        0.914 ARM B  #>  5  26.3      70       1        0    0.945    0.0282     0.999    0.894 ARM B  #>  6  57.5      69       1        0    0.932    0.0317     0.991    0.875 ARM B  #>  7  80.0      68       1        0    0.918    0.0350     0.983    0.857 ARM B  #>  8  81.6      67       1        0    0.904    0.0381     0.974    0.839 ARM B  #>  9  88.6      66       1        0    0.890    0.0411     0.965    0.822 ARM B  #> 10 102.       65       1        0    0.877    0.0439     0.955    0.804 ARM B  #> # ℹ 64 more rows #> # ℹ 1 more variable: censor <dbl> # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":null,"dir":"Reference","previous_headings":"","what":"ggplot Decomposition — h_decompose_gg","title":"ggplot Decomposition — h_decompose_gg","text":"elements composing ggplot extracted organized list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ggplot Decomposition — h_decompose_gg","text":"","code":"h_decompose_gg(gg)"},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ggplot Decomposition — h_decompose_gg","text":"gg (ggplot) graphic decompose.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ggplot Decomposition — h_decompose_gg","text":"named list elements: panel: panel. yaxis: y-axis. xaxis: x-axis. xlab: x-axis label. ylab: y-axis label. guide: legend.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_decompose_gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ggplot Decomposition — h_decompose_gg","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   yval = \"Survival\",   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"tt\",   footnotes = \"ff\" )  g_el <- h_decompose_gg(gg) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"red\", fill = \"gray85\", lwd = 5)) grid::grid.draw(g_el$panel)   grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"royalblue\", fill = \"gray85\", lwd = 5)) grid::grid.draw(with(g_el, cbind(ylab, yaxis)))  # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"","code":"h_format_row(x, format, labels = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"x (named list) list numerical values formatted optionally labeled. Elements x must numeric vectors. format (named character NULL) format patterns x. Names format must match names x. parameter passed directly rtables::format_rcell function format parameter. labels (named character NULL) optional labels x. Names labels must match names x. label specified element x, function tries use label names (order) attribute element (depending one exists NULL NA NaN). none attributes attached given element x, label automatically generated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"single row data.frame object.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_format_row.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to get the right formatting in the optional table in g_lineplot. — h_format_row","text":"","code":"mean_ci <- c(48, 51) x <- list(mean = 50, mean_ci = mean_ci) format <- c(mean = \"xx.x\", mean_ci = \"(xx.xx, xx.xx)\") labels <- c(mean = \"My Mean\") h_format_row(x, format, labels) #>   My Mean             V1 #> 1    50.0 (48.00, 51.00)  attr(mean_ci, \"label\") <- \"Mean 95% CI\" x <- list(mean = 50, mean_ci = mean_ci) h_format_row(x, format, labels) #>   My Mean    Mean 95% CI #> 1    50.0 (48.00, 51.00)"},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"Function generates simple line plot displaying parameter trends time.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"","code":"h_g_ipp(   df,   xvar,   yvar,   xlab,   ylab,   id_var,   title = \"Individual Patient Plots\",   subtitle = \"\",   caption = NULL,   add_baseline_hline = FALSE,   yvar_baseline = \"BASE\",   ggtheme = nestcolor::theme_nest(),   col = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (character scalar) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. col (character) lines colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"ggplot line plot.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_g_ipp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function To Create Simple Line Plot over Time — h_g_ipp","text":"","code":"library(dplyr) library(nestcolor)  # Select a small sample of data to plot. adlb <- tern_ex_adlb %>%   filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>%   slice(1:36)  p <- h_g_ipp(   df = adlb,   xvar = \"AVISIT\",   yvar = \"AVAL\",   xlab = \"Visit\",   id_var = \"USUBJID\",   ylab = \"SGOT/ALT (U/L)\",   add_baseline_hline = TRUE ) p"},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function: KM plot — h_ggkm","title":"Helper function: KM plot — h_ggkm","text":"Draw Kaplan-Meier plot using ggplot2.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function: KM plot — h_ggkm","text":"","code":"h_ggkm(   data,   xticks = NULL,   yval = \"Survival\",   censor_show,   xlab,   ylab,   ylim = NULL,   title,   footnotes = NULL,   max_time = NULL,   lwd = 1,   lty = NULL,   pch = 3,   size = 2,   col = NULL,   ci_ribbon = FALSE,   ggtheme = nestcolor::theme_nest() )"},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function: KM plot — h_ggkm","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric, number, NULL) numeric vector ticks single number spacing ticks x axis. NULL (default), labeling::extended() used determine optimal tick position x axis. yval (string) value y-axis. Options Survival (default) Failure probability. censor_show (flag) whether show censored. xlab (string) label x-axis. ylab (string) label y-axis. ylim (vector numeric) vector length 2 containing lower upper limits y-axis. NULL (default), minimum maximum y-values displayed used limits. title (string) title plot. footnotes (string) footnotes plot. max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL). lwd (numeric) line width. Length vector equal number strata survival::survfit(). lty (numeric) line type. Length vector equal number strata survival::survfit(). pch (numeric, string) value character points symbol indicate censored cases. size (numeric) size censored point, class unit. col (character) lines colors. Length vector equal number strata survival::survfit(). ci_ribbon (flag) draw confidence interval around Kaplan-Meier curve. ggtheme (theme) graphical theme provided ggplot2 control outlook Kaplan-Meier curve.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function: KM plot — h_ggkm","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_ggkm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function: KM plot — h_ggkm","text":"","code":"# \\donttest{ library(dplyr) library(survival)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks,   xlab = \"Days\",   yval = \"Survival\",   ylab = \"Survival Probability\",   title = \"Survival\" ) gg  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Poisson Models. — h_glm_count","title":"Helper Functions for Poisson Models. — h_glm_count","text":"Helper functions can used return results various Poisson models.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Poisson Models. — h_glm_count","text":"","code":"h_glm_poisson(.var, .df_row, variables, weights)  h_glm_quasipoisson(.var, .df_row, variables, weights)  h_glm_count(.var, .df_row, variables, distribution, weights)  h_ppmeans(obj, .df_row, arm, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Poisson Models. — h_glm_count","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. variables (named list strings) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". offset (numeric) numeric vector scalar adding offset. weights (character) character vector specifying weights used averaging predictions. Number weights must equal number levels included covariates. Weights option passed emmeans::emmeans(). distribution (character) character value specifying distribution used regression (poisson, quasipoisson). obj (glm.fit) fitted model object used derive mean rate estimates treatment arm. arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. conf_level (numeric) value used derive confidence interval rate.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Poisson Models. — h_glm_count","text":"h_glm_poisson() returns results Poisson model. h_glm_quasipoisson() returns results Quasi-Poisson model. h_glm_count() returns results selected model. h_ppmeans() returns estimated means.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_glm_count.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Poisson Models. — h_glm_count","text":"h_glm_poisson(): Helper function return results poisson model. h_glm_quasipoisson(): Helper function return results quasipoisson model. h_glm_count(): Helper function return results selected model (poisson, quasipoisson, negative binomial). h_ppmeans(): Helper function return estimated means.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: CoxPH Grob — h_grob_coxph","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"Grob rtable output h_tbl_coxph_pairwise()","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"","code":"h_grob_coxph(   ...,   x = 0,   y = 0,   width = grid::unit(0.4, \"npc\"),   ttheme = gridExtra::ttheme_default(padding = grid::unit(c(1, 0.5), \"lines\"), core =     list(bg_params = list(fill = c(\"grey95\", \"grey90\"), alpha = 0.5))) )"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"... arguments passed h_tbl_coxph_pairwise(). x (numeric) value 0 1 specifying x-location. y (numeric) value 0 1 specifying y-location. width (unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"grob table containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: CoxPH Grob — h_grob_coxph","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) data <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0) tbl_grob <- h_grob_coxph(   df = data,   variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\"),   control_coxph_pw = control_coxph(conf_level = 0.9), x = 0.5, y = 0.5 ) grid::grid.draw(tbl_grob)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: Survival Estimation Grob — h_grob_median_surv","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"survival fit transformed grob containing table groups rows characterized N, median 95% confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"","code":"h_grob_median_surv(   fit_km,   armval = \"All\",   x = 0.9,   y = 0.9,   width = grid::unit(0.3, \"npc\"),   ttheme = gridExtra::ttheme_default() )"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". x (numeric) value 0 1 specifying x-location. y (numeric) value 0 1 specifying y-location. width (unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"grob table containing statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: Survival Estimation Grob — h_grob_median_surv","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_grob_median_surv() %>%   grid::grid.draw()  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"Two graphical objects obtained, one corresponding row labeling second table numbers patients risk. title = TRUE, third object corresponding table title also obtained.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"","code":"h_grob_tbl_at_risk(data, annot_tbl, xlim, title = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"data (data.frame) survival data pre-processed h_data_plot. annot_tbl (data.frame) annotation prepared survival::summary.survfit() includes number patients risk given time points. xlim (numeric) maximum value x-axis (used ensure risk table aligns KM graph). title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"named list two gTree objects title = FALSE: at_risk label, three gTree objects title = TRUE: at_risk, label, title.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_tbl_at_risk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper: Patient-at-Risk Grobs — h_grob_tbl_at_risk","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)  data_plot <- h_data_plot(fit_km = fit_km)  xticks <- h_xticks(data = data_plot)  gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"tt\", footnotes = \"ff\", yval = \"Survival\" )  # The annotation table reports the patient at risk for a given strata and # time (`xticks`). annot_tbl <- summary(fit_km, time = xticks) if (is.null(fit_km$strata)) {   annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = \"All\")) } else {   strata_lst <- strsplit(sub(\"=\", \"equals\", levels(annot_tbl$strata)), \"equals\")   levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]   annot_tbl <- data.frame(     n.risk = annot_tbl$n.risk,     time = annot_tbl$time,     strata = annot_tbl$strata   ) }  # The annotation table is transformed into a grob. tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks))  # For the representation, the layout is estimated for which the decomposition # of the graphic element is necessary. g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\")  grid::grid.newpage() pushViewport(viewport(layout = lyt, height = .95, width = .95)) grid.rect(gp = grid::gpar(lty = 1, col = \"purple\", fill = \"gray85\", lwd = 1)) pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2)) grid.rect(gp = grid::gpar(lty = 1, col = \"orange\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$at_risk) popViewport() pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1)) grid.rect(gp = grid::gpar(lty = 1, col = \"green3\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$label)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"Build y-axis annotation decomposed ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"","code":"h_grob_y_annot(ylab, yaxis)"},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"ylab (gtable) y-lab graphical object derived ggplot. yaxis (gtable) y-axis graphical object derived ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"gTree object containing y-axis annotation ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_grob_y_annot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper: Grid Object with y-axis Annotation — h_grob_y_annot","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"title\", footnotes = \"footnotes\", yval = \"Survival\" )  g_el <- h_decompose_gg(gg)  grid::grid.newpage() pvp <- grid::plotViewport(margins = c(5, 4, 2, 20)) pushViewport(pvp) grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis)) grid.rect(gp = grid::gpar(lty = 1, col = \"gray35\", fill = NA))  # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"","code":"h_incidence_rate_normal(person_years, n_events, alpha = 0.05)  h_incidence_rate_normal_log(person_years, n_events, alpha = 0.05)  h_incidence_rate_exact(person_years, n_events, alpha = 0.05)  h_incidence_rate_byar(person_years, n_events, alpha = 0.05)  h_incidence_rate(person_years, n_events, control = control_incidence_rate())"},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"person_years (numeric) total person-years risk. n_events (integer) number events observed. alpha (numeric) two-sided alpha-level confidence interval. control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level: (proportion) confidence level estimated incidence rate. conf_type: (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit: (string)day, week, month, year (default) indicating time unit data input. num_pt_year: (numeric) time unit desired output (person-years).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"Estimated incidence rate rate associated confidence interval rate_ci.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"h_incidence_rate_normal(): Helper function estimate incidence rate associated confidence interval based normal approximation incidence rate. Unit one person-year. h_incidence_rate_normal_log(): Helper function estimate incidence rate associated confidence interval based normal approximation logarithm incidence rate. Unit one person-year. h_incidence_rate_exact(): Helper function estimate incidence rate associated exact confidence interval. Unit one person-year. h_incidence_rate_byar(): Helper function estimate incidence rate associated Byar's confidence interval. Unit one person-year. h_incidence_rate(): Helper function estimate incidence rate associated confidence interval.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Incidence Rate — h_incidence_rate","text":"","code":"h_incidence_rate_normal(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] -0.003859038  0.023859038 #>   h_incidence_rate_normal_log(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] 0.002500977 0.039984382 #>   h_incidence_rate_exact(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] 0.001211046 0.036123438 #>   h_incidence_rate_byar(200, 2) #> $rate #> [1] 0.01 #>  #> $rate_ci #> [1] 0.001994207 0.032054171 #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper: KM Layout — h_km_layout","title":"Helper: KM Layout — h_km_layout","text":"Prepares (5 rows) x (2 cols) layout Kaplan-Meier curve.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper: KM Layout — h_km_layout","text":"","code":"h_km_layout(   data,   g_el,   title,   footnotes,   annot_at_risk = TRUE,   annot_at_risk_title = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper: KM Layout — h_km_layout","text":"data (data.frame) survival data pre-processed h_data_plot. g_el (list gtable) list obtained h_decompose_gg(). title (string) title plot. footnotes (string) footnotes plot. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper: KM Layout — h_km_layout","text":"grid layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper: KM Layout — h_km_layout","text":"layout corresponds grid two columns five rows unequal dimensions. dimension fixed, curve flexible accommodate remaining free space. left column gets annotation ggplot (y-axis) names strata patient risk tabulation. main constraint width columns must allow writing strata name. right column receive ggplot, legend, x-axis patient risk table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_km_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper: KM Layout — h_km_layout","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid)  fit_km <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm(   data = data_plot,   censor_show = TRUE,   xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\",   title = \"tt\", footnotes = \"ff\", yval = \"Survival\" ) g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\") grid.show.layout(lyt)  # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"Helper functions used calculations logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"","code":"h_get_interaction_vars(fit_glm)  h_interaction_coef_name(   interaction_vars,   first_var_with_level,   second_var_with_level )  h_or_cat_interaction(   odds_ratio_var,   interaction_var,   fit_glm,   conf_level = 0.95 )  h_or_cont_interaction(   odds_ratio_var,   interaction_var,   fit_glm,   at = NULL,   conf_level = 0.95 )  h_or_interaction(   odds_ratio_var,   interaction_var,   fit_glm,   at = NULL,   conf_level = 0.95 )  h_simple_term_labels(terms, table)  h_interaction_term_labels(terms1, terms2, table, any = FALSE)  h_glm_simple_term_extract(x, fit_glm)  h_glm_interaction_extract(x, fit_glm)  h_glm_inter_term_extract(odds_ratio_var, interaction_var, fit_glm, ...)  h_logistic_simple_terms(x, fit_glm, conf_level = 0.95)  h_logistic_inter_terms(x, fit_glm, conf_level = 0.95, at = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"fit_glm (glm) logistic regression model fitted stats::glm() \"binomial\" family. Limited functionality also available conditional logistic regression models fitted survival::clogit(), currently used extract_rsp_biomarkers(). interaction_vars (character length 2) interaction variable names. first_var_with_level (character length 2) first variable name interaction level. second_var_with_level (character length 2) second variable name interaction level. odds_ratio_var (string) odds ratio variable. interaction_var (string) interaction variable. conf_level (proportion) confidence level interval. (NULL numeric) optional values interaction variable. Otherwise median used. terms (character) simple terms. table (table) table containing numbers terms. terms1 (character) terms first dimension (rows). terms2 (character) terms second dimension (rows). (flag) whether term1 term2 can fulfilled count number patients. case can scalar (strings). x (string character) variable interaction term fit_glm (depending helper function). ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"Vector names interaction variables. Name coefficient. Odds ratio. Odds ratio. Odds ratio. Term labels containing numbers patients. Term labels containing numbers patients. Tabulated main effect results logistic regression model. Tabulated interaction term results logistic regression model. data.frame tabulated interaction term results logistic regression model. Tabulated statistics given variable(s) logistic regression model. Tabulated statistics given variable(s) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"h_get_interaction_vars(): Helper function extract interaction variable names fitted model assuming one interaction term. h_interaction_coef_name(): Helper function get right coefficient name interaction variable names given levels. main value order first second variable checked interaction_vars input. h_or_cat_interaction(): Helper function calculate odds ratio estimates case odds ratio interaction variable categorical. h_or_cont_interaction(): Helper function calculate odds ratio estimates case either odds ratio interaction variable continuous. h_or_interaction(): Helper function calculate odds ratio estimates case interaction. wrapper h_or_cont_interaction() h_or_cat_interaction(). h_simple_term_labels(): Helper function construct term labels simple terms table numbers patients. h_interaction_term_labels(): Helper function construct term labels interaction terms table numbers patients. h_glm_simple_term_extract(): Helper function tabulate main effect results (conditional) logistic regression model. h_glm_interaction_extract(): Helper function tabulate interaction term results logistic regression model. h_glm_inter_term_extract(): Helper function tabulate interaction results logistic regression model. basically wrapper h_or_interaction() h_glm_simple_term_extract() puts results right data frame format. h_logistic_simple_terms(): Helper function tabulate results including odds ratios confidence intervals simple terms. h_logistic_inter_terms(): Helper function tabulate results including odds ratios confidence intervals interaction terms.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"provide function case variables continuous arise table, treatment arm variable always involved categorical.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_logistic_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Multivariate Logistic Regression — h_logistic_regression","text":"","code":"library(dplyr) library(broom)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred  h_glm_simple_term_extract(\"AGE\", mod1) #>   variable variable_label term term_label interaction interaction_label #> 1      AGE            Age  AGE        Age                               #>   reference reference_label  estimate  std_error df     pvalue #> 1                           0.1698216 0.09524116  1 0.07457501 #>   is_variable_summary is_term_summary #> 1               FALSE            TRUE h_glm_simple_term_extract(\"ARMCD\", mod1) #>   variable   variable_label  term              term_label interaction #> 1    ARMCD Planned Arm Code ARM A Reference ARM A, n = 64             #> 2    ARMCD Planned Arm Code ARM B           ARM B, n = 68             #> 3    ARMCD Planned Arm Code ARM C           ARM C, n = 52             #>   interaction_label reference reference_label  estimate std_error df    pvalue #> 1                                                                  2 0.3004308 #> 2                                             -1.774769  1.144405  1 0.1209443 #> 3                                               17.1922  3626.588  1 0.9962176 #>   is_variable_summary is_term_summary #> 1                TRUE           FALSE #> 2               FALSE            TRUE #> 3               FALSE            TRUE  h_glm_interaction_extract(\"ARMCD:AGE\", mod2) #>    variable                        variable_label  term              term_label #> 1 ARMCD:AGE Interaction of Planned Arm Code * Age ARM A Reference ARM A, n = 64 #> 2 ARMCD:AGE Interaction of Planned Arm Code * Age ARM B           ARM B, n = 68 #> 3 ARMCD:AGE Interaction of Planned Arm Code * Age ARM C           ARM C, n = 52 #>   interaction interaction_label reference reference_label   estimate std_error #> 1                                                                              #> 2                                                          0.3081205 0.2062392 #> 3                                                         0.02948826  548.5923 #>   df    pvalue is_variable_summary is_term_summary #> 1  2 0.3275837                TRUE           FALSE #> 2  1 0.1351767               FALSE            TRUE #> 3  1 0.9999571               FALSE            TRUE  h_glm_inter_term_extract(\"AGE\", \"ARMCD\", mod2) #>   variable variable_label term term_label interaction interaction_label #> 1      AGE            Age  AGE        Age                               #> 2      AGE            Age  AGE        Age       ARMCD  Planned Arm Code #> 3      AGE            Age  AGE        Age       ARMCD  Planned Arm Code #> 4      AGE            Age  AGE        Age       ARMCD  Planned Arm Code #>   reference reference_label    estimate std_error odds_ratio       lcl      ucl #> 1                           -0.03873898 0.1514322         NA        NA       NA #> 2     ARM A           ARM A          NA        NA  0.9620018 0.7149514 1.294420 #> 3     ARM B           ARM B          NA        NA  1.3091545 1.0021802 1.710157 #> 4     ARM C           ARM C          NA        NA  0.9907919 0.0000000      Inf #>   df   pvalue is_variable_summary is_term_summary is_reference_summary #> 1  1 0.798092               FALSE            TRUE                FALSE #> 2 NA       NA               FALSE           FALSE                 TRUE #> 3 NA       NA               FALSE           FALSE                 TRUE #> 4 NA       NA               FALSE           FALSE                 TRUE  h_logistic_simple_terms(\"AGE\", mod1) #>   variable variable_label term term_label interaction interaction_label #> 1      AGE            Age  AGE        Age                               #>   reference reference_label  estimate  std_error df     pvalue #> 1                           0.1698216 0.09524116  1 0.07457501 #>   is_variable_summary is_term_summary odds_ratio       lcl      ucl #> 1               FALSE            TRUE   1.185093 0.9832935 1.428308 #>                     ci #> 1 0.9832935, 1.4283084  h_logistic_inter_terms(c(\"RACE\", \"AGE\", \"ARMCD\", \"AGE:ARMCD\"), mod2) #>        variable                        variable_label                      term #> 1          RACE                                  Race                     ASIAN #> 2          RACE                                  Race BLACK OR AFRICAN AMERICAN #> 3          RACE                                  Race                     WHITE #> 13        ARMCD                      Planned Arm Code                     ARM A #> 23        ARMCD                      Planned Arm Code                     ARM B #> ARM B     ARMCD                      Planned Arm Code                     ARM B #> 33        ARMCD                      Planned Arm Code                     ARM C #> ARM C     ARMCD                      Planned Arm Code                     ARM C #> 11          AGE                                   Age                       AGE #> 21          AGE                                   Age                       AGE #> 31          AGE                                   Age                       AGE #> 4           AGE                                   Age                       AGE #> 12    AGE:ARMCD Interaction of Planned Arm Code * Age                     ARM A #> 22    AGE:ARMCD Interaction of Planned Arm Code * Age                     ARM B #> 32    AGE:ARMCD Interaction of Planned Arm Code * Age                     ARM C #>                              term_label interaction interaction_label reference #> 1              Reference ASIAN, n = 110                                         #> 2     BLACK OR AFRICAN AMERICAN, n = 40                                         #> 3                         WHITE, n = 34                                         #> 13              Reference ARM A, n = 64                                         #> 23                        ARM B, n = 68                                         #> ARM B                     ARM B, n = 68         AGE               Age        35 #> 33                        ARM C, n = 52                                         #> ARM C                     ARM C, n = 52         AGE               Age        35 #> 11                                  Age                                         #> 21                                  Age       ARMCD  Planned Arm Code     ARM A #> 31                                  Age       ARMCD  Planned Arm Code     ARM B #> 4                                   Age       ARMCD  Planned Arm Code     ARM C #> 12              Reference ARM A, n = 64                                         #> 22                        ARM B, n = 68                                         #> 32                        ARM C, n = 52                                         #>       reference_label    estimate std_error df     pvalue odds_ratio        lcl #> 1                                            2  0.9361139                       #> 2                        18.15018  3944.701  1  0.9963288   76299564          0 #> 3                      -0.3727152  1.025808  1  0.7163522  0.6888614 0.09224926 #> 13                                           2   0.253914         NA         NA #> 23                      -11.52715   6.96188  1 0.09777185         NA         NA #> ARM B              35          NA        NA NA         NA  0.4757148 0.03361147 #> 33                       16.33327  20278.39  1  0.9993573         NA         NA #> ARM C              35          NA        NA NA         NA   34808099          0 #> 11                    -0.03873898 0.1514322  1   0.798092         NA         NA #> 21              ARM A          NA        NA NA         NA  0.9620018  0.7149514 #> 31              ARM B          NA        NA NA         NA   1.309155    1.00218 #> 4               ARM C          NA        NA NA         NA  0.9907919          0 #> 12                                          NA         NA         NA         NA #> 22                      0.3081205 0.2062392  1  0.1351767         NA         NA #> 32                     0.02948826  548.5923  1  0.9999571         NA         NA #>            ucl is_variable_summary is_term_summary is_reference_summary #> 1                             TRUE           FALSE                FALSE #> 2          Inf               FALSE            TRUE                FALSE #> 3     5.143998               FALSE            TRUE                FALSE #> 13          NA                TRUE           FALSE                FALSE #> 23          NA               FALSE            TRUE                FALSE #> ARM B 6.732956               FALSE           FALSE                 TRUE #> 33          NA               FALSE            TRUE                FALSE #> ARM C      Inf               FALSE           FALSE                 TRUE #> 11          NA               FALSE            TRUE                FALSE #> 21     1.29442               FALSE           FALSE                 TRUE #> 31    1.710157               FALSE           FALSE                 TRUE #> 4          Inf               FALSE           FALSE                 TRUE #> 12          NA                TRUE           FALSE                FALSE #> 22          NA               FALSE            TRUE                FALSE #> 32          NA               FALSE            TRUE                FALSE #>                           ci #> 1                            #> 2                     0, Inf #> 3     0.09224926, 5.14399810 #> 13                    NA, NA #> 23                    NA, NA #> ARM B 0.03361147, 6.73295611 #> 33                    NA, NA #> ARM C                 0, Inf #> 11                    NA, NA #> 21      0.7149514, 1.2944200 #> 31        1.002180, 1.710157 #> 4                     0, Inf #> 12                    NA, NA #> 22                    NA, NA #> 32                    NA, NA"},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"Helper Function create map dataframe input dataset, can used argument trim_levels_to_map split function. Based different method, map constructed differently.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"","code":"h_map_for_count_abnormal(   df,   variables = list(anl = \"ANRIND\", split_rows = c(\"PARAM\"), range_low = \"ANRLO\",     range_high = \"ANRHI\"),   abnormal = list(low = c(\"LOW\", \"LOW LOW\"), high = c(\"HIGH\", \"HIGH HIGH\")),   method = c(\"default\", \"range\"),   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"df (data.frame) data set containing analysis variables. variables (named list string) list additional analysis variables. abnormal (named list) identifying abnormal range level(s) df. Based levels abnormality input dataset, can something like list(Low = \"LOW LOW\", High = \"HIGH HIGH\") abnormal = list(Low = \"LOW\", High = \"HIGH\")) method (string) indicates returned map constructed. Can \"default\" \"range\". na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"map data.frame.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"method \"default\", returned map abnormal directions observed df, records normal values excluded avoid error creating layout. method \"range\", returned map based rule least one observation low range > 0 low direction least one observation high range missing high direction.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_map_for_count_abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to create a map dataframe that can be used in trim_levels_to_map split function. — h_map_for_count_abnormal","text":"","code":"adlb <- df_explicit_na(tern_ex_adlb)  h_map_for_count_abnormal(   df = adlb,   variables = list(anl = \"ANRIND\", split_rows = c(\"LBCAT\", \"PARAM\")),   abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")),   method = \"default\",   na_str = \"<Missing>\" ) #>        LBCAT                                PARAM ANRIND #> 1  CHEMISTRY Alanine Aminotransferase Measurement    LOW #> 4  CHEMISTRY Alanine Aminotransferase Measurement   HIGH #> 7  CHEMISTRY Alanine Aminotransferase Measurement NORMAL #> 2  CHEMISTRY       C-Reactive Protein Measurement    LOW #> 3  CHEMISTRY       C-Reactive Protein Measurement   HIGH #> 8  CHEMISTRY       C-Reactive Protein Measurement NORMAL #> 5 IMMUNOLOGY         Immunoglobulin A Measurement    LOW #> 6 IMMUNOLOGY         Immunoglobulin A Measurement   HIGH #> 9 IMMUNOLOGY         Immunoglobulin A Measurement NORMAL  df <- data.frame(   USUBJID = c(rep(\"1\", 4), rep(\"2\", 4), rep(\"3\", 4)),   AVISIT = c(     rep(\"WEEK 1\", 2),     rep(\"WEEK 2\", 2),     rep(\"WEEK 1\", 2),     rep(\"WEEK 2\", 2),     rep(\"WEEK 1\", 2),     rep(\"WEEK 2\", 2)   ),   PARAM = rep(c(\"ALT\", \"CPR\"), 6),   ANRIND = c(     \"NORMAL\", \"NORMAL\", \"LOW\",     \"HIGH\", \"LOW\", \"LOW\", \"HIGH\", \"HIGH\", rep(\"NORMAL\", 4)   ),   ANRLO = rep(5, 12),   ANRHI = rep(20, 12) ) df$ANRIND <- factor(df$ANRIND, levels = c(\"LOW\", \"HIGH\", \"NORMAL\")) h_map_for_count_abnormal(   df = df,   variables = list(     anl = \"ANRIND\",     split_rows = c(\"PARAM\"),     range_low = \"ANRLO\",     range_high = \"ANRHI\"   ),   abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")),   method = \"range\",   na_str = \"<Missing>\" ) #>   PARAM ANRIND #> 1   ALT    LOW #> 3   ALT   HIGH #> 5   ALT NORMAL #> 2   CPR    LOW #> 4   CPR   HIGH #> 6   CPR NORMAL"},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"Functions calculate odds ratios estimate_odds_ratio().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"","code":"or_glm(data, conf_level)  or_clogit(data, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"data (data.frame) data frame containing least variables rsp grp, optionally strata or_clogit(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"named list elements or_ci n_tot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"or_glm(): Estimates odds ratio based stats::glm(). Note must exactly 2 groups data specified grp variable. or_clogit(): estimates odds ratio based survival::clogit(). done whole data set including groups, since results pairwise comparisons groups.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Odds Ratio Estimation — h_odds_ratio","text":"","code":"# Data with 2 groups. data <- data.frame(   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)),   grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)],   strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)],   stringsAsFactors = TRUE )  # Odds ratio based on glm. or_glm(data, conf_level = 0.95) #> $or_ci #>        est        lcl        ucl  #> 0.33333333 0.01669735 6.65441589  #>  #> $n_tot #> n_tot  #>     8  #>   # Data with 3 groups. data <- data.frame(   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)),   grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)],   strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)],   stringsAsFactors = TRUE )  # Odds ratio based on stratified estimation by conditional logistic regression. or_clogit(data, conf_level = 0.95) #> $or_ci #> $or_ci$b #>        est        lcl        ucl  #> 0.28814553 0.02981009 2.78522598  #>  #> $or_ci$c #>       est       lcl       ucl  #> 0.5367919 0.0673365 4.2791881  #>  #>  #> $n_tot #> n_tot  #>    20  #>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"","code":"h_pkparam_sort(pk_data, key_var = \"PARAMCD\")"},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"pk_data (data.frame)Pharmacokinetics dataframe key_var (character) key variable used merge pk_data metadata created d_pkparam()","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"PK data.frame sorted PARAM variable.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_pkparam_sort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort Data by PK PARAM Variable — h_pkparam_sort","text":"","code":"library(dplyr)  adpp <- tern_ex_adpp %>% mutate(PKPARAM = factor(paste0(PARAM, \" (\", AVALU, \")\"))) pk_ordered_data <- h_pkparam_sort(adpp)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"","code":"prop_diff_wald(rsp, grp, conf_level = 0.95, correct = FALSE)  prop_diff_ha(rsp, grp, conf_level)  prop_diff_nc(rsp, grp, conf_level, correct = FALSE)  prop_diff_cmh(rsp, grp, strata, conf_level = 0.95)  prop_diff_strat_nc(   rsp,   grp,   strata,   weights_method = c(\"cmh\", \"wilson_h\"),   conf_level = 0.95,   correct = FALSE )"},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"rsp (logical) whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). conf_level (proportion) confidence level interval. correct (logical) whether include continuity correction. information, see stats::prop.test(). strata (factor) variable one level per stratum length rsp. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"named list elements diff (proportion difference) diff_ci (proportion difference confidence interval).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"prop_diff_wald(): Wald interval follows usual textbook definition single proportion confidence interval using normal approximation. possible include continuity correction Wald's interval. prop_diff_ha(): Anderson-Hauck confidence interval. prop_diff_nc(): Newcombe confidence interval. based Wilson score confidence interval single binomial proportion. prop_diff_cmh(): Calculates weighted difference. defined difference response rates experimental treatment group control treatment group, adjusted stratification factors applying Cochran-Mantel-Haenszel (CMH) weights. CMH chi-squared test, use stats::mantelhaen.test(). prop_diff_strat_nc(): Calculates stratified Newcombe confidence interval difference response rates experimental treatment group control treatment group, adjusted stratification factors. implementation follows closely one proposed Yan Su (2010) . Weights can estimated heuristic proposed prop_strat_wilson() CMH-derived weights (see prop_diff_cmh()).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329--335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions to Calculate Proportion Difference — h_prop_diff","text":"","code":"# Wald confidence interval set.seed(2) rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20) grp <- factor(c(rep(\"A\", 10), rep(\"B\", 10))) prop_diff_wald(rsp = rsp, grp = grp, conf_level = 0.95, correct = FALSE) #> $diff #> [1] 0 #>  #> $diff_ci #> [1] -0.4382613  0.4382613 #>   # Anderson-Hauck confidence interval ## \"Mid\" case: 3/4 respond in group A, 1/2 respond in group B. rsp <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE) grp <- factor(c(\"A\", \"B\", \"A\", \"B\", \"A\", \"A\"), levels = c(\"B\", \"A\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.90) #> $diff #> [1] 0.25 #>  #> $diff_ci #> [1] -0.9195011  1.0000000 #>   ## Edge case: Same proportion of response in A and B. rsp <- c(TRUE, FALSE, TRUE, FALSE) grp <- factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.6) #> $diff #> [1] 0 #>  #> $diff_ci #> [1] -0.8451161  0.8451161 #>   # `Newcombe` confidence interval  set.seed(1) rsp <- c(   sample(c(TRUE, FALSE), size = 40, prob = c(3 / 4, 1 / 4), replace = TRUE),   sample(c(TRUE, FALSE), size = 40, prob = c(1 / 2, 1 / 2), replace = TRUE) ) grp <- factor(rep(c(\"A\", \"B\"), each = 40), levels = c(\"B\", \"A\")) table(rsp, grp) #>        grp #> rsp      B  A #>   FALSE 20 10 #>   TRUE  20 30 prop_diff_nc(rsp = rsp, grp = grp, conf_level = 0.9) #> $diff #> [1] 0.25 #>  #> $diff_ci #> [1] 0.07193388 0.40725819 #>   # Cochran-Mantel-Haenszel confidence interval  set.seed(2) rsp <- sample(c(TRUE, FALSE), 100, TRUE) grp <- sample(c(\"Placebo\", \"Treatment\"), 100, TRUE) grp <- factor(grp, levels = c(\"Placebo\", \"Treatment\")) strata_data <- data.frame(   \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   stringsAsFactors = TRUE )  prop_diff_cmh(   rsp = rsp, grp = grp, strata = interaction(strata_data),   conf_level = 0.90 ) #> $prop #>   Placebo Treatment  #> 0.5331117 0.3954251  #>  #> $prop_ci #> $prop_ci$Placebo #> [1] 0.4306536 0.6355698 #>  #> $prop_ci$Treatment #> [1] 0.2890735 0.5017768 #>  #>  #> $diff #> [1] -0.1376866 #>  #> $diff_ci #> [1] -0.285363076  0.009989872 #>  #> $weights #>       a.x       b.x       a.y       b.y       a.z       b.z  #> 0.1148388 0.2131696 0.1148388 0.2131696 0.1767914 0.1671918  #>  #> $n1 #> a.x b.x a.y b.y a.z b.z  #>   4  11   8  11  13  11  #>  #> $n2 #> a.x b.x a.y b.y a.z b.z  #>   8   9   4   9   6   6  #>   # Stratified `Newcombe` confidence interval  set.seed(2) data_set <- data.frame(   \"rsp\" = sample(c(TRUE, FALSE), 100, TRUE),   \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   \"grp\" = sample(c(\"Placebo\", \"Treatment\"), 100, TRUE),   stringsAsFactors = TRUE )  prop_diff_strat_nc(   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),   weights_method = \"cmh\",   conf_level = 0.90 ) #> $diff #> [1] -0.05777672 #>  #> $diff_ci #>      lower      upper  #> -0.2236537  0.1119331  #>   prop_diff_strat_nc(   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),   weights_method = \"wilson_h\",   conf_level = 0.90 ) #> $diff #> [1] -0.07771884 #>  #> $diff_ci #>      lower      upper  #> -0.2540844  0.1027720  #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions to Test Proportion Differences — h_prop_diff_test","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"Helper functions implement various tests difference two proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"","code":"prop_chisq(tbl)  prop_cmh(ary)  prop_schouten(tbl)  prop_fisher(tbl)"},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"tbl (matrix) matrix two groups rows binary response (TRUE/FALSE) columns. ary (array, 3 dimensions) array two groups rows, binary response (TRUE/FALSE) columns, strata third dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions to Test Proportion Differences — h_prop_diff_test","text":"prop_chisq(): performs Chi-Squared test. Internally calls stats::prop.test(). prop_cmh(): performs stratified Cochran-Mantel-Haenszel test. Internally calls stats::mantelhaen.test(). Note strata less two observations automatically discarded. prop_schouten(): performs Chi-Squared test Schouten correction. prop_fisher(): performs Fisher's exact test. Internally calls stats::fisher.test().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"Functions calculate different proportion confidence intervals use estimate_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"","code":"prop_wilson(rsp, conf_level, correct = FALSE)  prop_strat_wilson(   rsp,   strata,   weights = NULL,   conf_level = 0.95,   max_iterations = NULL,   correct = FALSE )  prop_clopper_pearson(rsp, conf_level)  prop_wald(rsp, conf_level, correct = FALSE)  prop_agresti_coull(rsp, conf_level)  prop_jeffreys(rsp, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"rsp (logical) whether subject responder . conf_level (proportion) confidence level interval. correct (flag) apply continuity correction. strata (factor) variable one level per stratum length rsp. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010)  minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"Confidence interval proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"prop_wilson(): Calculates Wilson interval calling stats::prop.test(). Also referred Wilson score interval. prop_strat_wilson(): Calculates stratified Wilson confidence interval unequal proportions described Yan Su (2010) prop_clopper_pearson(): Calculates Clopper-Pearson interval calling stats::binom.test(). Also referred exact method. prop_wald(): Calculates Wald interval following usual textbook definition single proportion confidence interval using normal approximation. prop_agresti_coull(): Calculates Agresti-Coull interval (created Alan Agresti Brent Coull) (95% CI) adding two successes two failures data using Wald formula construct CI. prop_jeffreys(): Calculates Jeffreys interval, equal-tailed interval based non-informative Jeffreys prior binomial proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329--335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_proportions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Calculating Proportion Confidence Intervals — h_proportions","text":"","code":"rsp <- c(   TRUE, TRUE, TRUE, TRUE, TRUE,   FALSE, FALSE, FALSE, FALSE, FALSE ) prop_wilson(rsp, conf_level = 0.9) #> [1] 0.2692718 0.7307282  # Stratified Wilson confidence interval with unequal probabilities  set.seed(1) rsp <- sample(c(TRUE, FALSE), 100, TRUE) strata_data <- data.frame(   \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   stringsAsFactors = TRUE ) strata <- interaction(strata_data) n_strata <- ncol(table(rsp, strata)) # Number of strata  prop_strat_wilson(   rsp = rsp, strata = strata,   conf_level = 0.90 ) #> $conf_int #>     lower     upper  #> 0.4072891 0.5647887  #>  #> $weights #>       a.x       b.x       a.y       b.y       a.z       b.z  #> 0.2074199 0.1776464 0.1915610 0.1604678 0.1351096 0.1277952  #>   # Not automatic setting of weights prop_strat_wilson(   rsp = rsp, strata = strata,   weights = rep(1 / n_strata, n_strata),   conf_level = 0.90 ) #> $conf_int #>     lower     upper  #> 0.4190436 0.5789733  #>   prop_clopper_pearson(rsp, conf_level = .95) #> [1] 0.3886442 0.5919637  prop_wald(rsp, conf_level = 0.95) #> [1] 0.3920214 0.5879786 prop_wald(rsp, conf_level = 0.95, correct = TRUE) #> [1] 0.3870214 0.5929786  prop_agresti_coull(rsp, conf_level = 0.95) #> [1] 0.3942193 0.5865206  prop_jeffreys(rsp, conf_level = 0.95) #> [1] 0.3934779 0.5870917"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"","code":"h_rsp_to_logistic_variables(variables, biomarker)  h_logistic_mult_cont_df(variables, data, control = control_logistic())  h_tab_rsp_one_biomarker(df, vars, .indent_mods = 0L)"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (named list) controls response definition confidence level produced control_logistic(). df (data.frame) results single biomarker, part returned extract_rsp_biomarkers() (needs couple columns added high-level function relative returned h_logistic_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables() returns named list elements response, arm, covariates, strata. h_logistic_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_rsp_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables(): helps converting \"response\" function variable list \"logistic regression\" variable list. reason currently inconsistency variable names accepted extract_rsp_subgroups() fit_logistic(). h_logistic_mult_cont_df(): prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates strat. h_tab_rsp_one_biomarker(): prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup — h_response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  # This is how the variable list is converted internally. h_rsp_to_logistic_variables(   variables = list(     rsp = \"RSP\",     covariates = c(\"A\", \"B\"),     strat = \"D\"   ),   biomarker = \"AGE\" ) #> $response #> [1] \"RSP\" #>  #> $arm #> [1] \"AGE\" #>  #> $covariates #> [1] \"A\" \"B\" #>  #> $strata #> [1] \"D\" #>   # For a single population, estimate separately the effects # of two biomarkers. df <- h_logistic_mult_cont_df(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\"   ),   data = adrs_f ) df #>   biomarker              biomarker_label n_tot n_rsp prop        or       lcl #> 1    BMRKR1 Continuous Level Biomarker 1   200   164 0.82 0.9755036 0.8804862 #> 2       AGE                          Age   200   164 0.82 0.9952416 0.9462617 #>        ucl conf_level      pval     pval_label #> 1 1.080775       0.95 0.6352602 p-value (Wald) #> 2 1.046757       0.95 0.8530389 p-value (Wald)  # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     strat = \"STRATA1\"   ),   data = adrs_f[NULL, ] ) #>   biomarker              biomarker_label n_tot n_tot_events median hr lcl ucl #> 1    BMRKR1 Continuous Level Biomarker 1     0            0     NA NA  NA  NA #> 2       AGE                          Age     0            0     NA NA  NA  NA #>   conf_level pval     pval_label #> 1       0.95   NA p-value (Wald) #> 2       0.95   NA p-value (Wald)  # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\"  h_tab_rsp_one_biomarker(   df1,   vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\") ) #>                Total n   Responders   Response (%)   Odds Ratio      95% CI      p-value (Wald) #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> All patients     200        164          82.0%          0.98      (0.88, 1.08)       0.6353"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"Helper functions tabulate data frame statistics response rate odds ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"","code":"h_proportion_df(rsp, arm)  h_proportion_subgroups_df(   variables,   data,   groups_lists = list(),   label_all = \"All Patients\" )  h_odds_ratio_df(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL)  h_odds_ratio_subgroups_df(   variables,   data,   groups_lists = list(),   conf_level = 0.95,   method = NULL,   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"rsp (logical) whether subject responder . arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame NULL) required stratified analysis performed. conf_level (proportion) confidence level interval. method (string) specifies test used calculate p-value difference two proportions. options, see s_test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"h_proportion_df() returns data.frame columns arm, n, n_rsp, prop. h_proportion_subgroups_df() returns data.frame columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. h_odds_ratio_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, optionally pval pval_label. h_odds_ratio_subgroups_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"h_proportion_df(): helper prepare data frame binary responses arm. h_proportion_subgroups_df(): summarizes proportion binary responses arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_odds_ratio_df(): helper prepare data frame estimates odds ratio treatment control arm. h_odds_ratio_subgroups_df(): summarizes estimates odds ratio treatment control arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Binary Response by Subgroup — h_response_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  h_proportion_df(   c(TRUE, FALSE, FALSE),   arm = factor(c(\"A\", \"A\", \"B\"), levels = c(\"A\", \"B\")) ) #>   arm n n_rsp prop #> 1   A 2     1  0.5 #> 2   B 1     0  0.0  h_proportion_subgroups_df(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Define groupings for BMRKR2 levels. h_proportion_subgroups_df(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>           arm  n n_rsp      prop        subgroup    var #> 1  B: Placebo 73    50 0.6849315    All Patients    ALL #> 2   A: Drug X 69    59 0.8550725    All Patients    ALL #> 3  B: Placebo 40    25 0.6250000               F    SEX #> 4   A: Drug X 38    36 0.9473684               F    SEX #> 5  B: Placebo 33    25 0.7575758               M    SEX #> 6   A: Drug X 31    23 0.7419355               M    SEX #> 7  B: Placebo 24    13 0.5416667             low BMRKR2 #> 8   A: Drug X 26    21 0.8076923             low BMRKR2 #> 9  B: Placebo 47    30 0.6382979      low/medium BMRKR2 #> 10  A: Drug X 52    44 0.8461538      low/medium BMRKR2 #> 11 B: Placebo 73    50 0.6849315 low/medium/high BMRKR2 #> 12  A: Drug X 69    59 0.8550725 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Unstratatified analysis. h_odds_ratio_df(   c(TRUE, FALSE, FALSE, TRUE),   arm = factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) ) #>   arm n_tot or        lcl      ucl conf_level #> 1         4  1 0.01984252 50.39681       0.95  # Include p-value. h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = \"chisq\") #>   arm n_tot    or      lcl      ucl conf_level       pval #> 1       142 2.714 1.180449 6.239827       0.95 0.01643036 #>                   pval_label #> 1 p-value (Chi-Squared Test)  # Stratatified analysis. h_odds_ratio_df(   rsp = adrs_f$rsp,   arm = adrs_f$ARM,   strata_data = adrs_f[, c(\"STRATA1\", \"STRATA2\")],   method = \"cmh\" ) #>   arm n_tot       or      lcl      ucl conf_level       pval #> 1       142 2.665586 1.146149 6.199324       0.95 0.02019665 #>                               pval_label #> 1 p-value (Cochran-Mantel-Haenszel Test)  # Unstratified analysis. h_odds_ratio_subgroups_df(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142  2.714000 1.1804488  6.239827       0.95 All Patients    ALL #> 2        78 10.800000 2.2669576 51.452218       0.95            F    SEX #> 3        64  0.920000 0.2966470  2.853223       0.95            M    SEX #> 4        50  3.553846 1.0047370 12.570277       0.95          LOW BMRKR2 #> 5        49  2.705882 0.5911718 12.385232       0.95       MEDIUM BMRKR2 #> 6        43  2.250000 0.3970298 12.750933       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis  # Stratified analysis. h_odds_ratio_subgroups_df(   variables = list(     rsp = \"rsp\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\"),     strat = c(\"STRATA1\", \"STRATA2\")   ),   data = adrs_f ) #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142 2.6655860 1.1461490  6.199324       0.95 All Patients    ALL #> 2        78 7.7065093 1.5817529 37.547132       0.95            F    SEX #> 3        64 0.9572284 0.2990954  3.063525       0.95            M    SEX #> 4        50 3.0323726 0.8833232 10.409875       0.95          LOW BMRKR2 #> 5        49 2.1264996 0.4312008 10.486995       0.95       MEDIUM BMRKR2 #> 6        43 2.5134820 0.4351747 14.517370       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis  # Define groupings of BMRKR2 levels. h_odds_ratio_subgroups_df(   variables = list(     rsp = \"rsp\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adrs_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>   arm n_tot        or      lcl       ucl conf_level        subgroup    var #> 1       142  2.714000 1.180449  6.239827       0.95    All Patients    ALL #> 2        78 10.800000 2.266958 51.452218       0.95               F    SEX #> 3        64  0.920000 0.296647  2.853223       0.95               M    SEX #> 4        50  3.553846 1.004737 12.570277       0.95             low BMRKR2 #> 5        99  3.116667 1.193409  8.139385       0.95      low/medium BMRKR2 #> 6       142  2.714000 1.180449  6.239827       0.95 low/medium/high BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Dataframe by Subgroups — h_split_by_subgroups","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"Split dataframe non-nested list subsets.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"","code":"h_split_by_subgroups(data, subgroups, groups_lists = list())"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"data (data.frame) dataset split. subgroups (character) names factor variables data used create subsets. Unused levels present data dropped. Note order vector determines order downstream table. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"list subset data (df) metadata subset (df_labels).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"Main functionality prepare data use forest plot layouts.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_by_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Dataframe by Subgroups — h_split_by_subgroups","text":"","code":"df <- data.frame(   x = c(1:5),   y = factor(c(\"A\", \"B\", \"A\", \"B\", \"A\"), levels = c(\"A\", \"B\", \"C\")),   z = factor(c(\"C\", \"C\", \"D\", \"D\", \"D\"), levels = c(\"D\", \"C\")) ) formatters::var_labels(df) <- paste(\"label for\", names(df))  h_split_by_subgroups(   data = df,   subgroups = c(\"y\", \"z\") ) #> $y.A #> $y.A$df #>   x y z #> 1 1 A C #> 2 3 A D #> 3 5 A D #>  #> $y.A$df_labels #>   subgroup var   var_label #> 1        A   y label for y #>  #>  #> $y.B #> $y.B$df #>   x y z #> 1 2 B C #> 2 4 B D #>  #> $y.B$df_labels #>   subgroup var   var_label #> 1        B   y label for y #>  #>  #> $z.D #> $z.D$df #>   x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #>  #> $z.D$df_labels #>   subgroup var   var_label #> 1        D   z label for z #>  #>  #> $z.C #> $z.C$df #>   x y z #> 1 1 A C #> 2 2 B C #>  #> $z.C$df_labels #>   subgroup var   var_label #> 1        C   z label for z #>  #>   h_split_by_subgroups(   data = df,   subgroups = c(\"y\", \"z\"),   groups_lists = list(     y = list(\"AB\" = c(\"A\", \"B\"), \"C\" = \"C\")   ) ) #> $y.AB #> $y.AB$df #>   x y z #> 1 1 A C #> 2 2 B C #> 3 3 A D #> 4 4 B D #> 5 5 A D #>  #> $y.AB$df_labels #>   subgroup var   var_label #> 1       AB   y label for y #>  #>  #> $z.D #> $z.D$df #>   x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #>  #> $z.D$df_labels #>   subgroup var   var_label #> 1        D   z label for z #>  #>  #> $z.C #> $z.C$df #>   x y z #> 1 1 A C #> 2 2 B C #>  #> $z.C$df_labels #>   subgroup var   var_label #> 1        C   z label for z #>  #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Split parameters — h_split_param","title":"Split parameters — h_split_param","text":"divides data vector param groups defined f based specified values. relevant rtables layers distribute parameters .stats ' .formats lists items corresponding specific analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split parameters — h_split_param","text":"","code":"h_split_param(param, value, f)"},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split parameters — h_split_param","text":"param (vector) parameter split. value (vector) value used split. f (list vectors) reference make split","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split parameters — h_split_param","text":"named list element names f, containing elements specified .stats.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_split_param.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split parameters — h_split_param","text":"","code":"f <- list(   surv = c(\"pt_at_risk\", \"event_free_rate\", \"rate_se\", \"rate_ci\"),   surv_diff = c(\"rate_diff\", \"rate_diff_ci\", \"ztest_pval\") )  .stats <- c(\"pt_at_risk\", \"rate_diff\") h_split_param(.stats, .stats, f = f) #> $surv #> [1] \"pt_at_risk\" #>  #> $surv_diff #> [1] \"rate_diff\" #>   # $surv # [1] \"pt_at_risk\" # # $surv_diff # [1] \"rate_diff\"  .formats <- c(\"pt_at_risk\" = \"xx\", \"event_free_rate\" = \"xxx\") h_split_param(.formats, names(.formats), f = f) #> $surv #>      pt_at_risk event_free_rate  #>            \"xx\"           \"xxx\"  #>  #> $surv_diff #> NULL #>   # $surv # pt_at_risk event_free_rate # \"xx\"           \"xxx\" # # $surv_diff # NULL"},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"Helper Function create new SMQ variable ADAE consists adverse events belonging selected Standardized/Customized queries. new dataset contain records adverse events belonging selected baskets. Remember na_str must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"h_stack_by_baskets(   df,   baskets = grep(\"^(SMQ|CQ).+NAM$\", names(df), value = TRUE),   smq_varlabel = \"Standardized MedDRA Query\",   keys = c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"AEDECOD\", \"AESEQ\"),   aag_summary = NULL,   na_level = lifecycle::deprecated(),   na_str = \"<Missing>\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"df (data.frame) data set containing analysis variables. baskets (character) variable names selected Standardized/Customized queries. smq_varlabel (string) label new variable created. keys (character) names key variables returned along new variable created. aag_summary (data.frame) containing SMQ baskets levels interest final SMQ variable. useful levels interest observed df dataset. two columns dataset named basket basket_name. na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"data.frame variables keys taken df new variable SMQ containing records belonging baskets selected via baskets argument.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_stack_by_baskets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"adae <- tern_ex_adae[1:20, ] %>% df_explicit_na() h_stack_by_baskets(df = adae) #> # A tibble: 8 × 6 #>   STUDYID USUBJID               ASTDTM              AEDECOD       AESEQ SMQ      #>   <fct>   <fct>                 <dttm>              <fct>         <int> <fct>    #> 1 AB12345 AB12345-BRA-11-id-8   2021-12-05 02:02:07 dcd D.2.1.5.3     2 D.2.1.5… #> 2 AB12345 AB12345-BRA-12-id-120 2020-02-05 01:42:29 dcd D.2.1.5.3     2 D.2.1.5… #> 3 AB12345 AB12345-BRA-1-id-171  2022-11-29 12:18:31 dcd C.1.1.1.3     2 C.1.1.1… #> 4 AB12345 AB12345-BRA-1-id-23   2020-07-10 07:32:49 dcd B.2.2.3.1     3 C.1.1.1… #> 5 AB12345 AB12345-BRA-1-id-59   2021-10-10 23:54:46 dcd C.1.1.1.3     4 C.1.1.1… #> 6 AB12345 AB12345-BRA-1-id-9    2021-06-01 14:39:09 dcd C.1.1.1.3     1 C.1.1.1… #> 7 AB12345 AB12345-BRA-11-id-8   2021-12-21 02:02:07 dcd C.1.1.1.3     3 C.1.1.1… #> 8 AB12345 AB12345-BRA-12-id-120 2020-10-01 01:42:29 dcd C.1.1.1.3     3 C.1.1.1…  aag <- data.frame(   NAMVAR = c(\"CQ01NAM\", \"CQ02NAM\", \"SMQ01NAM\", \"SMQ02NAM\"),   REFNAME = c(     \"D.2.1.5.3/A.1.1.1.1 AESI\", \"X.9.9.9.9/Y.8.8.8.8 AESI\",     \"C.1.1.1.3/B.2.2.3.1 AESI\", \"C.1.1.1.3/B.3.3.3.3 AESI\"   ),   SCOPE = c(\"\", \"\", \"BROAD\", \"BROAD\"),   stringsAsFactors = FALSE )  basket_name <- character(nrow(aag)) cq_pos <- grep(\"^(CQ).+NAM$\", aag$NAMVAR) smq_pos <- grep(\"^(SMQ).+NAM$\", aag$NAMVAR) basket_name[cq_pos] <- aag$REFNAME[cq_pos] basket_name[smq_pos] <- paste0(   aag$REFNAME[smq_pos], \"(\", aag$SCOPE[smq_pos], \")\" )  aag_summary <- data.frame(   basket = aag$NAMVAR,   basket_name = basket_name,   stringsAsFactors = TRUE )  result <- h_stack_by_baskets(df = adae, aag_summary = aag_summary) #> Warning: There are 0 basket levels in common between aag_summary$basket_name and df. all(levels(aag_summary$basket_name) %in% levels(result$SMQ)) #> [1] TRUE  h_stack_by_baskets(   df = adae,   aag_summary = NULL,   keys = c(\"STUDYID\", \"USUBJID\", \"AEDECOD\", \"ARM\"),   baskets = \"SMQ01NAM\" ) #> # A tibble: 6 × 5 #>   STUDYID USUBJID               AEDECOD       ARM            SMQ                 #>   <fct>   <fct>                 <fct>         <fct>          <fct>               #> 1 AB12345 AB12345-BRA-1-id-171  dcd C.1.1.1.3 B: Placebo     C.1.1.1.3/B.2.2.3.… #> 2 AB12345 AB12345-BRA-1-id-23   dcd B.2.2.3.1 A: Drug X      C.1.1.1.3/B.2.2.3.… #> 3 AB12345 AB12345-BRA-1-id-59   dcd C.1.1.1.3 A: Drug X      C.1.1.1.3/B.2.2.3.… #> 4 AB12345 AB12345-BRA-1-id-9    dcd C.1.1.1.3 C: Combination C.1.1.1.3/B.2.2.3.… #> 5 AB12345 AB12345-BRA-11-id-8   dcd C.1.1.1.3 A: Drug X      C.1.1.1.3/B.2.2.3.… #> 6 AB12345 AB12345-BRA-12-id-120 dcd C.1.1.1.3 A: Drug X      C.1.1.1.3/B.2.2.3.…"},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"Helper functions used internally STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"","code":"h_step_window(x, control = control_step())  h_step_trt_effect(data, model, variables, x)  h_step_survival_formula(variables, control = control_step())  h_step_survival_est(   formula,   data,   variables,   x,   subset = rep(TRUE, nrow(data)),   control = control_coxph() )  h_step_rsp_formula(variables, control = c(control_step(), control_logistic()))  h_step_rsp_est(   formula,   data,   variables,   x,   subset = rep(TRUE, nrow(data)),   control = control_logistic() )"},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"x (numeric) biomarker value(s) use (without NA). control (named list) output control_step(). data (data.frame) dataset containing variables summarize. model regression model object. variables (named list string) list additional analysis variables. formula (formula) regression model formula. subset (logical) subset vector.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"h_step_window() returns list containing window-selection matrix sel interval information matrix interval. h_step_trt_effect() returns vector elements est se. h_step_survival_formula() returns model formula. h_step_survival_est() returns matrix number observations n, events, log hazard ratio estimates loghr, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x. h_step_rsp_formula() returns model formula. h_step_rsp_est() returns matrix number observations n, log odds ratio estimates logor, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_step.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations — h_step","text":"h_step_window(): creates windows STEP, based control settings provided. h_step_trt_effect(): calculates estimated treatment effect estimate linear predictor scale corresponding standard error STEP model fitted data given variables specification, single biomarker value x. works coxph glm models, .e. calculating log hazard ratio log odds ratio estimates. h_step_survival_formula(): builds model formula used survival STEP calculations. h_step_survival_est(): estimates model formula built based variables data given subset control parameters Cox regression. h_step_rsp_formula(): builds model formula used response STEP calculations. h_step_rsp_est(): estimates model formula built based variables data given subset control parameters logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"","code":"h_surv_to_coxreg_variables(variables, biomarker)  h_coxreg_mult_cont_df(variables, data, control = control_coxreg())  h_tab_surv_one_biomarker(df, vars, time_unit, .indent_mods = 0L)"},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (list) list parameters returned helper function control_coxreg(). df (data.frame) results single biomarker, part returned extract_survival_biomarkers() (needs couple columns added high-level function relative returned h_coxreg_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot_events: Total number events per group. n_tot: Total number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables() returns named list elements time, event, arm, covariates, strata. h_coxreg_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_surv_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables(): helps converting \"survival\" function variable list \"Cox regression\" variable list. reason currently inconsistency variable names accepted extract_survival_subgroups() fit_coxreg_multivar(). h_coxreg_mult_cont_df(): prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables) optionally subgroups strat. h_tab_surv_one_biomarker(): prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup — h_survival_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = FALSE)  adtte_f <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  # This is how the variable list is converted internally. h_surv_to_coxreg_variables(   variables = list(     tte = \"AVAL\",     is_event = \"EVNT\",     covariates = c(\"A\", \"B\"),     strata = \"D\"   ),   biomarker = \"AGE\" ) #> $time #> [1] \"AVAL\" #>  #> $event #> [1] \"EVNT\" #>  #> $arm #> [1] \"AGE\" #>  #> $covariates #> [1] \"A\" \"B\" #>  #> $strata #> [1] \"D\" #>   # For a single population, estimate separately the effects # of two biomarkers. df <- h_coxreg_mult_cont_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     strata = c(\"STRATA1\", \"STRATA2\")   ),   data = adtte_f ) df #>   biomarker              biomarker_label n_tot n_tot_events   median       hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.000189 #> 2       AGE                          Age   200          141 753.5176 1.008267 #>         lcl      ucl conf_level      pval     pval_label #> 1 0.9511092 1.051802       0.95 0.9941244 p-value (Wald) #> 2 0.9845155 1.032591       0.95 0.4984743 p-value (Wald)  # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"REGION1\",     strata = c(\"STRATA1\", \"STRATA2\")   ),   data = adtte_f[NULL, ] ) #>   biomarker              biomarker_label n_tot n_tot_events median hr lcl ucl #> 1    BMRKR1 Continuous Level Biomarker 1     0            0     NA NA  NA  NA #> 2       AGE                          Age     0            0     NA NA  NA  NA #>   conf_level pval     pval_label #> 1       0.95   NA p-value (Wald) #> 2       0.95   NA p-value (Wald)  # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\" h_tab_surv_one_biomarker(   df1,   vars = c(\"n_tot\", \"n_tot_events\", \"median\", \"hr\", \"ci\", \"pval\"),   time_unit = \"days\" ) #>                Total n   Total Events   Median (days)   Hazard Ratio   95% Wald CI    p-value (Wald) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> All patients     200         141            753.5           1.00       (0.95, 1.05)       0.9941"},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"Helper functions tabulate data frame statistics median survival time hazard ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"","code":"h_survtime_df(tte, is_event, arm)  h_survtime_subgroups_df(   variables,   data,   groups_lists = list(),   label_all = \"All Patients\" )  h_coxph_df(tte, is_event, arm, strata_data = NULL, control = control_coxph())  h_coxph_subgroups_df(   variables,   data,   groups_lists = list(),   control = control_coxph(),   label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"tte (numeric) contains time--event duration values. is_event (logical)TRUE event, FALSE time event censored. arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame NULL) required stratified analysis performed. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"h_survtime_df() returns data.frame columns arm, n, n_events, median. h_survtime_subgroups_df() returns data.frame columns arm, n, n_events, median, subgroup, var, var_label, row_type. h_coxph_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval pval_label. h_coxph_subgroups_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"h_survtime_df(): helper prepare data frame median survival times arm. h_survtime_subgroups_df(): summarizes median survival times arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_coxph_df(): helper prepare data frame estimates treatment hazard ratio. h_coxph_subgroups_df(): summarizes estimates treatment hazard ratio across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strat. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_survival_duration_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     is_event = CNSR == 0   ) labels <- c(\"ARM\" = adtte_labels[[\"ARM\"]], \"SEX\" = adtte_labels[[\"SEX\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  # Extract median survival time for one group. h_survtime_df(   tte = adtte_f$AVAL,   is_event = adtte_f$is_event,   arm = adtte_f$ARM ) #>          arm  n n_events   median #> 1 B: Placebo 73       57 727.8043 #> 2  A: Drug X 69       44 974.6402  # Extract median survival time for multiple groups. h_survtime_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) #>           arm  n n_events    median     subgroup    var #> 1  B: Placebo 73       57  727.8043 All Patients    ALL #> 2   A: Drug X 69       44  974.6402 All Patients    ALL #> 3  B: Placebo 40       31  599.1772            F    SEX #> 4   A: Drug X 38       24 1016.2982            F    SEX #> 5  B: Placebo 33       26  888.4916            M    SEX #> 6   A: Drug X 31       20  974.6402            M    SEX #> 7  B: Placebo 24       21  735.4722          LOW BMRKR2 #> 8   A: Drug X 26       15  974.6402          LOW BMRKR2 #> 9  B: Placebo 23       14  731.8352       MEDIUM BMRKR2 #> 10  A: Drug X 26       17  964.2197       MEDIUM BMRKR2 #> 11 B: Placebo 26       22  654.8245         HIGH BMRKR2 #> 12  A: Drug X 17       12 1016.2982         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Define groupings for BMRKR2 levels. h_survtime_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>           arm  n n_events    median        subgroup    var #> 1  B: Placebo 73       57  727.8043    All Patients    ALL #> 2   A: Drug X 69       44  974.6402    All Patients    ALL #> 3  B: Placebo 40       31  599.1772               F    SEX #> 4   A: Drug X 38       24 1016.2982               F    SEX #> 5  B: Placebo 33       26  888.4916               M    SEX #> 6   A: Drug X 31       20  974.6402               M    SEX #> 7  B: Placebo 24       21  735.4722             low BMRKR2 #> 8   A: Drug X 26       15  974.6402             low BMRKR2 #> 9  B: Placebo 47       35  735.4722      low/medium BMRKR2 #> 10  A: Drug X 52       32  964.2197      low/medium BMRKR2 #> 11 B: Placebo 73       57  727.8043 low/medium/high BMRKR2 #> 12  A: Drug X 69       44  974.6402 low/medium/high BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis  # Extract hazard ratio for one group. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM) #>   arm n_tot n_tot_events        hr       lcl      ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.057337       0.95 0.09049511 #>           pval_label #> 1 p-value (log-rank)  # Extract hazard ratio for one group with stratification factor. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1) #>   arm n_tot n_tot_events        hr       lcl     ucl conf_level       pval #> 1       142          101 0.6646586 0.4399495 1.00414       0.95 0.05089188 #>           pval_label #> 1 p-value (log-rank)  # Extract hazard ratio for multiple groups. h_coxph_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        49           31 0.7651261 0.3641277 1.6077269       0.95 0.47860004 #> 6        43           34 0.6662356 0.3257413 1.3626456       0.95 0.26285846 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis  # Define groupings of BMRKR2 levels. h_coxph_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f,   groups_lists = list(     BMRKR2 = list(       \"low\" = \"LOW\",       \"low/medium\" = c(\"LOW\", \"MEDIUM\"),       \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\")     )   ) ) #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        99           67 0.7472958 0.4600419 1.2139136       0.95 0.23764314 #> 6       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #>           pval_label        subgroup    var                    var_label #> 1 p-value (log-rank)    All Patients    ALL                 All Patients #> 2 p-value (log-rank)               F    SEX                          Sex #> 3 p-value (log-rank)               M    SEX                          Sex #> 4 p-value (log-rank)             low BMRKR2 Continuous Level Biomarker 2 #> 5 p-value (log-rank)      low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 p-value (log-rank) low/medium/high BMRKR2 Continuous Level Biomarker 2 #>   row_type #> 1  content #> 2 analysis #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis  # Extract hazard ratio for multiple groups with stratification factors. h_coxph_subgroups_df(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\",     subgroups = c(\"SEX\", \"BMRKR2\"),     strat = c(\"STRATA1\", \"STRATA2\")   ),   data = adtte_f ) #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.6126133 0.3913507 0.9589739       0.95 0.03086774 #> 2        78           55 0.3934024 0.2027682 0.7632630       0.95 0.00469167 #> 3        64           46 0.9501768 0.4730073 1.9087145       0.95 0.88580522 #> 4        50           36 0.7378635 0.3140465 1.7336363       0.95 0.48408079 #> 5        49           31 0.9408062 0.4172095 2.1215148       0.95 0.88305965 #> 6        43           34 0.5125617 0.2125140 1.2362459       0.95 0.13124382 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"Please see h_tab_surv_one_biomarker() h_tab_rsp_one_biomarker(), use function examples. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"","code":"h_tab_one_biomarker(   df,   afuns,   colvars,   na_str = NA_character_,   .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"df (data.frame) results single biomarker. afuns (named list function) analysis functions. colvars (list vars labels) variables tabulate labels. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tab_one_biomarker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for Tabulation of a Single Biomarker Result — h_tab_one_biomarker","text":"rtables table object statistics columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"Create data.frame pairwise stratified unstratified CoxPH analysis results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"","code":"h_tbl_coxph_pairwise(df, variables, control_coxph_pw = control_coxph())"},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strat (character NULL) variable names indicating stratification factors. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"data.frame containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_coxph_pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: Pairwise CoxPH table — h_tbl_coxph_pairwise","text":"","code":"# \\donttest{ library(dplyr)  adtte <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0)  h_tbl_coxph_pairwise(   df = adtte,   variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\"),   control_coxph_pw = control_coxph(conf_level = 0.9) ) #>                  HR       90% CI p-value (log-rank) #> B: Placebo     1.41 (1.01, 1.96)             0.0905 #> C: Combination 1.81 (1.24, 2.64)             0.0086 # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function: Survival Estimations — h_tbl_median_surv","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"Transform survival fit table groups rows characterized N, median confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"","code":"h_tbl_median_surv(fit_km, armval = \"All\")"},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"summary table statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_tbl_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function: Survival Estimations — h_tbl_median_surv","text":"","code":"# \\donttest{ library(dplyr) library(survival)  adtte <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") fit <- survfit(   form = Surv(AVAL, 1 - CNSR) ~ ARMCD,   data = adtte ) h_tbl_median_surv(fit_km = fit) #>        N Median        95% CI #> ARM A 69  974.6   (687, 1625) #> ARM B 73  727.8 (555.8, 1156) #> ARM C 58  632.3   (393, 1001) # }"},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"Helper function count number patients fraction patients according highest post-baseline lab grade variable .var, baseline lab grade variable baseline_var, direction interest specified direction_var.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"h_worsen_counter(df, id, .var, baseline_var, direction_var)"},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"df (data.frame) data set containing analysis variables. id (string) subject variable name. .var (string) single variable name passed rtables requested statistics function. baseline_var (string) baseline lab grade variable direction_var (string) Direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"h_worsen_counter() returns counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\".","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/h_worsen_counter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function to Analyze Patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"library(dplyr)  # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>%   mutate(     GRADDR = case_when(       PARAMCD == \"ALT\" ~ \"B\",       PARAMCD == \"CRP\" ~ \"L\",       PARAMCD == \"IGA\" ~ \"H\"     )   ) %>%   filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\")  df <- h_adlb_worsen(   adlb,   worst_flag_low = c(\"WGRLOFL\" = \"Y\"),   worst_flag_high = c(\"WGRHIFL\" = \"Y\"),   direction_var = \"GRADDR\" )  # `h_worsen_counter` h_worsen_counter(   df %>% filter(PARAMCD == \"CRP\" & GRADDR == \"Low\"),   id = \"USUBJID\",   .var = \"ATOXGR\",   baseline_var = \"BTOXGR\",   direction_var = \"GRADDR\" ) #> $fraction #> $fraction$`1` #>   num denom  #>    25   180  #>  #> $fraction$`2` #>   num denom  #>    15   186  #>  #> $fraction$`3` #>   num denom  #>    18   191  #>  #> $fraction$`4` #>   num denom  #>    17   196  #>  #> $fraction$Any #>   num denom  #>    75   196  #>  #>"},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function: x tick positions — h_xticks","title":"Helper function: x tick positions — h_xticks","text":"Calculate positions ticks x-axis. However, xticks already exists kept . based function ggplot2 relies , required graphic patient--risk annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function: x tick positions — h_xticks","text":"","code":"h_xticks(data, xticks = NULL, max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function: x tick positions — h_xticks","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric, number, NULL) numeric vector ticks single number spacing ticks x axis. NULL (default), labeling::extended() used determine optimal tick position x axis. max_time (numeric) maximum value show X axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function: x tick positions — h_xticks","text":"vector positions use x-axis ticks ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/h_xticks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function: x tick positions — h_xticks","text":"","code":"# \\donttest{ library(dplyr) library(survival)  data <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%   h_data_plot()  h_xticks(data) #> [1]    0 1000 2000 3000 4000 5000 h_xticks(data, xticks = seq(0, 3000, 500)) #> [1]    0  500 1000 1500 2000 2500 3000 h_xticks(data, xticks = 500) #>  [1]    0  500 1000 1500 2000 2500 3000 3500 4000 4500 h_xticks(data, xticks = 500, max_time = 6000) #>  [1]    0  500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 h_xticks(data, xticks = c(0, 500), max_time = 300) #> [1]   0 500 h_xticks(data, xticks = 500, max_time = 300) #>  [1]    0  500 1000 1500 2000 2500 3000 3500 4000 4500 # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"","code":"imputation_rule(   df,   x_stats,   stat,   imp_rule,   post = FALSE,   avalcat_var = \"AVALCAT1\" )"},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"df (data.frame) data set containing analysis variables. x_stats (named list) named list statistics, typically results s_summary(). stat (character) statistic return value/NA level according imputation rule applied. imp_rule (character) imputation rule setting. Set \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. post (flag) whether data corresponds post-dose time-point (defaults FALSE). parameter used imp_rule set \"1/3\". avalcat_var (character) name variable indicates whether row df corresponds analysis value category \"BLQ\", \"LTR\", \"<PCLLOQ\", none (defaults \"AVALCAT1\"). Variable avalcat_var must present df.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"list containing statistic value (val) NA level (na_str) displayed according specified imputation rule.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/imputation_rule.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply 1/3 or 1/2 Imputation Rule to Data — imputation_rule","text":"","code":"set.seed(1) df <- data.frame(   AVAL = runif(50, 0, 1),   AVALCAT1 = sample(c(1, \"BLQ\"), 50, replace = TRUE) ) x_stats <- s_summary(df$AVAL) imputation_rule(df, x_stats, \"max\", \"1/3\") #> $val #>       max  #> 0.9919061  #>  #> $na_str #> [1] \"ND\" #>  imputation_rule(df, x_stats, \"geom_mean\", \"1/3\") #> $val #> [1] NA #>  #> $na_str #> [1] \"NE\" #>  imputation_rule(df, x_stats, \"mean\", \"1/2\") #> $val #> [1] NA #>  #> $na_str #> [1] \"ND\" #>"},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence Rate — incidence_rate","title":"Incidence Rate — incidence_rate","text":"Estimate event rate adjusted person-years risk, otherwise known incidence rate. Primary analysis variable person-years risk.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence Rate — incidence_rate","text":"","code":"s_incidence_rate(   df,   .var,   n_events,   is_event,   control = control_incidence_rate() )  a_incidence_rate(   df,   .var,   n_events,   is_event,   control = control_incidence_rate() )  estimate_incidence_rate(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence Rate — incidence_rate","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. n_events (integer) number events observed. is_event (logical)TRUE event, FALSE time event censored. control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level (proportion) confidence level estimated incidence rate. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric) time unit desired output (person-years). lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence Rate — incidence_rate","text":"s_incidence_rate() returns following statistics: person_years: Total person-years risk. n_events: Total number events observed. rate: Estimated incidence rate. rate_ci: Confidence interval incidence rate. a_incidence_rate() returns corresponding list formatted rtables::CellValue(). estimate_incidence_rate() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_incidence_rate() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Incidence Rate — incidence_rate","text":"s_incidence_rate(): Statistics function estimates incidence rate associated confidence interval. a_incidence_rate(): Formatted analysis function used afun estimate_incidence_rate(). estimate_incidence_rate(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence Rate — incidence_rate","text":"","code":"library(dplyr)  df <- data.frame(   USUBJID = as.character(seq(6)),   CNSR = c(0, 1, 1, 0, 0, 0),   AVAL = c(10.1, 20.4, 15.3, 20.8, 18.7, 23.4),   ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\")) ) %>%   mutate(is_event = CNSR == 0) %>%   mutate(n_events = as.integer(is_event))  basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   estimate_incidence_rate(     vars = \"AVAL\",     n_events = \"n_events\",     control = control_incidence_rate(       input_time_unit = \"month\",       num_pt_year = 100     )   ) %>%   build_table(df) #>                                            A                 B        #>                                          (N=3)             (N=3)      #> ————————————————————————————————————————————————————————————————————— #> Total patient-years at risk               3.8               5.2       #> Number of adverse events observed          1                 3        #> AE rate per 100 patient-years            26.20             57.23      #> 95% CI                              (-25.15, 77.55)   (-7.53, 122.00)"},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual Patient Plots — individual_patient_plot","title":"Individual Patient Plots — individual_patient_plot","text":"Line plot(s) displaying trend patients' parameter values time rendered. Patients' individual baseline values can added plot(s) reference.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual Patient Plots — individual_patient_plot","text":"","code":"g_ipp(   df,   xvar,   yvar,   xlab,   ylab,   id_var = \"USUBJID\",   title = \"Individual Patient Plots\",   subtitle = \"\",   caption = NULL,   add_baseline_hline = FALSE,   yvar_baseline = \"BASE\",   ggtheme = nestcolor::theme_nest(),   plotting_choices = c(\"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\"),   max_obs_per_plot = 4,   col = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual Patient Plots — individual_patient_plot","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (character scalar) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. plotting_choices (character) specifies options displaying plots. Must one \"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\". max_obs_per_plot (count) Number observations plotted one plot. Ignored plotting_choices \"separate_by_obs\". col (character) lines colors.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual Patient Plots — individual_patient_plot","text":"ggplot object list ggplot objects.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Individual Patient Plots — individual_patient_plot","text":"g_ipp(): Plotting function individual patient plots , depending user preference, renders single graphic compiles list graphics show trends individual's parameter values time.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/individual_patient_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual Patient Plots — individual_patient_plot","text":"","code":"library(dplyr) library(nestcolor)  # Select a small sample of data to plot. adlb <- tern_ex_adlb %>%   filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>%   slice(1:36)  plot_list <- g_ipp(   df = adlb,   xvar = \"AVISIT\",   yvar = \"AVAL\",   xlab = \"Visit\",   ylab = \"SGOT/ALT (U/L)\",   title = \"Individual Patient Plots\",   add_baseline_hline = TRUE,   plotting_choices = \"split_by_max_obs\",   max_obs_per_plot = 5 ) plot_list #> [[1]]  #>  #> [[2]]  #>"},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels or Names of List Elements — labels_or_names","title":"Labels or Names of List Elements — labels_or_names","text":"Internal helper function working nested statistic function results typically labels names can use.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels or Names of List Elements — labels_or_names","text":"","code":"labels_or_names(x)"},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels or Names of List Elements — labels_or_names","text":"x list","code":""},{"path":"https://insightsengineering.github.io/tern/reference/labels_or_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels or Names of List Elements — labels_or_names","text":"character vector labels names list elements.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"Layout-creating function creates multivariate column layout summarizing logistic regression results. function wrapper rtables::split_cols_by_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"","code":"logistic_regression_cols(lyt, conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"lyt (layout) input layout analyses added . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_regression_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic Regression Multivariate Column Layout Function — logistic_regression_cols","text":"layout object suitable passing layouting functions. Adding function rtable layout split table columns corresponding statistics df, estimate, std_error, odds_ratio, ci, pvalue.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"Constructor content functions used summarize_logistic() summarize logistic regression results. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"","code":"logistic_summary_by_flag(flag_var, na_str = NA_character_, .indent_mods = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"flag_var (string) variable name identifying row used content function. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/logistic_summary_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic Regression Summary Table Constructor Function — logistic_summary_by_flag","text":"content function.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Names Without Dots — make_names","title":"Make Names Without Dots — make_names","text":"Make Names Without Dots","code":""},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Names Without Dots — make_names","text":"","code":"make_names(nams)"},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Names Without Dots — make_names","text":"nams (character) vector original names.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/make_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Names Without Dots — make_names","text":"character  vector proper names, use dots contrast make.names().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of Months to Days — month2day","title":"Conversion of Months to Days — month2day","text":"Conversion Months Days. approximative calculation considers month average 30.4375 days.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of Months to Days — month2day","text":"","code":"month2day(x)"},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of Months to Days — month2day","text":"x (numeric) time months.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of Months to Days — month2day","text":"numeric vector time days.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/month2day.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of Months to Days — month2day","text":"","code":"x <- c(13.25, 8.15, 1, 2.834) month2day(x) #> [1] 403.29688 248.06563  30.43750  86.25988"},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":null,"dir":"Reference","previous_headings":"","what":"Muffled car::Anova — muffled_car_anova","title":"Muffled car::Anova — muffled_car_anova","text":"Applied survival models, car::Anova() signal strata terms dropped model formula present, function deliberately muffles message.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Muffled car::Anova — muffled_car_anova","text":"","code":"muffled_car_anova(mod, test_statistic)"},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Muffled car::Anova — muffled_car_anova","text":"mod (coxph) Cox regression model fitted survival::coxph(). test_statistic (string) method used estimation p.values; wald (default) likelihood.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/muffled_car_anova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Muffled car::Anova — muffled_car_anova","text":"Returns output car::Anova(), convergence message muffled.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of Available (Non-Missing Entries) in a Vector — n_available","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"Small utility function better readability.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"","code":"n_available(x)"},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"x () vector count non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/n_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of Available (Non-Missing Entries) in a Vector — n_available","text":"Number non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Odds Ratio Estimation — odds_ratio","title":"Odds Ratio Estimation — odds_ratio","text":"Compares bivariate responses two groups terms odds ratios along confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Odds Ratio Estimation — odds_ratio","text":"","code":"s_odds_ratio(   df,   .var,   .ref_group,   .in_ref_col,   .df_row,   variables = list(arm = NULL, strata = NULL),   conf_level = 0.95,   groups_list = NULL )  a_odds_ratio(   df,   .var,   .ref_group,   .in_ref_col,   .df_row,   variables = list(arm = NULL, strata = NULL),   conf_level = 0.95,   groups_list = NULL )  estimate_odds_ratio(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"hidden\",   table_names = vars,   .stats = \"or_ci\",   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Odds Ratio Estimation — odds_ratio","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. .df_row (data.frame) data frame across columns given row split. variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_odds_ratio(). show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Odds Ratio Estimation — odds_ratio","text":"s_odds_ratio() returns named list statistics or_ci (containing est, lcl, ucl) n_tot. a_odds_ratio() returns corresponding list formatted rtables::CellValue(). estimate_odds_ratio() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_odds_ratio() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Odds Ratio Estimation — odds_ratio","text":"function uses either logistic regression unstratified analyses, conditional logistic regression stratified analyses. Wald confidence interval specified confidence level calculated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Odds Ratio Estimation — odds_ratio","text":"s_odds_ratio(): Statistics function estimates odds ratio treatment control. variables list arm strata variable names must passed stratified analysis required. a_odds_ratio(): Formatted analysis function used afun estimate_odds_ratio(). estimate_odds_ratio(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Odds Ratio Estimation — odds_ratio","text":"stratified analyses, currently implementation conditional likelihood confidence intervals, therefore likelihood confidence interval yet available option. Besides, rsp contains responders non-responders, result values NA, odds ratio estimation possible.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Odds Ratio Estimation — odds_ratio","text":"","code":"set.seed(12) dta <- data.frame(   rsp = sample(c(TRUE, FALSE), 100, TRUE),   grp = factor(rep(c(\"A\", \"B\"), each = 50), levels = c(\"B\", \"A\")),   strata = factor(sample(c(\"C\", \"D\"), 100, TRUE)) )  # Unstratified analysis. s_odds_ratio(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   .df_row = dta ) #> $or_ci #>       est       lcl       ucl  #> 0.8484848 0.3831831 1.8788053  #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #>  #> $n_tot #> n_tot  #>   100  #> attr(,\"label\") #> [1] \"Total n\" #>   # Stratified analysis. s_odds_ratio(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   .df_row = dta,   variables = list(arm = \"grp\", strata = \"strata\") ) #> $or_ci #>       est       lcl       ucl  #> 0.7689750 0.3424155 1.7269154  #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #>  #> $n_tot #> n_tot  #>   100  #> attr(,\"label\") #> [1] \"Total n\" #>   a_odds_ratio(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   .df_row = dta ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name     formatted_cell indent_mod           row_label #> 1    or_ci 0.85 (0.38 - 1.88)          1 Odds Ratio (95% CI) #> 2    n_tot                100          0             Total n  dta <- data.frame(   rsp = sample(c(TRUE, FALSE), 100, TRUE),   grp = factor(rep(c(\"A\", \"B\"), each = 50)) )  l <- basic_table() %>%   split_cols_by(var = \"grp\", ref_group = \"B\") %>%   estimate_odds_ratio(vars = \"rsp\")  build_table(l, df = dta) #>                       B           A          #> ———————————————————————————————————————————— #> Odds Ratio (95% CI)       0.72 (0.33 - 1.60)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion Difference — prop_diff","text":"","code":"s_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\",     \"strat_newcombecc\"),   weights_method = \"cmh\" )  a_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   conf_level = 0.95,   method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\",     \"strat_newcombecc\"),   weights_method = \"cmh\" )  estimate_proportion_diff(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = vars,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion Difference — prop_diff","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_proportion_diff(). var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion Difference — prop_diff","text":"s_proportion_diff() returns named list elements diff diff_ci. a_proportion_diff() returns corresponding list formatted rtables::CellValue(). estimate_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion_diff() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Proportion Difference — prop_diff","text":"s_proportion_diff(): Statistics function estimating difference terms responder proportion. a_proportion_diff(): Formatted analysis function used afun estimate_proportion_diff(). estimate_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Proportion Difference — prop_diff","text":"performing unstratified analysis, methods \"cmh\", \"strat_newcombe\", \"strat_newcombecc\" permitted.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion Difference — prop_diff","text":"","code":"# Summary  ## \"Mid\" case: 4/4 respond in group A, 1/2 respond in group B. nex <- 100 # Number of example rows dta <- data.frame(   \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE),   \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE),   \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE),   stringsAsFactors = TRUE )  s_proportion_diff(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   conf_level = 0.90,   method = \"ha\" ) #> $diff #> [1] -8.05153 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #>  #> $diff_ci #> [1] -25.754477   9.651418 #> attr(,\"label\") #> [1] \"90% CI (Anderson-Hauck)\" #>   # CMH example with strata s_proportion_diff(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   variables = list(strata = c(\"f1\", \"f2\")),   conf_level = 0.90,   method = \"cmh\" ) #> $diff #> [1] -8.240459 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #>  #> $diff_ci #> [1] -24.002675   7.521757 #> attr(,\"label\") #> [1] \"90% CI (CMH, without correction)\" #>   a_proportion_diff(   df = subset(dta, grp == \"A\"),   .var = \"rsp\",   .ref_group = subset(dta, grp == \"B\"),   .in_ref_col = FALSE,   conf_level = 0.90,   method = \"ha\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod                       row_label #> 1     diff           -8.1          0 Difference in Response rate (%) #> 2  diff_ci   (-25.8, 9.7)          1         90% CI (Anderson-Hauck)  l <- basic_table() %>%   split_cols_by(var = \"grp\", ref_group = \"B\") %>%   estimate_proportion_diff(     vars = \"rsp\",     conf_level = 0.90,     method = \"ha\"   )  build_table(l, df = dta) #>                                   B        A       #> —————————————————————————————————————————————————— #> Difference in Response rate (%)           -8.1     #>   90% CI (Anderson-Hauck)             (-25.8, 9.7)"},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Difference Test for Two Proportions — prop_diff_test","title":"Difference Test for Two Proportions — prop_diff_test","text":"Various tests implemented test difference two proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Difference Test for Two Proportions — prop_diff_test","text":"","code":"s_test_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") )  a_test_proportion_diff(   df,   .var,   .ref_group,   .in_ref_col,   variables = list(strata = NULL),   method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") )  test_proportion_diff(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = vars,   show_labels = \"hidden\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Difference Test for Two Proportions — prop_diff_test","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. variables (named list string) list additional analysis variables. method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_test_proportion_diff(). var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Difference Test for Two Proportions — prop_diff_test","text":"s_test_proportion_diff() returns named list single item pval attribute label describing method used. p-value tests null hypothesis proportions two groups . a_test_proportion_diff() returns corresponding list formatted rtables::CellValue(). test_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_test_proportion_diff() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Difference Test for Two Proportions — prop_diff_test","text":"s_test_proportion_diff(): Statistics function tests difference two proportions. a_test_proportion_diff(): Formatted analysis function used afun test_proportion_diff(). test_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/prop_diff_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Difference Test for Two Proportions — prop_diff_test","text":"","code":"dta <- data.frame(   rsp = sample(c(TRUE, FALSE), 100, TRUE),   grp = factor(rep(c(\"A\", \"B\"), each = 50)),   strat = factor(rep(c(\"V\", \"W\", \"X\", \"Y\", \"Z\"), each = 20)) )  # With `rtables` pipelines. l <- basic_table() %>%   split_cols_by(var = \"grp\", ref_group = \"B\") %>%   test_proportion_diff(     vars = \"rsp\",     method = \"cmh\", variables = list(strata = \"strat\")   )  build_table(l, df = dta) #>                                            B     A    #> ————————————————————————————————————————————————————— #>   p-value (Cochran-Mantel-Haenszel Test)       1.0000"},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Table Pruning — prune_occurrences","title":"Occurrence Table Pruning — prune_occurrences","text":"Family constructor condition functions flexibly prune occurrence tables. condition functions always return whether row result higher threshold. Since class CombinationFunction() can logically combined condition functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Table Pruning — prune_occurrences","text":"","code":"keep_rows(row_condition)  keep_content_rows(content_row_condition)  has_count_in_cols(atleast, ...)  has_count_in_any_col(atleast, ...)  has_fraction_in_cols(atleast, ...)  has_fraction_in_any_col(atleast, ...)  has_fractions_difference(atleast, ...)  has_counts_difference(atleast, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Table Pruning — prune_occurrences","text":"row_condition (CombinationFunction) condition function works individual analysis rows flags whether kept pruned table. content_row_condition (CombinationFunction) condition function works individual first content rows leaf tables flags whether leaf tables kept pruned table. atleast (count proportion) threshold met order keep row. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Table Pruning — prune_occurrences","text":"keep_rows() returns pruning function can used rtables::prune_table() prune rtables table. keep_content_rows() returns pruning function checks condition first content row leaf tables table. has_count_in_cols() returns condition function sums counts specified column. has_count_in_any_col() returns condition function compares counts specified columns threshold. has_fraction_in_cols() returns condition function sums counts specified column, computes fraction dividing total column counts. has_fraction_in_any_col() returns condition function looks fractions specified columns checks whether fulfill threshold. has_fractions_difference() returns condition function extracts fractions specified column, computes difference minimum maximum. has_counts_difference() returns condition function extracts counts specified column, computes difference minimum maximum.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Table Pruning — prune_occurrences","text":"keep_rows(): Constructor creating pruning functions based row condition function. removes analysis rows (TableRow) pruned, .e., fulfill row condition. removes sub-tree children left. keep_content_rows(): Constructor creating pruning functions based condition (first) content row leaf tables. removes leaf tables first content row fulfill condition. check individual rows. proceeds recursively removing sub tree children left. has_count_in_cols(): Constructor creating condition functions total counts specified columns. has_count_in_any_col(): Constructor creating condition functions counts specified columns satisfying threshold. has_fraction_in_cols(): Constructor creating condition functions total fraction specified columns. has_fraction_in_any_col(): Constructor creating condition functions fraction specified columns. has_fractions_difference(): Constructor creating condition function checks difference fractions reported specified column. has_counts_difference(): Constructor creating condition function checks difference counts reported specified column.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Occurrence Table Pruning — prune_occurrences","text":"Since table specifications worded positively, name constructor condition functions positively, . However, note result keep_rows() says pruned, conform rtables::prune_table() interface.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/prune_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Table Pruning — prune_occurrences","text":"","code":"# \\donttest{ tab <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"RACE\") %>%   split_rows_by(\"STRATA1\") %>%   summarize_row_groups() %>%   analyze_vars(\"COUNTRY\", .stats = \"count_fraction\") %>%   build_table(DM) # }  # \\donttest{ # `keep_rows` is_non_empty <- !CombinationFunction(all_zero_or_na) prune_table(tab, keep_rows(is_non_empty)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # `keep_content_rows` # \\donttest{ more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab)) prune_table(tab, keep_content_rows(more_than_twenty)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>     CHE                         0            0              0        #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #>     GBR                         0            0              0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     RUS                         0            0              0        #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     NGA                         0            0              0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>     CHE                         0            0              0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #>     JPN                         0            0              0        #>     GBR                         0            0              0        #>     CAN                         0            0              0        #>     CHE                         0            0              0        # }  # \\donttest{ more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # \\donttest{ # `has_count_in_any_col` any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(any_more_than_one)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # \\donttest{ # `has_fraction_in_cols` more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #> BLACK OR AFRICAN AMERICAN                                            #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    # }  # \\donttest{ # `has_fraction_in_any_col` any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #> BLACK OR AFRICAN AMERICAN                                            #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    # }  # \\donttest{ # `has_fractions_difference` more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent_diff)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     PAK                      1 (8.3%)     1 (9.1%)      1 (14.3%)    #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }  # \\donttest{ more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one_diff)) #>                             A: Drug X    B: Placebo   C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN                                                                #>   A                         27 (22.3%)   20 (18.9%)     31 (24.0%)   #>     CHN                     14 (51.9%)    9 (45%)       12 (38.7%)   #>     USA                      2 (7.4%)      1 (5%)       8 (25.8%)    #>     BRA                      1 (3.7%)     4 (20%)        1 (3.2%)    #>     PAK                     3 (11.1%)     2 (10%)        2 (6.5%)    #>     NGA                     3 (11.1%)      1 (5%)        3 (9.7%)    #>     RUS                      2 (7.4%)      1 (5%)        1 (3.2%)    #>     JPN                         0          1 (5%)        2 (6.5%)    #>     GBR                         0          1 (5%)        1 (3.2%)    #>     CAN                      2 (7.4%)        0           1 (3.2%)    #>   B                         24 (19.8%)   29 (27.4%)     22 (17.1%)   #>     CHN                      12 (50%)    13 (44.8%)      11 (50%)    #>     USA                      2 (8.3%)    5 (17.2%)       1 (4.5%)    #>     BRA                     4 (16.7%)    3 (10.3%)       1 (4.5%)    #>     PAK                      2 (8.3%)     2 (6.9%)      4 (18.2%)    #>     NGA                      2 (8.3%)     1 (3.4%)      3 (13.6%)    #>     RUS                      1 (4.2%)     1 (3.4%)       2 (9.1%)    #>     JPN                         0        4 (13.8%)          0        #>     GBR                      1 (4.2%)        0              0        #>   C                         28 (23.1%)   19 (17.9%)     31 (24.0%)   #>     CHN                     13 (46.4%)   10 (52.6%)     16 (51.6%)   #>     USA                     3 (10.7%)    3 (15.8%)      4 (12.9%)    #>     BRA                      1 (3.6%)     1 (5.3%)      4 (12.9%)    #>     PAK                      1 (3.6%)     1 (5.3%)       3 (9.7%)    #>     NGA                     4 (14.3%)     1 (5.3%)       2 (6.5%)    #>     RUS                     4 (14.3%)    2 (10.5%)       1 (3.2%)    #>     JPN                      2 (7.1%)     1 (5.3%)       1 (3.2%)    #> BLACK OR AFRICAN AMERICAN                                            #>   A                          6 (5.0%)     7 (6.6%)       8 (6.2%)    #>     CHN                      3 (50%)     3 (42.9%)      5 (62.5%)    #>     USA                     2 (33.3%)    1 (14.3%)      1 (12.5%)    #>     BRA                         0        1 (14.3%)          0        #>     PAK                         0            0          1 (12.5%)    #>     NGA                         0            0          1 (12.5%)    #>     JPN                     1 (16.7%)        0              0        #>     GBR                         0        2 (28.6%)          0        #>   B                         10 (8.3%)     6 (5.7%)      12 (9.3%)    #>     CHN                      3 (30%)     2 (33.3%)      8 (66.7%)    #>     USA                      1 (10%)         0           1 (8.3%)    #>     BRA                      2 (20%)     1 (16.7%)       1 (8.3%)    #>     PAK                         0        1 (16.7%)          0        #>     RUS                         0        1 (16.7%)          0        #>     JPN                      2 (20%)         0           1 (8.3%)    #>     GBR                      1 (10%)         0           1 (8.3%)    #>     CAN                      1 (10%)     1 (16.7%)          0        #>   C                         12 (9.9%)    11 (10.4%)      7 (5.4%)    #>     CHN                     8 (66.7%)    5 (45.5%)      5 (71.4%)    #>     USA                      1 (8.3%)    2 (18.2%)      1 (14.3%)    #>     BRA                      1 (8.3%)    2 (18.2%)          0        #>     NGA                         0         1 (9.1%)          0        #>     RUS                      1 (8.3%)        0              0        #> WHITE                                                                #>   A                          3 (2.5%)     6 (5.7%)       6 (4.7%)    #>     CHN                     2 (66.7%)    2 (33.3%)       3 (50%)     #>     USA                     1 (33.3%)    1 (16.7%)          0        #>     PAK                         0        1 (16.7%)      1 (16.7%)    #>     NGA                         0        1 (16.7%)          0        #>     RUS                         0            0          1 (16.7%)    #>     JPN                         0        1 (16.7%)          0        #>     CAN                         0            0          1 (16.7%)    #>   B                          7 (5.8%)     5 (4.7%)       4 (3.1%)    #>     CHN                     4 (57.1%)     1 (20%)        3 (75%)     #>     USA                         0         1 (20%)           0        #>     BRA                         0         1 (20%)           0        #>     PAK                     1 (14.3%)        0              0        #>     NGA                     1 (14.3%)        0              0        #>     RUS                     1 (14.3%)        0           1 (25%)     #>     JPN                         0         1 (20%)           0        #>     CAN                         0         1 (20%)           0        #>   C                          4 (3.3%)     3 (2.8%)       8 (6.2%)    #>     CHN                      3 (75%)      3 (100%)       6 (75%)     #>     USA                      1 (25%)         0          1 (12.5%)    #>     JPN                         0            0          1 (12.5%)    # }"},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-implemented range() Default S3 method for numerical objects — range_noinf","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"function returns c(NA, NA) instead c(-Inf, Inf) zero-length data without warnings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"","code":"range_noinf(x, na.rm = FALSE, finite = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"x (numeric) sequence numbers range computed. na.rm (logical) indicating NA omitted. finite (logical) indicating non-finite elements removed.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/range_noinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Re-implemented range() Default S3 method for numerical objects — range_noinf","text":"2-element vector class numeric.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Reapply Variable Labels — reapply_varlabels","title":"Reapply Variable Labels — reapply_varlabels","text":"helper function used tests.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reapply Variable Labels — reapply_varlabels","text":"","code":"reapply_varlabels(x, varlabels, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reapply Variable Labels — reapply_varlabels","text":"x (vector) vector elements needs new labels. varlabels (character) vector labels x. ... parameters added list.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/reapply_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reapply Variable Labels — reapply_varlabels","text":"x variable labels reapplied.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"Tabulate estimated effects multiple continuous biomarker variables binary response endpoint across population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"","code":"tabulate_rsp_biomarkers(   df,   vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\"),   .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"df (data.frame) containing analysis variables, returned extract_rsp_biomarkers(). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"rtables table summarizing biomarker effects binary response subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"functions create layout starting data frame contains required statistics. tables typically used input forest plots.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"contrast tabulate_rsp_subgroups() tabulation function start input layout lyt. internally table created combining multiple subtables.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Biomarker Effects on Binary Response by Subgroup — response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  df <- extract_rsp_biomarkers(   variables = list(     rsp = \"rsp\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adrs_f )  # \\donttest{ ## Table with default columns. tabulate_rsp_biomarkers(df) #>                                  Total n   Responders   Response (%)   Odds Ratio      95% CI      p-value (Wald) #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age                                                                                                               #>   All Patients                     200        164          82.0%          1.00      (0.95, 1.05)       0.8530     #>   Continuous Level Biomarker 2                                                                                    #>     LOW                            70          53          75.7%          0.93      (0.85, 1.01)       0.0845     #>     MEDIUM                         68          58          85.3%          0.99      (0.88, 1.11)       0.8190     #>     HIGH                           62          53          85.5%          1.06      (0.96, 1.18)       0.2419     #> Continuous Level Biomarker 1                                                                                      #>   All Patients                     200        164          82.0%          0.98      (0.88, 1.08)       0.6353     #>   Continuous Level Biomarker 2                                                                                    #>     LOW                            70          53          75.7%          1.15      (0.95, 1.40)       0.1584     #>     MEDIUM                         68          58          85.3%          0.88      (0.73, 1.06)       0.1700     #>     HIGH                           62          53          85.5%          0.88      (0.72, 1.08)       0.2104      ## Table with a manually chosen set of columns: leave out \"pval\", reorder. tab <- tabulate_rsp_biomarkers(   df = df,   vars = c(\"n_rsp\", \"ci\", \"n_tot\", \"prop\", \"or\") )  ## Finally produce the forest plot. g_forest(tab, xlim = c(0.7, 1.4))  # }"},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Binary Response by Subgroup — response_subgroups","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"Tabulate statistics response rate odds ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"","code":"a_response_subgroups(   .formats = list(n = \"xx\", n_rsp = \"xx\", prop = \"xx.x%\", n_tot = \"xx\", or =     list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)), pval =     \"x.xxxx | (<0.0001)\") )  tabulate_rsp_subgroups(lyt, df, vars = c(\"n_tot\", \"n\", \"prop\", \"or\", \"ci\"))"},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":".formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. lyt (layout) input layout analyses added . df (list) data frames containing analysis variables. List created using extract_rsp_subgroups(). vars (character) names statistics reported among: n: Total number observations per group. n_rsp: Number responders per group. prop: Proportion responders. n_tot: Total number observations. : Odds ratio. ci : Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"a_response_subgroups() returns corresponding list formatted rtables::CellValue(). rtables table summarizing binary response subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"functions create layout starting data frame contains required statistics. Tables typically used part forest plot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"a_response_subgroups(): Formatted analysis function used afun tabulate_rsp_subgroups(). tabulate_rsp_subgroups(): Table-creating function creates table summarizing binary response subgroup. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Binary Response by Subgroup — response_subgroups","text":"","code":"library(dplyr) library(forcats)  adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs)  adrs_f <- adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>%   droplevels() %>%   mutate(     # Reorder levels of factor to make the placebo group the reference arm.     ARM = fct_relevel(ARM, \"B: Placebo\"),     rsp = AVALC == \"CR\"   ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\")  # Unstratified analysis. df <- extract_rsp_subgroups(   variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")),   data = adrs_f ) df #> $prop #>           arm  n n_rsp      prop     subgroup    var #> 1  B: Placebo 73    50 0.6849315 All Patients    ALL #> 2   A: Drug X 69    59 0.8550725 All Patients    ALL #> 3  B: Placebo 40    25 0.6250000            F    SEX #> 4   A: Drug X 38    36 0.9473684            F    SEX #> 5  B: Placebo 33    25 0.7575758            M    SEX #> 6   A: Drug X 31    23 0.7419355            M    SEX #> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2 #> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2 #> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2 #> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2 #> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2 #> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $or #>   arm n_tot        or       lcl       ucl conf_level     subgroup    var #> 1       142  2.714000 1.1804488  6.239827       0.95 All Patients    ALL #> 2        78 10.800000 2.2669576 51.452218       0.95            F    SEX #> 3        64  0.920000 0.2966470  2.853223       0.95            M    SEX #> 4        50  3.553846 1.0047370 12.570277       0.95          LOW BMRKR2 #> 5        49  2.705882 0.5911718 12.385232       0.95       MEDIUM BMRKR2 #> 6        43  2.250000 0.3970298 12.750933       0.95         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                          Sex analysis #> 3                          Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #>   ## Table with default columns. basic_table() %>%   tabulate_rsp_subgroups(df) #>                                             B: Placebo           A: Drug X                                  #> Baseline Risk Factors          Total n   n    Response (%)   n    Response (%)   Odds Ratio      95% CI     #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                     142     73      68.5%       69      85.5%          2.71      (1.18, 6.24)  #> Sex                                                                                                         #>   F                              78      40      62.5%       38      94.7%         10.80      (2.27, 51.45) #>   M                              64      33      75.8%       31      74.2%          0.92      (0.30, 2.85)  #> Continuous Level Biomarker 2                                                                                #>   LOW                            50      24      54.2%       26      80.8%          3.55      (1.00, 12.57) #>   MEDIUM                         49      23      73.9%       26      88.5%          2.71      (0.59, 12.39) #>   HIGH                           43      26      76.9%       17      88.2%          2.25      (0.40, 12.75)  ## Table with selected columns. basic_table() %>%   tabulate_rsp_subgroups(     df = df,     vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\")   ) #>                                                    B: Placebo                       A: Drug X                                         #> Baseline Risk Factors          Total n   n    Responders   Response (%)   n    Responders   Response (%)   Odds Ratio      95% CI     #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                     142     73       50          68.5%       69       59          85.5%          2.71      (1.18, 6.24)  #> Sex                                                                                                                                   #>   F                              78      40       25          62.5%       38       36          94.7%         10.80      (2.27, 51.45) #>   M                              64      33       25          75.8%       31       23          74.2%          0.92      (0.30, 2.85)  #> Continuous Level Biomarker 2                                                                                                          #>   LOW                            50      24       13          54.2%       26       21          80.8%          3.55      (1.00, 12.57) #>   MEDIUM                         49      23       17          73.9%       26       23          88.5%          2.71      (0.59, 12.39) #>   HIGH                           43      26       20          76.9%       17       15          88.2%          2.25      (0.40, 12.75)"},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":null,"dir":"Reference","previous_headings":"","what":"rtables Access Helper Functions — rtables_access","title":"rtables Access Helper Functions — rtables_access","text":"couple functions help accessing data rtables objects. Currently work occurrence tables, defined count first element fraction second element cell.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"rtables Access Helper Functions — rtables_access","text":"","code":"h_row_first_values(table_row, col_names = NULL, col_indices = NULL)  h_row_counts(table_row, col_names = NULL, col_indices = NULL)  h_row_fractions(table_row, col_names = NULL, col_indices = NULL)  h_col_counts(table, col_names = NULL, col_indices = NULL)  h_content_first_row(table)  is_leaf_table(table)  check_names_indices(table_row, col_names = NULL, col_indices = NULL)"},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"rtables Access Helper Functions — rtables_access","text":"table_row (TableRow) analysis row occurrence table. col_names (character) names columns extract . col_indices (integer) indices columns extract . col_names provided, inferred names table_row. Note currently works well single column split. table (VTableNodeInfo) occurrence table row.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"rtables Access Helper Functions — rtables_access","text":"h_row_first_values() returns vector numeric values. h_row_counts() returns vector numeric values. h_row_fractions() returns vector proportions. h_col_counts() returns vector column counts. h_content_first_row() returns row rtables table. is_leaf_table() returns logical value indicating whether current table leaf. check_names_indices returns column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"rtables Access Helper Functions — rtables_access","text":"h_row_first_values(): Helper function extract first values content cell specified columns TableRow. Defaults columns. h_row_counts(): Helper function extracts row values checks convertible integers (integerish values). h_row_fractions(): helper function extract fractions specified columns TableRow. specifically extracts second values content cell checks fraction. h_col_counts(): Helper function extract column counts specified columns table. h_content_first_row(): Helper function get first row content table current table. is_leaf_table(): Helper function says whether current table leaf tree. check_names_indices(): Internal helper function tests standard inputs column indices.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/rtables_access.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"rtables Access Helper Functions — rtables_access","text":"","code":"tbl <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"RACE\") %>%   analyze(\"AGE\", function(x) {     list(       \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.x (xx.x)\"),       \"n\" = length(x),       \"frac\" = rcell(c(0.1, 0.1), format = \"xx (xx)\")     )   }) %>%   build_table(tern_ex_adsl) %>%   prune_table() tree_row_elem <- collect_leaves(tbl[2, ])[[1]] result <- max(h_row_first_values(tree_row_elem)) result #> [1] 35.31214  # Row counts (integer values) # h_row_counts(tree_row_elem) # Fails because there are no integers # Using values with integers tree_row_elem <- collect_leaves(tbl[3, ])[[1]] result <- h_row_counts(tree_row_elem) # result  # Row fractions tree_row_elem <- collect_leaves(tbl[4, ])[[1]] h_row_fractions(tree_row_elem) #>      A: Drug X     B: Placebo C: Combination  #>            0.1            0.1            0.1"},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"Analyses based multivariate Cox model usually performed Controlled Substance Reporting regulatory documents serve exploratory purposes (e.g., publication). practice, model usually includes main effects (without interaction terms). produces hazard ratio estimates covariates included model. analysis follows principles (e.g., stratified vs. unstratified analysis tie handling) usual Cox model analysis. Since usually pre-specified hypothesis testing analysis, p.values need interpreted caution. (Statistical Analysis Clinical Trials Data R, NEST's bookdown)","code":""},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"","code":"s_cox_multivariate(   formula,   data,   conf_level = 0.95,   pval_method = c(\"wald\", \"likelihood\"),   ... )"},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"formula (formula) formula corresponding investigated survival::Surv() survival model including covariates. data (data.frame) data frame includes variable formula covariates. conf_level (proportion) confidence level hazard ratio interval estimations. Default 0.95. pval_method (character) method used estimation p-values, one \"wald\" (default) \"likelihood\". ... Optional parameters passed survival::coxph(). Can include ties, character string specifying method tie handling, one exact (default), efron, breslow.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"list elements mod, msum, aov, coef_inter.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"output limited single effect terms. Work ongoing estimation interaction terms scope defined  Global Data Standards Repository (GDS_Standard_TLG_Specs_Tables_2.doc).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/s_cox_multivariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multivariate Cox Model - Summarized Results — s_cox_multivariate","text":"","code":"library(dplyr)  adtte <- tern_ex_adtte adtte_f <- subset(adtte, PARAMCD == \"OS\") # _f: filtered adtte_f <- filter(   adtte_f,   PARAMCD == \"OS\" &     SEX %in% c(\"F\", \"M\") &     RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\") ) adtte_f$SEX <- droplevels(adtte_f$SEX) adtte_f$RACE <- droplevels(adtte_f$RACE)"},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Strings to NA — sas_na","title":"Convert Strings to NA — sas_na","text":"SAS imports missing data empty strings strings whitespaces . helper function can used convert values NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Strings to NA — sas_na","text":"","code":"sas_na(x, empty = TRUE, whitespaces = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Strings to NA — sas_na","text":"x (factor character vector) values missing values substituted. empty (logical) TRUE empty strings get replaced NA. whitespaces (logical) TRUE strings made whitespaces get replaced NA.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Strings to NA — sas_na","text":"x \"\" /whitespace-values substituted NA, depending values empty whitespaces.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/sas_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Strings to NA — sas_na","text":"","code":"sas_na(c(\"1\", \"\", \" \", \"   \", \"b\")) #> [1] \"1\" NA  NA  NA  \"b\" sas_na(factor(c(\"\", \" \", \"b\"))) #> [1] <NA> <NA> b    #> Levels: b  is.na(sas_na(c(\"1\", \"\", \" \", \"   \", \"b\"))) #> [1] FALSE  TRUE  TRUE  TRUE FALSE"},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence Table Sorting — score_occurrences","title":"Occurrence Table Sorting — score_occurrences","text":"Functions score occurrence table subtables rows can used sorting occurrence tables.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence Table Sorting — score_occurrences","text":"","code":"score_occurrences(table_row)  score_occurrences_cols(...)  score_occurrences_subtable(...)  score_occurrences_cont_cols(...)"},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence Table Sorting — score_occurrences","text":"table_row (TableRow) analysis row occurrence table. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence Table Sorting — score_occurrences","text":"score_occurrences() returns sum counts across columns table row. score_occurrences_cols() returns function sums counts across specified columns table row. score_occurrences_subtable() returns function sums counts subtable across specified columns. score_occurrences_cont_cols() returns function sums counts first content row specified columns.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence Table Sorting — score_occurrences","text":"score_occurrences(): Scoring function sums counts across columns. fail anything else counts used. score_occurrences_cols(): Scoring functions can produced constructor include specific columns scoring. See h_row_counts() information. score_occurrences_subtable(): Scoring functions produced constructor can used subtables: sum specified column counts subtable. useful available content row summing counts. score_occurrences_cont_cols(): Produce score function sorting table summing first content row specified columns. Note extending rtables::cont_n_onecol() rtables::cont_n_allcols().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/score_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence Table Sorting — score_occurrences","text":"","code":"lyt <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   analyze_num_patients(     vars = \"USUBJID\",     .stats = c(\"unique\"),     .labels = c(\"Total number of patients with at least one event\")   ) %>%   split_rows_by(\"AEBODSYS\", child_labels = \"visible\", nested = FALSE) %>%   summarize_num_patients(     var = \"USUBJID\",     .stats = c(\"unique\", \"nonunique\"),     .labels = c(       \"Total number of patients with at least one event\",       \"Total number of events\"     )   ) %>%   count_occurrences(vars = \"AEDECOD\")  tbl <- build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) %>%   prune_table()  tbl_sorted <- tbl %>%   sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_occurrences)  tbl_sorted #>                                                      A: Drug X    B: Placebo   C: Combination #>                                                        (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event     59 (85.5%)   57 (78.1%)     48 (82.8%)   #> cl D.1                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     20 (34.5%)   #>   Total number of events                                 39           35             28       #>     dcd D.1.1.4.2                                    16 (23.2%)   13 (17.8%)     16 (27.6%)   #>     dcd D.1.1.1.1                                    17 (24.6%)   18 (24.7%)     7 (12.1%)    #> cl C.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   13 (17.8%)     18 (31.0%)   #>   Total number of events                                 17           15             19       #>     dcd C.1.1.1.3                                    15 (21.7%)   13 (17.8%)     18 (31.0%)   #> cl C.2                                                                                        #>   Total number of patients with at least one event   20 (29.0%)   14 (19.2%)     10 (17.2%)   #>   Total number of events                                 21           15             14       #>     dcd C.2.1.2.1                                    20 (29.0%)   14 (19.2%)     10 (17.2%)   #> cl B.2                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     24 (41.4%)   #>   Total number of events                                 40           38             33       #>     dcd B.2.2.3.1                                    17 (24.6%)   15 (20.5%)     16 (27.6%)   #>     dcd B.2.1.2.1                                    17 (24.6%)   16 (21.9%)     13 (22.4%)   #> cl B.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   19 (26.0%)     15 (25.9%)   #>   Total number of events                                 19           19             18       #>     dcd B.1.1.1.1                                    15 (21.7%)   19 (26.0%)     15 (25.9%)   #> cl D.2                                                                                        #>   Total number of patients with at least one event   21 (30.4%)   20 (27.4%)     12 (20.7%)   #>   Total number of events                                 27           22             15       #>     dcd D.2.1.5.3                                    21 (30.4%)   20 (27.4%)     12 (20.7%)   #> cl A.1                                                                                        #>   Total number of patients with at least one event   31 (44.9%)   24 (32.9%)     27 (46.6%)   #>   Total number of events                                 39           33             35       #>     dcd A.1.1.1.1                                    17 (24.6%)   17 (23.3%)     14 (24.1%)   #>     dcd A.1.1.1.2                                    17 (24.6%)   14 (19.2%)     17 (29.3%)    score_cols_a_and_b <- score_occurrences_cols(col_names = c(\"A: Drug X\", \"B: Placebo\"))  # Note that this here just sorts the AEDECOD inside the AEBODSYS. The AEBODSYS are not sorted. # That would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>%   sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_cols_a_and_b)  tbl_sorted #>                                                      A: Drug X    B: Placebo   C: Combination #>                                                        (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event     59 (85.5%)   57 (78.1%)     48 (82.8%)   #> cl D.1                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     20 (34.5%)   #>   Total number of events                                 39           35             28       #>     dcd D.1.1.1.1                                    17 (24.6%)   18 (24.7%)     7 (12.1%)    #>     dcd D.1.1.4.2                                    16 (23.2%)   13 (17.8%)     16 (27.6%)   #> cl C.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   13 (17.8%)     18 (31.0%)   #>   Total number of events                                 17           15             19       #>     dcd C.1.1.1.3                                    15 (21.7%)   13 (17.8%)     18 (31.0%)   #> cl C.2                                                                                        #>   Total number of patients with at least one event   20 (29.0%)   14 (19.2%)     10 (17.2%)   #>   Total number of events                                 21           15             14       #>     dcd C.2.1.2.1                                    20 (29.0%)   14 (19.2%)     10 (17.2%)   #> cl B.2                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     24 (41.4%)   #>   Total number of events                                 40           38             33       #>     dcd B.2.1.2.1                                    17 (24.6%)   16 (21.9%)     13 (22.4%)   #>     dcd B.2.2.3.1                                    17 (24.6%)   15 (20.5%)     16 (27.6%)   #> cl B.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   19 (26.0%)     15 (25.9%)   #>   Total number of events                                 19           19             18       #>     dcd B.1.1.1.1                                    15 (21.7%)   19 (26.0%)     15 (25.9%)   #> cl D.2                                                                                        #>   Total number of patients with at least one event   21 (30.4%)   20 (27.4%)     12 (20.7%)   #>   Total number of events                                 27           22             15       #>     dcd D.2.1.5.3                                    21 (30.4%)   20 (27.4%)     12 (20.7%)   #> cl A.1                                                                                        #>   Total number of patients with at least one event   31 (44.9%)   24 (32.9%)     27 (46.6%)   #>   Total number of events                                 39           33             35       #>     dcd A.1.1.1.1                                    17 (24.6%)   17 (23.3%)     14 (24.1%)   #>     dcd A.1.1.1.2                                    17 (24.6%)   14 (19.2%)     17 (29.3%)    score_subtable_all <- score_occurrences_subtable(col_names = names(tbl))  # Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That # would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>%   sort_at_path(path = c(\"AEBODSYS\"), scorefun = score_subtable_all, decreasing = FALSE)  tbl_sorted #>                                                      A: Drug X    B: Placebo   C: Combination #>                                                        (N=69)       (N=73)         (N=58)     #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event     59 (85.5%)   57 (78.1%)     48 (82.8%)   #> cl C.2                                                                                        #>   Total number of patients with at least one event   20 (29.0%)   14 (19.2%)     10 (17.2%)   #>   Total number of events                                 21           15             14       #>     dcd C.2.1.2.1                                    20 (29.0%)   14 (19.2%)     10 (17.2%)   #> cl C.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   13 (17.8%)     18 (31.0%)   #>   Total number of events                                 17           15             19       #>     dcd C.1.1.1.3                                    15 (21.7%)   13 (17.8%)     18 (31.0%)   #> cl B.1                                                                                        #>   Total number of patients with at least one event   15 (21.7%)   19 (26.0%)     15 (25.9%)   #>   Total number of events                                 19           19             18       #>     dcd B.1.1.1.1                                    15 (21.7%)   19 (26.0%)     15 (25.9%)   #> cl D.2                                                                                        #>   Total number of patients with at least one event   21 (30.4%)   20 (27.4%)     12 (20.7%)   #>   Total number of events                                 27           22             15       #>     dcd D.2.1.5.3                                    21 (30.4%)   20 (27.4%)     12 (20.7%)   #> cl D.1                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     20 (34.5%)   #>   Total number of events                                 39           35             28       #>     dcd D.1.1.1.1                                    17 (24.6%)   18 (24.7%)     7 (12.1%)    #>     dcd D.1.1.4.2                                    16 (23.2%)   13 (17.8%)     16 (27.6%)   #> cl B.2                                                                                        #>   Total number of patients with at least one event   29 (42.0%)   27 (37.0%)     24 (41.4%)   #>   Total number of events                                 40           38             33       #>     dcd B.2.1.2.1                                    17 (24.6%)   16 (21.9%)     13 (22.4%)   #>     dcd B.2.2.3.1                                    17 (24.6%)   15 (20.5%)     16 (27.6%)   #> cl A.1                                                                                        #>   Total number of patients with at least one event   31 (44.9%)   24 (32.9%)     27 (46.6%)   #>   Total number of events                                 39           33             35       #>     dcd A.1.1.1.1                                    17 (24.6%)   17 (23.3%)     14 (24.1%)   #>     dcd A.1.1.1.2                                    17 (24.6%)   14 (19.2%)     17 (29.3%)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"","code":"split_cols_by_groups(lyt, var, groups_list = NULL, ref_group = NULL, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"lyt (layout) input layout analyses added . var (string) single variable name passed rtables requested statistics function. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. ref_group (data.frame vector) data corresponding reference group. ... additional arguments rtables::split_cols_by() order. instance, control formats (format), add joint column groups (incl_all).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"layout object suitable passing layouting functions. Adding function rtable layout add column split including given groups table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/split_cols_by_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Columns by Groups of Levels — split_cols_by_groups","text":"","code":"# 1 - Basic use  # Without group combination `split_cols_by_groups` is # equivalent to [rtables::split_cols_by()]. basic_table() %>%   split_cols_by_groups(\"ARM\") %>%   add_colcounts() %>%   analyze(\"AGE\") %>%   build_table(DM) #>        A: Drug X   B: Placebo   C: Combination #>         (N=121)     (N=106)        (N=129)     #> —————————————————————————————————————————————— #> Mean     34.91       33.02          34.57       # Add a reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   analyze(     \"AGE\",     afun = function(x, .ref_group, .in_ref_col) {       if (.in_ref_col) {         in_rows(\"Diff Mean\" = rcell(NULL))       } else {         in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\"))       }     }   ) %>%   build_table(DM) #>             B: Placebo   A: Drug X   C: Combination #>              (N=106)      (N=121)       (N=129)     #> ——————————————————————————————————————————————————— #> Diff Mean                  1.89           1.55       # 2 - Adding group specification  # Manual preparation of the groups. groups <- list(   \"Arms A+B\" = c(\"A: Drug X\", \"B: Placebo\"),   \"Arms A+C\" = c(\"A: Drug X\", \"C: Combination\") )  # Use of split_cols_by_groups without reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", groups) %>%   add_colcounts() %>%   analyze(\"AGE\") %>%   build_table(DM) #>        Arms A+B   Arms A+C #>        (N=227)    (N=250)  #> —————————————————————————— #> Mean    34.03      34.73    # Including differentiated output in the reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", groups_list = groups, ref_group = \"Arms A+B\") %>%   analyze(     \"AGE\",     afun = function(x, .ref_group, .in_ref_col) {       if (.in_ref_col) {         in_rows(\"Diff. of Averages\" = rcell(NULL))       } else {         in_rows(\"Diff. of Averages\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\"))       }     }   ) %>%   build_table(DM) #>                     Arms A+B   Arms A+C #> ——————————————————————————————————————— #> Diff. of Averages                0.71    # 3 - Binary list dividing factor levels into reference and treatment  # `combine_groups` defines reference and treatment. groups <- combine_groups(   fct = DM$ARM,   ref = c(\"A: Drug X\", \"B: Placebo\") ) groups #> $`A: Drug X/B: Placebo` #> [1] \"A: Drug X\"  \"B: Placebo\" #>  #> $`C: Combination` #> [1] \"C: Combination\" #>   # Use group definition without reference column. basic_table() %>%   split_cols_by_groups(\"ARM\", groups_list = groups) %>%   add_colcounts() %>%   analyze(\"AGE\") %>%   build_table(DM) #>        A: Drug X/B: Placebo   C: Combination #>              (N=227)             (N=129)     #> ———————————————————————————————————————————— #> Mean          34.03               34.57       # Use group definition with reference column (first item of groups). basic_table() %>%   split_cols_by_groups(\"ARM\", groups, ref_group = names(groups)[1]) %>%   add_colcounts() %>%   analyze(     \"AGE\",     afun = function(x, .ref_group, .in_ref_col) {       if (.in_ref_col) {         in_rows(\"Diff Mean\" = rcell(NULL))       } else {         in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\"))       }     }   ) %>%   build_table(DM) #>             A: Drug X/B: Placebo   C: Combination #>                   (N=227)             (N=129)     #> ————————————————————————————————————————————————— #> Diff Mean                               0.54"},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Text According To Available Text Width — split_text_grob","title":"Split Text According To Available Text Width — split_text_grob","text":"Dynamically wrap text.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Text According To Available Text Width — split_text_grob","text":"","code":"split_text_grob(   text,   x = grid::unit(0.5, \"npc\"),   y = grid::unit(0.5, \"npc\"),   width = grid::unit(1, \"npc\"),   just = \"centre\",   hjust = NULL,   vjust = NULL,   default.units = \"npc\",   name = NULL,   gp = grid::gpar(),   vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Text According To Available Text Width — split_text_grob","text":"text character string x numeric vector unit object specifying x-values. y numeric vector unit object specifying y-values. width unit object specifying max width text just justification text     relative (x, y) location.  two values, first     value specifies horizontal justification second value specifies     vertical justification.  Possible string values : \"left\",     \"right\", \"centre\", \"center\", \"bottom\",     \"top\".  numeric values, 0 means left (bottom) alignment     1 means right (top) alignment. hjust numeric vector specifying horizontal justification.     specified, overrides just setting. vjust numeric vector specifying vertical justification.     specified, overrides just setting. default.units string indicating default units use     x y     given numeric vectors. name character identifier. gp object class \"gpar\", typically output     call function gpar.  basically     list graphical parameter settings. vp Grid viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Text According To Available Text Width — split_text_grob","text":"text grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/split_text_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Text According To Available Text Width — split_text_grob","text":"code taken R Graphics Paul Murell, 2nd edition","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack Multiple Grobs — stack_grobs","title":"Stack Multiple Grobs — stack_grobs","text":"Stack grobs new grob 1 column multiple rows layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack Multiple Grobs — stack_grobs","text":"","code":"stack_grobs(   ...,   grobs = list(...),   padding = grid::unit(2, \"line\"),   vp = NULL,   gp = NULL,   name = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack Multiple Grobs — stack_grobs","text":"... grobs. grobs list grobs. padding unit length 1, space grob. vp viewport() object (NULL). gp gpar() object. name character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack Multiple Grobs — stack_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stack_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stack Multiple Grobs — stack_grobs","text":"","code":"library(grid)  g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid.newpage() grid.draw(stack_grobs(g1, g2, g3))  showViewport()   grid.newpage() pushViewport(viewport(layout = grid.layout(1, 2))) vp1 <- viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(stack_grobs(g1, g2, g3, vp = vp1, name = \"test\"))  showViewport()  grid.ls(grobs = TRUE, viewports = TRUE, print = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Mean — stat_mean_ci","title":"Confidence Interval for Mean — stat_mean_ci","text":"Convenient function calculating mean confidence interval. calculates arithmetic well geometric mean. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Mean — stat_mean_ci","text":"","code":"stat_mean_ci(   x,   conf_level = 0.95,   na.rm = TRUE,   n_min = 2,   gg_helper = TRUE,   geom_mean = FALSE )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Mean — stat_mean_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. n_min (number) minimum number non-missing x estimate confidence interval mean. gg_helper (logical)TRUE output aligned use ggplot. geom_mean (logical)TRUE geometric mean calculated.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Mean — stat_mean_ci","text":"named vector values mean_ci_lwr mean_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Mean — stat_mean_ci","text":"","code":"stat_mean_ci(sample(10), gg_helper = FALSE) #> mean_ci_lwr mean_ci_upr  #>    3.334149    7.665851   p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +   ggplot2::geom_point()  p + ggplot2::stat_summary(   fun.data = stat_mean_ci,   geom = \"errorbar\" )   p + ggplot2::stat_summary(   fun.data = stat_mean_ci,   fun.args = list(conf_level = 0.5),   geom = \"errorbar\" )   p + ggplot2::stat_summary(   fun.data = stat_mean_ci,   fun.args = list(conf_level = 0.5, geom_mean = TRUE),   geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":null,"dir":"Reference","previous_headings":"","what":"p-Value of the Mean — stat_mean_pval","title":"p-Value of the Mean — stat_mean_pval","text":"Convenient function calculating two-sided p-value mean.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"p-Value of the Mean — stat_mean_pval","text":"","code":"stat_mean_pval(x, na.rm = TRUE, n_min = 2, test_mean = 0)"},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"p-Value of the Mean — stat_mean_pval","text":"x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. n_min (numeric) minimum number non-missing x estimate p-value mean. test_mean (numeric) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"p-Value of the Mean — stat_mean_pval","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_mean_pval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"p-Value of the Mean — stat_mean_pval","text":"","code":"stat_mean_pval(sample(10)) #>     p_value  #> 0.000278196   stat_mean_pval(rnorm(10), test_mean = 0.5) #>    p_value  #> 0.01876644"},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Median — stat_median_ci","title":"Confidence Interval for Median — stat_median_ci","text":"Convenient function calculating median confidence interval. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Median — stat_median_ci","text":"","code":"stat_median_ci(x, conf_level = 0.95, na.rm = TRUE, gg_helper = TRUE)"},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Median — stat_median_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. gg_helper (logical)TRUE output aligned use ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Median — stat_median_ci","text":"named vector values median_ci_lwr median_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence Interval for Median — stat_median_ci","text":"function adapted DescTools/versions/0.99.35/source","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_median_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Median — stat_median_ci","text":"","code":"stat_median_ci(sample(10), gg_helper = FALSE) #> median_ci_lwr median_ci_upr  #>             2             9  #> attr(,\"conf_level\") #> [1] 0.9785156  p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +   ggplot2::geom_point() p + ggplot2::stat_summary(   fun.data = stat_median_ci,   geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion Difference and Confidence Interval — stat_propdiff_ci","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"Function calculating proportion (risk) difference confidence interval arm X (reference group) arm Y. Risk difference calculated subtracting cumulative incidence arm Y cumulative incidence arm X.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci(   x,   y,   N_x,   N_y,   list_names = NULL,   conf_level = 0.95,   pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"x (list integer) list number occurrences arm X (reference group). y (list integer) list number occurrences arm Y. Must equal length x. N_x (numeric) total number records arm X. N_y (numeric) total number records arm Y. list_names (character) names variable/level corresponding pair proportions x y. Must equal length x y. conf_level (proportion) confidence level interval. pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"List proportion differences CIs corresponding pair number occurrences x y. list element consists 3 statistics: proportion difference, CI lower bound, CI upper bound.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/stat_propdiff_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion Difference and Confidence Interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci(   x = list(0.375), y = list(0.01), N_x = 5, N_y = 5, list_names = \"x\", conf_level = 0.9 ) #> $x #> [1]   7.30000 -12.35184  26.95184 #>   stat_propdiff_ci(   x = list(0.5, 0.75, 1), y = list(0.25, 0.05, 0.5), N_x = 10, N_y = 20, pct = FALSE ) #> [[1]] #> [1]  0.0375000 -0.1060891  0.1810891 #>  #> [[2]] #> [1]  0.07250000 -0.09220915  0.23720915 #>  #> [[3]] #> [1]  0.0750000 -0.1231285  0.2731285 #>"},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"function wraps estimation stratified percentiles assume approximation large numbers. necessary case proportions strata unequal.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"","code":"strata_normal_quantile(vars, weights, conf_level)"},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"vars (character) variable names primary analysis variable iterated . weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010)  minimizes weighted squared length confidence interval. conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"Stratified quantile.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/strata_normal_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for the Estimation of Stratified Quantiles — strata_normal_quantile","text":"","code":"strata_data <- table(data.frame(   \"f1\" = sample(c(TRUE, FALSE), 100, TRUE),   \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE),   stringsAsFactors = TRUE )) ns <- colSums(strata_data) ests <- strata_data[\"TRUE\", ] / ns vars <- ests * (1 - ests) / ns weights <- rep(1 / length(ns), length(ns)) strata_normal_quantile(vars, weights, 0.95) #> [1] 1.131693"},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":null,"dir":"Reference","previous_headings":"","what":"Indicate Study Arm Variable in Formula — study_arm","title":"Indicate Study Arm Variable in Formula — study_arm","text":"use study_arm indicate study arm variable tern formulas.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indicate Study Arm Variable in Formula — study_arm","text":"","code":"study_arm(x)"},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indicate Study Arm Variable in Formula — study_arm","text":"x arm information","code":""},{"path":"https://insightsengineering.github.io/tern/reference/study_arm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indicate Study Arm Variable in Formula — study_arm","text":"x","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"Summarize results ANCOVA. can used analyze multiple endpoints /multiple timepoints within response variable .var.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"","code":"s_ancova(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   conf_level,   interaction_y = FALSE,   interaction_item = NULL )  a_ancova(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   conf_level,   interaction_y = FALSE,   interaction_item = NULL )  summarize_ancova(   lyt,   vars,   var_labels,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"visible\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL,   interaction_y = FALSE,   interaction_item = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data frame across columns given row split. variables (named list string) list additional analysis variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. conf_level (proportion) confidence level interval. interaction_y (character) selected item inside interaction_item column used select specific ANCOVA results. interaction needed, default option FALSE. interaction_item (character) name variable interactions arm. interaction needed, default option NULL. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"s_ancova() returns named list 5 statistics: n: Count complete sample size group. lsmean: Estimated marginal means group. lsmean_diff: Difference estimated marginal means comparison reference group. working reference group, empty. lsmean_diff_ci: Confidence level difference estimated marginal means comparison reference group. pval: p-value (adjusted multiple comparisons). a_ancova() returns corresponding list formatted rtables::CellValue(). summarize_ancova() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_ancova() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"s_ancova(): Statistics function produces named list results investigated linear model. a_ancova(): Formatted analysis function used afun summarize_ancova(). summarize_ancova(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary for analysis of covariance (ANCOVA). — summarize_ancova","text":"","code":"library(dplyr)  df <- iris %>% filter(Species == \"virginica\") .df_row <- iris .var <- \"Petal.Length\" variables <- list(arm = \"Species\", covariates = \"Sepal.Length * Sepal.Width\") .ref_group <- iris %>% filter(Species == \"setosa\") conf_level <- 0.95  basic_table() %>%   split_cols_by(\"Species\", ref_group = \"setosa\") %>%   add_colcounts() %>%   summarize_ancova(     vars = \"Petal.Length\",     variables = list(arm = \"Species\", covariates = NULL),     table_names = \"unadj\",     conf_level = 0.95, var_labels = \"Unadjusted comparison\",     .labels = c(lsmean = \"Mean\", lsmean_diff = \"Difference in Means\")   ) %>%   summarize_ancova(     vars = \"Petal.Length\",     variables = list(arm = \"Species\", covariates = c(\"Sepal.Length\", \"Sepal.Width\")),     table_names = \"adj\",     conf_level = 0.95, var_labels = \"Adjusted comparison (covariates: Sepal.Length and Sepal.Width)\"   ) %>%   build_table(iris) #>                                                                  setosa    versicolor     virginica   #>                                                                  (N=50)      (N=50)         (N=50)    #> ————————————————————————————————————————————————————————————————————————————————————————————————————— #> Unadjusted comparison                                                                                 #>   n                                                                50          50             50      #>   Mean                                                            1.46        4.26           5.55     #>   Difference in Means                                                         2.80           4.09     #>     95% CI                                                                (2.63, 2.97)   (3.92, 4.26) #>     p-value                                                                 <0.0001        <0.0001    #> Adjusted comparison (covariates: Sepal.Length and Sepal.Width)                                        #>   n                                                                50          50             50      #>   Adjusted Mean                                                   2.02        4.19           5.07     #>   Difference in Adjusted Means                                                2.17           3.05     #>     95% CI                                                                (1.96, 2.38)   (2.81, 3.29) #>     p-value                                                                 <0.0001        <0.0001"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"primary analysis variable .var indicates numerical change baseline results, additional required secondary analysis variables value baseline_flag. Depending baseline flag, either absolute baseline values (baseline) change baseline values (post-baseline) summarized.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"","code":"s_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)  a_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)  summarize_change(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   table_names = vars,   .stats = c(\"n\", \"mean_sd\", \"median\", \"range\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. variables (named list string) list additional analysis variables. na.rm (flag) whether NA values removed x prior analysis. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"s_change_from_baseline() returns values returned s_summary.numeric(). a_change_from_baseline() returns corresponding list formatted rtables::CellValue(). summarize_change() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_change_from_baseline() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"s_change_from_baseline(): Statistics function summarizes baseline post-baseline visits. a_change_from_baseline(): Formatted analysis function used afun summarize_change(). summarize_change(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"data df must either baseline post-baseline visits. Otherwise error thrown. used split visits layout, data subset contains either baseline post-baseline data.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the Change from Baseline or Absolute Baseline Values — summarize_change","text":"","code":"df <- data.frame(   chg = c(1, 2, 3),   is_bl = c(TRUE, TRUE, TRUE),   val = c(4, 5, 6) )  # `summarize_change()`  ## Fabricated dataset. library(dplyr)  dta_test <- data.frame(   USUBJID = rep(1:6, each = 3),   AVISIT = rep(paste0(\"V\", 1:3), 6),   ARM = rep(LETTERS[1:3], rep(6, 3)),   AVAL = c(9:1, rep(NA, 9)) ) %>%   mutate(ABLFLL = AVISIT == \"V1\") %>%   group_by(USUBJID) %>%   mutate(     BLVAL = AVAL[ABLFLL],     CHG = AVAL - BLVAL   ) %>%   ungroup()  results <- basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   summarize_change(\"CHG\", variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\")) %>%   build_table(dta_test) # \\donttest{ Viewer(results) # }"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Variables in Columns — summarize_colvars","title":"Summarize Variables in Columns — summarize_colvars","text":"analyze function uses S3 generic function s_summary() summarize different variables arranged columns. Additional standard formatting arguments available. minimal wrapper rtables::analyze_colvars(). latter function meant add different analysis methods column variables different rows. analysis methods column labels, please refer analyze_vars_in_cols().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize Variables in Columns — summarize_colvars","text":"","code":"summarize_colvars(   lyt,   ...,   na_level = lifecycle::deprecated(),   na_str = NA_character_,   .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize Variables in Columns — summarize_colvars","text":"lyt (layout) input layout analyses added . ... arguments passed s_summary(). na_level Please use na_str argument instead. na_str (string) string used replace NA empty values output. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize Variables in Columns — summarize_colvars","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout summarize given variables, arrange output columns, add table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/summarize_colvars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize Variables in Columns — summarize_colvars","text":"","code":"dta_test <- data.frame(   USUBJID = rep(1:6, each = 3),   PARAMCD = rep(\"lab\", 6 * 3),   AVISIT = rep(paste0(\"V\", 1:3), 6),   ARM = rep(LETTERS[1:3], rep(6, 3)),   AVAL = c(9:1, rep(NA, 9)),   CHG = c(1:9, rep(NA, 9)) )  ## Default output within a `rtables` pipeline. basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>%   summarize_colvars() %>%   build_table(dta_test) #>                         A                       B                 C      #>                 AVAL         CHG        AVAL         CHG      AVAL   CHG #> ———————————————————————————————————————————————————————————————————————— #> V1                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   7.5 (2.1)   2.5 (2.1)   3.0 (NA)    7.0 (NA)     NA    NA  #>   Median         7.5         2.5         3.0         7.0       NA    NA  #>   Min - Max   6.0 - 9.0   1.0 - 4.0   3.0 - 3.0   7.0 - 7.0    NA    NA  #> V2                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   6.5 (2.1)   3.5 (2.1)   2.0 (NA)    8.0 (NA)     NA    NA  #>   Median         6.5         3.5         2.0         8.0       NA    NA  #>   Min - Max   5.0 - 8.0   2.0 - 5.0   2.0 - 2.0   8.0 - 8.0    NA    NA  #> V3                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   5.5 (2.1)   4.5 (2.1)   1.0 (NA)    9.0 (NA)     NA    NA  #>   Median         5.5         4.5         1.0         9.0       NA    NA  #>   Min - Max   4.0 - 7.0   3.0 - 6.0   1.0 - 1.0   9.0 - 9.0    NA    NA   ## Selection of statistics, formats and labels also work. basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>%   summarize_colvars(     .stats = c(\"n\", \"mean_sd\"),     .formats = c(\"mean_sd\" = \"xx.x, xx.x\"),     .labels = c(n = \"n\", mean_sd = \"Mean, SD\")   ) %>%   build_table(dta_test) #>                       A                    B               C      #>                AVAL       CHG       AVAL       CHG     AVAL   CHG #> ————————————————————————————————————————————————————————————————— #> V1                                                                #>   n             2          2          1         1       0      0  #>   Mean, SD   7.5, 2.1   2.5, 2.1   3.0, NA   7.0, NA    NA    NA  #> V2                                                                #>   n             2          2          1         1       0      0  #>   Mean, SD   6.5, 2.1   3.5, 2.1   2.0, NA   8.0, NA    NA    NA  #> V3                                                                #>   n             2          2          1         1       0      0  #>   Mean, SD   5.5, 2.1   4.5, 2.1   1.0, NA   9.0, NA    NA    NA   ## Use arguments interpreted by `s_summary`. basic_table() %>%   split_cols_by(\"ARM\") %>%   split_rows_by(\"AVISIT\") %>%   split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>%   summarize_colvars(na.rm = FALSE) %>%   build_table(dta_test) #>                         A                       B                 C      #>                 AVAL         CHG        AVAL         CHG      AVAL   CHG #> ———————————————————————————————————————————————————————————————————————— #> V1                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   7.5 (2.1)   2.5 (2.1)   3.0 (NA)    7.0 (NA)     NA    NA  #>   Median         7.5         2.5         3.0         7.0       NA    NA  #>   Min - Max   6.0 - 9.0   1.0 - 4.0   3.0 - 3.0   7.0 - 7.0    NA    NA  #> V2                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   6.5 (2.1)   3.5 (2.1)   2.0 (NA)    8.0 (NA)     NA    NA  #>   Median         6.5         3.5         2.0         8.0       NA    NA  #>   Min - Max   5.0 - 8.0   2.0 - 5.0   2.0 - 2.0   8.0 - 8.0    NA    NA  #> V3                                                                       #>   n               2           2           1           1        0      0  #>   Mean (SD)   5.5 (2.1)   4.5 (2.1)   1.0 (NA)    9.0 (NA)     NA    NA  #>   Median         5.5         4.5         1.0         9.0       NA    NA  #>   Min - Max   4.0 - 7.0   3.0 - 6.0   1.0 - 1.0   9.0 - 9.0    NA    NA"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Functions — summarize_functions","title":"Summarize Functions — summarize_functions","text":"functions wrappers rtables::summarize_row_groups(), applying corresponding tern content functions add summary rows given table layout: add_rowcounts() estimate_multinomial_response() (rtables::analyze()) h_tab_one_biomarker() (probably deprecate) logistic_summary_by_flag() summarize_num_patients() summarize_occurrences_by_grade() summarize_patients_events_in_cols() summarize_patients_exposure_in_cols() tabulate_rsp_subgroups() Additionally, summarize_coxreg() function utilizes rtables::summarize_row_groups() (combination several rtables functions like rtables::analyze_colvars()) output Cox regression summary table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary for Poisson Negative Binomial. — summarize_glm_count","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"Summarize results Poisson Negative Binomial Regression. can used analyze count /frequency data using linear model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"","code":"s_glm_count(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   distribution,   conf_level,   rate_mean_method,   weights,   scale = 1 )  a_glm_count(   df,   .var,   .df_row,   variables,   .ref_group,   .in_ref_col,   distribution,   conf_level,   rate_mean_method,   weights,   scale = 1 )  summarize_glm_count(   lyt,   vars,   var_labels,   na_str = NA_character_,   nested = TRUE,   ...,   show_labels = \"visible\",   table_names = vars,   .stats = NULL,   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data frame across columns given row split. variables (named list string) list additional analysis variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. distribution (character) character value specifying distribution used regression (poisson, quasipoisson). conf_level (proportion) confidence level interval. weights (character) character vector specifying weights used averaging predictions. Number weights must equal number levels included covariates. Weights option passed emmeans::emmeans(). lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) character label. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"s_glm_count() returns named list 5 statistics: n: Count complete sample size group. rate: Estimated event rate per follow-time. rate_ci: Confidence level estimated rate per follow-time. rate_ratio: Ratio event rates treatment arm reference arm. rate_ratio_ci: Confidence level rate ratio. pval: p-value. a_glm_count() returns corresponding list formatted rtables::CellValue(). summarize_glm_count() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_glm_count() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"s_glm_count(): Statistics function produces named list results investigated Poisson model. a_glm_count(): Formatted analysis function used afun summarize_glm_count(). summarize_glm_count(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_glm_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary for Poisson Negative Binomial. — summarize_glm_count","text":"","code":"library(dplyr) anl <- tern_ex_adtte %>% filter(PARAMCD == \"TNE\") anl$AVAL_f <- as.factor(anl$AVAL)  lyt <- basic_table() %>%   split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>%   add_colcounts() %>%   analyze_vars(     \"AVAL_f\",     var_labels = \"Number of exacerbations per patient\",     .stats = c(\"count_fraction\"),     .formats = c(\"count_fraction\" = \"xx (xx.xx%)\"),     .label = c(\"Number of exacerbations per patient\")   ) %>%   summarize_glm_count(     vars = \"AVAL\",     variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = NULL),     conf_level = 0.95,     distribution = \"poisson\",     rate_mean_method = \"emmeans\",     var_labels = \"Unadjusted exacerbation rate (per year)\",     table_names = \"unadj\",     .stats = c(\"rate\"),     .labels = c(rate = \"Rate\")   ) %>%   summarize_glm_count(     vars = \"AVAL\",     variables = list(arm = \"ARM\", offset = \"lgTMATRSK\", covariates = c(\"REGION1\")),     conf_level = 0.95,     distribution = \"quasipoisson\",     rate_mean_method = \"ppmeans\",     var_labels = \"Adjusted (QP) exacerbation rate (per year)\",     table_names = \"adj\",     .stats = c(\"rate\", \"rate_ci\", \"rate_ratio\", \"rate_ratio_ci\", \"pval\"),     .labels = c(       rate = \"Rate\", rate_ci = \"Rate CI\", rate_ratio = \"Rate Ratio\",       rate_ratio_ci = \"Rate Ratio CI\", pval = \"p value\"     )   ) build_table(lyt = lyt, df = anl) #>                                                 B: Placebo         A: Drug X        C: Combination  #>                                                   (N=73)             (N=69)             (N=58)      #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Number of exacerbations per patient                                                                 #>   0                                             8 (10.96%)         3 (4.35%)          6 (10.34%)    #>   1                                             9 (12.33%)        11 (15.94%)         6 (10.34%)    #>   2                                            15 (20.55%)        18 (26.09%)         9 (15.52%)    #>   3                                            11 (15.07%)        14 (20.29%)        15 (25.86%)    #>   4                                             9 (12.33%)        10 (14.49%)         9 (15.52%)    #>   5                                             9 (12.33%)         7 (10.14%)         8 (13.79%)    #>   6                                             4 (5.48%)          4 (5.80%)          4 (6.90%)     #>   7                                             8 (10.96%)         2 (2.90%)          0 (0.00%)     #>   10                                            0 (0.00%)          0 (0.00%)          1 (1.72%)     #> Unadjusted exacerbation rate (per year)                                                             #>   Rate                                            9.1554             8.2061             7.8551      #> Adjusted (QP) exacerbation rate (per year)                                                          #>   Rate                                            3.4860             3.1214             2.6152      #>   Rate CI                                    (1.9833, 6.1272)   (1.7307, 5.6294)   (1.3661, 5.0065) #>   Rate Ratio                                                         0.8954             0.7502      #>   Rate Ratio CI                                                 (0.3975, 2.0170)   (0.3067, 1.8348) #>   p value                                                            0.7897             0.5288"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Logistic Regression Table — summarize_logistic","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"Layout-creating function summarizes logistic variable regression binary outcome categorical/continuous covariates model statement. covariate category (categorical) specified values (continuous), present degrees freedom, regression parameter estimate standard error (SE) relative reference group category. Report odds ratios covariate category specified values corresponding Wald confidence intervals default allow user specify confidence levels. Report p-value Wald chi-square test null hypothesis covariate effect response model containing specified covariates. Allow option include one two-way interaction present similar output interaction degree freedom.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"","code":"summarize_logistic(   lyt,   conf_level,   drop_and_remove_str = \"\",   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"lyt (layout) input layout analyses added . conf_level (proportion) confidence level interval. drop_and_remove_str (character) string dropped removed. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add logistic regression variable summary table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"formula, variable names need standard data.frame column names without special characters.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multivariate Logistic Regression Table — summarize_logistic","text":"","code":"library(dplyr) library(broom)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred  df <- tidy(mod1, conf_level = 0.99) df2 <- tidy(mod2, conf_level = 0.99)  # flagging empty strings with \"_\" df <- df_explicit_na(df, na_level = \"_\") df2 <- df_explicit_na(df2, na_level = \"_\")  result1 <- basic_table() %>%   summarize_logistic(     conf_level = 0.95,     drop_and_remove_str = \"_\"   ) %>%   build_table(df = df) result1 #>                                       Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Planned Arm Code                              2                                                                                 0.3004  #>   Reference ARM A, n = 64                                                                                                               #>   ARM B, n = 68                               1                  -1.775             1.144           0.17       (<0.01, 3.23)    0.1209  #>   ARM C, n = 52                               1                  17.192            3626.588       >999.99     (0.00, >999.99)   0.9962  #> Age                                                                                                                                     #>   Age                                         1                  0.170              0.095           1.19       (0.93, 1.51)     0.0746  #> Race                                          2                                                                                 0.7967  #>   Reference ASIAN, n = 110                                                                                                              #>   BLACK OR AFRICAN AMERICAN, n = 40           1                  17.923            4001.705       >999.99     (0.00, >999.99)   0.9964  #>   WHITE, n = 34                               1                  -0.656             0.974           0.52       (0.04, 6.37)     0.5002   result2 <- basic_table() %>%   summarize_logistic(     conf_level = 0.95,     drop_and_remove_str = \"_\"   ) %>%   build_table(df = df2) result2 #>                                         Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Race                                            2                                                                                 0.9361  #>   Reference ASIAN, n = 110                                                                                                                #>   BLACK OR AFRICAN AMERICAN, n = 40             1                  18.150            3944.701       >999.99     (0.00, >999.99)   0.9963  #>   WHITE, n = 34                                 1                  -0.373             1.026           0.69       (0.05, 9.68)     0.7164  #> Planned Arm Code                                2                                                                                 0.2539  #>   Reference ARM A, n = 64                                                                                                                 #>   ARM B, n = 68                                 1                 -11.527             6.962                                       0.0978  #>     Age                                                                                                                                   #>       35                                                                                              0.48       (0.01, 15.48)            #>   ARM C, n = 52                                 1                  16.333           20278.387                                     0.9994  #>     Age                                                                                                                                   #>       35                                                                                            >999.99     (0.00, >999.99)           #> Age                                                                                                                                       #>   Age                                           1                  -0.039             0.151                                       0.7981  #>     Planned Arm Code                                                                                                                      #>       ARM A                                                                                           0.96       (0.65, 1.42)             #>       ARM B                                                                                           1.31       (0.92, 1.86)             #>       ARM C                                                                                           0.99      (0.00, >999.99)           #> Interaction of Planned Arm Code * Age           2                                                                                 0.3276  #>   Reference ARM A, n = 64                                                                                                                 #>   ARM B, n = 68                                 1                  0.308              0.206                                       0.1352  #>   ARM C, n = 52                                 1                  0.029             548.592                                      1.0000"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of Patients — summarize_num_patients","title":"Number of Patients — summarize_num_patients","text":"Count number unique non-unique patients column (variable).","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of Patients — summarize_num_patients","text":"","code":"s_num_patients(   x,   labelstr,   .N_col,   count_by = NULL,   unique_count_suffix = TRUE )  s_num_patients_content(   df,   labelstr = \"\",   .N_col,   .var,   required = NULL,   count_by = NULL,   unique_count_suffix = TRUE )  summarize_num_patients(   lyt,   var,   na_str = NA_character_,   .stats = NULL,   .formats = NULL,   .labels = c(unique = \"Number of patients with at least one event\", nonunique =     \"Number of events\"),   indent_mod = lifecycle::deprecated(),   .indent_mods = 0L,   riskdiff = FALSE,   ... )  analyze_num_patients(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   .stats = NULL,   .formats = NULL,   .labels = c(unique = \"Number of patients with at least one event\", nonunique =     \"Number of events\"),   show_labels = c(\"default\", \"visible\", \"hidden\"),   indent_mod = lifecycle::deprecated(),   .indent_mods = 0L,   riskdiff = FALSE,   ... )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of Patients — summarize_num_patients","text":"x (character factor) vector patient IDs. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. count_by (character factor) optional vector combined x counting nonunique records. unique_count_suffix (logical) \"(n)\" suffix added unique_count labels. Defaults TRUE. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. required (character NULL) optional name variable required non-missing. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). indent_mod Please use .indent_mods argument instead. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. ... additional arguments lower level functions. vars (character) variable names primary analysis variable iterated . nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\".","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of Patients — summarize_num_patients","text":"s_num_patients() returns named list 3 statistics: unique: Vector counts percentages. nonunique: Vector counts. unique_count: Counts. s_num_patients_content() returns values s_num_patients(). summarize_num_patients() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_num_patients_content() table layout. analyze_num_patients() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_num_patients_content() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of Patients — summarize_num_patients","text":"general, functions starts analyze* expected work like rtables::analyze(), functions starts summarize* based upon rtables::summarize_row_groups(). latter provides value dividing split row column space, , bound fundamental splits, repeated design every page pagination involved.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Number of Patients — summarize_num_patients","text":"s_num_patients(): Statistics function counts number unique patients, corresponding percentage taken respect total number patients, number non-unique patients. s_num_patients_content(): Statistics function counts number unique patients column (variable), corresponding percentage taken respect total number patients, number non-unique patients column. summarize_num_patients(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). analyze_num_patients(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Number of Patients — summarize_num_patients","text":"opposed summarize_num_patients(), function repeat produced rows.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_num_patients.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Number of Patients — summarize_num_patients","text":"","code":"# Use the statistics function to count number of unique and nonunique patients. s_num_patients(x = as.character(c(1, 1, 1, 2, 4, NA)), labelstr = \"\", .N_col = 6L) #> $unique #> [1] 3.0 0.5 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 5 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>  s_num_patients(   x = as.character(c(1, 1, 1, 2, 4, NA)),   labelstr = \"\",   .N_col = 6L,   count_by = as.character(c(1, 1, 2, 1, 1, 1)) ) #> $unique #> [1] 3.0 0.5 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 4 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>   # Count number of unique and non-unique patients. df <- data.frame(   USUBJID = as.character(c(1, 2, 1, 4, NA)),   EVENT = as.character(c(10, 15, 10, 17, 8)) ) s_num_patients_content(df, .N_col = 5, .var = \"USUBJID\") #> $unique #> [1] 3.0 0.6 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 4 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>   df_by_event <- data.frame(   USUBJID = as.character(c(1, 2, 1, 4, NA)),   EVENT = as.character(c(10, 15, 10, 17, 8)) ) s_num_patients_content(df_by_event, .N_col = 5, .var = \"USUBJID\") #> $unique #> [1] 3.0 0.6 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 4 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>  s_num_patients_content(df_by_event, .N_col = 5, .var = \"USUBJID\", count_by = \"EVENT\") #> $unique #> [1] 3.0 0.6 #> attr(,\"label\") #> [1] \"\" #>  #> $nonunique #> [1] 3 #> attr(,\"label\") #> [1] \"\" #>  #> $unique_count #> [1] 3 #> attr(,\"label\") #> [1] \"(n)\" #>   df_tmp <- data.frame(   USUBJID = as.character(c(1, 2, 1, 4, NA, 6, 6, 8, 9)),   ARM = c(\"A\", \"A\", \"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"B\"),   AGE = c(10, 15, 10, 17, 8, 11, 11, 19, 17) ) tbl <- basic_table() %>%   split_cols_by(\"ARM\") %>%   add_colcounts() %>%   analyze_num_patients(\"USUBJID\", .stats = c(\"unique\")) %>%   build_table(df_tmp) tbl #>                                                  A           B     #>                                                (N=5)       (N=4)   #> —————————————————————————————————————————————————————————————————— #> Number of patients with at least one event   3 (60.0%)   3 (75.0%)"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"Counting number patients summing analysis value (.e exposure values) across patients column table layout required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"","code":"s_count_patients_sum_exposure(   df,   ex_var = \"AVAL\",   id = \"USUBJID\",   labelstr = \"\",   .stats = c(\"n_patients\", \"sum_exposure\"),   .N_col,   custom_label = NULL )  a_count_patients_sum_exposure(   df,   var = NULL,   ex_var = \"AVAL\",   id = \"USUBJID\",   labelstr = \"\",   add_total_level = FALSE,   .N_col,   .stats,   .formats = list(n_patients = \"xx (xx.x%)\", sum_exposure = \"xx\"),   custom_label = NULL )  summarize_patients_exposure_in_cols(   lyt,   var,   na_str = NA_character_,   ...,   .stats = c(\"n_patients\", \"sum_exposure\"),   .labels = c(n_patients = \"Patients\", sum_exposure = \"Person time\"),   .indent_mods = NULL,   col_split = TRUE )  analyze_patients_exposure_in_cols(   lyt,   var = NULL,   ex_var = \"AVAL\",   col_split = TRUE,   add_total_level = FALSE,   .stats = c(\"n_patients\", \"sum_exposure\"),   .labels = c(n_patients = \"Patients\", sum_exposure = \"Person time\"),   .indent_mods = 0L,   ... )"},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"df (data.frame) data set containing analysis variables. ex_var (character) name variable within df containing exposure values. id (string) subject variable name. labelstr (character) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .stats (character) statistics select table. .N_col (integer) column-wise N (column count) full column analyzed typically passed rtables. custom_label (string NULL) provided labelstr empty used label. var (string) single variable name passed rtables requested statistics function. add_total_level (flag) adds \"total\" level others includes levels constitute split. custom label can set level via custom_label argument. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. lyt (layout) input layout analyses added . na_str (string) string used replace NA empty values output. ... additional arguments lower level functions. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. col_split (flag) whether columns split. Set FALSE required column split done already earlier layout pipe.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"s_count_patients_sum_exposure() returns named list statistics: n_patients: Number unique patients df. sum_exposure: Sum ex_var across patients df. a_count_patients_sum_exposure() returns formatted rtables::CellValue(). summarize_patients_exposure_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows, statistics s_count_patients_sum_exposure() arranged columns, table layout. analyze_patients_exposure_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted data rows, statistics s_count_patients_sum_exposure() arranged columns, table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"s_count_patients_sum_exposure(): Statistics function counts numbers patients sum exposure across patients. a_count_patients_sum_exposure(): Analysis function used afun rtables::analyze_colvars() within analyze_patients_exposure_in_cols() cfun rtables::summarize_row_groups() within summarize_patients_exposure_in_cols(). summarize_patients_exposure_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::split_cols_by_multivar() rtables::summarize_row_groups(). analyze_patients_exposure_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::split_cols_by_multivar() rtables::analyze_colvars().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"opposed summarize_patients_exposure_in_cols() generates content rows, analyze_patients_exposure_in_cols() generates data rows repeated multiple pages pagination used.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/summarize_patients_exposure_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counting Patients Summing Exposure Across All Patients in Columns — summarize_patients_exposure_in_cols","text":"","code":"set.seed(1) df <- data.frame(   USUBJID = c(paste(\"id\", seq(1, 12), sep = \"\")),   ARMCD = c(rep(\"ARM A\", 6), rep(\"ARM B\", 6)),   SEX = c(rep(\"Female\", 6), rep(\"Male\", 6)),   AVAL = as.numeric(sample(seq(1, 20), 12)),   stringsAsFactors = TRUE ) adsl <- data.frame(   USUBJID = c(paste(\"id\", seq(1, 12), sep = \"\")),   ARMCD = c(rep(\"ARM A\", 2), rep(\"ARM B\", 2)),   SEX = c(rep(\"Female\", 2), rep(\"Male\", 2)),   stringsAsFactors = TRUE )  a_count_patients_sum_exposure(   df = df,   var = \"SEX\",   .N_col = nrow(df),   .stats = \"n_patients\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #>   row_name formatted_cell indent_mod row_label #> 1   Female      6 (50.0%)          0    Female #> 2     Male      6 (50.0%)          0      Male  lyt <- basic_table() %>%   summarize_patients_exposure_in_cols(var = \"AVAL\", col_split = TRUE) result <- build_table(lyt, df = df, alt_counts_df = adsl) result #>                                       Patients     Person time #> —————————————————————————————————————————————————————————————— #> Total patients numbers/person time   12 (100.0%)       114      lyt2 <- basic_table() %>%   summarize_patients_exposure_in_cols(var = \"AVAL\", col_split = TRUE, .stats = \"sum_exposure\") result2 <- build_table(lyt2, df = df, alt_counts_df = adsl) result2 #>                                      Person time #> ———————————————————————————————————————————————— #> Total patients numbers/person time       114      lyt3 <- basic_table() %>%   split_cols_by(\"ARMCD\", split_fun = add_overall_level(\"Total\", first = FALSE)) %>%   summarize_patients_exposure_in_cols(var = \"AVAL\", col_split = TRUE) %>%   analyze_patients_exposure_in_cols(var = \"SEX\", col_split = FALSE) result3 <- build_table(lyt3, df = df, alt_counts_df = adsl) result3 #>                                               ARM A                      ARM B                       Total           #>                                       Patients    Person time    Patients    Person time    Patients     Person time #> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total patients numbers/person time   6 (100.0%)       46        6 (100.0%)       68        12 (100.0%)       114     #>   Female                             6 (100.0%)       46         0 (0.0%)         0         6 (50.0%)        46      #>   Male                                0 (0.0%)         0        6 (100.0%)       68         6 (50.0%)        68       lyt4 <- basic_table() %>%   split_cols_by(\"ARMCD\", split_fun = add_overall_level(\"Total\", first = FALSE)) %>%   summarize_patients_exposure_in_cols(     var = \"AVAL\", col_split = TRUE,     .stats = \"n_patients\", custom_label = \"some custom label\"   ) %>%   analyze_patients_exposure_in_cols(var = \"SEX\", col_split = FALSE, ex_var = \"AVAL\") result4 <- build_table(lyt4, df = df, alt_counts_df = adsl) result4 #>                       ARM A        ARM B         Total    #>                      Patients     Patients     Patients   #> ————————————————————————————————————————————————————————— #> some custom label   6 (100.0%)   6 (100.0%)   12 (100.0%) #>   Female            6 (100.0%)    0 (0.0%)     6 (50.0%)  #>   Male               0 (0.0%)    6 (100.0%)    6 (50.0%)   lyt5 <- basic_table() %>%   analyze_patients_exposure_in_cols(var = \"SEX\", col_split = TRUE, ex_var = \"AVAL\") result5 <- build_table(lyt5, df = df, alt_counts_df = adsl) result5 #>          Patients    Person time #> ———————————————————————————————— #> Female   6 (50.0%)       46      #> Male     6 (50.0%)       68       # Adding total levels and custom label lyt <- basic_table(   show_colcounts = TRUE ) %>%   analyze_patients_exposure_in_cols(     var = \"ARMCD\",     col_split = TRUE,     add_total_level = TRUE,     custom_label = \"TOTAL\"   ) %>%   append_topleft(c(\"\", \"Sex\"))  tbl <- build_table(lyt, df = df, alt_counts_df = adsl) tbl #>          Patients     Person time #> Sex       (N=12)        (N=12)    #> ————————————————————————————————— #> ARM A    6 (50.0%)        46      #> ARM B    6 (50.0%)        68      #> TOTAL   12 (100.0%)       114"},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"Tabulate estimated effects multiple continuous biomarker variables across population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"","code":"tabulate_survival_biomarkers(   df,   vars = c(\"n_tot\", \"n_tot_events\", \"median\", \"hr\", \"ci\", \"pval\"),   time_unit = NULL,   .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"df (data.frame) containing analysis variables, returned extract_survival_biomarkers(). vars (character) names statistics reported among: n_tot_events: Total number events per group. n_tot: Total number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"rtables table summarizing biomarker effects survival subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"functions create layout starting data frame contains required statistics. tables typically used input forest plots.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"tabulate_survival_biomarkers(): Table-creating function creates table summarizing biomarker effects survival subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"contrast tabulate_survival_subgroups() tabulation function start input layout lyt. internally table created combining multiple subtables.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/survival_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Biomarker Effects on Survival by Subgroup — survival_biomarkers_subgroups","text":"","code":"library(dplyr)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_biomarkers(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     biomarkers = c(\"BMRKR1\", \"AGE\"),     strata = \"STRATA1\",     covariates = \"SEX\",     subgroups = \"BMRKR2\"   ),   data = adtte_f ) df #>   biomarker              biomarker_label n_tot n_tot_events   median        hr #> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939 #> 2       AGE                          Age   200          141 753.5176 1.0106406 #> 3    BMRKR1 Continuous Level Biomarker 1    70           52 735.4722 0.9905065 #> 4       AGE                          Age    70           52 735.4722 1.0106279 #> 5    BMRKR1 Continuous Level Biomarker 1    68           42 858.9952 0.9623210 #> 6       AGE                          Age    68           42 858.9952 1.0360765 #> 7    BMRKR1 Continuous Level Biomarker 1    62           47 727.8043 1.0770946 #> 8       AGE                          Age    62           47 727.8043 1.0009890 #>         lcl      ucl conf_level      pval     pval_label     subgroup    var #> 1 0.9538978 1.050625       0.95 0.9646086 p-value (Wald) All Patients    ALL #> 2 0.9871004 1.034742       0.95 0.3787395 p-value (Wald) All Patients    ALL #> 3 0.9142220 1.073156       0.95 0.8155443 p-value (Wald)          LOW BMRKR2 #> 4 0.9621192 1.061582       0.95 0.6735773 p-value (Wald)          LOW BMRKR2 #> 5 0.8708694 1.063376       0.95 0.4509368 p-value (Wald)       MEDIUM BMRKR2 #> 6 0.9727439 1.103532       0.95 0.2707796 p-value (Wald)       MEDIUM BMRKR2 #> 7 0.9756250 1.189118       0.95 0.1412524 p-value (Wald)         HIGH BMRKR2 #> 8 0.9678535 1.035259       0.95 0.9541048 p-value (Wald)         HIGH BMRKR2 #>                      var_label row_type #> 1                 All Patients  content #> 2                 All Patients  content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis  ## Table with default columns. tabulate_survival_biomarkers(df) #>                                  Total n   Total Events   Median   Hazard Ratio   95% Wald CI    p-value (Wald) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age                                                                                                             #>   All Patients                     200         141        753.5        1.01       (0.99, 1.03)       0.3787     #>   Continuous Level Biomarker 2                                                                                  #>     LOW                            70           52        735.5        1.01       (0.96, 1.06)       0.6736     #>     MEDIUM                         68           42        859.0        1.04       (0.97, 1.10)       0.2708     #>     HIGH                           62           47        727.8        1.00       (0.97, 1.04)       0.9541     #> Continuous Level Biomarker 1                                                                                    #>   All Patients                     200         141        753.5        1.00       (0.95, 1.05)       0.9646     #>   Continuous Level Biomarker 2                                                                                  #>     LOW                            70           52        735.5        0.99       (0.91, 1.07)       0.8155     #>     MEDIUM                         68           42        859.0        0.96       (0.87, 1.06)       0.4509     #>     HIGH                           62           47        727.8        1.08       (0.98, 1.19)       0.1413      ## Table with a manually chosen set of columns: leave out \"pval\", reorder. tab <- tabulate_survival_biomarkers(   df = df,   vars = c(\"n_tot_events\", \"ci\", \"n_tot\", \"median\", \"hr\"),   time_unit = as.character(adtte_f$AVALU[1]) )  ## Finally produce the forest plot. # \\donttest{ g_forest(tab, xlim = c(0.8, 1.2))  # }"},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise CoxPH model — survival_coxph_pairwise","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"Summarize p-value, HR CIs stratified unstratified CoxPH model.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"","code":"s_coxph_pairwise(   df,   .ref_group,   .in_ref_col,   .var,   is_event,   strat = NULL,   control = control_coxph() )  a_coxph_pairwise(   df,   .ref_group,   .in_ref_col,   .var,   is_event,   strat = NULL,   control = control_coxph() )  coxph_pairwise(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = \"CoxPH\",   show_labels = \"visible\",   table_names = vars,   .stats = c(\"pvalue\", \"hr\", \"hr_ci\"),   .formats = NULL,   .labels = NULL,   .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"df (data.frame) data set containing analysis variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. .var (string) single variable name passed rtables requested statistics function. is_event (logical)TRUE event, FALSE time event censored. strat (character NULL) variable names indicating stratification factors. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"s_coxph_pairwise() returns statistics: pvalue: p-value test HR = 1. hr: Hazard ratio. hr_ci: Confidence interval hazard ratio. n_tot: Total number observations. n_tot_events: Total number events. a_coxph_pairwise() returns corresponding list formatted rtables::CellValue(). coxph_pairwise() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_coxph_pairwise() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"s_coxph_pairwise(): Statistics function analyzes HR, CIs HR p-value coxph model. a_coxph_pairwise(): Formatted analysis function used afun coxph_pairwise(). coxph_pairwise(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_coxph_pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise CoxPH model — survival_coxph_pairwise","text":"","code":"library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(is_event = CNSR == 0) df <- adtte_f %>%   filter(ARMCD == \"ARM A\") df_ref_group <- adtte_f %>%   filter(ARMCD == \"ARM B\")  basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   coxph_pairwise(     vars = \"AVAL\",     is_event = \"is_event\",     var_labels = \"Unstratified Analysis\"   ) %>%   build_table(df = adtte_f) #>                         ARM A       ARM B          ARM C     #>                         (N=69)      (N=73)         (N=58)    #> ———————————————————————————————————————————————————————————— #> Unstratified Analysis                                        #>   p-value (log-rank)                0.0905         0.0086    #>   Hazard Ratio                       1.41           1.81     #>   95% CI                         (0.95, 2.09)   (1.16, 2.84)  basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   coxph_pairwise(     vars = \"AVAL\",     is_event = \"is_event\",     var_labels = \"Stratified Analysis\",     strat = \"SEX\",     control = control_coxph(pval_method = \"wald\")   ) %>%   build_table(df = adtte_f) #>                       ARM A       ARM B          ARM C     #>                       (N=69)      (N=73)         (N=58)    #> —————————————————————————————————————————————————————————— #> Stratified Analysis                                        #>   p-value (wald)                  0.0784         0.0066    #>   Hazard Ratio                     1.44           1.89     #>   95% CI                       (0.96, 2.15)   (1.19, 2.98)"},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"Tabulate statistics median survival time hazard ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"","code":"a_survival_subgroups(   .formats = list(n = \"xx\", n_events = \"xx\", n_tot_events = \"xx\", median = \"xx.x\", n_tot     = \"xx\", hr = list(format_extreme_values(2L)), ci =     list(format_extreme_values_ci(2L)), pval = \"x.xxxx | (<0.0001)\") )  tabulate_survival_subgroups(   lyt,   df,   vars = c(\"n_tot_events\", \"n_events\", \"median\", \"hr\", \"ci\"),   time_unit = NULL,   na_str = NA_character_ )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":".formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. lyt (layout) input layout analyses added . df (list) data frames containing analysis variables. List created using extract_survival_subgroups(). vars (character) name statistics reported among: n_tot_events: Total number events per group. n_events: Number events per group. n_tot: Total number observations per group. n: Number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"a_survival_subgroups() returns corresponding list formatted rtables::CellValue(). rtables table summarizing survival subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"functions create layout starting data frame contains required statistics. Tables typically used part forest plot.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"a_survival_subgroups(): Formatted analysis function used afun tabulate_survival_subgroups(). tabulate_survival_subgroups(): Table-creating function creates table summarizing survival subgroup. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/survival_duration_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate Survival Duration by Subgroup — survival_duration_subgroups","text":"","code":"library(dplyr) library(forcats)  adtte <- tern_ex_adtte  # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte)  adtte_f <- adtte %>%   filter(     PARAMCD == \"OS\",     ARM %in% c(\"B: Placebo\", \"A: Drug X\"),     SEX %in% c(\"M\", \"F\")   ) %>%   mutate(     # Reorder levels of ARM to display reference arm before treatment arm.     ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")),     SEX = droplevels(SEX),     AVALU = as.character(AVALU),     is_event = CNSR == 0   ) labels <- c(   \"ARM\" = adtte_labels[[\"ARM\"]],   \"SEX\" = adtte_labels[[\"SEX\"]],   \"AVALU\" = adtte_labels[[\"AVALU\"]],   \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- labels  df <- extract_survival_subgroups(   variables = list(     tte = \"AVAL\",     is_event = \"is_event\",     arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")   ),   data = adtte_f ) df #> $survtime #>           arm  n n_events    median     subgroup    var #> 1  B: Placebo 73       57  727.8043 All Patients    ALL #> 2   A: Drug X 69       44  974.6402 All Patients    ALL #> 3  B: Placebo 40       31  599.1772            F    SEX #> 4   A: Drug X 38       24 1016.2982            F    SEX #> 5  B: Placebo 33       26  888.4916            M    SEX #> 6   A: Drug X 31       20  974.6402            M    SEX #> 7  B: Placebo 24       21  735.4722          LOW BMRKR2 #> 8   A: Drug X 26       15  974.6402          LOW BMRKR2 #> 9  B: Placebo 23       14  731.8352       MEDIUM BMRKR2 #> 10  A: Drug X 26       17  964.2197       MEDIUM BMRKR2 #> 11 B: Placebo 26       22  654.8245         HIGH BMRKR2 #> 12  A: Drug X 17       12 1016.2982         HIGH BMRKR2 #>                       var_label row_type #> 1                  All Patients  content #> 2                  All Patients  content #> 3                           Sex analysis #> 4                           Sex analysis #> 5                           Sex analysis #> 6                           Sex analysis #> 7  Continuous Level Biomarker 2 analysis #> 8  Continuous Level Biomarker 2 analysis #> 9  Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #>  #> $hr #>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval #> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511 #> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759 #> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028 #> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030 #> 5        49           31 0.7651261 0.3641277 1.6077269       0.95 0.47860004 #> 6        43           34 0.6662356 0.3257413 1.3626456       0.95 0.26285846 #>           pval_label     subgroup    var                    var_label row_type #> 1 p-value (log-rank) All Patients    ALL                 All Patients  content #> 2 p-value (log-rank)            F    SEX                          Sex analysis #> 3 p-value (log-rank)            M    SEX                          Sex analysis #> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis #>   ## Table with default columns. basic_table() %>%   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1]) #>                                                     B: Placebo               A: Drug X                                      #> Baseline Risk Factors          Total Events   Events   Median (DAYS)   Events   Median (DAYS)   Hazard Ratio   95% Wald CI  #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                       101          57         727.8         44         974.6           0.71       (0.48, 1.06) #> Sex                                                                                                                         #>   F                                 55          31         599.2         24        1016.3           0.56       (0.32, 0.96) #>   M                                 46          26         888.5         20         974.6           0.91       (0.50, 1.65) #> Continuous Level Biomarker 2                                                                                                #>   LOW                               36          21         735.5         15         974.6           0.76       (0.39, 1.51) #>   MEDIUM                            31          14         731.8         17         964.2           0.77       (0.36, 1.61) #>   HIGH                              34          22         654.8         12        1016.3           0.67       (0.33, 1.36)  ## Table with a manually chosen set of columns: adding \"pval\". basic_table() %>%   tabulate_survival_subgroups(     df = df,     vars = c(\"n_tot_events\", \"n_events\", \"median\", \"hr\", \"ci\", \"pval\"),     time_unit = adtte_f$AVALU[1]   ) #>                                                     B: Placebo               A: Drug X                                                           #> Baseline Risk Factors          Total Events   Events   Median (DAYS)   Events   Median (DAYS)   Hazard Ratio   95% Wald CI    p-value (log-rank) #> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients                       101          57         727.8         44         974.6           0.71       (0.48, 1.06)         0.0905       #> Sex                                                                                                                                              #>   F                                 55          31         599.2         24        1016.3           0.56       (0.32, 0.96)         0.0341       #>   M                                 46          26         888.5         20         974.6           0.91       (0.50, 1.65)         0.7558       #> Continuous Level Biomarker 2                                                                                                                     #>   LOW                               36          21         735.5         15         974.6           0.76       (0.39, 1.51)         0.4324       #>   MEDIUM                            31          14         731.8         17         964.2           0.77       (0.36, 1.61)         0.4786       #>   HIGH                              34          22         654.8         12        1016.3           0.67       (0.33, 1.36)         0.2629"},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival Time Analysis — survival_time","title":"Survival Time Analysis — survival_time","text":"Summarize median survival time CIs, percentiles survival times, survival time range censored/event patients.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival Time Analysis — survival_time","text":"","code":"s_surv_time(df, .var, is_event, control = control_surv_time())  a_surv_time(df, .var, is_event, control = control_surv_time())  surv_time(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   var_labels = \"Time to Event\",   table_names = vars,   .stats = c(\"median\", \"median_ci\", \"quantiles\", \"range_censor\", \"range_event\"),   .formats = NULL,   .labels = NULL,   .indent_mods = c(median = 0L, median_ci = 1L, quantiles = 0L, range_censor = 0L,     range_event = 0L, range = 0L) )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Survival Time Analysis — survival_time","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. is_event (logical)TRUE event, FALSE time event censored. control (list) parameters comparison details, specified using helper function control_surv_time(). possible parameter options : conf_level (proportion) confidence level interval survival time. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" supported. quantiles (numeric) vector length two specify quantiles survival time. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) character label. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Survival Time Analysis — survival_time","text":"s_surv_time() returns statistics: median: Median survival time. median_ci: Confidence interval median time. quantiles: Survival time two specified quantiles. range_censor: Survival time range censored observations. range_event: Survival time range observations events. range: Survival time range observations. a_surv_time() returns corresponding list formatted rtables::CellValue(). surv_time() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_surv_time() table layout.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Survival Time Analysis — survival_time","text":"s_surv_time(): Statistics function analyzes survival times. a_surv_time(): Formatted analysis function used afun surv_time(). surv_time(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Survival Time Analysis — survival_time","text":"","code":"library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVAL = day2month(AVAL),     is_event = CNSR == 0   ) df <- adtte_f %>% filter(ARMCD == \"ARM A\")  basic_table() %>%   split_cols_by(var = \"ARMCD\") %>%   add_colcounts() %>%   surv_time(     vars = \"AVAL\",     var_labels = \"Survival Time (Months)\",     is_event = \"is_event\",     control = control_surv_time(conf_level = 0.9, conf_type = \"log-log\")   ) %>%   build_table(df = adtte_f) #>                             ARM A          ARM B          ARM C     #>                             (N=69)         (N=73)         (N=58)    #> ——————————————————————————————————————————————————————————————————— #> Survival Time (Months)                                              #>   Median                     32.0           23.9           20.8     #>     90% CI               (22.6, 46.5)   (18.3, 29.2)   (12.9, 25.9) #>   25% and 75%-ile         17.4, 65.3     9.8, 42.0      7.3, 37.1   #>   Range (censored)       0.8 to 63.5    6.2 to 78.9    3.4 to 52.4  #>   Range (event)          0.3 to 155.5   0.1 to 154.1   0.6 to 80.7"},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival Time Point Analysis — survival_timepoint","title":"Survival Time Point Analysis — survival_timepoint","text":"Summarize patients' survival rate difference survival rates groups time point.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival Time Point Analysis — survival_timepoint","text":"","code":"s_surv_timepoint(   df,   .var,   time_point,   is_event,   control = control_surv_timepoint() )  a_surv_timepoint(   df,   .var,   time_point,   is_event,   control = control_surv_timepoint() )  s_surv_timepoint_diff(   df,   .var,   .ref_group,   .in_ref_col,   time_point,   control = control_surv_timepoint(),   ... )  a_surv_timepoint_diff(   df,   .var,   .ref_group,   .in_ref_col,   time_point,   control = control_surv_timepoint(),   ... )  surv_timepoint(   lyt,   vars,   na_str = NA_character_,   nested = TRUE,   ...,   table_names_suffix = \"\",   var_labels = \"Time\",   show_labels = \"visible\",   method = c(\"surv\", \"surv_diff\", \"both\"),   .stats = c(\"pt_at_risk\", \"event_free_rate\", \"rate_ci\", \"rate_diff\", \"rate_diff_ci\",     \"ztest_pval\"),   .formats = NULL,   .labels = NULL,   .indent_mods = if (method == \"both\") {      c(rate_diff = 1L, rate_diff_ci = 2L,     ztest_pval = 2L)  } else {      c(rate_diff_ci = 1L, ztest_pval = 1L)  } )"},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Survival Time Point Analysis — survival_timepoint","text":"df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. time_point (number) survival time point interest. is_event (logical)TRUE event, FALSE time event censored. control (list) parameters comparison details, specified using helper function control_surv_timepoint(). possible parameter options : conf_level (proportion) confidence level interval survival rate. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported. time_point (number) survival time point interest. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (logical)TRUE working reference level, FALSE otherwise. ... additional arguments lower level functions. lyt (layout) input layout analyses added . vars (character) variable names primary analysis variable iterated . na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. table_names_suffix (string) optional suffix table_names used rtables avoid warnings duplicate table names. var_labels (character) character label. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". method (string) either surv (survival estimations), surv_diff (difference survival control) . .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Survival Time Point Analysis — survival_timepoint","text":"s_surv_timepoint() returns statistics: pt_at_risk: Patients remaining risk. event_free_rate: Event-free rate (%). rate_se: Standard error event free rate. rate_ci: Confidence interval event free rate. a_surv_timepoint() returns corresponding list formatted rtables::CellValue(). s_surv_timepoint_diff() returns statistics: rate_diff: Event-free rate difference two groups. rate_diff_ci: Confidence interval difference. ztest_pval: p-value test difference 0. a_surv_timepoint_diff() returns corresponding list formatted rtables::CellValue(). surv_timepoint() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_surv_timepoint() /s_surv_timepoint_diff() table layout depending value method.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Survival Time Point Analysis — survival_timepoint","text":"s_surv_timepoint(): Statistics function analyzes survival rate. a_surv_timepoint(): Formatted analysis function used afun surv_timepoint() method = \"surv\". s_surv_timepoint_diff(): Statistics function analyzes difference two survival rates. a_surv_timepoint_diff(): Formatted analysis function used afun surv_timepoint() method = \"surv_diff\". surv_timepoint(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze().","code":""},{"path":"https://insightsengineering.github.io/tern/reference/survival_timepoint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Survival Time Point Analysis — survival_timepoint","text":"","code":"library(dplyr)  adtte_f <- tern_ex_adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(     AVAL = day2month(AVAL),     is_event = CNSR == 0   ) df <- adtte_f %>%   filter(ARMCD == \"ARM A\")  df_ref_group <- adtte_f %>%   filter(ARMCD == \"ARM B\")  # Survival at given time points. basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   surv_timepoint(     vars = \"AVAL\",     var_labels = \"Months\",     is_event = \"is_event\",     time_point = 7   ) %>%   build_table(df = adtte_f) #>                                    ARM A            ARM B            ARM C      #>                                    (N=69)           (N=73)           (N=58)     #> ——————————————————————————————————————————————————————————————————————————————— #> 7 Months                                                                        #>   Patients remaining at risk         54               57               42       #>   Event Free Rate (%)              84.89            79.43            75.50      #>   95% CI                       (76.24, 93.53)   (70.15, 88.71)   (64.33, 86.67)  # Difference in survival at given time points. basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   surv_timepoint(     vars = \"AVAL\",     var_labels = \"Months\",     is_event = \"is_event\",     time_point = 9,     method = \"surv_diff\",     .indent_mods = c(\"rate_diff\" = 0L, \"rate_diff_ci\" = 2L, \"ztest_pval\" = 2L)   ) %>%   build_table(df = adtte_f) #>                                   ARM A        ARM B            ARM C      #>                                   (N=69)       (N=73)           (N=58)     #> —————————————————————————————————————————————————————————————————————————— #> 9 Months                                                                   #>   Difference in Event Free Rate                -9.64            -13.03     #>       95% CI                               (-22.80, 3.52)   (-27.59, 1.53) #>       p-value (Z-test)                         0.1511           0.0794      # Survival and difference in survival at given time points. basic_table() %>%   split_cols_by(var = \"ARMCD\", ref_group = \"ARM A\") %>%   add_colcounts() %>%   surv_timepoint(     vars = \"AVAL\",     var_labels = \"Months\",     is_event = \"is_event\",     time_point = 9,     method = \"both\"   ) %>%   build_table(df = adtte_f) #>                                       ARM A            ARM B            ARM C      #>                                       (N=69)           (N=73)           (N=58)     #> —————————————————————————————————————————————————————————————————————————————————— #> 9 Months                                                                           #>   Patients remaining at risk            53               53               39       #>   Event Free Rate (%)                 84.89            75.25            71.86      #>   95% CI                          (76.24, 93.53)   (65.32, 85.17)   (60.14, 83.57) #>   Difference in Event Free Rate                        -9.64            -13.03     #>     95% CI                                         (-22.80, 3.52)   (-27.59, 1.53) #>     p-value (Z-test)                                   0.1511           0.0794"},{"path":"https://insightsengineering.github.io/tern/reference/tern-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tern Package — tern-package","title":"tern Package — tern-package","text":"Package create tables, listings graphs analyze clinical trials data.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tern-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tern Package — tern-package","text":"Maintainer: Joe Zhu joe.zhu@roche.com Authors: Daniel Sabanés Bové daniel.sabanes_bove@roche.com Jana Stoilova jana.stoilova@roche.com Heng Wang wang.heng@gene.com Francois Collin Adrian Waddell adrian.waddell@gene.com Pawel Rucki pawel.rucki@roche.com Chendi Liao chendi.liao@roche.com Jennifer Li li.jing@gene.com contributors: F. Hoffmann-La Roche AG [copyright holder, funder]","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom Tidy Method for Binomial GLM Results — tidy.glm","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"Helper method (broom::tidy()) prepare data frame glm object binomial family.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"","code":"# S3 method for glm tidy(x, conf_level = 0.95, at = NULL, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"x logistic regression model fitted stats::glm() \"binomial\" family. conf_level (proportion) confidence level interval. (NULL numeric) optional values interaction variable. Otherwise median used. ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"data.frame containing tidied model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy.glm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Tidy Method for Binomial GLM Results — tidy.glm","text":"","code":"library(dplyr) library(broom)  adrs_f <- tern_ex_adrs %>%   filter(PARAMCD == \"BESRSPI\") %>%   filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>%   mutate(     Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0),     RACE = factor(RACE),     SEX = factor(SEX)   ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\")   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic(   data = adrs_f,   variables = list(     response = \"Response\",     arm = \"ARMCD\",     covariates = c(\"AGE\", \"RACE\"),     interaction = \"AGE\"   ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred  df <- tidy(mod1, conf_level = 0.99) df2 <- tidy(mod2, conf_level = 0.99)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom Tidy Method for STEP Results — tidy.step","title":"Custom Tidy Method for STEP Results — tidy.step","text":"Tidy STEP results tibble format ready plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom Tidy Method for STEP Results — tidy.step","text":"","code":"# S3 method for step tidy(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom Tidy Method for STEP Results — tidy.step","text":"x (step matrix) results fit_survival_step(). ... used .","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom Tidy Method for STEP Results — tidy.step","text":"tibble one row per STEP subgroup. estimates CIs HR scale, respectively. Additional attributes carry metadata also used plotting.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy.step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Tidy Method for STEP Results — tidy.step","text":"","code":"library(survival) lung$sex <- factor(lung$sex) vars <- list(   time = \"time\",   event = \"status\",   arm = \"sex\",   biomarker = \"age\" ) step_matrix <- fit_survival_step(   variables = vars,   data = lung,   control = c(control_coxph(), control_step(num_points = 10, degree = 2)) ) broom::tidy(step_matrix) #> # A tibble: 10 × 12 #>    `Percentile Center` `Percentile Lower` `Percentile Upper` `Interval Center` #>  *               <dbl>              <dbl>              <dbl>             <dbl> #>  1              0.0909             0                   0.341              50   #>  2              0.182              0                   0.432              54   #>  3              0.273              0.0227              0.523              57   #>  4              0.364              0.114               0.614              59.5 #>  5              0.455              0.205               0.705              62   #>  6              0.545              0.295               0.795              64   #>  7              0.636              0.386               0.886              67   #>  8              0.727              0.477               0.977              69   #>  9              0.818              0.568               1                  71   #> 10              0.909              0.659               1                  74   #> # ℹ 8 more variables: `Interval Lower` <dbl>, `Interval Upper` <dbl>, n <dbl>, #> #   events <dbl>, `Hazard Ratio` <dbl>, se <dbl>, ci_lower <dbl>, #> #   ci_upper <dbl>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"","code":"# S3 method for summary.coxph tidy(x, ...)  # S3 method for coxreg.univar tidy(x, ...)  # S3 method for coxreg.multivar tidy(x, ...)"},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"x (list) Result Cox regression model fitted fit_coxreg_univar() (univariate models) fit_coxreg_multivar() (multivariate models). ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"tidy() returns: summary.coxph objects,  data.frame columns: Pr(>|z|), exp(coef), exp(-coef), lower .95, upper .95, level, n. coxreg.univar objects, data.frame columns: effect, term, term_label, level, n, hr, lcl, ucl, pval, ci. coxreg.multivar objects, data.frame columns: term, pval, term_label, hr, lcl, ucl, level, ci.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"tidy(summary.coxph): Custom tidy method survival::coxph() summary results. Tidy survival::coxph() results data.frame extract model results. tidy(coxreg.univar): Custom tidy method univariate Cox regression. Tidy result Cox regression model fitted fit_coxreg_univar(). tidy(coxreg.multivar): Custom tidy method multivariate Cox regression. Tidy result Cox regression model fitted fit_coxreg_multivar().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/tidy_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Tidy Methods for Cox Regression — tidy_coxreg","text":"","code":"library(survival) library(broom)  set.seed(1, kind = \"Mersenne-Twister\")  dta_bladder <- with(   data = bladder[bladder$enum < 5, ],   data.frame(     time = stop,     status = event,     armcd = as.factor(rx),     covar1 = as.factor(enum),     covar2 = factor(       sample(as.factor(enum)),       levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\")     )   ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)  formula <- \"survival::Surv(time, status) ~ armcd + covar1\" msum <- summary(coxph(stats::as.formula(formula), data = dta_bladder)) tidy(msum) #>       Pr(>|z|) exp(coef) exp(-coef)  lower .95 upper .95   level   n #> 1 1.287954e-02 0.6110123   1.636628 0.41442417 0.9008549  armcd2 340 #> 2 6.407916e-04 0.4460731   2.241785 0.28061816 0.7090818 covar12 340 #> 3 5.272933e-06 0.3075864   3.251119 0.18517346 0.5109230 covar13 340 #> 4 2.125359e-08 0.1808795   5.528541 0.09943722 0.3290258 covar14 340  ## Cox regression: arm + 1 covariate. mod1 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = \"covar1\"   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91) )  ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates. mod2 <- fit_coxreg_univar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder,   control = control_coxreg(conf_level = 0.91, interaction = TRUE) )  tidy(mod1) #>            effect   term        term_label level   n        hr       lcl #> ref    Treatment:  armcd  2 vs control (1)     2 340 0.6386426 0.4557586 #> covar1 Covariate: covar1 A Covariate Label     2 340  0.607037 0.4324571 #>              ucl       pval                   ci #> ref    0.8949131 0.02423805 0.4557586, 0.8949131 #> covar1 0.8520935 0.01257339 0.4324571, 0.8520935 tidy(mod2) #>                           effect   term        term_label level   n        hr #> ref                   Treatment:  armcd  2 vs control (1)     2 340 0.6386426 #> covar1.1              Covariate: covar1 A Covariate Label       340           #> covar1.armcd2/covar11 Covariate: covar1                 1     1     0.6284569 #> covar1.armcd2/covar12 Covariate: covar1                 2     2     0.5806499 #> covar1.armcd2/covar13 Covariate: covar1                 3     3     0.5486103 #> covar1.armcd2/covar14 Covariate: covar1                 4     4     0.6910725 #> covar2.1              Covariate: covar2         Sex (F/M)       340           #> covar2.armcd2/covar2F Covariate: covar2                 F     F     0.6678243 #> covar2.armcd2/covar2M Covariate: covar2                 M     M     0.5954021 #>                             lcl       ucl       pval pval_inter #> ref                   0.4557586 0.8949131 0.02423805            #> covar1.1                     NA        NA             0.9883021 #> covar1.armcd2/covar11 0.3450471 1.1446499                       #> covar1.armcd2/covar12 0.2684726 1.2558239                       #> covar1.armcd2/covar13 0.2226814 1.3515868                       #> covar1.armcd2/covar14 0.2308006 2.0692373                       #> covar2.1                     NA        NA             0.7759013 #> covar2.armcd2/covar2F 0.3649842 1.2219413                       #> covar2.armcd2/covar2M 0.3572772 0.9922368                       #>                                         ci #> ref                   0.4557586, 0.8949131 #> covar1.1                                   #> covar1.armcd2/covar11 0.3450471, 1.1446499 #> covar1.armcd2/covar12 0.2684726, 1.2558239 #> covar1.armcd2/covar13 0.2226814, 1.3515868 #> covar1.armcd2/covar14 0.2308006, 2.0692373 #> covar2.1                                   #> covar2.armcd2/covar2F 0.3649842, 1.2219413 #> covar2.armcd2/covar2M 0.3572772, 0.9922368  multivar_model <- fit_coxreg_multivar(   variables = list(     time = \"time\", event = \"status\", arm = \"armcd\",     covariates = c(\"covar1\", \"covar2\")   ),   data = dta_bladder ) broom::tidy(multivar_model) #>                       term         pval                        term_label #> armcd.1              armcd                            ARM (reference = 1) #> armcd.2                ARM   0.01232761                                 2 #> covar1.1            covar1 7.209956e-09 A Covariate Label (reference = 1) #> covar1.2 A Covariate Label  0.001120332                                 2 #> covar1.3 A Covariate Label 6.293725e-06                                 3 #> covar1.4 A Covariate Label 3.013875e-08                                 4 #> covar2.1            covar2                      Sex (F/M) (reference = F) #> covar2.2         Sex (F/M)    0.1910521                                 M #>                 hr        lcl       ucl level                     ci #> armcd.1                    NA        NA  <NA>                        #> armcd.2  0.6062777 0.40970194 0.8971710     2   0.4097019, 0.8971710 #> covar1.1                   NA        NA  <NA>                        #> covar1.2 0.4564763 0.28481052 0.7316115     2   0.2848105, 0.7316115 #> covar1.3 0.3069612 0.18386073 0.5124813     3   0.1838607, 0.5124813 #> covar1.4 0.1817017 0.09939435 0.3321668     4 0.09939435, 0.33216684 #> covar2.1                   NA        NA  <NA>                        #> covar2.2  1.289373 0.88087820 1.8873019     M   0.8808782, 1.8873019"},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Replicate Entries of a Vector if Required — to_n","title":"Replicate Entries of a Vector if Required — to_n","text":"Replicate entries vector required.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replicate Entries of a Vector if Required — to_n","text":"","code":"to_n(x, n)"},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replicate Entries of a Vector if Required — to_n","text":"x (numeric) vector numbers want analyze. n (count) many entries need.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replicate Entries of a Vector if Required — to_n","text":"x required length already NULL, otherwise scalar replicated version n entries.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_n.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Replicate Entries of a Vector if Required — to_n","text":"function fail x length n /scalar.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Table into Matrix of Strings — to_string_matrix","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"Helper function use mostly within tests. with_spacesparameter allows test content also indentation table structure. print_txt_to_copy instead facilitate testing development returning well formatted text needs copied pasted expected output.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"","code":"to_string_matrix(x, with_spaces = FALSE, print_txt_to_copy = FALSE)"},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"x rtables table. with_spaces tested table keep indentation relevant spaces? print_txt_to_copy Utility way copy input table directly expected variable instead copying manually.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/to_string_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Table into Matrix of Strings — to_string_matrix","text":"matrix strings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":null,"dir":"Reference","previous_headings":"","what":"tryCatch around car::Anova — try_car_anova","title":"tryCatch around car::Anova — try_car_anova","text":"Captures warnings executing car::Anova.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"tryCatch around car::Anova — try_car_anova","text":"","code":"try_car_anova(mod, test.statistic)"},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"tryCatch around car::Anova — try_car_anova","text":"mod lm, aov, glm, multinom, polr     mlm, coxph, coxme, lme, mer, merMod, svyglm, svycoxph,     rlm, suitable model object. test.statistic generalized linear model, whether calculate     \"LR\" (likelihood-ratio), \"Wald\", \"F\" tests; Cox     Cox mixed-effects model, whether calculate \"LR\" (partial-likelihood ratio)     \"Wald\" tests; default case linear mixed models fit     lmer, whether calculate Wald \"Chisq\" Kenward-Roger     \"F\" tests Satterthwaite degrees freedom (warning: KR F-tests     can time-consuming).     multivariate linear model, multivariate test statistic compute --- one     \"Pillai\", \"Wilks\", \"Hotelling-Lawley\", \"Roy\",     \"Pillai\" default. summary method Anova.mlm     objects permits specification one multivariate     test statistic, default report four.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"tryCatch around car::Anova — try_car_anova","text":"list item aov result model error_text captured warnings.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/try_car_anova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"tryCatch around car::Anova — try_car_anova","text":"","code":"# `car::Anova` on cox regression model including strata and expected # a likelihood ratio test triggers a warning as only `Wald` method is # accepted.  library(survival)  mod <- coxph(   formula = Surv(time = futime, event = fustat) ~ factor(rx) + strata(ecog.ps),   data = ovarian )"},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Ungroup Non-Numeric Statistics — ungroup_stats","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"Ungroups grouped non-numeric statistics within input vectors .formats, .labels, .indent_mods.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"","code":"ungroup_stats(x, .formats, .labels, .indent_mods)"},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"x (named list numeric) list numeric statistics containing statistics ungroup. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/ungroup_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ungroup Non-Numeric Statistics — ungroup_stats","text":"list modified elements x, .formats, .labels, .indent_mods.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Univariate Formula Special Term — univariate","title":"Univariate Formula Special Term — univariate","text":"special term univariate indicate model fitted individually every variable included univariate.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Univariate Formula Special Term — univariate","text":"","code":"univariate(x)"},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Univariate Formula Special Term — univariate","text":"x vector variable name separated commas.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Univariate Formula Special Term — univariate","text":"used within model formula, produces univariate models variable provided.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/univariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Univariate Formula Special Term — univariate","text":"provided alongside pairwise specification, model y ~ ARM + univariate(SEX, AGE, RACE) lead study comparison models y ~ ARM y ~ ARM + SEX y ~ ARM + AGE y ~ ARM + RACE","code":""},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Blank for Missing Input — unlist_and_blank_na","title":"Blank for Missing Input — unlist_and_blank_na","text":"Helper function use tabulating model results.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blank for Missing Input — unlist_and_blank_na","text":"","code":"unlist_and_blank_na(x)"},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blank for Missing Input — unlist_and_blank_na","text":"x (vector) input cell.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/unlist_and_blank_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Blank for Missing Input — unlist_and_blank_na","text":"empty character vector entries x missing (NA), otherwise unlisted version x.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"function wraps iteration procedure allows estimate weights proportional strata. assumes minimize weighted squared length confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"","code":"update_weights_strat_wilson(   vars,   strata_qnorm,   initial_weights,   n_per_strata,   max_iterations = 50,   conf_level = 0.95,   tol = 0.001 )"},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"vars (numeric) normalized proportions strata. strata_qnorm (numeric) initial estimation identical weights quantiles. initial_weights (numeric) initial weights used calculate strata_qnorm. can optimized future need estimate better initial weights. n_per_strata (numeric) number elements strata. max_iterations (count) maximum number iterations tried. Convergence always checked. conf_level (proportion) confidence level interval. tol (number) tolerance threshold convergence.","code":""},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"list 3 elements: n_it, weights, diff_v.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/reference/update_weights_strat_wilson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper Function for the Estimation of Weights for prop_strat_wilson — update_weights_strat_wilson","text":"","code":"vs <- c(0.011, 0.013, 0.012, 0.014, 0.017, 0.018) sq <- 0.674 ws <- rep(1 / length(vs), length(vs)) ns <- c(22, 18, 17, 17, 14, 12)  update_weights_strat_wilson(vs, sq, ws, ns, 100, 0.95, 0.001) #> $n_it #> [1] 3 #>  #> $weights #> [1] 0.2067191 0.1757727 0.1896962 0.1636346 0.1357615 0.1284160 #>  #> $diff_v #> [1] 1.458717e-01 1.497223e-03 1.442189e-06 #>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-9-2","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.9.2","text":"Added na_str argument analyze & summarize_row_groups wrapper functions count_abnormal, count_abnormal_by_baseline, count_abnormal_by_marked, count_abnormal_by_worst_grade, count_abnormal_lab_worsen_by_baseline, count_cumulative, count_missed_doses, count_occurrences, count_occurrences_by_grade, summarize_occurrences_by_grade, summarize_patients_events_in_cols, count_patients_with_event, count_patients_with_flags, count_values, estimate_multinomial_response, estimate_proportion, h_tab_one_biomarker, estimate_incidence_rate, logistic_summary_by_flag, estimate_odds_ratio, estimate_proportion_diff, test_proportion_diff, summarize_ancova, summarize_change, summarize_glm_count, summarize_num_patients, analyze_num_patients, summarize_patients_exposure_in_cols, coxph_pairwise, tabulate_survival_subgroups, surv_time, surv_timepoint.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-9-2","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.9.2","text":"Added formatting function format_count_fraction_lt10 formatting count_fraction special consideration count less 10. Updated s_summary.logical output count_fraction denominator zero display NA instead 0 tables. Updated analyze_vars_in_cols allow character input indicate whether nominal time point post-dose pre-dose applying 1/3 imputation rule.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-9-2","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.9.2","text":"Fixed bug g_km causing error converting certain annotation width units.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-9-2","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.9.2","text":"Began deprecation na_level argument s_count_abnormal_by_baseline, a_summary, analyze_vars, analyze_vars_in_cols, compare_vars, h_map_for_count_abnormal, h_stack_by_baskets, summarize_colvars, a_coxreg, summarize_coxreg replaced na_str argument.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-9-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.9.1","text":"Added imputation_rule function apply imputation rule data. Added new format function format_sigfig allow numeric value formatting specified number significant figures. Added vectors containing default statistic formats labels tern_default_formats tern_default_labels, respectively. Added function get_stats return methods given statistical method groups. Added function get_formats_from_stats return formats get_labels_from_stats return labels given set statistics. Added \"auto\" option .formats. uses format_auto determine automatically number digits. Added title argument h_grob_tbl_at_risk annot_at_risk_title argument g_km h_km_layout allows user add “Patients Risk” title Kaplan-Meier risk annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-9-1","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.9.1","text":"Refactored tabulate_rsp_subgroups pass sanitation checks preventing creation degenerate subtables. Updated analyze_vars_in_cols use caching, allow implementation imputation rule via imp_rule argument, allow user specify cell alignment via .aligns argument. Updated add_rowcounts allow addition row counts alt_counts_df using alt_counts argument. Added gp argument g_forest control graphical parameters font size.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-9-1","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.9.1","text":"Grouped functions relating valid method names default formats labels new source file utils_defaults_handling.R. Started deprecation summary_custom() a_summary() S3 method. Renamed statistical method p-value discrete case pval_counts. Removed a_summary_internal() favor one main a_summary().","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-090","dir":"Changelog","previous_headings":"","what":"tern 0.9.0","title":"tern 0.9.0","text":"CRAN release: 2023-09-01","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-9-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.9.0","text":"Added stat_propdiff_ci function calculate proportion/risk difference CI. Added risk difference column functionality via riskdiff argument functions count_occurrences, count_occurrences_by_grade, count_patients_with_event, count_patients_with_flags, analyze_num_patients, summarize_num_patients.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-9-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.9.0","text":"Refactored function a_summary longer use helper function create_afun_summary. Refactored functions summarize_vars compare_vars use refactored a_summary function. Created new internal helper functions ungroup_stats ungroup statistics calculated factor variables, a_summary_internal perform calculations a_summary.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.9.0","text":"Fixed bug s_count_occurrences_by_grade “missing” grade always appears final level. Fixed bug analyze_vars_in_cols categorical data used. Fixed bug s_count_occurrences_by_grade levels relabeled reordering account “missing” grades.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-9-0","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.9.0","text":"Fixed swapped descriptions .N_row .N_col parameters. Removed internal calls df_explicit_na. Changes NA values happen externally tern functions, depending users’ needs. Reinstated correct soft deprecation create_afun_summary create_afun_compare.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-5","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.5","text":"Added ylim argument g_km allow user set custom limits y-axis. Added assertion g_km checks whether one arm present data annot_coxph true. Added flag_labels argument s_count_patients_with_flags enable label handling options count_patients_by_flags. Added nested argument analyze wrapper functions count_abnormal, count_abnormal_by_baseline, count_abnormal_by_marked, count_abnormal_by_worst_grade, count_abnormal_lab_worsen_by_baseline, count_cumulative, count_missed_doses, count_occurrences, count_occurrences_by_grade, count_patients_with_event, count_patients_with_flags, count_values, estimate_multinomial_response, estimate_proportion, estimate_incidence_rate, estimate_odds_ratio, estimate_proportion_diff, test_proportion_diff, summarize_ancova, summarize_change, summarize_glm_count, analyze_num_patients, coxph_pairwise, surv_time, surv_timepoint.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-5","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.5","text":"Started deprecation cycle summarize_vars control_summarize_vars. Renamed analyze_vars control_analyze_vars reflect underlying rtables machinery keeping backward compatibility aliases.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-084","dir":"Changelog","previous_headings":"","what":"tern 0.8.4","title":"tern 0.8.4","text":"CRAN release: 2023-06-27","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-4","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.4","text":"Added method character class h_coxreg_inter_effect enabling character covariates summarize_coxreg.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-4","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.4","text":"Began deprecation time_unit_input time_unit_output arguments replaced input_time_unit num_pt_year, respectively, control_incidence_rate. Removed deprecated pairwise function. Deprecated a_compare replaced a_summary argument compare = TRUE. Deprecated helper functions create_afun_summary create_afun_compare longer used a_summary a_compare respectively.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-4","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.4","text":"Fixed long double assertion check sum(weights) M1mac installation.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-083","dir":"Changelog","previous_headings":"","what":"tern 0.8.3","title":"tern 0.8.3","text":"CRAN release: 2023-06-19","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.3","text":"Added explicit zero counts g_km plot “risk” annotation tables. Added flag total level split analyze_patients_exposure_in_cols. Implemented .indent_mods argument functions h_tab_one_biomarker, h_tab_rsp_one_biomarker, h_tab_surv_one_biomarker, summarize_logistic, logistic_summary_by_flag, tabulate_rsp_biomarkers, a_coxreg, summarize_coxreg, tabulate_survival_biomarkers, surv_time, surv_timepoint, cfun_by_flag. Updated summarize_coxreg print covariates data rows univariate Cox regression interactions content rows otherwise. Removed “baseline status” text d_count_abnormal_by_baseline labels. Improved default sizing annotation tables g_km added dynamic scaling surv_med coxph annotation tables, customization via width_annots argument.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-3","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.3","text":"Fixed bug split_text_grob preventing titles footnotes properly formatted printed decorate_grob. Fixed bug g_lineplot preventing addition lines plot midpoint statistic calculations result NA value(s). Fixed tern:::tidy.glm formals respect broom:::tidy.default formals.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.3","text":"Updated README include installation instructions CRAN. Remove examples unexported functions. Export functions has_count_in_cols, has_counts_difference, combine_counts, h_tab_rsp_one_biomarker, arrange_grobs, a_count_patients_sum_exposure, a_coxreg, groups_list_to_df, forest_viewport. Updated README include installation instructions CRAN. Began deprecation indent_mod argument replaced .indent_mods argument summarize_num_patients analyze_num_patients.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"breaking-changes-0-8-2","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"tern 0.8.2","text":"Refactored s_coxreg summarize_coxregto work new analysis function a_coxreg.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-2","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.2","text":"Added section_div na_level arguments summarize_vars. Added median_range numeric variable statistic option summarize_vars. Corrected ordering factor levels d_onco_rsp_label. Added new “Analyze Functions”, “Summarize Functions”, “Analyze Functions Columns” overview pages. Consolidated KM plot documentation within g_km function. Added a_count_patients_sum_exposure summarize_patients_exposure_in_cols new analyze function analyze_patients_exposure_in_cols. Added informative error user selects invalid method unstratified analyses s_proportion_diff. Updated s_summary s_compare allow NA values input variables. factor variables NAs, na.rm = FALSE explicit NA level automatically added. na.rm = TRUE also consider \"<Missing>\" values exclude . Updated purpose na_level parameter s_summary s_compare align tern functions. Instead string consider NA setting na.rm = TRUE, now defines string print place NA values output table.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-2","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.2","text":"Fixed missing label TRTEDTM tern datasets. Fixed improper implementation na_level argument summarize_vars preventing effect.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-2","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.2","text":"Implemented lubridate package date variables tern datasets. Organized .gitignore .Rbuildignore files. Removed deprecated footnotes functions related files. Started deprecation cycle pairwise function. Moved count_patients_with_flags functions count_patients_with_event.R count_patients_with_flags.R.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-8-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.8.0","text":"Added summarize_glm_count function analyze count data using linear model. Added legend g_step. Added formatting functions format_fraction_fixed_dp format_count_fraction_fixed_dp fixed single decimal place percentages. Added na_level labelstr arguments summarize_vars_in_cols. Added analyze_num_patients include summary beginning repeat paginating. Added h_row_first_values function general helper function retrieve first values specific rows. Added option remove \"(n)\" suffix unique_count labels s_num_patients. Added options g_km annotate statistics (annot_stats) add corresponding vertical lines (annot_stats_lines).","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-8-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.8.0","text":"Fixed bug causing incorrect ordering numeric grade levels missing grades present s_count_occurrences_by_grade. Refactored summarize_vars_in_cols work pagination machinery. Fixed bug allow passing conf_level argument emmeans::contrast() s_ancova. Fixed bugs rtables_access.R caused checking specific combinations (also standard values never used) column indices names. Fixed single applicable record bug count_abnormal_by_grade. Fixed bug add_rowcounts caused row count row values count zero. Fixed bug h_col_indices causing error pruning combination columns. Fixed bug test_proportion_diff missing argument var_labels.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"documentation-and-tests-0-8-0","dir":"Changelog","previous_headings":"","what":"Documentation and Tests","title":"tern 0.8.0","text":"Added tests increase code coverage. Created separate documentation files functions different sections pkgdown reference. Created separate .R files logistic regression cox regression helper functions. Fixed table tests using analyze_num_patients generate initial summary repetition paginating. Updated tests use testthat 3rd edition replaced applicable tests snapshot testing. Updated summarize_ancova examples use iris dataset instead scda data. Created vignette saves cached synthetic CDISC dataset files data/ folder generated cached synthetic datasets. Updated examples/tests use datasets data/ folder instead scda datasets. Removed template tests tern. tests internal repo scda.test.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-8-0","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.8.0","text":"Renamed summarize_vars_in_cols analyze_vars_in_cols reflect appropriate analyze logic. Removed redundant summary_in_cols helper functions. Exported function format_xx. Replaced deprecated ggplot2 functions/arguments fix warnings. Replaced deprecated function forcats::fct_explicit_na forcats::fct_na_value_to_level. Removed deprecated wrap_text function related files. Started deprecation cycle footnotes functions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-10","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.7.10","text":"Added stratified Newcombe stratified Wilson statistics estimate_proportion estimate_proportion_diff relative tests. Added stat_mean_pval, new summary statistic calculate p-value mean. Added statistic mean_se (mean standard error) summarize_variables related functions. Introduced Rdpack references.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-10","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.10","text":"Redesign data handling tests removing repetitive data loads library calls. Added DescTools::BinomDiffCI function within tern. Added new parameter summarize_logistic specify pivoted value use analysis. Updated s_coxph_pairwise generate log-rank p-value using original log-rank test instead Cox Proportional-Hazards Model. Implemented nestcolor examples adapting g_km, g_ipp, g_waterfall, g_step, g_lineplot, g_forest. Added parameters interaction_y interaction_item ANCOVA make interaction calculations available. Added new parameter footnotes add footnotes g_km.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"migration-from-assertthat-to-checkmate-0-7-10","dir":"Changelog","previous_headings":"","what":"Migration from assertthat to checkmate","title":"tern 0.7.10","text":"Implemented checkmate::assert_vector, checkmate::assert_set_equal, checkmate::assert_int check vector type, length, values. Replaced standard assertions checkmate following functions: all_elements_in_ref, is_df_with_nlevels_factor, is_df_with_no_na_level, is_proportion_vector, is_quantiles_vector, is_character_or_factor, is_nonnegative_count, is_valid_character, assert_character_or_factor, assert_equal_length has_tabletree_colnames. Modified is_proportion, is_equal_length, is_df_with_no_na_level, is_df_with_nlevels_factor, is_variables, is_df_with_variables, is_df_with_factors, is_valid_factor use assertion logic. Added verbose warnings as_factor_keep_attributes. Made assert_df_with_factors assert_proportion_value internal functions. Renamed assertthat.R test-assertthat.R utils_checkmate.R test-utils_checkmate.R.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"documentation-and-namespace-polishing-0-7-10","dir":"Changelog","previous_headings":"","what":"Documentation and NAMESPACE Polishing","title":"tern 0.7.10","text":"count_abnormal_by_marked (reference abnormal_by_marked), count_abnormal_lab_worsen_by_baseline h_adlb_worsen (reference abnormal_by_worst_grade_worsen_from_baseline), count_abnormal_by_worst_grade (reference abnormal_by_worst_grade), to_string_matrix, tidy.summary.coxph, tidy.step, surv_timepoint, (reference survival_timepoint), surv_time (reference survival_time), coxph_pairwise (reference survival_coxph_pairwise), extract_survival_subgroups tabulate_survival_subgroups (reference survival_duration_subgroups), extract_survival_biomarkers tabulate_survival_biomarkers (reference survival_biomarkers_subgroups), control_summarize_vars, s_summary a_summary (reference summarize_variables) kept S3 method tree. summarize_patients_exposure_in_cols, summarize_num_patients s_num_patients, s_num_patients_content, summarize_num_patients. count_cumulative, count_missed_doses, count_patients_events_in_cols, summarize_colvars, summarize_change, summarize_ancova,.rtable, color_palette, add_footnotes. (statistical function controls) control_coxreg, control_coxph, control_incidence_rate, control_lineplot_vars, control_surv_time, control_surv_timepoint, control_logisitic, control_step. stat_mean_ci, stat_median_ci, split_cols_by_groups, explicit_na, sas_na, extract_rsp_subgroups, tabulate_rsp_subgroups, extract_rsp_biomarkers, tabulate_rsp_biomarkers, keep_rows, keep_content_rows, has_count_in_any_col, has_fraction_in_cols, has_fraction_in_any_col, has_fractions_difference, test_proportion_diff, pairwise, logistic_regression, estimate_incidence_rate, control_incidence_rate (reference incidence_rate), cut_quantile_bins, estimate_multinomial_rsp, decorate_grob_set, extreme_format, fit_rsp_step, fit_survival_step, footnotes, footnotes-set, format_count_fraction, format_fraction_threshold, formatting_functions, format_fraction, combination_function (S4 method), compare_variables (S3 method), kaplan_meier. (chain functions, reference abnormal_by_marked) s_count_abnormal_by_marked, a_count_abnormal_by_marked. (chain functions, reference abnormal_by_worst_grade_worsen_from_baseline) a_count_abnormal_lab_worsen_by_baseline, s_count_abnormal_lab_worsen_by_baseline. (chain functions, reference abnormal_by_worst_grade) s_count_abnormal_by_worst_grade, a_count_abnormal_by_worst_grade. (chain functions, reference survival_timepoint) s_surv_timepoint, s_surv_timepoint_diff, a_surv_timepoint, a_surv_timepoint_diff. (chain functions, reference survival_time) s_surv_time, a_surv_time. (chain functions, reference survival_coxph_pairwise) s_coxph_pairwise, a_coxph_pairwise. (chain functions, reference survival_duration_subgroups) a_survival_subgroups. (chain functions, reference count_cumulative) s_count_cumulative, a_count_cumulative. (chain functions, reference count_missed_doses) s_count_nonmissing, s_count_missed_doses, a_count_missed_doses. (chain functions, reference count_patients_events_in_cols) s_count_patients_and_multiple_events, summarize_patients_events_in_cols. (chain functions, reference incidence_rate) s_incidence_rate, a_incidence_rate. (cox regression helper) cox_regression_inter, decorate_grob_factory, draw_grob, estimate_coef. summary_labels, summary_formats, s_count_patients_sum_exposure, a_change_from_baseline s_change_from_baseline, a_ancova, s_ancova, arrange_grobs, as_factor_keep_attributes, combine_levels, split_text_grob, groups_list_to_df, s_cox_multivariate, is_leaf_table, a_response_subgroups, range_noinf, has_count_in_cols, has_counts_difference, prop_chisq, prop_cmh, prop_schouten, prop_fisher, s_test_proportion_diff, a_test_proportion_diff, fct_discard, fct_explicit_na_if.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-10","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.10","text":"Fixed retrieval method stats::ancova output due version inconsistency. Fixed tests respect new standard print NA coming rtables. Fixed error tests coming changes formatters::var_labels. Fixed prop_diff functions respect success responses (TRUE values). Fixed error coming comparing factors vector characters vector. Fixed empty vector exception cut_quantile_bins. Fixed exception error empty strings present pivoted columns (rtables split functions) Fixed bug s_ancova causing error first level arm factor control arm. Fixed bug s_abnormal_by_worst_grade one PARAM level. Fixed bug prop_diff_wald selecting responders, updated tests accordingly. Fixed bug h_ancova caused error deselecting covariates.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-10","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.10","text":"Added deprecated badge g_mmrm. Removed internal function calls examples (tern:::) added dontrun internal function examples. Removed warnings messages console occurring examples tests. Deprecated functions color_palette h_set_nest_theme favor nestcolor::color_palette nestcolor::theme_nest, respectively. Removed deprecated functions: color_palette, color_palette_core, h_set_nest_theme, s_cox_univariate. Removed deprecated mmrm functions: fit_mmrm, g_mmrm_diagnostic, g_mmrm_lsmeans, .rtable.mmrm, h_mmrm_fixed, h_mmrm_cov, h_mmrm_diagnostic, tidy.mmrm, s_mmrm_lsmeans, s_mmrm_lsmeans_single, summarize_lsmeans. Renamed functions arm study_arm extract extract_by_name. Renamed rtables.R utils_rtables.R. Extracted cox_regression_inter separate file cox_regression. Renamed estimate_incidence_rate.R incidence_rate.R match documentation grouping name. Extracted control_incidence_rate separate file produces separate documentation file. Added @md removed @order incidence_rate.R. Modified examples accordingly. Removed hyperlink prop_schouten function documentation. Exported draw_grob function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"fix-0-7-8","dir":"Changelog","previous_headings":"","what":"Fix","title":"tern 0.7.8","text":"h_split_by_subgroups documentation warning fix wrong placing example block","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"documentation-and-namespace-polishing-0-7-8","dir":"Changelog","previous_headings":"","what":"Documentation and NAMESPACE polishing","title":"tern 0.7.8","text":"Adopting standard badges @description instead every @descriptionIn function. Corrected accordingly summarize_variables_in_cols Added stable badge g_lineplot, g_step, g_waterfall, cox_regression, score_occurrences, add_rowcounts, odds_ratio, count_occurrences, count_occurrences_by_grade, explicit_na, df_explicit_na, count_patients_with_event, decorate_grob, combine_groups, append_varlabels, univariate, stack_grobs, count_abnormal (reference abnormal), count_abnormal_by_baseline (reference abnormal_by_baseline). (helper functions) h_format_row, h_map_for_count_abnormal (utils functions) make_names, month2day, day2month empty_vector_if_na, aesi_label, n_available, format_xx, arm. count_values_funs, prop_difference, combine_counts. (chain functions) s_count_abnormal, a_count_abnormal. (chain functions) s_count_abnormal_by_baseline, a_count_abnormal_by_baseline, d_count_abnormal_by_baseline. Deprecated s_cox_univariate function now deprecated badge.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-8","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.8","text":"Enhanced g_lineplot table automatically scale table height return ggplot object. Enhanced g_ipp caption argument adjust position. Enhanced prop_diff, tern function related functions able apply continuity correction Newcombe method. Enhanced summarize_numeric_in_columns summarize_variables allow factor/character summary able summarize number BLQs AVALC ADPC dataset. Updated order summarize variables stats manual order consistency. Added sum option summarize_variables. Use consistent color palette plotting (stream default). Enhanced h_pkparam_sort function argument key_var allow data different column names.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-8","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.8","text":"Updated test-table_aet02.R variant 12. Changed scda data version ‘2022-02-28’. Added template pkgdown site. Removed package dependencies grDevices, stringr, viridisLite. Renaming summarize_numeric_in_columns summarize_variables_in_columns. Renaming summarize_vars_numeric_in_cols summarize_vars_in_cols. Fixed bug points g_lineplot plot connected missing values. Updated package authors.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"breaking-changes-0-7-7","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tern 0.7.7","text":"Move MMRM separate package tern.mmrm.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-7","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.7","text":"Added h_pkparam_sort order PK PARAM value based order dataset generated d_pkparam(). Added d_pkparam generate PK parameter map sorting.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-7","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.7","text":"Changed nudge_y argument h_g_ipp dependent data, fixing issue whereby baseline labels offset incorrectly. Enhanced stat_mean_ci s_summary.numeric calculate geometric mean confidence intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-7","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.7","text":"Updated dependencies internal adjustments rtables package refactor. Removed with_label, var_labels, var_labels<- resolve conflict formatters package, new dependency. Added new “Introduction tern” “tern tabulation” vignettes.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-6","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.6","text":"Added h_map_for_count_abnormal create map used trim_levels_to_map split function calling helper function. supports two methods: one observed mapping, one least low limit zero least one non missing high limit. Added s_summary_numeric_in_cols summarize_vars_numeric_in_cols functions generate summary statistics columns, mainly used PK datasets. Added five statistics s_summary.numeric use s_summary_numeric_in_cols.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-6","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.6","text":"Enhanced functions tabulate_survival_subgroups tabulate_rsp_subgroups (Survival Duration Best Response analyses) calculate N-s based records considered create model. Enhanced function estimate_proportion related functions able apply continuity correction Wilson method. Refactored count_abnormal_by_marked related statistics formatting functions use efficient layout .spl_context argument used determining denominators trim_levels_to_map split function split_rows_by show desired levels table. breaking change. Refactored count_abnormal_by_worst_grade related statistics formatting functions use efficient layout .spl_context argument used determining denominators trim_levels_to_map split function split_rows_by show desired levels table. breaking change. Refactored count_abnormal function related statistics formatting functions use efficient layout trim_levels_to_map split function split_rows_by show desired levels table. Also updated abnormal argument able consider one level direction. breaking change. Enhanced function estimate_incidence_rate related functions consider week time unit data input.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-6","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tern 0.7.6","text":"Fixed bug assertthat functions output wrong data frame names limited length failure message outputs.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-6","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.6","text":"Removed dependency utils.nest using checkmate purrr packages validation moved get_free_cores skip_if_too_deep functions utils.nest tern.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-5","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.5","text":"Added functions estimate continuous biomarker effects across subgroups survival binary response endpoints, used produce corresponding forest plots, see survival_biomarkers_subgroups response_biomarkers_subgroups. Added g_lineplot plot function, including new h_format_row helper function control_lineplot_vars function. Removed g_summary_by. Added new safety helper function h_stack_by_baskets stack events SMQ /CQ basket flag ADAE data set.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-5","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.5","text":"Added couple new statistics s_summary.numeric. Added names attribute element final list returned s_summary.numeric function. Added summary_formats summary_labels helper functions. Added option also convert logical variables factor variables df_explicit_na. Refactored h_append_grade_groups improve flexibility, robustness clearness, make sure result ordering according order grade_groups. Also, added remove_single argument controls whether elements one-element grade groups output removed. Added var_labels show_labels arguments count_occurrences count_patients_with_flags allow creation title row. Added na_level argument count_abnormal_by_baseline. Updated h_append_grade_groups longer fill-empty grade groups zeros.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-5","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.5","text":"Fixed prop_diff_cmh handle edge case FALSE (TRUE) responses. Enhanced g_mmrm_diagnostic improve error handling data amenable Locally Weighted Scatterplot Smoothing. Plot can now display combination annotation tables number patients risk, median survival time, CoxPH summary. Function return warning instead error arm variable includes single level annot_coxph = TRUE. Lines plot now start time 0 probability 1. Category labels can include equals sign.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-5","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.5","text":"Fixed day2month month2day work NA data. Added parameters stat_mean_ci stat_median_ci may return different outputs. Added functionality h_row_counts handle analysis rows NULL cells. Updated LICENCE README new package references. Added error_on_lint: TRUE .lintr.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-4","dir":"Changelog","previous_headings":"","what":"New features","title":"tern 0.7.4","text":"count_abnormal_by_marked tabulates marked laboratory abnormalities. summarize_patients_exposure_in_cols tabulates patient counts sum exposure across patients.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-4","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.4","text":"Enhanced mmrm related functions fitting models without arm variable. Updated cox_regression work without covariates. Also case interaction model summary, p-values main effect coefficients longer displayed. Descriptive statistics returned summarize_vars now include quantiles. summarize_vars now accepts control function control_summarize_vars specify details confidence level mean median quantile details. control argument replaces conf_level. Added var_labels show_labels arguments count_occurrences_by_grade. Changed indent argument append_varlabels accept non-negative integer represent indent space defined user. Previous calls Boolean indent integer conversion produce warning.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-4","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.4","text":"Corrected tabulate_survival_subgroups related survival forest plot functions use total number events, instead observations, default scaling symbol sizes plot. (user might still use total number observations manually wish .) Helper function h_adsl_adlb_merge_using_worst_flag now impute BTOXGR missing visits.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-4","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.4","text":"Deleted count_abnormal_by_worst_grade_by_baseline related statistic analysis functions simpler design create lab abnormality tables. Switched data examples use scda instead random.cdisc.data package.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-features-0-7-3","dir":"Changelog","previous_headings":"","what":"New Features","title":"tern 0.7.3","text":"Added Subgroup Treatment Effect Pattern (STEP) model fitting functions fit_rsp_step fit_survival_step, corresponding tidy method tidy.step well graph function g_step. Added new layout function compare_vars compares variables different types columns produces p-value comparison reference column. Function built top summarize_vars functionality. cut_quantile_bins cuts numeric vector quantile bins. fct_collapse_only collapses levels factor keeps order provided. fct_explicit_na_if inserts explicit missings factor based condition. range_noinf kind wrapper function base::range. returns c(NA, NA) instead c(-Inf, Inf) zero-length data.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"enhancements-0-7-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tern 0.7.3","text":"Cox regression via fit_coxreg_univar fit_coxreg_multivar now also possible without treatment arm. univariate case means fits separate univariate models provided covariates tabulation corresponding effect estimates can later occur. Added fraction result returned s_count_occurrences. contains list numerators denominators one element per occurrence. Updated sum_num_patients count_occurrences result unique count_fraction return (0, 0) input empty. Added new argument groups_lists extract_survival_subgroups, extract_rsp_subgroups associated helper functions allows group factor levels subgroup variables manually defined groups, enhancing flexibility resulting forest graphs. Forest graph function g_forest now extracts default arguments attributes input table produced tabulate_rsp_subgroups tabulate_survival_subgroups user manually anymore. Remove arm variable name arm labels plot legend annotation tables. Show symbol used mark censored cases match order arm variable factor levels legend. Display hazard ratio confidence interval two decimal places. Updated default position hazard ratio table stay left bottom corner x-axis. Use arm value strata name treatment arm variable single level. Updated s_surv_time function use newly created function range_noinf instead base::range. New argument no_fillin_visits added h_adsl_adlb_merge_using_worst_flag specify excluded visits post-baseline worst toxicity grade output. Improved h_adsl_adlb_merge_using_worst_flag include variables shared adsl adlb, along PARAM, PARAMCD, ATOXGR, BTOXGR optionally AVISIT, AVISITN by_visit = TRUE. Prior output contained USUBJID, ARMCD, PARAMCD, ATOXGR, BTOXGR.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"bug-fixes-0-7-3","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"tern 0.7.3","text":"Fix bug s_surv_timepoint cases zero patients risk. Modified stat_median_ci function passing empty var empty name, row names contain missing values error show.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"miscellaneous-0-7-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"tern 0.7.3","text":"Deprecated s_cox_univariate function, use fit_coxreg_univar function instead. Updated default formats hr hr_ci a_coxph_pairwise median s_surv_time align STREAM. Updated pre-processing code files test-table_ttet01.R test-table_dort01.R make sure analysis variable EVNT1 levels factor defined. Improved error message number levels factor variable data frame expected.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-072","dir":"Changelog","previous_headings":"","what":"tern 0.7.2","title":"tern 0.7.2","text":"Fixed column label internal test errors.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-071","dir":"Changelog","previous_headings":"","what":"tern 0.7.1","title":"tern 0.7.1","text":"New argument position_surv_med added g_km move position annotation table median survival times. Fixed bug g_km related ignored arguments pch size passed helper function h_ggkm. Updated xticks max_time arguments g_km greater functionality. max_time added argument h_xticks allow . Fixed bug prop_diff_cmh led NaN weighted proportion difference estimates missing confidence intervals. change, including patients one treatment arm least one stratum estimation lead numeric results. Fixed bug prop_cmh giving error case least one stratum containing less two observations. New argument n_events added estimate_incidence_rate. New argument denom added count_occurrences. New arguments yval ci_ribbon added g_km. Add new individual patient plot function g_ipp along helpers h_g_ipp h_set_nest_theme. Fixed bug count_patients_with_events, now shows zero counts without percentage. Fixed bug get_mmrm_lsmeans allow MMRM analysis 3000 observations. Updated stat_mean_ci stat_median_ci handle edge cases number elements input series equal 1. cases, NA_real_ now returned, instead NA +/-Inf confidence interval (CI) estimates. Rename n_lim argument stat_mean_ci n_min better reflect desired meaning.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-070","dir":"Changelog","previous_headings":"","what":"tern 0.7.0","title":"tern 0.7.0","text":"version tern introduces major rewriting tern due change layout based tabulation rtables. tern now build tables directly anymore, instead provides analysis functions build tables, see examples. * Counting patients abnormal values post-baseline count_abnormal. * Counting patients graded abnormal values count_abnormal_by_worst_grade. * Counting patients abnormal values baseline status count_abnormal_by_baseline. * Counting patients missed doses s_count_missed_doses count_missed_doses. * Counting patients event flags count_patients_with_event count_patients_with_flags. * Summarizing variables summarize_vars (supports numeric, factor, character logical variables). Note factors need NAs converted na_level use. * Summarizing change baseline summarize_change. * Summarizing variables columns summarize_colvars. * Estimating difference responder proportions estimate_proportion_diff. * Estimating difference Odds Ratio estimate_odds_ratio. * Testing difference responder proportions test_proportion_diff. * Estimating responder proportion level factor estimate_multinomial_response. * Fitting tabulating results Cox regressions fit_coxreg_univar, fit_coxreg_multivar summarize_coxreg, respectively. * Pruning occurrence tables (tables counts fractions) flexible rules, see ?prune_occurrences details. * Sorting occurrence tables using different options, see ?score_occurrences details. * Fitting tabulating MMRM models fit_mmrm .rtable summarize_lsmeans, see ?tabulate_mmrm details. * Counting number unique non-unique patients summarize_num_patients. * Counting occurrences count_occurrences. * Counting occurrences grade summarize_occurrences_by_grade count_occurrences_by_grade. * Counting patients events columns summarize_patients_events_in_cols. * Tabulating binary outcome response subgroup extract_rsp_subgroups tabulate_rsp_subgroups. * Tabulating survival duration subgroup extract_survival_subgroups tabulate_survival_subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-069","dir":"Changelog","previous_headings":"","what":"tern 0.6.9","title":"tern 0.6.9","text":"Removing used imports.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-068","dir":"Changelog","previous_headings":"","what":"tern 0.6.8","title":"tern 0.6.8","text":"Improved handling missing data aggregation functions: a_mean_sd, a_median, a_n_true_and_freq, a_count, a_q1q3, a_iqr, a_range. New default p-value method s_test_proportion_diff: Chi-Squared Test Schouten Correction. Add new function t_contingency contingency tables. Renamed class splitText dynamicSplitText resolve name conflict package ggpubr. Add rreplace_format tabulation post-processing. Add new tern function t_ancova create ANCOVA tables, well corresponding elementary table function t_el_ancova summary function s_ancova. Add new tern function s_odds_ratio estimate Odds Ratio response categories, well corresponding elementary table function t_el_odds_ratio. Added new CI methods (Agresti-Coull, Jeffreys) s_proportion. Added new CI methods anderson-hauck newcombe s_proportion_diff. Added new p-value methods (Fisher’s Exact, Chi-Squared Test Schouten Correction) s_test_proportion_diff. binary summary table function t_binary_outcome takes now lists (instead character vectors) specified helper function control_binary_comparison arguments strat_analysis unstrat_analysis. Odds Ratio estimates CIs now removable included default, similarly subsections arm comparison analyses. Also added argument rsp_multinomial. Add new table function t_el_multinomial_proportion. Add new table function t_abn_shift. Add new MMRM analysis function s_mmrm, well corresponding table functions t_mmrm_lsmeans, t_mmrm_cov, t_mmrm_diagnostic, t_mmrm_fixed, plot functions g_mmrm_lsmeans, g_mmrm_diagnostic. results match SAS results (numeric precision). Deprecated old MMRM functions a_mmrm t_mmrm (give deprecation warning still work) remove next release. reason results functions don’t match SAS results. Fix bug g_km related numbers patients risk table correct numbers integer time--event variable inputs.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-067","dir":"Changelog","previous_headings":"","what":"tern 0.6.7","title":"tern 0.6.7","text":"functions row_by argument, inputs longer require use nested_by. Add stat_mean_ci stat_median_ci error bars ggplot2. Add new tern function t_coxreg single interface diverse cox regression types. Add compound table binary endpoint: t_binary_endpoint elementary functions: t_el_proportion, t_el_proportion_diff t_el_test_proportion_diff. supporting summary functions added : s_proportion, s_adj_proportion_diff, s_proportion_diff s_test_proportion_diff. Added new tern function t_events_patyear create event table adjusted person-years. Added new tern function t_abnormality elementary table function t_el_abnormality. Removed grade_levels argument t_events_term_grade_id functions. Post-processing reordering leaves table tree creates different ordering rows required. Creating helper function occur later time. Added prune_zero_rows argument t_events_per_term_grade_id t_max_grade_per_id show rows zeros can clutter visualization Shiny app make slower. Fixed position (N=xx) t_summary_by output summarizing numeric columns parallel compare_in_header. Rename t_coxph t_coxph_pairwise reflect model process, add details documentation. Remove test.nest dependency. Keep column labels splitting data tree.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-066","dir":"Changelog","previous_headings":"","what":"tern 0.6.6","title":"tern 0.6.6","text":"Remove test.nest dependency.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-065","dir":"Changelog","previous_headings":"","what":"tern 0.6.5","title":"tern 0.6.5","text":"Change default option denominator N t_summary. Fix IQR bug: IQR Q3 minus Q1. Add new function t_logistic multi-variable logistic regression table. Add new function df_explicit_na replace NA explicit values. Added possibility t_tte specify confidence level independent survfit, coxph, ztest, see manual. Fixed bug t_rsp showing p-value, odds ratio CIs strata_data NULL. Added stratified analysis t_forest_rsp t_forest_tte, footnotes g_forest. Added footnotes, footnotes<- add_footnotes<- functions deal footnotes. Added argument conf_int confidence interval level t_el_forest_rps, t_forest_rsp, t_el_forest_tte, t_forest_tte. Added argument col_symbol_size g_forest control relative size symbols used plot. Added s_coxph_pairwise function perform pairwise testing, used t_tte t_coxph. Added methods t_count_true replacing t_summary_true. Updated t_count_unique create analysis subsets, added t_el_count_unique vectors. Fixed bug t_events_term_id table sort order decreasing frequency instead alphabetical. Added function color_palette new nest color palette.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-064","dir":"Changelog","previous_headings":"","what":"tern 0.6.4","title":"tern 0.6.4","text":"Refactored renaming functions utils.nest. Added event_type argument t_events_per_term_grade_id. Added one/two-arm t-test functions. Improvements t_summary_by. Internal code refactoring.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-063","dir":"Changelog","previous_headings":"","what":"tern 0.6.3","title":"tern 0.6.3","text":"related tree functions see reference trees. Moved label functions rtables. Deleted keys keys<- functions. Exported tabulate_pairwise. get_N, col_N_add_total, check_id. na_as_level. as_factor_keep_attributes. r_by. Disposition elementary table t_el_disposition. t_el_forest_tte, t_el_forest_rsp. Added table_tree argument returns node object. Added f_numeric choose statistics calculate. denominator now also allows omit wanting omit percentages. Renamed row_by. Changed functionality group_data using row_by_list. Removed na_omit_group. Removed indent argument, use indent function rtables. instead","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-062","dir":"Changelog","previous_headings":"","what":"tern 0.6.2","title":"tern 0.6.2","text":"Use cached data random.cdisc.data speed testing. Added t_summary.Date method. Added save_join.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-061","dir":"Changelog","previous_headings":"","what":"tern 0.6.1","title":"tern 0.6.1","text":"Fixed colors Kaplan-Meier Plot Changed width_row.names argument g_forest function width_row_names. Changed censor.show argument g_km function censor_show. Changed col.legend.title argument g_waterfall function col_legend_title. Changed na.rm argument t_count_unique function na_rm. Changed row.name argument t_count_unique function row_name. Changed na.omit.group argument t_forest_rsp function na_omit_group. Changed na.omit.group argument t_forest_tte function na_omit_group. Changed row.name.TRUE row.name.FALSE arguments t_summary.logical row_name_true row_name_false respectively. Rename splotTextGrob split_text_grob. Fix examples. Refactor internal functions code.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-060","dir":"Changelog","previous_headings":"","what":"tern 0.6.0","title":"tern 0.6.0","text":"Removed functions addTable, t_summarize_by_visit, t_summarize_variables. Added t_summary_by function. Refactor g_km function, renamed kmGrob kmCurveGrob. Refactor t_events_* family functions. Updated examples.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-tlgs-0-5-0-3","dir":"Changelog","previous_headings":"","what":"New TLGs","title":"tern 0.5.0.3","text":"t_summary methods data.frame, numeric, logical, character, factor, Date objects. t_events_per_term_id, t_events_per_term_grade_id: Adverse Events & Concomitant Treatment Tables. t_max_grade_per_id, t_count_unique, t_events_summary elementary tables used Adverse Events & Concomitant Treatment Tables. g_waterfall: Horizontal Waterfall Plot.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"new-helper-functions-0-5-0-3","dir":"Changelog","previous_headings":"","what":"New Helper Functions","title":"tern 0.5.0.3","text":"decorate_grob, decorate_grob_set, decorate_grob_factory, splitTextGrob. stack_grobs, arrange_grobs, draw_grob.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tlg-changes-0-5-0-3","dir":"Changelog","previous_headings":"","what":"TLG changes","title":"tern 0.5.0.3","text":"t_tte now shows two rows ranges event censored times, respectively. g_km works one arm survfit objects. forest plot functions, added formatting display extreme values “>999.9”. t_summarise_variables uses now n instead N denominator calculating percentages factors default. t_rsp now works response values TRUE FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"deprecated-functions-0-5-0-3","dir":"Changelog","previous_headings":"","what":"Deprecated Functions","title":"tern 0.5.0.3","text":"Deprecated t_summarize_variables t_summary powerful. Replacing t_summarize_by_visit t_summary_by occur upcoming release.","code":""},{"path":"https://insightsengineering.github.io/tern/news/index.html","id":"tern-050","dir":"Changelog","previous_headings":"","what":"tern 0.5.0","title":"tern 0.5.0","text":"First version harmonized analysis functions names arguments.","code":""}]