Restore files from main

book/tables/ADA/adat03.qmd

@@ -39,6 +39,85 @@ anl <- merge(anl, adpc, by = c("USUBJID", "NFRLT")) %>%
   mutate(AVAL_LT = ifelse(AVAL <= max_conc, TRUE, FALSE))
 ```
 
+## Standard Table (μg/mL)
+
+```{r variant1, test = list(result_v1 = "result")}
+# parameters in columns
+adat03_stats <- c("n", "mean", "sd", "median", "min", "max", "cv", "geom_mean", "count_fraction")
+adat03_lbls <- c(
+  n = "Total Number\nof Measurable\n Samples",
+  mean = "Mean",
+  sd = "SD",
+  median = "Median",
+  min = "Minimum",
+  max = "Maximum",
+  cv = "CV (%)",
+  geom_mean = "Geometric Mean",
+  count_fraction = paste0("Samples with\nConcentration\n≤ ", max_conc, "μg/mL")
+)
+adat03_fmts <- c(
+  n = "xx.",
+  mean = format_sigfig(3),
+  sd = format_sigfig(3),
+  median = format_sigfig(3),
+  min = format_sigfig(3),
+  max = format_sigfig(3),
+  cv = "xx.x",
+  geom_mean = format_sigfig(3),
+  count_fraction = format_count_fraction
+)
+
+afun_list <- lapply(
+  1:9,
+  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = "Overall")
+)
+
+# lyt creation
+lyt <- basic_table() %>%
+  split_rows_by(
+    var = "ARM",
+    label_pos = "topleft",
+    split_label = "Treatment Group",
+    split_fun = drop_split_levels,
+    section_div = ""
+  ) %>%
+  add_rowcounts() %>%
+  split_rows_by(
+    var = "VISIT",
+    label_pos = "topleft",
+    split_label = "Visit",
+    split_fun = drop_split_levels,
+    child_labels = "hidden"
+  ) %>%
+  analyze_vars_in_cols(
+    vars = c(rep("AVAL", 8), "AVAL_LT"),
+    .stats = adat03_stats,
+    .labels = adat03_lbls,
+    .formats = adat03_fmts
+  ) %>%
+  analyze_colvars(
+    afun_list,
+    nested = FALSE,
+    extra_args = list(".labels" = "Overall")
+  )
+
+result <- build_table(lyt, anl, alt_counts_df = adsl)
+
+main_title(result) <- paste(
+  "Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n
+  Protocol:", unique(adab$PARCAT1)[1]
+)
+subtitles(result) <- paste("Analyte:", unique(adab$PARAMCD)[1])
+fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL")) <- "Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with
+valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value."
+fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL_LT")) <- "In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of
+Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA,
+or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]"
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
 
 ## `teal` App
 

book/tables/pharmacokinetic/pkct01.qmd

@@ -10,6 +10,140 @@ subtitle: Summary Concentration Table
 ::: panel-tabset
 ## Data Setup
 
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(scda)
+library(dplyr)
+library(tern)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl") %>%
+  filter(ACTARM == "A: Drug X")
+adpc <- synthetic_cdisc_dataset("latest", "adpc") %>%
+  filter(ACTARM == "A: Drug X", PARAM == "Plasma Drug X")
+
+# Setting up the data
+adpc_1 <- adpc %>%
+  mutate(
+    NFRLT = as.factor(NFRLT),
+    AVALCAT1 = as.factor(AVALCAT1)
+  ) %>%
+  select(NFRLT, ACTARM, VISIT, AVAL, PARAM, AVALCAT1) %>%
+  var_relabel(NFRLT = "Nominal Time from First Dose (hr)")
+
+# Row structure
+lyt_rows <- basic_table() %>%
+  split_rows_by(
+    var = "ACTARM",
+    split_fun = drop_split_levels,
+    split_label = "Treatment Group",
+    label_pos = "topleft"
+  ) %>%
+  add_rowcounts(alt_counts = TRUE) %>%
+  split_rows_by(
+    var = "VISIT",
+    split_fun = drop_split_levels,
+    split_label = "Visit",
+    label_pos = "topleft"
+  ) %>%
+  split_rows_by(
+    var = "NFRLT",
+    split_fun = drop_split_levels,
+    split_label = obj_label(adpc_1$NFRLT),
+    label_pos = "topleft",
+    child_labels = "hidden"
+  )
+```
+
+## Standard Table (Stats in Columns)
+
+```{r variant1, test = list(result_v1 = "result")}
+lyt <- lyt_rows %>%
+  analyze_vars_in_cols(
+    vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
+    .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
+    .formats = c(
+      n = "xx.", n_blq = "xx.", mean = format_sigfig(3), sd = format_sigfig(3), cv = "xx.x", median = format_sigfig(3),
+      geom_mean = format_sigfig(3), geom_cv = "xx.x", min = format_sigfig(3), max = format_sigfig(3)
+    ),
+    .labels = c(
+      n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
+      geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
+    ),
+    na_level = "NE",
+    .aligns = "decimal"
+  )
+
+result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()
+
+# Decorating
+main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
+subtitles(result) <- c("Protocol: xxxxx", paste("Analyte: ", unique(adpc_1$PARAM)), paste("Treatment:", unique(adpc_1$ACTARM)))
+main_footer(result) <- "NE: Not Estimable"
+
+result
+```
+
+## Table Implementing 1/3 Imputation Rule
+
+```{r variant2, test = list(result_v2 = "result")}
+lyt <- lyt_rows %>%
+  analyze_vars_in_cols(
+    vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
+    .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
+    .formats = c(
+      n = "xx.", n_blq = "xx.", mean = format_sigfig(3), sd = format_sigfig(3), cv = "xx.x", median = format_sigfig(3),
+      geom_mean = format_sigfig(3), geom_cv = "xx.x", min = format_sigfig(3), max = format_sigfig(3)
+    ),
+    .labels = c(
+      n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
+      geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
+    ),
+    imp_rule = "1/3",
+    .aligns = "decimal"
+  )
+
+result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()
+
+# Decorating
+main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
+subtitles(result) <- c("Protocol: xxxxx", paste("Analyte: ", unique(adpc_1$PARAM)), paste("Treatment:", unique(adpc_1$ACTARM)))
+main_footer(result) <- c("NE: Not Estimable", "ND: Not Derived")
+
+result
+```
+
+## Table Implementing 1/2 Imputation Rule
+
+```{r variant3, test = list(result_v3 = "result")}
+lyt <- lyt_rows %>%
+  analyze_vars_in_cols(
+    vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
+    .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
+    .formats = c(
+      n = "xx.", n_blq = "xx.", mean = format_sigfig(3), sd = format_sigfig(3), cv = "xx.x", median = format_sigfig(3),
+      geom_mean = format_sigfig(3), geom_cv = "xx.x", min = format_sigfig(3), max = format_sigfig(3)
+    ),
+    .labels = c(
+      n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
+      geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
+    ),
+    imp_rule = "1/2",
+    .aligns = "decimal"
+  )
+
+result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()
+
+# Decorate table
+main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
+subtitles(result) <- c("Protocol: xxxxx", paste("Analyte: ", unique(adpc_1$PARAM)), paste("Treatment:", unique(adpc_1$ACTARM)))
+main_footer(result) <- "ND: Not Derived"
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
 ## `teal` App
 
 ```{r teal, message=FALSE, opts.label='skip_if_testing'}

book/tables/pharmacokinetic/pkpt02.qmd

@@ -23,6 +23,66 @@ adpp <- adpp %>% filter(PPSPEC == "Plasma", AVISIT == "CYCLE 1 DAY 1")
 
 ## Standard Table -- Plasma
 
+```{r}
+# lyt creation
+lyt <- basic_table() %>%
+  split_cols_by(
+    var = "ARMCD",
+    split_fun = trim_levels_in_group("ARMCD"),
+    # label_pos = "topleft",
+    split_label = "Treatment Arm"
+  ) %>%
+  split_rows_by(
+    var = "PKPARAM",
+    label_pos = "topleft",
+    split_label = "PK Parameter"
+  ) %>%
+  tern::analyze_vars(
+    vars = "AVAL",
+    .stats = c("n", "mean_sd", "cv", "geom_mean", "geom_cv", "median", "range"),
+    .formats = c(
+      n = "xx.",
+      mean_sd = format_sigfig(3, "xx (xx)"),
+      cv = "xx.x",
+      geom_mean = format_sigfig(3),
+      geom_cv = "xx.x",
+      median = format_sigfig(3),
+      range = format_sigfig(3, "xx - xx")
+    )
+  )
+```
+
+#### Plasma Drug X
+
+```{r variant1, test = list(result_v1 = "result")}
+adpp0 <- adpp %>%
+  filter(PPCAT == "Plasma Drug X") %>%
+  h_pkparam_sort() %>%
+  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, " (", AVALU, ")"))) %>%
+  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))
+
+result <- build_table(lyt, df = adpp0)
+main_title(result) <- paste("Summary of", unique(adpp0$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
+subtitles(result) <- paste("Analyte:", unique(adpp0$PPCAT), "\nVisit:", unique(adpp0$AVISIT))
+result
+```
+
+#### Plasma Drug Y
+
+```{r variant2, test = list(result_v2 = "result")}
+adpp1 <- adpp %>%
+  filter(PPCAT == "Plasma Drug Y") %>%
+  h_pkparam_sort() %>%
+  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, " (", AVALU, ")"))) %>%
+  mutate(PKPARAM = reorder(PKPARAM, TLG_ORDER))
+
+result <- build_table(lyt, df = adpp1)
+main_title(result) <- paste("Summary of", unique(adpp1$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
+subtitles(result) <- paste("Analyte:", unique(adpp1$PPCAT), "\nVisit:", unique(adpp1$AVISIT))
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
 
 ## `teal` App