Revert to use AVALCAT1 instead of FLAGSUM

insightsengineering · Sep 7, 2023 · 685b01d · 685b01d
1 parent 73a1164
commit 685b01d
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Showing 4 changed files with 13 additions and 17 deletions.
diff --git a/R/imputation_rule.R b/R/imputation_rule.R
@@ -10,9 +10,9 @@
 #'   rule or `"1/2"` to implement 1/2 imputation rule.
 #' @param post (`flag`)\cr whether the data corresponds to a post-dose time-point (defaults to `FALSE`).
 #'   This parameter is only used when `imp_rule` is set to `"1/3"`.
-#' @param avalcat_flagvar (`character`)\cr name of variable that indicates whether or not a row in `df` is
-#'   flagged as `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or similar (defaults to `"FLAGSUM"`). Variable
-#'   `avalcat_flagvar` must be present in `df`.
+#' @param avalcat_var (`character`)\cr name of variable that indicates whether a row in `df` corresponds
+#'   to an analysis value in category `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or none of the above
+#'   (defaults to `"AVALCAT1"`). Variable `avalcat_var` must be present in `df`.
 #'
 #' @return A `list` containing statistic value (`val`) and NA level (`na_level`) that should be displayed
 #'   according to the specified imputation rule.
@@ -26,32 +26,31 @@
 #'   AVAL = runif(50, 0, 1),
 #'   AVALCAT1 = sample(c(1, "BLQ"), 50, replace = TRUE)
 #' )
-#' df$FLAGSUM <- df$AVALCAT1 == "BLQ"
 #' x_stats <- s_summary(df$AVAL)
 #' imputation_rule(df, x_stats, "max", "1/3")
 #' imputation_rule(df, x_stats, "geom_mean", "1/3")
 #' imputation_rule(df, x_stats, "mean", "1/2")
 #'
 #' @export
-imputation_rule <- function(df, x_stats, stat, imp_rule, post = FALSE, avalcat_flagvar = "FLAGSUM") {
-  checkmate::assert_choice(avalcat_flagvar, names(df))
+imputation_rule <- function(df, x_stats, stat, imp_rule, post = FALSE, avalcat_var = "AVALCAT1") {
+  checkmate::assert_choice(avalcat_var, names(df))
   checkmate::assert_choice(imp_rule, c("1/3", "1/2"))
-  flagsum <- sum(df[[avalcat_flagvar]])
-  flagsum_ratio <- flagsum / max(1, nrow(df))
+  n_blq <- sum(df[[avalcat_var]] %in% c("BLQ", "LTR", "<PCLLOQ"))
+  ltr_blq_ratio <- n_blq / max(1, nrow(df))
 
   # defaults
   val <- x_stats[[stat]]
   na_level <- "NE"
 
   if (imp_rule == "1/3") {
     if (!post && stat == "geom_mean") val <- NA # 1/3_pre_LT, 1/3_pre_GT
-    if (flagsum_ratio > 1 / 3) {
+    if (ltr_blq_ratio > 1 / 3) {
       if (stat != "geom_mean") na_level <- "ND" # 1/3_pre_GT, 1/3_post_GT
       if (!post && !stat %in% c("median", "max")) val <- NA # 1/3_pre_GT
       if (post && !stat %in% c("median", "max", "geom_mean")) val <- NA # 1/3_post_GT
     }
   } else if (imp_rule == "1/2") {
-    if (flagsum_ratio > 1 / 2 && !stat == "max") {
+    if (ltr_blq_ratio > 1 / 2 && !stat == "max") {
       val <- NA # 1/2_GT
       na_level <- "ND" # 1/2_GT
     }

diff --git a/man/imputation_rule.Rd b/man/imputation_rule.Rd
diff --git a/tests/testthat/test-analyze_vars_in_cols.R b/tests/testthat/test-analyze_vars_in_cols.R
@@ -280,7 +280,6 @@ testthat::test_that("analyze_vars_in_cols works with imputation rule", {
     VISIT = with_label(as.factor(rep(c(rep("Day 1", 5), rep("Day 2", 4)), 18)), "Visit"),
     NFRLT = with_label(as.factor(rep(c(0, seq(0, 42, 6)), 18)), "Nominal Time")
   )
-  df$FLAGSUM <- df$AVALCAT1 == "BLQ"
 
   # 1/3 imputation rule
   lyt <- basic_table() %>%

diff --git a/tests/testthat/test-imputation_rule.R b/tests/testthat/test-imputation_rule.R
@@ -6,7 +6,6 @@ df <- data.frame(
   VISIT = with_label(as.factor(rep(c(rep("Day 1", 5), rep("Day 2", 4)), 18)), "Visit"),
   NFRLT = with_label(as.factor(rep(c(0, seq(0, 42, 6)), 18)), "Nominal Time")
 )
-df$FLAGSUM <- df$AVALCAT1 == "BLQ"
 
 testthat::test_that("imputation_rule works correctly for 1/3 imputation rule", {
   x_stats <- s_summary(df$AVAL[df$NFRLT == 0])