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insightsengineering · Sep 18, 2023 · f21a939 · f21a939
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diff --git a/book/tables/pharmacokinetic/pkct01.qmd b/book/tables/pharmacokinetic/pkct01.qmd
@@ -10,141 +10,6 @@ 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$NFRLT),
-#     label_pos = "topleft",
-#     child_labels = "hidden"
-#   )
-```
-
-## Standard Table (Stats in Columns)
-
-```{r variant1, test = list(result_v1 = "result")}
-# lyt <- lyt_rows %>%
-#   analyze_vars_in_cols(
-#     vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
-#     .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
-#     .formats = c(
-#       n = "xx.", n_blq = "xx.", mean = format_sigfig(3), sd = format_sigfig(3), cv = "xx.x", median = format_sigfig(3),
-#       geom_mean = format_sigfig(3), geom_cv = "xx.x", min = format_sigfig(3), max = format_sigfig(3)
-#     ),
-#     .labels = c(
-#       n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
-#       geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
-#     ),
-#     cache = TRUE,
-#     na_level = "NE",
-#     .aligns = "decimal"
-#   )
-
-# result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()
-
-# Decorating
-# main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
-# subtitles(result) <- c("Protocol: xxxxx", paste("Analyte: ", unique(adpc_1$PARAM)), paste("Treatment:", unique(adpc_1$ACTARM)))
-# main_footer(result) <- "NE: Not Estimable"
-
-# result
-```
-
-## Table Implementing 1/3 Imputation Rule
-
-```{r variant2, test = list(result_v2 = "result")}
-# lyt <- lyt_rows %>%
-#   analyze_vars_in_cols(
-#     vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
-#     .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
-#     .formats = c(
-#       n = "xx.", n_blq = "xx.", mean = format_sigfig(3), sd = format_sigfig(3), cv = "xx.x", median = format_sigfig(3),
-#       geom_mean = format_sigfig(3), geom_cv = "xx.x", min = format_sigfig(3), max = format_sigfig(3)
-#     ),
-#     .labels = c(
-#       n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
-#       geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
-#     ),
-#     cache = TRUE,
-#     imp_rule = "1/3",
-#     .aligns = "decimal"
-#   )
-
-# result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()
-
-# Decorating
-# main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
-# subtitles(result) <- c("Protocol: xxxxx", paste("Analyte: ", unique(adpc_1$PARAM)), paste("Treatment:", unique(adpc_1$ACTARM)))
-# main_footer(result) <- c("NE: Not Estimable", "ND: Not Derived")
-
-# result
-```
-
-## Table Implementing 1/2 Imputation Rule
-
-```{r variant3, test = list(result_v3 = "result")}
-# lyt <- lyt_rows %>%
-#   analyze_vars_in_cols(
-#     vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
-#     .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
-#     .formats = c(
-#       n = "xx.", n_blq = "xx.", mean = format_sigfig(3), sd = format_sigfig(3), cv = "xx.x", median = format_sigfig(3),
-#       geom_mean = format_sigfig(3), geom_cv = "xx.x", min = format_sigfig(3), max = format_sigfig(3)
-#     ),
-#     .labels = c(
-#       n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
-#       geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
-#     ),
-#     cache = TRUE,
-#     imp_rule = "1/2",
-#     .aligns = "decimal"
-#   )
-
-# result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()
-
-# Decorate table
-# main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
-# subtitles(result) <- c("Protocol: xxxxx", paste("Analyte: ", unique(adpc_1$PARAM)), paste("Treatment:", unique(adpc_1$ACTARM)))
-# main_footer(result) <- "ND: Not Derived"
-
-# result
-```
-
 
 ## `teal` App