TSICM04

Concomitant Medications Received by =xx% of Subjects

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Output

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# Program Name:              tsicm04.R

# Prep Environment

library(envsetup)
library(tern)
library(dplyr)
library(rtables)
library(junco)

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSICM04"
fileid <- tblid
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map


trtvar <- "TRT01A"
popfl <- "SAFFL"

atcvar <- "CMLVL1"
atclevel <- "1"

include_top_row <- FALSE

combined_colspan_trt <- TRUE

if (combined_colspan_trt == TRUE) {
  # Set up levels and label for the required combined columns
  add_combo <- add_combo_facet(
    "Combined",
    label = "Combined",
    levels = c("Xanomeline High Dose", "Xanomeline Low Dose")
  )

  # choose if any facets need to be removed - e.g remove the combined column for placebo
  rm_combo_from_placebo <- cond_rm_facets(
    facets = "Combined",
    ancestor_pos = NA,
    value = " ",
    split = "colspan_trt"
  )

  mysplit <- make_split_fun(post = list(add_combo, rm_combo_from_placebo))
}

# Process Data:

adsl <- pharmaverseadamjnj::adsl %>%
  filter(!!rlang::sym(popfl) == "Y") %>%
  select(USUBJID, all_of(trtvar), all_of(popfl))

adcm <- pharmaverseadamjnj::adcm %>%
  filter(ONTRTFL == "Y") %>%
  select(USUBJID, ONTRTFL, all_of(atcvar), CMBASPRF)

# Convert medications to sentence case
adcm[[atcvar]] <- as.factor(stringr::str_to_sentence(adcm[[atcvar]]))
adcm$CMBASPRF <- as.factor(stringr::str_to_sentence(adcm$CMBASPRF))

adsl$colspan_trt <- factor(
  ifelse(adsl[[trtvar]] == "Placebo", " ", "Active Study Agent"),
  levels = c("Active Study Agent", " ")
)

# join data together
cm <- adcm %>% inner_join(., adsl, by = c("USUBJID"))

if (length(adcm$ONTRTFL) == 0) {
  cm <- adcm %>% right_join(., adsl, by = c("USUBJID"))
}

colspan_trt_map <- create_colspan_map(
  adsl,
  non_active_grp = "Placebo",
  non_active_grp_span_lbl = " ",
  active_grp_span_lbl = "Active Study Agent",
  colspan_var = "colspan_trt",
  trt_var = trtvar
)

# Define layout and build table:

extra_args_1 <- list(.stats = "count_unique_fraction")

lyt <- rtables::basic_table(
  top_level_section_div = " ",
  show_colcounts = TRUE,
  colcount_format = "N=xx"
) %>%
  split_cols_by(
    "colspan_trt",
    split_fun = trim_levels_to_map(map = colspan_trt_map)
  )

if (combined_colspan_trt == TRUE) {
  lyt <- lyt %>%
    split_cols_by(trtvar, split_fun = mysplit)
} else {
  lyt <- lyt %>%
    split_cols_by(trtvar)
}

lyt <- lyt %>%
  add_overall_col("Total")

if (include_top_row == TRUE) {
  lyt <- lyt %>%
    analyze(
      "ONTRTFL",
      afun = a_freq_j,
      extra_args = append(
        extra_args_1,
        list(label = "Subjects with >=1 concomitant medication")
      )
    )
}

lyt <- lyt %>%
  split_rows_by(
    atcvar,
    child_labels = "hidden",
    split_label = paste0("ATC Level ", atclevel),
    label_pos = "topleft",
    split_fun = trim_levels_in_group("CMBASPRF"),
    section_div = c(" "),
    indent_mod = 0L
  ) %>%
  summarize_row_groups(atcvar, cfun = a_freq_j, extra_args = extra_args_1) %>%
  analyze("CMBASPRF", afun = a_freq_j, extra_args = (extra_args_1)) %>%
  append_topleft("  Base Preferred Term")

result <- build_table(lyt, cm, alt_counts_df = adsl)

# Post-Processing step to sort by descending count on total column:

if (length(adcm$ONTRTFL) != 0) {
  result <- sort_at_path(
    result,
    c("root", atcvar),
    scorefun = jj_complex_scorefun(colpath = "Total")
  )
  result <- sort_at_path(
    result,
    c("root", atcvar, "*", "CMBASPRF"),
    scorefun = jj_complex_scorefun(colpath = "Total")
  )

  ################################################################################
  # Prune table to only keep those that meet x% criteria on the total column, but top row is not subject to pruning (if optionally displayed)
  ################################################################################
  result <- safe_prune_table(
    result,
    prune_func = bspt_pruner(
      fraction = 0.05,
      keeprowtext = "Subjects with >=1 concomitant medication",
      cols = c("Total")
    )
  )
}

# If optional top row is required but there is no data remove it row and display "No data to display" text
if (length(adcm$ONTRTFL) == 0) {
  result <- safe_prune_table(
    result,
    prune_func = remove_rows(
      removerowtext = "Subjects with >=1 concomitant medication"
    )
  )
}

# Add titles and footnotes:

result <- set_titles(result, tab_titles)

# Convert to tbl file and output table

tt_to_tlgrtf(string_map = string_map, tt = result, file = fileid, orientation = "landscape")

TSICM04: Concomitant Medications Received by =[xx]% of Subjects; Safety Analysis Set (Study jjcs - core)
	Active Study Agent
ATC Level 1	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo	Total
Base Preferred Term	N=53	N=73	N=126	N=59	N=185
Uncoded	34 (64.2%)	35 (47.9%)	69 (54.8%)	34 (57.6%)	103 (55.7%)
Uncoded: hydrocortisone,  topical	11 (20.8%)	12 (16.4%)	23 (18.3%)	4 (6.8%)	27 (14.6%)
Uncoded: cortisone	8 (15.1%)	4 (5.5%)	12 (9.5%)	4 (6.8%)	16 (8.6%)
Uncoded: benadryl	7 (13.2%)	6 (8.2%)	13 (10.3%)	1 (1.7%)	14 (7.6%)
Systemic hormonal  preparations, excl.	7 (13.2%)	9 (12.3%)	16 (12.7%)	1 (1.7%)	17 (9.2%)
Hydrocortisone	7 (13.2%)	9 (12.3%)	16 (12.7%)	1 (1.7%)	17 (9.2%)
Note: Concomitant medications are defined as any therapy used on or after the same day as the first dose of study treatment [up to last dose of study treatment +xx days}, including those that started before and continue on after the first dose of study treatment.

Download RTF file