TSICM08

Concomitant Medications of Interest

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Output

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# Program Name:              tsicm08.R

# Prep Environment

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

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSICM08"
fileid <- tblid
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

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

codedtermvar <- "CMBASPRF"

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:

# Read in required 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(codedtermvar), starts_with("CQ")) %>%
  select(USUBJID, ONTRTFL, all_of(codedtermvar), ends_with("NAM"))

# Work out how many CQzzNAM vars we have
cqzznamvars <- adcm %>%
  select(starts_with("CQ"))

countcqzznamvars <- length(names(cqzznamvars))

# Create new variable that binds all special interest data together in vertical structure
mat <- matrix(ncol = 0, nrow = 0)
adcm_v <- data.frame(mat)

for (i in 1:countcqzznamvars) {
  if (i < 10) {
    ix <- paste0("0", i)
  } else {
    ix <- i
  }

  cqdata <- adcm %>%
    mutate(cqnamvar = as.factor(!!as.name(paste0("CQ", ix, "NAM")))) %>%
    filter(!is.na(cqnamvar))

  adcm_v <- bind_rows(adcm_v, cqdata)
}

# Convert medications to sentence case
adcm_v[[codedtermvar]] <- as.factor(stringr::str_to_sentence(adcm_v[[
  codedtermvar
]]))

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

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

if (length(adcm$ONTRTFL) == 0) {
  cm <- adcm_v %>% 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(
  denom = "n_altdf",
  .stats = c("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 (length(adcm$ONTRTFL) == 0) {
  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(
    "cqnamvar",
    child_labels = "hidden",
    split_label = "Interest Category",
    label_pos = "topleft",
    split_fun = trim_levels_in_group(codedtermvar),
    section_div = c(" "),
    indent_mod = 0L
  ) %>%
  summarize_row_groups(
    "cqnamvar",
    cfun = a_freq_j,
    extra_args = extra_args_1
  ) %>%
  analyze(codedtermvar, afun = a_freq_j, extra_args = (extra_args_1)) %>%
  append_topleft("  Base Preferred Term")

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

# If there is no data remove top 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"
    )
  )
}

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

if (length(adcm$ONTRTFL) != 0) {
  result <- sort_at_path(
    result,
    c("cqnamvar"),
    scorefun = jj_complex_scorefun(colpath = "Total")
  )
  result <- sort_at_path(
    result,
    c("cqnamvar", "*", codedtermvar),
    scorefun = jj_complex_scorefun(colpath = "Total")
  )
}

# 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")

TSICM08: Concomitant Medications of Interest; Safety Analysis Set (Study jjcs - core)
	Active Study Agent
Interest Category	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo	Total
Base Preferred Term	N=53	N=73	N=126	N=59	N=185
Steroid	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%)
Acetylsalicylic Acid	0	1 (1.4%)	1 (0.8%)	1 (1.7%)	2 (1.1%)
Acetylsalicylic acid	0	1 (1.4%)	1 (0.8%)	1 (1.7%)	2 (1.1%)
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.

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