################################################################################
# TABLE     : t_baseline_characteristics  (Parallel-group)
# TITLE     : Baseline Disease and Clinical Characteristics
# POPULATION: Safety Population (SAFFL == "Y")
# INPUT     : ADSL  (+ baseline records from ADVS/ADLB if pulled via BASE)
# NOTE      : PSEUDOCODE. Complements t_demographics: baseline clinical state
#             (vitals, eGFR/creatinine clearance, smoking/alcohol, baseline
#             disease severity). Continuous: n, Mean (SD), Median, Min-Max;
#             categorical: n (%). Columns = treatment arms (TRT01A) + Total.
################################################################################
source("../00_setup_helpers.R")
env <- setup(study = "CP-101", adam = "/data/adam", out = "/data/tfl")
dv  <- design_vars("PARALLEL")                 # -> trtvar = TRT01A, trtnvar = TRT01AN

adsl <- adam$adsl %>% filter(SAFFL == "Y")     # one row per participant

## column denominators (N=) per arm + Total
denom <- bign(adsl, trtvar = dv$trtvar, popfl = "SAFFL")

## --- continuous baseline clinical block -----------------------------------
## Baseline vitals/renal carried on ADSL as ...BL fields (SBPBL/DBPBL/PULSEBL/
## CRCLBL/EGFRBL). If not on ADSL, derive from ADVS/ADLB ABLFL=="Y" via BASE.
cont_block <- function(var, label, dp = 1L, ord) {
  descstat(adsl, var = var, by = dv$trtvar, dp = dp) %>%
    transmute(trt = .data[[dv$trtvar]], characteristic = label, ord = ord,
              `n`         = as.character(n),
              `Mean (SD)` = paste(c_mean, c_sd),
              `Median`    = c_median,
              `Min, Max`  = c_minmax) %>%
    pivot_longer(c(`n`,`Mean (SD)`,`Median`,`Min, Max`),
                 names_to = "stat", values_to = "value")
}
cont <- bind_rows(
  cont_block("SBPBL",  "Systolic BP (mmHg)",          1L, 1),
  cont_block("DBPBL",  "Diastolic BP (mmHg)",         1L, 2),
  cont_block("PULSEBL","Pulse Rate (bpm)",            1L, 3),
  cont_block("CRCLBL", "Creatinine Clearance (mL/min)",1L, 4),
  cont_block("EGFRBL", "eGFR (mL/min/1.73m^2)",       1L, 5))

## --- categorical baseline block -------------------------------------------
cat_block <- function(var, label, ord) {
  catfreq(adsl, var = var, by = dv$trtvar, denom = denom) %>%
    transmute(trt = .data[[dv$trtvar]], characteristic = label, ord, stat = cat, value = disp)
}
catg <- bind_rows(
  cat_block("SMOKSTAT", "Smoking Status n (%)",       6),
  cat_block("ALCOHOL",  "Alcohol Use n (%)",          7),
  cat_block("RENALCAT", "Renal Function Group n (%)", 8),   # Normal/Mild/Moderate
  cat_block("DISSEVBL", "Baseline Disease Severity n (%)", 9))

## --- stack, one column per treatment, render ------------------------------
tab <- bind_rows(cont, catg) %>%
  pivot_wider(names_from = trt, values_from = value) %>%
  arrange(ord)

ttl <- tfl_titles(num = "14.1.3", type = "Table",
   text = "Baseline Disease and Clinical Characteristics",
   pop  = "Safety Population",
   foot = "Percentages based on the number of participants in the Safety Population per arm. Baseline = last non-missing value on or before first dose (ABLFL).")

lyt <- basic_table(title = ttl$titles[3], main_footer = ttl$footnotes) %>%
  split_rows_by("characteristic", page_by = FALSE) %>%
  analyze("stat", afun = function(x) in_rows(.list = as.list(x)))
# tbl <- build_table(lyt, tab)   ## or gt::gt(tab)
print(tab)