################################################################################
# TABLE     : t_ae_by_soc_pt  (Crossover - 2x2 or Williams)
# TITLE     : Treatment-Emergent Adverse Events by System Organ Class and
#             Preferred Term
# POPULATION: Safety Population (SAFFL == "Y")
# INPUT     : ADAE (TRTEMFL == "Y"); ADEX for per-treatment denominators
# NOTE      : PSEUDOCODE. Counts = PARTICIPANTS with >=1 event (n_distinct USUBJID),
#             NOT event rows. Columns = actual treatment (TRTA), since in a
#             crossover an AE is attributed to the treatment in effect when it
#             emerged. % denominator = participants exposed to that treatment from
#             ADEX (period-bearing), NOT ADSL. SOC sorted by overall participant
#             frequency desc; PT within SOC desc. An optional by-APERIOD split
#             (dv$byperiod) uses per-period ADEX denominators.
################################################################################
source("../00_setup_helpers.R")
env <- setup(study = "CP-101", adam = "/data/adam", out = "/data/tfl")
dv  <- design_vars("CROSSOVER")                 # trtvar=TRTA, byperiod=APERIOD/APERIODC

## --- per-TREATMENT denominators from ADEX (participants dosed per TRTA) ----------
denom <- adam$adex %>%
  filter(SAFFL == "Y") %>%
  group_by(trt = .data[[dv$trtvar]]) %>%
  summarise(N = n_distinct(USUBJID), .groups = "drop") %>%
  bind_rows(tibble(trt = "Total",
                   N = n_distinct(adam$adex$USUBJID[adam$adex$SAFFL == "Y"])))

adae <- adam$adae %>% filter(SAFFL == "Y", TRTEMFL == "Y")   # treatment-emergent

## 1) "Any TEAE" overall row (distinct participants, any event) per treatment + Total
any_te <- bind_rows(
  adae %>% group_by(trt = .data[[dv$trtvar]]) %>%
    summarise(nsubj = n_distinct(USUBJID), .groups = "drop"),
  tibble(trt = "Total", nsubj = n_distinct(adae$USUBJID))) %>%
  mutate(AESOC = NA_character_, AEDECOD = NA_character_, level = 0L,
         term = "Participants with any TEAE")

## 2) by SOC (distinct participants within SOC), per treatment + Total
soc <- bind_rows(
  aecount(adae, trtvar = dv$trtvar, byvars = "AESOC"),
  adae %>% group_by(AESOC) %>%
    summarise(nsubj = n_distinct(USUBJID), .groups = "drop") %>% mutate(trt = "Total")) %>%
  mutate(level = 1L, term = AESOC)

## 3) by SOC*PT (distinct participants within SOC and PT), per treatment + Total
socpt <- bind_rows(
  aecount(adae, trtvar = dv$trtvar, byvars = c("AESOC","AEDECOD")),
  adae %>% group_by(AESOC, AEDECOD) %>%
    summarise(nsubj = n_distinct(USUBJID), .groups = "drop") %>% mutate(trt = "Total")) %>%
  mutate(level = 2L, term = paste0("   ", AEDECOD))   # indent PT under SOC

## ordering: SOC by overall participant count desc; PT within SOC desc
soc_ord <- adae %>% group_by(AESOC) %>%
  summarise(socn = n_distinct(USUBJID), .groups = "drop")
pt_ord  <- adae %>% group_by(AESOC, AEDECOD) %>%
  summarise(ptn = n_distinct(USUBJID), .groups = "drop")

## assemble: Any TEAE -> SOC -> indented PT, n (%) per treatment + Total
rep <- bind_rows(any_te, soc, socpt) %>%
  left_join(denom, by = "trt") %>%
  mutate(value = n_pct(nsubj, N)) %>%
  left_join(soc_ord, by = "AESOC") %>%
  left_join(pt_ord,  by = c("AESOC","AEDECOD")) %>%
  arrange(desc(socn), AESOC, level, desc(ptn)) %>%
  select(term, level, trt, value) %>%
  pivot_wider(names_from = trt, values_from = value)

ttl <- tfl_titles(num = "14.3.1.2", type = "Table",
   text = "Treatment-Emergent Adverse Events by System Organ Class and Preferred Term",
   pop  = "Safety Population",
   foot = paste0("A participant is counted once at each level. Columns = actual treatment (TRTA); ",
                 "AE attributed to treatment in effect at onset. % = participants with the event / N exposed (ADEX). {meddra}."))

## rtables/gt rendering; SOC bold (level 1), PT indented (level 2)
print(rep)