FDA Table 33

Subjects With Adverse Events by Male-Specific OCMQ (Narrow) and Preferred Term, Male Safety Population, Pooled Analysis (or Trial X)

table

FDA

safety

adverse events

Code

# Load libraries & data -------------------------------------
library(dplyr)
library(cards)
library(gtsummary)

adsl <- random.cdisc.data::cadsl
adae <- random.cdisc.data::cadae

set.seed(1)
adae <- rename(adae, OCMQ01SC = SMQ01SC, OCMQ01NAM = SMQ01NAM)
levels(adae$OCMQ01SC) <- c("BROAD", "NARROW")
adae$OCMQ01SC[is.na(adae$OCMQ01SC)] <- "NARROW"
adae$OCMQ01NAM <- factor(adae$OCMQ01NAM, levels = c(unique(adae$OCMQ01NAM), "Erectile Dysfunction", "Gynecomastia"))
adae$OCMQ01NAM[adae$SEX == "M"] <- as.factor(sample(c("Erectile Dysfunction", "Gynecomastia"), sum(adae$SEX == "M"), replace = TRUE))

# Pre-processing --------------------------------------------
data <- adae |>
  filter(
    SAFFL == "Y",
    SEX == "M",
    OCMQ01SC == "NARROW"
  ) |>
  select(OCMQ01SC, TRT01A, OCMQ01NAM, AEDECOD, USUBJID) |>
  # setting an explicit level for NA values so empty strata combinations are shown.
  mutate(across(everything(), ~ {
    if (anyNA(.)) {
      forcats::fct_na_value_to_level(as.factor(.), level = "<Missing>")
    } else {
      .
    }
  }))

# denominator values include only Male subjects in the arm with AEs
denom <- data |> distinct(USUBJID, TRT01A)

Code

tbl <- data |>
  tbl_hierarchical(
    by = TRT01A,
    variables = c(OCMQ01NAM, AEDECOD),
    id = USUBJID,
    denominator = denom,
    # variables to calculate rates for
    include = c(AEDECOD),
    label = list(
      OCMQ01NAM ~ "OCMQ (Narrow)",
      AEDECOD ~ "Preferred Term"
    )
  )

tbl

Code

ard <- gather_ard(tbl)
ard

$tbl_hierarchical

{cards} data frame: 153 x 15

   group1 group1_level    group2 group2_level variable variable_level   context stat_name stat_label  stat stat_fmt fmt_fun warning error gts_column
1    <NA>                   <NA>                TRT01A      A: Drug X  tabulate         n          n    49       49       0                   stat_1
2    <NA>                   <NA>                TRT01A      A: Drug X  tabulate         N          N   147      147       0                   stat_1
3    <NA>                   <NA>                TRT01A      A: Drug X  tabulate         p          % 0.333     33.3    <fn>                   stat_1
4    <NA>                   <NA>                TRT01A      B: Place…  tabulate         n          n    43       43       0                   stat_2
5    <NA>                   <NA>                TRT01A      B: Place…  tabulate         N          N   147      147       0                   stat_2
6    <NA>                   <NA>                TRT01A      B: Place…  tabulate         p          % 0.293     29.3    <fn>                   stat_2
7    <NA>                   <NA>                TRT01A      C: Combi…  tabulate         n          n    55       55       0                   stat_3
8    <NA>                   <NA>                TRT01A      C: Combi…  tabulate         N          N   147      147       0                   stat_3
9    <NA>                   <NA>                TRT01A      C: Combi…  tabulate         p          % 0.374     37.4    <fn>                   stat_3
10 TRT01A    A: Drug X OCMQ01NAM    Erectile…  AEDECOD      dcd A.1.… hierarch…         n          n    13       13    <fn>                   stat_1

ℹ 143 more rows

ℹ Use `print(n = ...)` to see more rows