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

FDA Table 45

table

FDA

safety

adverse events

Code

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

adsl <- pharmaverseadam::adsl
adae <- pharmaverseadam::adae

set.seed(1)
adae <- adae |>
  mutate(
    OCMQ01SC = as.factor(sample(c("BROAD", "NARROW"), nrow(adae), replace = TRUE)),
    OCMQ01NAM = if_else(
      SEX == "F",
      as.factor(sample(
        c("Abnormal Uterine Bleeding", "Amenorrhea", "Bacterial Vaginosis", "Decreased Menstrual Bleeding"),
        n(),
        replace = TRUE
      )),
      NA_character_
    )
  )

# Pre-processing --------------------------------------------
data <- adae |>
  filter(
    SAFFL == "Y",
    SEX == "F",
    OCMQ01SC == "BROAD",
    # Filtering to reduce the size of the demo table
    AEDECOD %in% c("APPLICATION SITE IRRITATION", "ERYTHEMA", "APPLICATION SITE PRURITUS", "BACK PAIN")
  ) |>
  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 Female 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 (Broad)",
      AEDECOD ~ "Preferred Term"
    )
  )

tbl

Code

ard <- gather_ard(tbl)
ard

$tbl_hierarchical

{cards} data frame: 126 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        Placebo  tabulate         n          n     7        7       0                   stat_1
2    <NA>                   <NA>                TRT01A        Placebo  tabulate         N          N    40       40       0                   stat_1
3    <NA>                   <NA>                TRT01A        Placebo  tabulate         p          % 0.175     17.5    <fn>                   stat_1
4    <NA>                   <NA>                TRT01A      Xanomeli…  tabulate         n          n    13       13       0                   stat_2
5    <NA>                   <NA>                TRT01A      Xanomeli…  tabulate         N          N    40       40       0                   stat_2
6    <NA>                   <NA>                TRT01A      Xanomeli…  tabulate         p          % 0.325     32.5    <fn>                   stat_2
7    <NA>                   <NA>                TRT01A      Xanomeli…  tabulate         n          n    20       20       0                   stat_3
8    <NA>                   <NA>                TRT01A      Xanomeli…  tabulate         N          N    40       40       0                   stat_3
9    <NA>                   <NA>                TRT01A      Xanomeli…  tabulate         p          %   0.5     50.0    <fn>                   stat_3
10 TRT01A      Placebo OCMQ01NAM    Abnormal…  AEDECOD      APPLICAT… hierarch…         n          n     1        1    <fn>                   stat_1

ℹ 116 more rows

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