FDA Table 18

Patients With Adverse Events by Female-Specific FDA Medical Query (Broad) and Preferred Term, Female Safety Population, Pooled Analyses

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 <- dplyr::rename(adae, FMQ01SC = SMQ01SC, FMQ01NAM = SMQ01NAM)
levels(adae$FMQ01SC) <- c("BROAD", "NARROW")
adae$FMQ01SC[is.na(adae$FMQ01SC)] <- "NARROW"
adae$FMQ01NAM <- factor(adae$FMQ01NAM, levels = c(
  unique(adae$FMQ01NAM), "Abnormal Uterine Bleeding", "Amenorrhea",
  "Bacterial Vaginosis", "Decreased Menstrual Bleeding"
))
adae$FMQ01NAM[adae$SEX == "F"] <- as.factor(
  sample(c(
    "Abnormal Uterine Bleeding", "Amenorrhea",
    "Bacterial Vaginosis", "Decreased Menstrual Bleeding"
  ), sum(adae$SEX == "F"), replace = TRUE)
)

# Pre-processing --------------------------------------------
adae <- adae |>
  filter(
    SAFFL == "Y",
    SEX == "F",
    FMQ01SC == "BROAD"
  )

adsl <- adsl |>
  filter(SAFFL == "Y") # safety population

Code

tbl <- adae |>
  select(FMQ01SC, ARM, FMQ01NAM, 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 {
      .
    }
  })) |>
  tbl_hierarchical(
    by = ARM,
    variables = c(FMQ01NAM, AEDECOD),
    id = USUBJID,
    denominator = adsl,
    # variables to calculate rates for
    include = c(AEDECOD),
    label = list(FMQ01NAM ~ "FMQ (Broad)", AEDECOD ~ "Preferred Term")
  )
tbl

Code

ard <- gather_ard(tbl)

ard

$tbl_hierarchical

{cards} data frame: 81 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>                   ARM      A: Drug X categori…         n          n   134      134       0                   stat_1
2    <NA>                  <NA>                   ARM      A: Drug X categori…         N          N   400      400       0                   stat_1
3    <NA>                  <NA>                   ARM      A: Drug X categori…         p          % 0.335     33.5    <fn>                   stat_1
4    <NA>                  <NA>                   ARM      B: Place… categori…         n          n   134      134       0                   stat_2
5    <NA>                  <NA>                   ARM      B: Place… categori…         N          N   400      400       0                   stat_2
6    <NA>                  <NA>                   ARM      B: Place… categori…         p          % 0.335     33.5    <fn>                   stat_2
7    <NA>                  <NA>                   ARM      C: Combi… categori…         n          n   132      132       0                   stat_3
8    <NA>                  <NA>                   ARM      C: Combi… categori…         N          N   400      400       0                   stat_3
9    <NA>                  <NA>                   ARM      C: Combi… categori…         p          %  0.33     33.0    <fn>                   stat_3
10    ARM    A: Drug X FMQ01NAM    Abnormal…  AEDECOD      dcd B.2.… hierarch…         n          n     6        6    <fn>                   stat_1

ℹ 71 more rows

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