Age groups are not mutually exclusive and our results do not match Table 2 Spec

[ddsjoberg]

Table 2 is the first table most visitors to the site will take a look at. In the data prep section we have this code:

# Pre-Processing - Add any variables needed in your table to df
adsl <- adsl %>%
  dplyr::mutate(AGEGR1 = as.factor(dplyr::case_when(
    AGE >= 17 & AGE < 65 ~ ">=17 to <65",
    AGE >= 65 ~ ">=65",
    AGE >= 65 & AGE < 75 ~ ">=65 to <75",
    AGE >= 75 ~ ">=75"
  )))

This variable we're creating is a bit confusing because these categories are not mutually exclusive. For example, one patient who is 70 years old would fall into ">=65" and ">=65 to <75". But this can't be summarized from a single variable, but the code make it look like we're trying to do so.

I see from the spec, that these non-exclusive groups appear. But to match the spec, we'd need to update our summary code:

I would expect we'd need something more like this:

library(gtsummary)

adsl <- 
  scda::synthetic_cdisc_dataset("rcd_2022_10_13", "adsl") %>%
  mutate(
    AGEGR1 = dplyr::case_when(
      AGE >= 17 & AGE < 65 ~ ">=17 to <65",
      AGE >= 65 ~ ">=65"
    ) %>%
      formatters::with_label("Age Group, years"),
    AGEGR1_v2 = (AGE >= 65 & AGE < 75) %>% 
      formatters::with_label(">=65 to <75"),
    AGEGRP_v3 = (AGE >= 75) %>% 
      formatters::with_label(">=75")
  )

tbl <-
  tbl_summary(
  adsl, 
  include = c(AGEGR1, AGEGR1_v2, AGEGRP_v3),
  by = ARM
) |> 
  modify_table_styling(
    columns = label, 
    rows = variable %in% c("AGEGR1_v2", "AGEGRP_v3"),
    text_format = "indent"
  )

^{Created on 2023-11-08 with reprex v2.0.2}