gtsummary R package: pre-post summary table with paired 2-sample tests?

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Is it possible to use the gtsummary R package to make a pre-post summary table with 2 columns that summarize multiple variables at 2 different time points?

I know the arsenal R package supports this, but I would prefer to use gtsummary if possible since it supports the tidyverse.

For example, is it possible to make a pre-post summary table using gtsummary that is similar to the table in this example? Here is a simpler version of the dataset from their example:

 dat <- data.frame(
  tp = paste0("Time Point ", c(1, 2, 1, 2, 1, 2, 1, 2, 1, 2)),
  id = c(1, 1, 2, 2, 3, 3, 4, 4, 5, 6),
  Cat = c("A", "A", "A", "B", "B", "B", "B", "A", NA, "B"),
  Fac = factor(c("A", "B", "C", "A", "B", "C", "A", "B", "C", "A")),
  Num = c(1, 2, 3, 4, 4, 3, 3, 4, 0, NA),
  stringsAsFactors = FALSE)

Note the dataset is in "long format": tp is the 2 pre-post time points, and id is the subject ID for the 2 repeated measures. To make the table, Cat and Fac are categorical variables that would be summarized as count(%) at each time point, and use McNemar's test to compare if they change over time. Num is a numeric variable that would be summarized as mean(standard deviation) at each time point, and use paired t-test to assess change over time.

gtsummary r
1 Answers

Yes, as of gtsummary v1.3.6, there is a function called add_difference() for this express purpose. The function supports both paired (e.g. pre- and post-responses), and unpaired data. The method is specified in the test= argument.

Worked example here: http://www.danieldsjoberg.com/gtsummary/articles/gallery.html#paired-test

Here's an unpaired example:

  trial %>%
  select(trt, age, marker, response, death) %>%
  tbl_summary(
    by = trt,
    statistic =
      list(
        all_continuous() ~ "{mean} ({sd})",
        all_dichotomous() ~ "{p}%"
      ),
    missing = "no"
  ) %>%
  add_n() %>%
  add_difference()

enter image description here

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