Adding the prevalence of an outcome to a gtsummary::tbl_uvregression univariable regression table

Question

I would like to add the prevalence of an outcome to a univariable regression table generated with gtsummary::tbl_uvregression. Is there a way to do this without converting the tbl_uvregression object to a table and calculating it with the dplyr::mutate function as in the example below?

PS: Daniel Sjoberg, thank you for a very helpful package!

options(digits = 3)
library(gtsummary)
#> #BlackLivesMatter
library(tidyverse)
library(knitr)
library(geepack)

packageVersion("gtsummary")
#> [1] '1.5.1'

data(dietox, package = "geepack")

dietox %>%
  mutate(big_pig = if_else(Weight > 50, 1, 0)) %>%
  select(big_pig, Pig, Evit, Cu, Litter) %>%
 
  tbl_uvregression(
    y = big_pig,
    method = geeglm,
    method.args = list(id = Pig, family = poisson, corstr = "independence"),
    include = -Pig,
    exponentiate = TRUE
  ) %>%
  add_n(location = c("level", "label")) %>%
  add_nevent(location = c("level", "label")) %>%
  as_tibble() %>%
  mutate(Prevalence = (`**Event N**` / as.integer(`**N**`))*100) %>%
  select(`**Characteristic**`,`**N**`,`**Event N**`,Prevalence, everything()) %>%
  kable()

Characteristic	N	Event N	Prevalence	IRR	95% CI	p-value
Evit	861	519	60.3	NA	NA	NA
Evit000	276	167	60.5	NA	NA	NA
Evit100	286	178	62.2	1.03	0.95, 1.12	0.5
Evit200	299	174	58.2	0.96	0.88, 1.05	0.4
Cu	861	519	60.3	NA	NA	NA
Cu000	274	164	59.9	NA	NA	NA
Cu035	300	176	58.7	0.98	0.91, 1.06	0.6
Cu175	287	179	62.4	1.04	0.96, 1.13	0.3
Litter	861	519	60.3	NA	NA	NA
1	48	31	64.6	NA	NA	NA
2	36	25	69.4	1.08	0.99, 1.17	0.091
3	48	25	52.1	0.81	0.64, 1.02	0.077
4	48	29	60.4	0.94	0.86, 1.01	0.10
5	36	25	69.4	1.08	0.90, 1.28	0.4
6	48	31	64.6	1.00	0.93, 1.08	>0.9
7	24	12	50.0	0.77	0.61, 0.98	0.035
8	48	28	58.3	0.90	0.75, 1.08	0.3
9	33	18	54.5	0.84	0.72, 0.99	0.043
10	36	24	66.7	1.03	0.98, 1.09	0.3
11	36	20	55.6	0.86	0.78, 0.95	0.002
12	36	21	58.3	0.90	0.78, 1.04	0.2
13	36	21	58.3	0.90	0.86, 0.95	<0.001
16	48	32	66.7	1.03	0.93, 1.14	0.5
17	36	24	66.7	1.03	0.98, 1.09	0.3
18	36	21	58.3	0.90	0.86, 0.95	<0.001
20	48	30	62.5	0.97	0.89, 1.05	0.5
21	48	30	62.5	0.97	0.79, 1.19	0.8
22	48	27	56.2	0.87	0.74, 1.03	0.11
23	48	24	50.0	0.77	0.63, 0.95	0.016
24	36	21	58.3	0.90	0.86, 0.95	<0.001

^{Created on 2022-02-11 by the reprex package (v2.0.1)}

Answer 1

A gtsummary table contains an internal data frame called .$table_body, and you can modify that data frame directly using the modify_table_body() function. In the example below, I calculate the prevalence and format the results to display in the table. I also expose the already existing column exposure, which is the sum of the exposure/followup time. Happy Programming!

library(gtsummary)
library(tidyverse)
library(geepack)

packageVersion("gtsummary")
#> [1] '1.5.2'

data(dietox, package = "geepack")

tbl <- 
  dietox %>%
  mutate(big_pig = if_else(Weight > 50, 1, 0)) %>%
  select(big_pig, Pig, Evit, Cu) %>%
  tbl_uvregression(
    y = big_pig,
    method = geeglm,
    method.args = list(id = Pig, family = poisson, corstr = "independence"),
    include = -Pig,
    exponentiate = TRUE
  ) %>%
  add_n(location = c("level", "label")) %>%
  add_nevent(location = c("level", "label")) %>%
  # calculate prev
  modify_table_body(
    ~.x %>%
      mutate(
        prev = stat_nevent / stat_n * 100
      ) %>%
      relocate(prev, exposure, .after = stat_nevent)
  ) %>%
  # format the newly added stats
  modify_header(prev = "**Prevalence**", exposure = "**Total Exposure Time**") %>%
  modify_fmt_fun(list(prev = function(x) paste0(style_sigfig(x), "%"), 
                      exposure = style_sigfig))

^{Created on 2022-02-11 by the reprex package (v2.0.1)}