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

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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)

gtsummary r
1 Answers

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))

enter image description here Created on 2022-02-11 by the reprex package (v2.0.1)

user_87112760
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