Effect size estimates for tbl_svysummary - gtsummary

Question

I had a quick question. Is there any way to add effect size estimates (i.e., Cohen's D and/or Cramer's V) for tbl_svysummary when comparing demographic factors to one another? I am looking for pretty much the same answer that was provided for this post (How to add the Chi-square effect size Cramer's V in the summary table using R package “gtsummary”?)
library(gtsummary)
my_ES_test <- function(data, variable, by, ...) {
rstatix::cohens_d(data, as.formula(glue::glue("{variable} ~
{by}")))$effsize
}
my_cramer_v <- function(data, variable, by, ...) {
table(data[[variable]], data[[by]]) %>%
rstatix::cramer_v()
}
gtTable <-
mtcars %>%
select(hp, vs, am) %>%
tbl_summary(by = vs) %>%
add_p() %>%
add_stat(
fns = list(all_continuous() ~ my_ES_test,
all_categorical() ~ my_cramer_v)) %>%
modify_header(add_stat_1 ~ "**Effect size**")
however, when I tried the methodology suggested therein, it did not work for tbl_svysummary. Example below:
library(tidyverse)
library(rstatix)
library(gtsummary)
my_ES_test <- function(data, variable, by, ...) {
rstatix::cohens_d(data, as.formula(glue::glue("{variable} ~
{by}")))$effsize
}
my_cramer_v <- function(data, variable, by, ...) {
table(data[[variable]], data[[by]]) %>%
rstatix::cramer_v()
}
tbl_svysummary_ex1 <-
survey::svydesign(~1, data = as.data.frame(Titanic), weights =
~Freq) %>%
tbl_svysummary(by = Survived, percent = "row", include =
c(Class, Age)) %>%
add_p(test = list(all_categorical() ~ "svy.chisq.test")) %>%
add_stat(
fns = list(all_continuous() ~ my_ES_test,
all_categorical() ~ my_cramer_v)) %>%
modify_header(add_stat_1 ~ "**Effect size**")
Furthermore, on the gtsummary website, there do not seem to be any instructions for how to do this in tbl_svysummary either. Any guidance here would be much appreciated!

Answer 1

The examples below shoe the statistic and the DFs. If you want an effect size that is not returned by default, then you'll need to write a custom method for add_difference() that includes the estimate.
library(gtsummary)
# create summary table
tbl_svysummary_ex1 <-
survey::svydesign(~1, data = as.data.frame(Titanic), weights = ~Freq) %>%
tbl_svysummary(by = Survived, percent = "row", include = c(Class, Age)) %>%
add_p(test = list(all_categorical() ~ "svy.chisq.test"))
tbl_svysummary_ex1$table_body |> names()
#> [1] "variable" "test_name" "var_type" "var_label" "row_type"
#> [6] "label" "stat_1" "stat_2" "test_result" "statistic"
#> [11] "p.value" "ndf" "ddf"
# unhide the statistic and DF columns by assigning a header
tbl_svysummary_ex1 |>
modify_header(
statistic = "**Chi-square**",
ndf = "**ndf**",
ddf = "**ddf**"
) |>
modify_fmt_fun(c(statistic, ndf, ddf) ~ style_sigfig) |>
as_kable()
Characteristic
No, N = 1,490
Yes, N = 711
Chi-square
p-value
ndf
ddf
Class
0.41
0.7
2.6
81
1st
122 (38%)
203 (62%)
2nd
167 (59%)
118 (41%)
3rd
528 (75%)
178 (25%)
Crew
673 (76%)
212 (24%)
Age
0.63
0.4
1.0
31
Child
52 (48%)
57 (52%)
Adult
1,438 (69%)
654 (31%)
Created on 2022-11-20 with reprex v2.0.2