Add the difference between two groups (typically mean difference), along with the difference confidence interval and pvalue.
add_difference( x, test = NULL, group = NULL, adj.vars = NULL, test.args = NULL, conf.level = 0.95, include = everything(), pvalue_fun = NULL, estimate_fun = style_sigfig )
x 


test  List of formulas specifying statistical tests to perform for each variable,
e.g. 
group  Column name (unquoted or quoted) of an ID or grouping variable.
The column can be used to calculate pvalues with correlated data.
Default is 
adj.vars  Variables to include in mean difference adjustment (e.g. in ANCOVA models) 
test.args  List of formulas containing additional arguments to pass to
tests that accept arguments. For example, add an argument for all ttests,
use 
conf.level  Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval. 
include  Variables to include in output. Input may be a vector of
quoted variable names, unquoted variable names, or tidyselect select helper
functions. Default is 
pvalue_fun  Function to round and format pvalues.
Default is style_pvalue.
The function must have a numeric vector input (the numeric, exact pvalue),
and return a string that is the rounded/formatted pvalue (e.g.

estimate_fun  Function to round and format difference. Default is 
Example 1
Example 2
# Example 1  add_difference_ex1 < trial %>% select(trt, age, marker) %>% tbl_summary(by = trt, statistic = all_continuous() ~ "{mean} ({sd})", missing = "no") %>% add_n() %>% add_difference() # Example 2  add_difference_ex2 < trial %>% select(trt, response, death) %>% tbl_summary(by = trt, statistic = all_dichotomous() ~ "{p}%", missing = "no") %>% modify_footnote(all_stat_cols() ~ NA) %>% add_n() %>% add_difference(estimate_fun = ~paste0(style_sigfig(. * 100), "%"))