add_p_test_safe.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables

testing statistical tests

Code
  .get_add_p_test_fun(class = "tbl_summary", test = "t.test") %>%
    .run_add_p_test_fun(data = trial, variable = "age", by = "trt", type = "continuous",
      group = NULL) %>% purrr::pluck("df_result")
Output
  # A tibble: 1 x 10
    estimate statistic parameter conf.low conf.high p.value method       estimate1
       <dbl>     <dbl>     <dbl>    <dbl>     <dbl>   <dbl> <chr>            <dbl>
  1   -0.438    -0.209      184.    -4.57      3.69   0.834 Welch Two S~      47.0
  # i 2 more variables: estimate2 <dbl>, alternative <chr>

Code
  .get_add_p_test_fun(class = "tbl_summary", test = "lme4") %>%
    .run_add_p_test_fun(data = trial, variable = "age", by = "trt", type = "continuous",
      group = "stage") %>% purrr::pluck("df_result")
Message
  boundary (singular) fit: see help('isSingular')
  boundary (singular) fit: see help('isSingular')
Output
  # A tibble: 1 x 2
    p.value method                              
      <dbl> <chr>                               
  1   0.833 random intercept logistic regression

Code
  .get_add_p_test_fun(class = "tbl_summary", test = "lme4") %>%
    .run_add_p_test_fun(data = trial, variable = "response", by = "trt", type = "categorical",
      group = "stage") %>% purrr::pluck("df_result")
Message
  boundary (singular) fit: see help('isSingular')
  boundary (singular) fit: see help('isSingular')
Output
  # A tibble: 1 x 2
    p.value method                              
      <dbl> <chr>                               
  1   0.530 random intercept logistic regression