Format the covariate effect simulation results for printing

Usage

print_coveff(
  coveffsim,
  n_sigfig = 3,
  use_seps = TRUE,
  drop_trailing_dec_mark = TRUE
)

Arguments

coveffsim: an object of class coveffsim
n_sigfig: Number of significant figures to form value_label of continuous variables. See gt::vec_fmt_number() for details.
use_seps: Whether to use separators for thousands in printing numbers. See gt::vec_fmt_number() for details.
drop_trailing_dec_mark: Whether to drop the trailing decimal mark (".") in value_label of continuous variables. See gt::vec_fmt_number() for details.

Value

A data frame with the formatted covariate effect simulation results with the following columns:

var_label: the label of the covariate
value_label: the label of the covariate value
value_annot: the annotation of the covariate value
Odds ratio or Response difference: the odds ratio (for binary models) or response difference (for linear models) of the covariate effect
95% CI: the 95% credible interval of the covariate effect

Details

Note that n_sigfig, use_seps, and drop_trailing_dec_mark are only applied to the response difference/odds ratio and 95% CI columns; value_label column was already generated in an earlier step in build_spec_coveff() or sim_coveff().

Examples

# \donttest{
data(d_sim_binom_cov_hgly2)

ermod_bin <- dev_ermod_bin(
  data = d_sim_binom_cov_hgly2,
  var_resp = "AEFLAG",
  var_exposure = "AUCss_1000",
  var_cov = "BHBA1C_5",
)

print_coveff(sim_coveff(ermod_bin))
#> # A tibble: 6 × 5
#>   var_label  value_label value_annot `Odds ratio` `95% CI`        
#>   <chr>      <chr>       <chr>       <chr>        <chr>           
#> 1 AUCss_1000 0.868       5th         0.548        "[0.460, 0.646]"
#> 2 AUCss_1000 2.21        median      1            " "             
#> 3 AUCss_1000 5.30        95th        3.98         "[2.73, 5.95]"  
#> 4 BHBA1C_5   5.75        5th         0.276        "[0.202, 0.372]"
#> 5 BHBA1C_5   7.97        median      1            " "             
#> 6 BHBA1C_5   10.4        95th        4.20         "[3.01, 5.93]"  

# Linear regression model example
data(d_sim_lin)

ermod_lin <- dev_ermod_lin(
  data = d_sim_lin,
  var_resp = "response",
  var_exposure = "AUCss",
  var_cov = c("SEX", "BAGE"),
)

print_coveff(sim_coveff(ermod_lin))
#> # A tibble: 8 × 5
#>   var_label value_label value_annot `Response difference` `95% CI`         
#>   <chr>     <chr>       <chr>       <chr>                 <chr>            
#> 1 AUCss     5.00        5th         −20.4                 "[−23.0, −17.8]" 
#> 2 AUCss     50.0        median      0                     " "              
#> 3 AUCss     95.0        95th        20.4                  "[17.8, 23.0]"   
#> 4 SEX       F           1st freq    0                     " "              
#> 5 SEX       M           2nd freq    −4.14                 "[−7.54, −0.692]"
#> 6 BAGE      33.5        5th         −8.50                 "[−11.3, −5.56]" 
#> 7 BAGE      50.4        median      0                     " "              
#> 8 BAGE      67.0        95th        8.34                  "[5.46, 11.1]"   
# }