rmsMD (version 1.0.1)

modelsummary_rms: Create model summary for rms models

Description

The modelsummary_rms function processes the output from models fitted using the rms package and generates a summarized dataframe of the results. This summary is tailored for publication in medical journals, presenting effect estimates, confidence intervals, and p-values.

Usage

modelsummary_rms(
  modelfit,
  combine_ci = TRUE,
  round_dp_coef = 3,
  round_dp_p = 3,
  rcs_overallp = TRUE,
  hide_rcs_coef = TRUE,
  exp_coef = NULL,
  fullmodel = FALSE,
  MI_lrt = FALSE
)

Value

Returns a dataframe of results. This can easily be outputted to word using packages such as flextable and officer.

Arguments

modelfit

The output from an rms model.

combine_ci

If TRUE, combines the effect estimates and 95% confidence intervals into a single column. Default is TRUE.

round_dp_coef

Specifies the number of decimal places to display for the effect estimates. Default is 3.

round_dp_p

Specifies the number of decimal places to display for P values. Default is 3.

rcs_overallp

If TRUE, provides an overall P value for Restricted Cubic Spline (RCS) terms, sourced from anova(modelfit). Automatically selects appropriate test (LR, F or Wald)

hide_rcs_coef

If TRUE, hides the individual coefficients for Restricted Cubic Spline (RCS) variables.

exp_coef

If TRUE, outputs the exponentiated coefficients (exp(coef)) as the effect estimates. Applicable only for model types other than ols, lrm, or cph. If NULL, no exponentiation is performed. Default is NULL.

fullmodel

If TRUE, includes all intermediate steps in the summary, allowing users to verify and compare with standard model outputs.

MI_lrt

If TRUE then overall p-values for RCS terms from models with multiple imputed data from fit.mult.impute will represent likelihood ratio chi-square tests from rms::processMI(), rather than Wald tests.

Examples

# For detailed examples please see the provided vignettes