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.
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
)The output from an rms model.
If TRUE, combines the effect estimates and 95% confidence intervals into a single column. Default is TRUE.
Specifies the number of decimal places to display for the effect estimates. Default is 3.
Specifies the number of decimal places to display for P values. Default is 3.
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)
If TRUE, hides the individual coefficients for Restricted Cubic Spline (RCS) variables.
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.
If TRUE, includes all intermediate steps in the summary, allowing users to verify and compare with standard model outputs.
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.
Returns a dataframe of results. This can easily be outputted to word using packages such as flextable and officer.
Examples# For detailed examples please see the provided vignettes
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