multi_reg: Multivariable Regression (Adjusted Odds, Risk, or Rate...

In gtregression: Tools for Creating Publication-Ready Regression Tables

Multivariable Regression (Adjusted Odds, Risk, or Rate Ratios)

Description

Fits multivariable regression models for binary, count, or continuous outcomes and returns a publication-ready summary table using 'gtsummary'. Depending on the specified 'approach', the function estimates adjusted Odds Ratios (OR), Risk Ratios (RR), Incidence Rate Ratios (IRR), or Beta coefficients.

Usage

multi_reg(data, outcome, exposures, approach = "logit")

Arguments

data	A data frame containing the analysis variables.
outcome	The name of the outcome variable. Must be a character string.
exposures	A character vector of predictor variables to include.
approach	Modeling approach to use. One of: - '"logit"' for logistic regression (OR), - '"log-binomial"' for log-binomial regression (RR), - '"poisson"' for Poisson regression (IRR), - '"robpoisson"' for robust Poisson regression (RR), - '"linear"' for linear regression (Beta coefficients), - '"negbin"' for negative binomial regression (IRR).

Value

An object of class 'multi_reg', extending 'gtsummary::tbl_regression'. Additional components can be accessed using:

• x$models: List of fitted model objects.

• x$model_summaries: List of summary outputs.

• x$reg_check: Regression diagnostics (only for linear models).

• x$table: Returns the main regression table.

Accessors

$models

List of fitted model objects.

$model_summaries

A tibble of tidy regression summaries for each model.

See Also

[uni_reg()], [plot_reg()], [plot_reg_combine()]

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data(PimaIndiansDiabetes2, package = "mlbench")
  pima <- dplyr::mutate(PimaIndiansDiabetes2,
  diabetes = ifelse(diabetes == "pos", 1, 0))
  model <- multi_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "mass"),
    approach = "logit"
  )
 print(model)
}

gtregression documentation built on Aug. 18, 2025, 5:23 p.m.