stratified_uni_reg: Performs univariate regression for each exposure on a binary,...

In gtregression: Tools for Creating Publication-Ready Regression Tables

Performs univariate regression for each exposure on a binary, count, or continuous outcome, stratified by a specified variable. Produces a stacked 'gtsummary' table with one column per stratum, along with underlying models and diagnostics.

Description

Performs univariate regression for each exposure on a binary, count, or continuous outcome, stratified by a specified variable. Produces a stacked 'gtsummary' table with one column per stratum, along with underlying models and diagnostics.

Usage

stratified_uni_reg(data, outcome, exposures, stratifier, approach = "logit")

Arguments

data	A data frame containing the variables.
outcome	name of the outcome variable.
exposures	A vector specifying the predictor (exposure) variables.
stratifier	A character string specifying the stratifier
approach	Modeling approach to use. One of: '"logit"' (Odds Ratios), '"log-binomial"' (Risk Ratios), '"poisson"' (Incidence Rate Ratios), '"robpoisson"' (Robust RR), '"linear"' (Beta coefficients), '"negbin"' (Incidence Rate Ratios),.

Value

An object of class 'stratified_uni_reg', which includes: - 'table': A 'gtsummary::tbl_stack' object with stratified results, - 'models': A list of fitted models for each stratum, - 'model_summaries': A tidy list of model summaries, - 'reg_check': A tibble of regression diagnostics (when available).

Accessors

$table

Stacked stratified regression table.

$models

List of fitted model objects for each stratum.

$model_summaries

List of tidy model summaries.

$reg_check

Diagnostic check results (when applicable).

See Also

[multi_reg()], [plot_reg()], [identify_confounder()]

Examples

if (requireNamespace("mlbench", quietly = TRUE) &&
  requireNamespace("dplyr", quietly = TRUE)) {
  data(PimaIndiansDiabetes2, package = "mlbench")
  pima <- dplyr::mutate(
    PimaIndiansDiabetes2,
    diabetes = ifelse(diabetes == "pos", 1, 0),
    glucose_cat = dplyr::case_when(
      glucose < 140 ~ "Normal",
      glucose >= 140 ~ "High"
    )
  )
  stratified_uni <- stratified_uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "mass"),
    stratifier = "glucose_cat",
    approach = "logit"
  )
  stratified_uni$table
}

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