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.

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
}
#> Running stratified univariate regression by: glucose_cat
#>   > Stratum: glucose_cat = High
#>   > Stratum: glucose_cat = Normal


  
Characteristic
      

        
glucose_cat = High
      
      

        
glucose_cat = Normal
      
    
N
      
OR
      
95% CI
      
p-value
      
N
      
OR
      
95% CI
      
p-value
    
age
197
1.01
0.99, 1.04
0.3
566
1.04
1.02, 1.06
<0.001
mass
197
1.07
1.02, 1.12
0.010
555
1.10
1.07, 1.14
<0.001
Abbreviations: CI = Confidence Interval, OR = Odds Ratio