---
title: "Stratified Analysis"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Stratified Analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Stratified Analysis

Stratified regression repeats the analysis inside each subgroup and places the
results side by side. It is useful when the same association may look different
across groups.

```{r strata-setup, message=FALSE, warning=FALSE}
library(gtregression)
library(dplyr)

data("data_birthwt", package = "gtregression")

birthwt_data <- data_birthwt |>
  mutate(
    race = factor(race, levels = c(1, 2, 3),
                  labels = c("White", "Black", "Other")),
    smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
    ht = factor(ht, levels = c(0, 1), labels = c("No", "Yes")),
    ui = factor(ui, levels = c(0, 1), labels = c("No", "Yes")),
    low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW")),
    ptl_cat = factor(ifelse(ptl > 0, "Yes", "No"), levels = c("No", "Yes"))
  )
```

## Univariable by Stratum

```{r strata-uni, message=FALSE, warning=FALSE}
strata_uni <- stratified_uni_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("age", "lwt", "smoke", "ht", "ui", "ptl_cat"),
  stratifier = "race",
  approach = "logit",
  theme = clinical
)

strata_uni$table
```

## Adjusted by Stratum

Use `adjust_for` when each exposure should be adjusted for the same variables
within each stratum.

```{r strata-multi, message=FALSE, warning=FALSE}
strata_multi <- stratified_multi_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat"),
  stratifier = "race",
  adjust_for = c("age", "lwt"),
  approach = "logit",
  theme = striped
)

strata_multi$table
```

## What To Inspect

- `$table`: rendered side-by-side table.
- `$table_display`: wide data used to build the table.
- `$models`: fitted models by stratum.
- `$reg_check`: diagnostics for linear models.