Estimate Marginal Subgroup Treatment Effects

The function uses a counterfactual, marginal approach based on the posterior predictive distribution. It averages over all other covariates to provide robust estimates of subgroup-specific effects.

Usage

estimate_subgroup_effects(
  brms_fit,
  original_data,
  trt_var,
  subgroup_vars = "auto",
  response_type = c("continuous", "binary", "count", "survival"),
  ndraws = NULL
)

Arguments

brms_fit

A fitted `brmsfit` object from `fit_brms_model()` or `run_brms_analysis()`.

original_data

A `data.frame` containing the data. While this parameter is called "original_data" for backward compatibility, the function internally uses the processed data from `brms_fit$data` to ensure consistency with the model's factor coding and contrasts. This parameter is mainly used for validation purposes.

trt_var

A character string specifying the name of the treatment variable.

subgroup_vars

A character vector of subgroup variable names found in `original_data`. If set to `"auto"` (the default), the function attempts to automatically identify subgroup variables from the model's formula.

response_type

The type of outcome variable. One of "binary", "count", "continuous", or "survival".

ndraws

An integer specifying the number of posterior draws to use. If `NULL` (default), all available draws are used.

Value

A `data.frame` (tibble) where each row corresponds to a subgroup (or the "Overall" effect), providing the estimated marginal effect and posterior summaries (e.g., `mean`, `sd`, `q2.5`, `q97.5`).

Details

This post-processing function estimates marginal treatment effects for specified subgroups from a fitted `brmsfit` object.