Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals

TL;DR

This paper develops methods to extend causal inferences from randomized trial participants to all eligible individuals, including those not randomized, using baseline covariate data and estimators evaluated through simulations and real-world application.

Contribution

It introduces new estimators for generalizing trial results to broader populations using baseline covariates and assesses their performance.

Findings

Estimators accurately estimate treatment effects in simulations.

Application to coronary artery disease trial demonstrates practical utility.

Method improves generalizability of trial findings.

Abstract

We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.