Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals.
Pubmed ID: 30488513
Publication Date: June 1, 2019
Affiliation: Harvard-MIT Division of Health Sciences and Technology, Boston, Massachusetts.
MeSH Terms: Humans, Randomized Controlled Trials as Topic, Chronic Disease, Treatment Outcome, Computer Simulation, Coronary Artery Bypass, Coronary Artery Disease, Patient Selection, Causality
Grants: R01 AI104459, R37 AI102634
Authors: Hernán MA, Dahabreh IJ, Robertson SE, Stuart EA, Tchetgen EJ
Cite As: Dahabreh IJ, Robertson SE, Tchetgen EJ, Stuart EA, Hernán MA. Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals. Biometrics 2019 Jun;75(2):685-694. Epub 2019 Jun 21.
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.