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

2017/09/30 by Issa J. Dahabreh, Issa Dahabreh, Sarah Robertson +7 · 159 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Advanced Causal Inference Techniques #Causal inference #Covariate #Estimator #Genetic Associations and Epidemiology #Identifiability #Inference #Outcome (game theory) #Population #Randomized controlled trial #Randomized experiment #Statistical Methods and Bayesian Inference #stat.ME

paper · pdf · doi:10.1111/biom.13009

published in Biometrics 75(2), 685-694 (Oxford University Press)

openalex publication_date 2018/11/29 · openalex created_date 2018/12/11 · arxiv created 2019/10/29 · arxiv updated 2019/10/30 · openalex updated_date 2026/08/05

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.

Citations

• Generalizing Evidence From Randomized Clinical Trials to Target Populations: The ACTG 320 Trial
• Doubly Robust Estimation in Missing Data and Causal Inference Models

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