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Biometrics. 2019 Jun;75(2):685-694. doi: 10.1111/biom.13009. Epub 2019 Jun 21.

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

Author information

1
Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, Rhode Island.
2
Departments of Health Services, Policy & Practice and Epidemiology, Brown University, Providence, Rhode Island.
3
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
4
Department of Statistics, Wharton Business School, University of Pennsylvania, Philadelphia, Pennsylvania.
5
Departments of Mental Health, Biostatistics, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
6
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
7
Harvard-MIT Division of Health Sciences and Technology, Boston, Massachusetts.

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.

KEYWORDS:

causal inference; clinical trials; double robustness; generalizability; observational studies; transportability

PMID:
30488513
DOI:
10.1111/biom.13009

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