Volume 39, Issue 14 pp. 1999-2014
TUTORIAL IN BIOSTATISTICS

Extending inferences from a randomized trial to a new target population

Issa J. Dahabreh

Corresponding Author

Issa J. Dahabreh

Center for Evidence Synthesis in Health, Brown University, Providence, Rhode Island

Department of Health Services, Policy & Practice, Brown University, Providence, Rhode Island

Department of Epidemiology, Brown University, Providence, Rhode Island

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

Issa J. Dahabreh MD ScD, Box G-S121-8; Brown University, Providence, RI 02912.

Email: [email protected]

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Sarah E. Robertson

Sarah E. Robertson

Center for Evidence Synthesis in Health, Brown University, Providence, Rhode Island

Department of Health Services, Policy & Practice, Brown University, Providence, Rhode Island

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Jon A. Steingrimsson

Jon A. Steingrimsson

Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island

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Elizabeth A. Stuart

Elizabeth A. Stuart

Departments of Mental Health, Biostatistics, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

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Miguel A. Hernán

Miguel A. Hernán

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

Harvard-MIT Division of Health Sciences and Technology, Boston, Massachusetts

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First published: 06 April 2020
Citations: 134
Abbreviations: DR, doubly robust; IO, inverse odds; OM, outcome model-based.

Abstract

When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses.

CONFLICT OF INTEREST

The authors have no conflicts of interest to report.

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