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

Issa J. Dahabreh; Sarah E. Robertson; Eric J. Tchetgen; Elizabeth A. Stuart; Miguel A. Hernán

doi:10.1111/biom.13009

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

Issa J. Dahabreh, Sarah E. Robertson, Eric J. Tchetgen, Elizabeth A. Stuart, Miguel A. Hernán

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

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.

Original language	English (US)
Pages (from-to)	685-694
Number of pages	10
Journal	Biometrics
Volume	75
Issue number	2
DOIs	https://doi.org/10.1111/biom.13009
State	Published - 2019

Keywords

causal inference
clinical trials
double robustness
generalizability
observational studies
transportability

ASJC Scopus subject areas

Statistics and Probability
General Biochemistry, Genetics and Molecular Biology
General Immunology and Microbiology
General Agricultural and Biological Sciences
Applied Mathematics

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10.1111/biom.13009

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title = "Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals",

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",

author = "Dahabreh, {Issa J.} and Robertson, {Sarah E.} and Tchetgen, {Eric J.} and Stuart, {Elizabeth A.} and Hern{\'a}n, {Miguel A.}",

note = "Funding Information: This work was supported by Patient-Centered Outcomes Research Institute (PCORI) awards ME-1306-03758 and ME-1502-27794 and National Institutes of Health (NIH) grant R37 AI102634. Statements in this paper do not necessarily represent the views of the PCORI, its Board of Governors, the Methodology Committee, or the NIH. The data analyses in our paper used CASS research materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. This paper does not necessarily reflect the opinions or views of the CASS or the NHLBI. The authors thank Christopher H. Schmid and Bora Youn (both at Brown University) for helpful discussions. Publisher Copyright: {\textcopyright} 2019 The International Biometric Society",

year = "2019",

doi = "10.1111/biom.13009",

language = "English (US)",

volume = "75",

pages = "685--694",

journal = "Biometrics",

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AU - Dahabreh, Issa J.

AU - Robertson, Sarah E.

AU - Tchetgen, Eric J.

AU - Stuart, Elizabeth A.

AU - Hernán, Miguel A.

N1 - Funding Information: This work was supported by Patient-Centered Outcomes Research Institute (PCORI) awards ME-1306-03758 and ME-1502-27794 and National Institutes of Health (NIH) grant R37 AI102634. Statements in this paper do not necessarily represent the views of the PCORI, its Board of Governors, the Methodology Committee, or the NIH. The data analyses in our paper used CASS research materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. This paper does not necessarily reflect the opinions or views of the CASS or the NHLBI. The authors thank Christopher H. Schmid and Bora Youn (both at Brown University) for helpful discussions. Publisher Copyright: © 2019 The International Biometric Society

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - causal inference

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KW - double robustness

KW - generalizability

KW - observational studies

KW - transportability

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

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Keywords

ASJC Scopus subject areas

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