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

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

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)685-694
Number of pages10
JournalBiometrics
Volume75
Issue number2
DOIs
StatePublished - Jan 1 2019

Fingerprint

Causal Inference
Randomized Trial
Coronary Artery Bypass
Surgery
Estimator
therapeutics
Therapy
Health Services Needs and Demand
coronary vessels
Average Treatment Effect
Coronary Artery Disease
surgery
Coronary Artery
Identifiability
Covariates
Baseline
Therapeutics
Simulation Study
Entire
Target

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals. / Dahabreh, Issa J.; Robertson, Sarah E.; Tchetgen, Eric J.; Stuart, Elizabeth; Hernán, Miguel A.

In: Biometrics, Vol. 75, No. 2, 01.01.2019, p. 685-694.

Research output: Contribution to journalArticle

Dahabreh, Issa J. ; Robertson, Sarah E. ; Tchetgen, Eric J. ; Stuart, Elizabeth ; Hernán, Miguel A. / Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals. In: Biometrics. 2019 ; Vol. 75, No. 2. pp. 685-694.
@article{a9e70d66b98b43da91da8c9f85b397ba,
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 Elizabeth Stuart and Hern{\'a}n, {Miguel A.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1111/biom.13009",
language = "English (US)",
volume = "75",
pages = "685--694",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "2",

}

TY - JOUR

T1 - Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals

AU - Dahabreh, Issa J.

AU - Robertson, Sarah E.

AU - Tchetgen, Eric J.

AU - Stuart, Elizabeth

AU - Hernán, Miguel A.

PY - 2019/1/1

Y1 - 2019/1/1

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

KW - clinical trials

KW - double robustness

KW - generalizability

KW - observational studies

KW - transportability

UR - http://www.scopus.com/inward/record.url?scp=85071543984&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071543984&partnerID=8YFLogxK

U2 - 10.1111/biom.13009

DO - 10.1111/biom.13009

M3 - Article

C2 - 30488513

AN - SCOPUS:85071543984

VL - 75

SP - 685

EP - 694

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 2

ER -