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Efficient and robust methods for causally interpretable meta‐analysis: Transporting inferences from multiple randomized trials to a target population
Author(s) -
Dahabreh Issa J.,
Robertson Sarah E.,
Petito Lucia C.,
Hernán Miguel A.,
Steingrimsson Jon A.
Publication year - 2023
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13716
Subject(s) - covariate , identifiability , estimator , causal inference , population , statistics , randomized controlled trial , econometrics , sample size determination , computer science , outcome (game theory) , randomized experiment , mathematics , medicine , environmental health , surgery , mathematical economics
We present methods for causally interpretable meta‐analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.