Open AccessToward Causally Interpretable Meta-analysis
Author(s) -
Issa J Dahabreh,
Lucia C. Petito,
Sarah E. Robertson,
Miguel A. Hernán,
Jon A. Steingrimsson
Publication year - 2020
Publication title -
epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.901
H-Index - 173
eISSN - 1531-5487
pISSN - 1044-3983
DOI - 10.1097/ede.0000000000001177
Subject(s) - identifiability , pooling , causal inference , computer science , population , identification (biology) , data collection , estimator , econometrics , machine learning , data mining , artificial intelligence , statistics , medicine , mathematics , botany , environmental health , biology
We take steps toward causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target population, one trial at a time and pooling all trials. We discuss identifiability conditions for average treatment effects in the target population and provide identification results. We show that the assumptions that allow inferences to be transported from all trials in the collection to the same target population have implications for the law underlying the observed data. We propose average treatment effect estimators that rely on different working models and provide code for their implementation in statistical software. We discuss how to use the data to examine whether transported inferences are homogeneous across the collection of trials, sketch approaches for sensitivity analysis to violations of the identifiability conditions, and describe extensions to address nonadherence in the trials. Last, we illustrate the proposed methods using data from the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis Trial.