Causally Interpretable Meta-Analysis: Transporting Inferences from Multiple Randomized Trials to a Target Population

• Activity Number: 236 - New Developments in Integrated Analysis of Complex Data from Multiple Sources
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Biometrics Section
• Author(s): Issa J. Dahabreh* and Sarah E. Robertson and Lucia C. Petito and Miguel Hernán and Jon Steingrimsson
• Companies: Harvard T. H. Chan School of Public Health and Brown University and Northwestern University and Harvard T. H. Chan School of Public Health and Brown University
• Keywords: meta-analysis; causal inference; evidence synthesis; transportability

Abstract

We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to estimate potential (counterfactual) outcome means and average treatment effects in a target population. 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 (counterfactual) 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 the estimator 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 its finite sample properties in simulation studies and demonstrate its implementation using data from a multi-center randomized trial.