Toward Causally Interpretable Meta-analysis: Transporting Inferences from Multiple Randomized Trials to a New Target Population

Issa J. Dahabreh*, Lucia C. Petito, Sarah E. Robertson, Miguel A. Hernán, Jon A. Steingrimsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)334-344
Number of pages11
JournalEpidemiology
Volume31
Issue number3
DOIs
StatePublished - May 1 2020

Keywords

  • Causal inference
  • Conditional average treatment effect
  • Evidence synthesis
  • Inverse probability weighting
  • Meta-analysis
  • Transportability

ASJC Scopus subject areas

  • Epidemiology

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