Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1057-1072
Number of pages16
JournalBiometrics
Volume79
Issue number2
DOIs
StatePublished - Jun 2023

Keywords

  • causal inference
  • combining information
  • evidence synthesis
  • generalizability
  • meta-analysis
  • research synthesis
  • transportability

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population'. Together they form a unique fingerprint.

Cite this