Causally-interpretable meta-analysis: clearly-defined causal effects and two case studies

TL;DR

This paper introduces causally-interpretable meta-analysis methods that explicitly relate treatment effects to a target population, demonstrating their application through two case studies and comparing them to traditional methods.

Contribution

The paper presents a new causally-interpretable meta-analysis framework that transports treatment effects to specific populations, enhancing interpretability and applicability.

Findings

Causally-interpretable methods perform better with effect-modifying covariates.

Traditional methods work well with little effect heterogeneity.

Causally-interpretable approach provides a solid theoretical foundation.

Abstract

Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently-introduced causally-interpretable meta-analysis methods and apply their treatment effect estimators to two individual-participant data sets. These estimators transport estimated treatment effects from studies in the meta-analysis to a specified target population using individuals' potentially effect-modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated-data meta-analysis methods. In our applications, certain versions of the causally-interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally-interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally-interpretable approach gives meta-analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.