  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.
