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Individualized treatment decisions can improve health outcomes, but usingdata to make these decisions in a reliable, precise, and generalizable way ischallenging with a single dataset. Leveraging multiple randomized controlledtrials allows for the combination of datasets with unconfounded treatmentassignment to better estimate heterogeneous treatment effects. This paperdiscusses several non-parametric approaches for estimating heterogeneoustreatment effects using data from multiple trials. We extend single-studymethods to a scenario with multiple trials and explore their performancethrough a simulation study, with data generation scenarios that have differinglevels of cross-trial heterogeneity. The simulations demonstrate that methodsthat directly allow for heterogeneity of the treatment effect across trialsperform better than methods that do not, and that the choice of single-studymethod matters based on the functional form of the treatment effect. Finally,we discuss which methods perform well in each setting and then apply them tofour randomized controlled trials to examine effect heterogeneity of treatmentsfor major depressive disorder.

Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effects