  Individualized treatment decisions can improve health outcomes, but using
data to make these decisions in a reliable, precise, and generalizable way is
challenging with a single dataset. Leveraging multiple randomized controlled
trials allows for the combination of datasets with unconfounded treatment
assignment to better estimate heterogeneous treatment effects. This paper
discusses several non-parametric approaches for estimating heterogeneous
treatment effects using data from multiple trials. We extend single-study
methods to a scenario with multiple trials and explore their performance
through a simulation study, with data generation scenarios that have differing
levels of cross-trial heterogeneity. The simulations demonstrate that methods
that directly allow for heterogeneity of the treatment effect across trials
perform better than methods that do not, and that the choice of single-study
method matters based on the functional form of the treatment effect. Finally,
we discuss which methods perform well in each setting and then apply them to
four randomized controlled trials to examine effect heterogeneity of treatments
for major depressive disorder.
