Data integration approaches to estimate heterogeneous treatment effects

In many fields, including medicine, education, and public policy, researchers and practitioners are interested in determining what works for whom - in other words, identifying subgroups for whom specific interventions work particularly well. By finding these subgroups, we can more effectively focus resources and give interventions to those who might benefit the most. These individualized treatment decisions can improve outcomes, but answering these types of questions is challenging with a single dataset. Specifically, randomized controlled trials have unconfounded treatment assignment but are often too small to reliably estimate heterogeneous treatment effects, while larger observational datasets might suffer from confounding. Data integration methods can utilize the benefits of different sources of data while accounting for bias. In this talk, I first discuss non-parametric data integration approaches for combining multiple randomized controlled trials to estimate the effect of treatments conditional on observed characteristics. I explore the performance of these methods through a simulation study, and I apply the approaches to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder. I then discuss methods for applying these multi-study treatment effect models to an external, observational target sample represented by electronic health records of a set of patients. With these methods, we can utilize individual-level data across sources to improve our ability to make intervention decisions that are tailored to individuals or communities, and we can ultimately apply our conclusions to a given target population.