Data integration approaches to estimate heterogeneous treatment effects

Clinicians and practitioners are often motivated to determine which treatment would work best for a given individual based on their observed characteristics, but doing so can be challenging because sample sizes are typically not large enough, and the variables involved in the true treatment effect heterogeneity are often unknown. To better understand treatment effect heterogeneity, researchers can rely on combining information from multiple sources, e.g., multiple randomized controlled trials (RCTs). However, combining data requires taking into account that the data comes from heterogeneous sources with different site-level characteristics that can impact treatment effects. Methods that combine RCTs also often yield treatment effect estimates that are conditional on trial membership, so applying these models to a new target population is not straightforward. This presentation introduces approaches for integrating multiple RCTs to estimate the conditional average treatment effect (CATE) function. We then discuss an approach that extends the CATE model estimated using multiple RCTs to an external target population, drawing from meta-analytic prediction intervals and extended to non-parametric methods. We examine performance in simulations and ultimately apply the approaches to real data comparing major depression treatments to investigate potential effect heterogeneity and estimate effects in a target population of patients in a health care system.

Clinical trials

Conditional average treatment effect

Depression

Meta analysis

Nonparametric

Treatment effect heterogeneity