Assessing Heterogeneous Treatment Effects in Bayesian Growth Mixture Mediation Models

• In Event: Advanced Techniques in Multilevel Modeling and Treatment Effect Analysis

Abstract

Often in educational and psychological randomized trials, researchers discover that sub-groups of individuals respond to the same intervention differently due to underlying individual differences. Previous studies have investigated such heterogeneous treatment effects (HTE) across different latent classes for both cross-sectional and longitudinal (repeated measures) outcomes, but not in the context of longitudinal mediation analysis. This study develops Bayesian longitudinal mediation models (BGMMMs) to assess the HTE of the intervention variable X on the longitudinal dependent variable Y, via the longitudinal mediator M. We consider Y following linear or nonlinear trajectories and incorporate class predictive and growth predictive covariates. Monte Carlo simulations were conducted to evaluate model performance, and an empirical example demonstrates the model's application.

Authors

• Ziwei Zhang, University of Minnesota

• Nidhi Kohli, University of Minnesota

• Eric F. Lock, University of Minnesota