Assessing Heterogeneous Treatment Effects Using Bayesian (Non)Linear Growth Mixture Mediation Models

Often in educational and behavioral randomized trials, researchers discover that subgroups of individuals respond differently to the same intervention 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 (nonlinear) growth mixture mediation models, B(N)GMMMs, 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.