RMME/STAT Joint Colloquium

Bayesian Longitudinal (Non)Linear Mediation Models

Dr. Nidhi Kohli

University of Minnesota, Twin Cities

Friday, May 2, at 11 AM ET

https://tinyurl.com/rmme-Kohli

This study develops Bayesian (non)linear random effects mediation models (B(N)REMM) to directly estimate both linear and nonlinear longitudinal mediation effects, overcoming limitations in existing structural equation modeling (SEM) approaches. We propose two models: a linear trend model (L-BREMM) and a segmented trend model using linear-linear piecewise functions with random changepoints (P-BREMM). We also examine the impact of omitting confounders in (non)linear mediation models using data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K). Furthermore, we extend this framework to develop Bayesian (nonlinear) growth mixture mediation models (B(N)GMMM), which assess heterogeneous treatment effects (HTE) of the intervention variable X on the longitudinal dependent variable Y, mediated by longitudinal variable M. To evaluate the robustness of these methods, we conducted a comprehensive Monte Carlo simulation study for all the models.

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