Bayesian dynamic mediation analysis.
Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record
Department of Biostatistics, The University of Texas School of Public Health.
Jing
J
Huang
Department of Biostatistics, The University of Texas MD Anderson Cancer Center.
Ying
Y
Yuan
D001499:Bayes Theorem; D035841:Behavioral Research / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D011584:Psychology / Q000379:methods*; D018709:Statistics, Nonparametric