Department of Biostatistics, The University of Texas School of Public Health.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center.
Psychol Methods. 2017 Dec;22(4):667-686. doi: 10.1037/met0000073. Epub 2016 Apr 28.
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
大多数中介分析方法假设中介是一个稳定的、时不变的过程，这忽略了许多人类心理过程和行为活动固有的动态本质。在本文中，我们将中介视为一个随时间不断变化的动态过程。我们提出了贝叶斯多层次时变系数模型来描述和估计这种动态中介效应。通过采用非参数惩罚样条方法，该方法具有灵活性，能够适应时间与中介效应之间关系的任何形状。模拟研究表明，所提出的方法效果良好，忠实地反映了中介过程的真实本质。通过将中介效应非参数化为时间的连续函数，我们的方法为帮助研究人员更好地理解心理和行为现象背后的中介过程的动态本质提供了一种有价值的工具。我们还简要讨论了一种使用动态自回归中介模型来估计动态中介效应的替代方法。提供了计算机代码来实现所提出的贝叶斯动态中介分析。（PsycINFO 数据库记录）
