Bayesian Inference of Dynamic Mediation Models for Longitudinal Data

Abstract

Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time, which overlooks the dynamic nature of mediation effect. To address this issue, we propose dynamic mediation models that can capture the dynamic nature of the mediation effect. Specifically, we model the path parameters of mediation models as auto-regressive (AR) processes of time that can vary over time. Additionally, we define the mediation effect under the potential outcome framework, and examine its identification and causal interpretation. Bayesian methods utilizing Gibbs sampling are adopted to estimate unknown parameters in the proposed dynamic mediation models. We further evaluate our proposed models and methods through extensive simulations and illustrate their application through a real data application.

Keywords:

• AR process
• Bayesian inference
• causal mediation effect
• dynamic mediation

Additional information

Funding

The work of SZ and HZ was supported by the National Natural Science Foundation of China [No. 72091212, 11771096]. The work of ZZ was supported by a grant from the US Department of Education [R305D210023]. However, the contents of paper do not necessarily represent the policy of the US Department of Education, and you should not assume endorsement by the Federal Government.