A Bayesian nonparametric approach for multiple mediators with applications in mental health studies
Year: 2024
Type: article
Abstract: Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much of the existing literature either only allow estimation of the joint mediation effect or estimate the joint mediation effect just as the sum of individual mediator effects, ignoring the interaction among the mediators. In this article, we propose a novel Bayesian nonparametric method that overcomes the two aforementioned drawbacks. We model the joint distribution of the observed data (outcome, mediators, treatment, and confounders) flexibly using an enriched Dirichlet process mixture with three levels. We use standardization (g-computation) to compute all possible mediation effects, including pairwise and all other possible interaction among the mediators. We thoroughly explore our method via simulations and apply our method to a mental health data from Wisconsin Longitudinal Study, where we estimate how the effect of births from unintended pregnancies on later life mental depression (CES-D) among the mothers is mediated through lack of self-acceptance and autonomy, employment instability, lack of social participation, and increased family stress. Our method identified significant individual mediators, along with some significant pairwise effects.
Source: Biostatistics
Authors: Samrat Roy, Michael J Daniels, Jason Roy
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: BAYESIAN APPROACHES FOR MISSINGNESS AND CAUSALITY IN CANCER AND BEHAVIOR STUDIES, Combining longitudinal cohort studies to examine cardiovascular risk factor trajectories across the adult lifespan and their association with disease, G791901357, Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studies
OpenAlex ID: W4391685649