High-Dimensional Mediation Analysis for Generalized Linear Models Using Bayesian Variable Selection Guided by Mediator Correlation

2026-02-12

AI Summary

This study addresses the challenges of high-dimensional mediation analysis, where mediators are numerous, highly interdependent, and associated with non-continuous outcomes—conditions under which conventional methods often neglect mediator correlations, leading to reduced statistical power. To overcome these limitations, the authors propose a Bayesian generalized linear framework that models mediators jointly via a multivariate distribution and incorporates a Markov random field prior to capture structural dependencies between the exposure and mediators. Additionally, an ordered subset Bernoulli prior is introduced to enforce sparsity in the mediator–outcome pathways, enabling simultaneous variable selection and pathway identification. Simulations demonstrate that the method achieves superior selection accuracy and power when mediators are correlated, maintains proper control of the family-wise error rate under the global null, and remains robust to model misspecification. Applied to metabolomics data, it successfully identifies key metabolites mediating the effect of Mediterranean diet on cardiometabolic outcomes.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Bayesian Learning
• Multiagent Systems: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

High-dimensional mediation analysis aims to identify mediating pathways and to estimate indirect effects linking an exposure to an outcome. In this paper, we propose a Bayesian framework to address key challenges in these analyses, including high dimensionality, complex dependence among omics mediators, and non-continuous outcomes. Furthermore, commonly used approaches assume independent mediators or ignore correlations in the selection stage, which can reduce power when mediators are highly correlated. Addressing these challenges leads to a non-Gaussian likelihood and specialized selection priors, which in turn require efficient and adaptive posterior computation. Our proposed framework selects active pathways under generalized linear models while accounting for mediator dependence. Specifically, the mediators are modeled using a multivariate distribution, exposure-mediator selection is guided by a Markov random field prior on inclusion indicators, and mediator-outcome activation is restricted to mediators supported in the exposure-mediator model through a sequential subsetting Bernoulli prior. Simulation studies show improved operating characteristics in correlated-mediator settings, with appropriate error control under the global null and stable performance under model misspecification. We illustrate the method using real-world metabolomics data to study metabolites that mediate the association between adherence to the Alternate Mediterranean Diet score and two cardiometabolic outcomes.

Problem

• high-dimensional mediation analysis
• mediator correlation
• generalized linear models
• non-continuous outcomes
• omics mediators

Innovation

• Bayesian variable selection
• high-dimensional mediation analysis
• mediator correlation
• Markov random field prior
• generalized linear models