Bayesian Mediation Analysis with Latent Exposure Mixtures

Music City Center 

Understanding the mechanisms by which exposure mixtures impact human health is an important topic in environmental health. One of the key challenges lies in the latent nature of information about co-occurring exposures, which complicates the identification of pathways linking mixtures to health outcomes. To address this, we propose a Bayesian framework that integrates mediation analysis with latent factor modeling. This method simultaneously estimates mediation effects associated with both individual exposures and latent exposure mixtures, capturing shared variation within exposure mixtures to enable a comprehensive investigation of their collective and component-specific effects on health outcomes. We evaluate the performance of the proposed framework through simulation studies and assess its applicability using real-world data, illustrating its potential to elucidate meaningful pathways in complex exposure-outcome relationships.

Causal mediation analysis

Latent factor modeling

Environmental health

Bayesian inference

Exposure mixtures 

ENAR