Bayesian Varying Coefficient Multiple Index Model for Longitudinal Exposure Effect Estimation

1735 

Contributed Abstract 

Poster 

Wei Jia (1), Roman Jandarov (1)

(1) University of Cincinnati, N/A

Roman Jandarov  
University of Cincinnati

Wei Jia  
University of Cincinnati

Wei Jia  
University of Cincinnati

We introduce the Bayesian Varying Coefficient Multiple Index Model (BVCMIM), a novel statistical approach designed to estimate the longitudinal effects of environmental chemical exposure mixtures on human health outcomes. Traditional methods in environmental health often focus on single chemical exposures or rely on linear models that may not capture the complex, non-linear, and non-additive relationships inherent in chemical mixtures. BVCMIM overcomes these limitations by allowing for the estimation of non-linear relationships between exposure indices and health outcomes, while also accounting for interactions among different chemical exposures over time. The model incorporates the use of horseshoe priors for sparsity, ensuring that only the most relevant exposures are included in the analysis, and applies Hamiltonian Monte Carlo for uncertainty quantification. Through a series of simulations, we demonstrate that BVCMIM provides robust and interpretable estimates of both baseline and longitudinal health effects, even in scenarios with intricate chemical interactions.

Bayesian Inference|Longitudinal Data Analysis|Chemical Exposure Mixture Analysis|Machine Learning| |

Section on Bayesian Statistical Science

Applications in Life Sciences and Medicine