Compositional Outcomes and Environmental Chemical Mixtures: the Dirichlet-Bayesian Weighted Quantile Sum Regression

Music City Center 

Environmental mixture approaches currently struggle to accommodate compositional outcomes, consisting of vectors constrained onto the unit simplex. This limitation poses challenges in effectively evaluating the associations between multiple concurrent environmental exposures and their respective impacts on the outcomes. As a result, there is a pressing need for the development of analytical methods that can more accurately assess the complexity of these relationships.
Here, we extend the Bayesian weighted quantile sum regression (BWQS) framework for jointly modeling compositional outcomes and environmental mixtures using a Dirichlet distribution with a multinomial logit link function. The proposed approach, named Dirichlet-BWQS (D-BWQS), allows for the simultaneous estimation of mixture weights associated with each exposure mixture component as well as the association between the overall exposure mixture index and each of the outcome proportions.