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Activity Number: 473 - Advances in Bayesian Methods and Mixture Modeling for Health Data
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #323369
Title: Incorporating Biological Knowledge in Analyses of Environmental Mixtures and Health
Author(s): Glen McGee* and Ander Wilson and Brent Coull and Thomas Webster
Companies: University of Waterloo and Colorado State University and Harvard University and Boston University
Keywords: Bayesian methods; Environmental health; Multipollutant mixtures; Informative priors; Kernel machine regression; Index models

A key goal of environmental health research is to assess the risk posed by mixtures of pollutants. As studies of mixtures can be expensive to conduct, it behooves researchers to incorporate available knowledge about mixtures into their analyses. We propose several strategies for incorporating knowledge from toxicology in epidemiological analyses of mixtures. This builds on the recent Bayesian multiple index model, which combines the flexibility of response surface methods—allowing for non-linearity and interactions among groups of exposures—and the interpretability of linear index models—which decompose mixture effects into component contributions via interpretable weights. To incorporate prior knowledge, we: place constraints on weights, structure weights (eg impose partial effect rankings; smoothness over time; etc) via transformations, and adopt novel informative priors based on relative potency factors. When prior knowledge is correct, the proposed informative priors improve inferences, and when it is incorrect, they protect against misspecification suffered by naïve toxicological models. Different strategies may be combined for different indices to suit available information.

Authors who are presenting talks have a * after their name.

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