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Title: Bayesian multiple index models for environmental mixtures Authors:  Glen McGee - University of Waterloo (Canada) [presenting]
Ander Wilson - Colorado State University (United States)
Thomas Webster - Boston University (United States)
Brent Coull - Harvard University (United States)
Abstract: An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response-surface methods and linear-index methods. Response-surface methods estimate high-dimensional surfaces and are highly flexible but difficult to interpret. Linear-index methods decompose coefficients from a linear model into an overall mixture effect and component weights; these models yield easily interpretable effect estimates and efficient inferences but can be overly restrictive. We propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non-linear and non-additive relationships between exposure indices and a health outcome, while reducing dimensionality and estimating index weights. The proposed framework allows one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability, and it contains both response-surface and linear-index models as special cases. Unlike fully non-parametric alternatives, the framework also provides a means of incorporating prior knowledge about mixtures in future analyses.