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A key goal of environmental health research is to assess the risk posed bymixtures of pollutants. As epidemiologic studies of mixtures can be expensiveto conduct, it behooves researchers to incorporate prior knowledge aboutmixtures into their analyses. This work extends the Bayesian multiple indexmodel (BMIM), which assumes the exposure-response function is a non-parametricfunction of a set of linear combinations of pollutants formed with a set ofexposure-specific weights. The framework is attractive because it combines theflexibility of response-surface methods with the interpretability of linearindex models. We propose three strategies to incorporate prior toxicologicalknowledge into construction of indices in a BMIM: (a) constraining indexweights, (b) structuring index weights by exposure transformations, and (c)placing informative priors on the index weights. We propose a novel priorspecification that combines spike-and-slab variable selection with informativeDirichlet distribution based on relative potency factors often derived fromprevious toxicological studies. In simulations we show that the proposed priorsimprove inferences when prior information is correct and can protect againstmisspecification suffered by naive toxicological models when prior informationis incorrect. Moreover, different strategies may be mixed-and-matched fordifferent indices to suit available information (or lack thereof). Wedemonstrate the proposed methods on an analysis of data from the NationalHealth and Nutrition Examination Survey and incorporate prior information onrelative chemical potencies obtained from toxic equivalency factors availablein the literature.

Integrating Biological Knowledge in Kernel-Based Analyses of Environmental Mixtures and Health