Identifying main effects and interactions among exposures using Gaussian processes

TL;DR

This paper introduces a Bayesian Gaussian process method for identifying main effects and interactions among chemical exposures on health, emphasizing interpretability and model selection with uncertainty quantification.

Contribution

It develops a novel MixSelect framework with heredity constraints and identifiability enforcement for nonparametric interaction modeling.

Findings

Effective in simulation studies

Applied successfully to NHANES data

Improves interpretability of exposure effects

Abstract

This article is motivated by the problem of studying the joint effect of different chemical exposures on human health outcomes. This is essentially a nonparametric regression problem, with interest being focused not on a black box for prediction but instead on selection of main effects and interactions. For interpretability, we decompose the expected health outcome into a linear main effect, pairwise interactions, and a non-linear deviation. Our interest is in model selection for these different components, accounting for uncertainty and addressing non-identifability between the linear and nonparametric components of the semiparametric model. We propose a Bayesian approach to inference, placing variable selection priors on the different components, and developing a Markov chain Monte Carlo (MCMC) algorithm. A key component of our approach is the incorporation of a heredity constraint to only include interactions in the presence of main effects, effectively reducing dimensionality of the model search. We adapt a projection approach developed in the spatial statistics literature to enforce identifiability in modeling the nonparametric component using a Gaussian process. We also employ a dimension reduction strategy to sample the non-linear random effects that aids the mixing of the MCMC algorithm. The proposed MixSelect framework is evaluated using a simulation study, and is illustrated using data from the National Health and Nutrition Examination Survey (NHANES). Code is available on GitHub.