On the Use of Bayesian Model Averaging for Covariate Selection in Epidemiological Modeling
Bayesian model averaging (BMA) is a powerful technique to address model selection uncertainty and recent computational advances have led to a proliferation of usage. BMA methods are of particular interest in environmental health risk assessment because of the high degree of uncertainty that typically arises in that context. In this article, we review a variety of approaches to conducting BMA and compare four implementations in a setting where there are a number of potential predictors. We then use these four methods to calculate risk assessment measures that account for the uncertainty involved in modeling environmental exposures. These methods are used to reexamine data from a study conducted by Walkowiak et al. (2001) to investigate the effects of maternal polychlorinated biphenyl exposure on cognitive development in early childhood. This case study reveals that different strategies for implementing BMA can yield varying risk assessment results. We conclude with some practical recommendations.
KeywordsBenchmark dose Bayesian information criterion (BIC) Covariate selection Gibbs variable selection (GVS) Model selection Polychlorinated biphenyl (PCB) Reversible jump Markov-chain Monte Carlo (RJ MCMC) Model uncertainty
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