Assessing potential associations between exposures to complex mixtures and health outcomes may be complicated by a lack of knowledge of causal components of the mixture, highly correlated mixture components, potential synergistic effects of mixture components, and difficulties in measurement. We extend recently proposed nonparametric Bayes shrinkage priors for model selection to investigations of complex mixtures by developing a formal hierarchical modeling framework to allow different degrees of shrinkage for main effects and interactions and to handle truncation of exposures at a limit of detection. The methods are used to shed light on data from a study of endometriosis and exposure to environmental polychlorinated biphenyl congeners.
From the Department of Biostatistics, UNC Gillings School of Global Public Health and Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC.
Submitted 14 December 2008; accepted 24 November 2009.
Supported in part with funding from the American Chemistry Council and the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The author's work was also supported by Maternal and Child Health Bureau R40MC08952 and by EPA RD-83184301-0.
Correspondence: Amy H. Herring, Department of Biostatistics, UNC Gillings School of Global Public Health and Carolina Population Center, CB 7420, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420. E-mail: email@example.com.