Nonparametric bayes shrinkage for assessing exposures to mixtures subject to limits of detection.
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.
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 27599-7420, USA. aherring@bios.unc.edu
Amy H
AH
Herring
D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D004715:Endometriosis / Q000139:chemically induced; D004781:Environmental Exposure / Q000009:adverse effects / Q000032:analysis*; D005260:Female; D006801:Humans; D057230:Limit of Detection; D011078:Polychlorinated Biphenyls / Q000097:blood; D055815:Young Adult
D011078:Polychlorinated Biphenyls