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.
Epidemiology. 2010 Jul;21 Suppl 4(Suppl 4):S71-6. doi: 10.1097/EDE.0b013e3181cf0058.
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.
对混合物的因果成分缺乏了解、混合物成分高度相关、混合物成分的潜在协同作用以及测量方面的困难。我们通过开发正式的分层建模框架，为复杂混合物的研究扩展了最近提出的用于模型选择的非参数贝叶斯收缩先验，以允许对主效应和相互作用进行不同程度的收缩，并处理检测限处暴露的截断。这些方法用于阐明子宫内膜异位症和接触环境多氯联苯同系物的研究数据。
