Thomas Duncan C
Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089-9011, USA.
J Expo Sci Environ Epidemiol. 2007 Dec;17 Suppl 2:S71-4. doi: 10.1038/sj.jes.7500630.
Teasing out the health effects of constituents of complex mixtures poses formidable statistical challenges owing to the problem of multicollinearity. While statistical devices such as regression on principal components, model selection, and model averaging offer some approaches to this problem, incorporation of external information is likely to be more helpful. I explore a general hierarchical modeling framework that would allow such information as sources, genetic interactions, and toxicology to be included in the higher levels of the model.
由于多重共线性问题，梳理复杂混合物成分对健康的影响带来了巨大的统计挑战。虽然诸如主成分回归、模型选择和模型平均等统计方法为解决这一问题提供了一些途径，但纳入外部信息可能会更有帮助。我探索了一个通用的层次建模框架，该框架能够将诸如来源、基因相互作用和毒理学等信息纳入模型的较高层次。
