부산대학교 도서관

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. Journal of Exposure Science and Environmental Epidemiology (2007) 17, S71-S74; doi: 10.1038/sj.jes.7500630 Keywords: multicollinearity, complex mixtures, gene-environment interactions, hierarchical models, biomarkers
Author(s): Duncan C Thomas [1] Introduction Suppose one were interested in determining the etiologically relevant constituents of some complex mixture X on some disease Y, and suppose the specific constituents [...]