Bayesian Methods for High-Dimensional Epidemiologic Data

描述（由申请人提供）：我们开发了新的统计技术，用于高维协变量数据的非参数贝叶斯分析，直接受到迄今为止对出生缺陷原因进行的最大规模的基于人群的研究的启发。我们开发的方法将能够在高维环境、生物医学、药理学和社会人口风险因素（以及它们之间的相互作用）和众多出生缺陷（其中许多太罕见而无法单独研究）中借用信息和缩小。利用由胚胎发育直接激发的层次结构，可以通过我们对胚胎机械发育的了解来了解信息的借用。这些新方法可能会对罕见先天性畸形的研究产生重大影响。待开发的方法在公共卫生和医学中具有广泛的应用，在这些领域中，感兴趣的暴露或特征可能数量很多，并且相互作用很重要，例如通过环境和基因-基因相互作用检查高维基因。

DESCRIPTION (provided by applicant): We develop novel statistical techniques for nonparametric Bayes analysis of high-dimensional covariate data, directly motivated by the largest population-based study ever conducted on the causes of birth defects. The methods we develop will enable borrowing of information and shrinkage across high-dimensional environmental, biomedical, pharmacological, and sociodemographic risk factors (and interactions among them) and across a multitude of birth defects, many of which are too rare to be studied in isolation. Using a hierarchical structure directly motivated by embryonic development, the borrowing of information can be informed by our knowledge of mechanistic development of the embryo. These novel methods may significantly impact the study of rare congenital malformations. The methods to be developed have broad application in public health and medicine, where exposures or characteristics of interest may be great in number and interactions are important, such as the examination high-dimensional gene by environment and gene-gene interactions.