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，直接受到有史以来对出生缺陷原因进行的最大规模的基于人群的研究的推动。我们开发的方法将使信息的借用和缩小跨越高维度的环境、生物医学、药理学和社会人口学风险因素(以及它们之间的相互作用)，并跨越大量出生缺陷，其中许多出生缺陷太罕见，不能单独研究。利用胚胎发育直接驱动的层级结构，信息的借用可以通过我们对胚胎机械发育的知识来获得。这些新的方法可能会对罕见的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有广泛的应用，这些领域中暴露或感兴趣的特征可能数量很多，相互作用也很重要，例如环境对高维基因的检测和基因与基因的相互作用。

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.