Bayesian Variable Selection Methods for Matched Case-Control Studies

描述（由申请人提供）：本研究计划的长期目标是制定变量选择程序，该程序可以有效地结合所使用的研究设计和数据结构。从生物医学的角度来看，这一发展将是有利的，因为它将允许更准确地识别生物特征，如遗传标记或区分不同疾病组的成像措施。反过来，这种对重要疾病生物标志物的改进识别将有助于更深入地了解许多疾病和失调的性质和病因。匹配病例对照设计目前在广泛的生物医学应用中使用，因为它们控制了重要的潜在混淆的影响，这些混淆可能扭曲特征和诊断组成员之间的真实关系。在使用这种设计的研究中，一个关键的兴趣是确定区分病例和对照的重要特征。确保高效率和统计

DESCRIPTION (provided by applicant): The long term goal of this research proposal is to develop variable selection procedures that can effectively incorporate both the study design used and the structure of the data. From a biomedical perspective, this development will be advantageous in that it will allow for a more accurate identification of biological features, such s genetic markers or imaging measures that distinguish among different disease groups. In turn, this improved identification of important disease biomarkers will contribute to deeper insights into the nature and etiology of many diseases and disorders. Matched case-control designs are currently used in a wide range of biomedical applications because they control for the effects of important potential confounds that can distort the true relationship between features and diagnostic group membership. In studies that use this design, a key interest is to identify important features in discriminating cases from controls. To ensure high efficiency and statistical power in identifying relevant features in distinguishing among disease groups, it is important to take into account the matched design that is used. However, in many instances, particularly those including high dimensional data analysis, there are few variable selection methods that account for matching. Bayesian approaches to variable selection are beneficial in that they offer efficient methods for handling high dimensional biological data. They yield tractable models that incorporate the biological structure of the data through the selection of prior distributions. The proposed methodology consists of a novel variable selection approach to effectively account for matching in case-control studies by formulating conditional logistic regression models in a Bayesian framework. This methodology will be carefully developed to handle a wide range of settings that have direct relevance to biomedical applications, including high dimensional data settings, interactions among different features, complex data structures, usage of different matched case- control designs, and ordering among disease groups or disorders. The proposed variable selection approach will be investigated in numerous simulation studies employing several types of matching, a brain imaging study in matched samples of stroke patients aimed at finding brain regions predictive of hospital acquired pneumonia, and a matched case-control study aimed at finding biomarkers in blood plasma for cardiovascular events. Its performance in the context of matched case-control studies will also be evaluated in comparison with other variable selection techniques.