Bayesian Variable Selection Methods for Matched Case-Control Studies

描述(由申请人提供)：这项研究提案的长期目标是开发变量选择程序，能够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学特征，如S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记物的改进识别将有助于更深入地了解许多疾病和障碍的性质和病因。配对病例对照设计目前在广泛的生物医学应用中被使用，因为它们控制重要的潜在混杂的影响，这些混杂可以扭曲特征和诊断小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。确保高效率和高统计 在区分疾病组时确定相关特征的能力，重要的是要考虑到所使用的匹配设计。然而，在许多情况下，特别是那些包括高维数据分析的情况下，几乎没有考虑匹配的变量选择方法。变量选择的贝叶斯方法是有益的，因为它们为处理高维生物数据提供了有效的方法。它们产生易于处理的模型，通过选择先前的分布来纳入数据的生物结构。所提出的方法包括一种新的变量选择方法，通过在贝叶斯框架下建立条件Logistic回归模型来有效地解释病例对照研究中的匹配。将仔细开发这一方法，以处理与生物医学应用直接相关的广泛设置，包括高维数据设置、不同特征之间的相互作用、复杂数据结构、不同匹配病例对照设计的使用以及疾病组或疾病之间的排序。建议的变量选择方法将在采用几种匹配类型的众多模拟研究中进行研究，在中风患者匹配样本中进行的大脑成像研究旨在寻找预测医院获得性肺炎的大脑区域，以及旨在在血浆中寻找心血管事件的生物标记物的匹配病例对照研究。它在配对病例对照研究中的表现也将与其他变量选择技术进行比较。

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.