Bayesian Variable Selection Methods for Matched Case-Control Studies

描述（由申请人提供）：本研究提案的长期目标是开发变量选择程序，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，例如区分不同疾病组的遗传标记或成像措施。反过来，这种对重要疾病生物标志物的改进鉴定将有助于更深入地了解许多疾病和病症的性质和病因。匹配的病例对照设计目前被广泛用于生物医学应用，因为它们控制了可能扭曲特征和诊断组成员之间真实关系的重要潜在混淆的影响。在使用这种设计的研究中，一个关键的兴趣是识别区分病例和对照的重要特征。为了确保高效率和统计 为了确定区分疾病组的相关特征，重要的是要考虑所使用的匹配设计。然而，在许多情况下，特别是那些包括高维数据分析，有几个变量选择方法，考虑匹配。贝叶斯方法的变量选择是有益的，因为它们提供了有效的方法来处理高维生物数据。它们产生易于处理的模型，通过选择先验分布将数据的生物结构结合起来。所提出的方法包括一种新的变量选择方法，有效地考虑匹配的病例对照研究，制定条件logistic回归模型在贝叶斯框架。该方法将被仔细开发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、复杂的数据结构、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。提出的变量选择方法将在大量的模拟研究中进行研究，采用几种类型的匹配，脑成像研究，在匹配的中风患者样本中，旨在寻找预测医院获得性肺炎的脑区域，以及匹配的病例对照研究，旨在寻找血浆中的生物标志物的心血管事件。它的性能匹配的病例对照研究的背景下，也将评估与其他变量选择技术相比。

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.