Bayesian Variable Selection Methods for Matched Case-Control Studies

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-01-31 DOI:10.1515/ijb-2016-0043

J. Asafu-Adjei, M. Tadesse, B. Coull, R. Balasubramanian, M. Lev, L. Schwamm, R. Betensky

引用次数: 5

Abstract

Abstract Matched case-control designs are currently used in many biomedical applications. To ensure high efficiency and statistical power in identifying features that best discriminate cases from controls, it is important to account for the use of matched designs. However, in the setting of high dimensional data, few variable selection methods account for matching. Bayesian approaches to variable selection have several advantages, including the fact that such approaches visit a wider range of model subsets. In this paper, we propose a variable selection method to account for case-control matching in a Bayesian context and apply it using simulation studies, a matched brain imaging study conducted at Massachusetts General Hospital, and a matched cardiovascular biomarker study conducted by the High Risk Plaque Initiative.

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匹配病例对照研究的贝叶斯变量选择方法

摘要匹配病例对照设计目前在许多生物医学应用中使用。为了确保在识别最能区分病例与对照组的特征方面具有高效率和统计能力，重要的是要考虑到匹配设计的使用。然而，在高维数据的设置中，很少有变量选择方法考虑匹配。变量选择的贝叶斯方法有几个优点，包括这样的方法访问更广泛的模型子集。在本文中，我们提出了一种变量选择方法来解释贝叶斯背景下的病例对照匹配，并使用模拟研究、马萨诸塞州总医院进行的匹配脑成像研究和高危斑块倡议进行的匹配心血管生物标志物研究将其应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.