Department of Biostatistics, State University of New York at Buffalo, 706 Kimball Tower, 3435 Main Street, Buffalo, NY 14214, U.S.A.
Stat Med. 2013 Feb 20;32(4):631-43. doi: 10.1002/sim.5542. Epub 2012 Aug 3.
Many researchers have addressed the problem of finding the optimal linear combination of biomarkers to maximize the area under receiver operating characteristic (ROC) curves for scenarios with binary disease status. In practice, many disease processes such as Alzheimer can be naturally classified into three diagnostic categories such as normal, mild cognitive impairment and Alzheimer's disease (AD), and for such diseases the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. In this article, we propose a few parametric and nonparametric approaches to address the problem of finding the optimal linear combination to maximize the VUS. We carried out simulation studies to investigate the performance of the proposed methods. We apply all of the investigated approaches to a real data set from a cohort study in early stage AD.
许多研究人员已经解决了这个问题，即找到最优的线性组合生物标志物，以最大化用于二元疾病状态情况下的接收器操作特性 (ROC) 曲线下面积。在实践中，许多疾病过程，如阿尔茨海默病，可以自然地分为三个诊断类别，如正常、轻度认知障碍和阿尔茨海默病 (AD)，对于这类疾病，ROC 曲线下面积 (VUS) 是最常用的诊断准确性指标。在本文中，我们提出了一些参数和非参数方法来解决寻找最优线性组合以最大化 VUS 的问题。我们进行了模拟研究来评估所提出方法的性能。我们将所有研究的方法应用于来自早期 AD 队列研究的真实数据集。
