Bantis Leonidas E, Feng Ziding
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, U.S.A..
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, U.S.A.
Stat Med. 2016 Oct 30;35(24):4352-4367. doi: 10.1002/sim.7008. Epub 2016 Jun 20.
The receiver operating characteristic (ROC) curve is the most popular statistical tool for evaluating the discriminatory capability of a given continuous biomarker. The need to compare two correlated ROC curves arises when individuals are measured with two biomarkers, which induces paired and thus correlated measurements. Many researchers have focused on comparing two correlated ROC curves in terms of the area under the curve (AUC), which summarizes the overall performance of the marker. However, particular values of specificity may be of interest. We focus on comparing two correlated ROC curves at a given specificity level. We propose parametric approaches, transformations to normality, and nonparametric kernel-based approaches. Our methods can be straightforwardly extended for inference in terms of ROC (t). This is of particular interest for comparing the accuracy of two correlated biomarkers at a given sensitivity level. Extensions also involve inference for the AUC and accommodating covariates. We evaluate the robustness of our techniques through simulations, compare them with other known approaches, and present a real-data application involving prostate cancer screening. Copyright © 2016 John Wiley & Sons, Ltd.
受试者工作特征（ROC）曲线是评估给定连续生物标志物鉴别能力最常用的统计工具。当使用两种生物标志物对个体进行测量时，就会出现比较两条相关ROC曲线的需求，这会导致成对测量，进而产生相关测量值。许多研究人员专注于根据曲线下面积（AUC）来比较两条相关的ROC曲线，AUC总结了标志物的整体性能。然而，特定的特异性值可能也很重要。我们专注于在给定的特异性水平下比较两条相关的ROC曲线。我们提出了参数方法、向正态性的变换以及基于核的非参数方法。我们的方法可以直接扩展用于ROC(t)的推断。这对于在给定敏感性水平下比较两种相关生物标志物的准确性尤为重要。扩展还涉及AUC的推断和协变量的处理。我们通过模拟评估我们技术的稳健性，将它们与其他已知方法进行比较，并展示一个涉及前列腺癌筛查的实际数据应用。版权所有© 2016约翰威立父子有限公司。
