In medical research, diagnostic tests with continuous values are widely employed to attempt to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a test (or a biomarker) can be assessed by using the receiver operating characteristic (ROC) curve of the test. To summarize the ROC curve and primarily to determine an "optimal" threshold for test results to use in practice, several approaches may be considered, such as those based on the Youden index, on the so-called close-to-(0,1) point, on the concordance probability and on the symmetry point. In this paper, we focus on the symmetry point-based approach, that simultaneously controls the probabilities of the two types of correct classifications (healthy as healthy and diseased as diseased), and show how to get joint nonparametric confidence regions for the corresponding optimal cutpoint and the associated sensitivity (= specificity) value. Extensive simulation experiments are conducted to evaluate the finite sample performances of the proposed method. Real datasets are also used to illustrate its application.

中文翻译：

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基于对称点的最佳切割点的非参数置信区域和连续尺度诊断测试的相关灵敏度

在医学研究中，具有连续值的诊断测试被广泛用于尝试区分患病和非患病受试者。测试（或生物标志物）的诊断准确性可以通过使用测试的受试者工作特征 (ROC) 曲线来评估。为了总结 ROC 曲线并主要确定在实践中使用的测试结果的“最佳”阈值，可以考虑几种方法，例如基于 Youden 指数的方法，在所谓的 close-to-(0,1 ) 点，关于一致性概率和对称点。在本文中，我们专注于基于对称点的方法，该方法同时控制两种类型正确分类（健康为健康和患病为患病）的概率，并展示如何为相应的最佳切割点和相关的灵敏度（= 特异性）值获得联合非参数置信区域。进行了广泛的模拟实验以评估所提出方法的有限样本性能。还使用真实数据集来说明其应用。