Huang Yijian, Parakati Isaac, Patil Dattatraya H, Sanda Martin G
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA.
Stat Sin. 2023 Jan;33(1):193-214. doi: 10.5705/ss.202021.0020.
The receiver operating characteristic (ROC) curve provides a comprehensive performance assessment of a continuous biomarker over the full threshold spectrum. Nevertheless, a medical test often dictates to operate at a certain high level of sensitivity or specificity. A diagnostic accuracy metric directly targeting the clinical utility is specificity at the controlled sensitivity level, or vice versa. While the empirical point estimation is readily adopted in practice, the nonparametric interval estimation is challenged by the fact that the variance involves density functions due to estimated threshold. In addition, even with a fixed threshold, many standard confidence intervals including the Wald interval for binomial proportion could have erratic behaviors. In this article, we are motivated by the superior performance of the score interval for binomial proportion and propose a novel extension for the biomarker problem. Meanwhile, we develop exact bootstrap and establish consistency of the bootstrap variance estimator. Both single-biomarker evaluation and two-biomarker comparison are investigated. Extensive simulation studies were conducted, demonstrating competitive performance of our proposals. An illustration with aggressive prostate cancer diagnosis is provided.
受试者工作特征（ROC）曲线提供了对连续生物标志物在整个阈值范围内的全面性能评估。然而，医学检验通常要求在一定的高灵敏度或特异性水平下进行操作。直接针对临床效用的诊断准确性指标是在控制灵敏度水平下的特异性，反之亦然。虽然经验点估计在实践中很容易采用，但非参数区间估计面临着由于估计阈值导致方差涉及密度函数这一事实的挑战。此外，即使阈值固定，许多标准置信区间，包括二项比例的Wald区间，也可能表现不稳定。在本文中，我们受二项比例得分区间的卓越性能启发，为生物标志物问题提出了一种新颖的扩展方法。同时，我们开发了精确的自助法并建立了自助方差估计器的一致性。我们研究了单生物标志物评估和双生物标志物比较。进行了广泛的模拟研究，证明了我们提出的方法具有竞争力的性能。还提供了一个侵袭性前列腺癌诊断的示例。
