Shaw Pamela A, Pepe Margaret S, Alonzo Todd A, Etzioni Ruth
Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892.
Stat Biopharm Res. 2009 Feb 1;1(1):18-25. doi: 10.1198/sbr.2009.0002.
Biomarkers that can be used in combination with established screening tests to reduce false positive rates are in considerable demand. In this article, we present methods for evaluating the diagnostic performance of combination tests that require positivity on a biomarker test in addition to a standard screening test. These methods rely on relative true and false positive rates to measure the loss in sensitivity and gain in specificity associated with the combination relative to the standard test. Inference about the relative rates follows from noting their interpretation as conditional probabilities. These methods are extended to evaluate combinations with continuous biomarker tests by introducing a new statistical entity, the relative receiver operating characteristic (rROC) curve. The rROC curve plots the relative true positive rate versus the relative false positive rate as the biomarker threshold for positivity varies. Inference can be made by applying existing ROC methodology. We illustrate the methods with two examples: a breast cancer biomarker study proposed by the Early Detection Research Network (EDRN) and a prostate cancer case-control study examining the ability of free prostate-specific antigen (PSA) to improve the specificity of the standard PSA test.
人们迫切需要能够与现有筛查测试相结合以降低假阳性率的生物标志物。在本文中，我们提出了评估联合测试诊断性能的方法，这些联合测试除了标准筛查测试外，还需要生物标志物测试呈阳性。这些方法依靠相对真阳性率和假阳性率来衡量与标准测试相比，联合测试在敏感性方面的损失和特异性方面的提高。关于相对率的推断源于将它们解释为条件概率。通过引入一个新的统计实体——相对接受者操作特征（rROC）曲线，这些方法被扩展到评估连续生物标志物测试的联合情况。rROC曲线绘制了随着生物标志物阳性阈值的变化，相对真阳性率与相对假阳性率的关系。可以通过应用现有的ROC方法进行推断。我们用两个例子来说明这些方法：早期检测研究网络（EDRN）提出的一项乳腺癌生物标志物研究，以及一项前列腺癌病例对照研究，该研究考察游离前列腺特异性抗原（PSA）提高标准PSA测试特异性的能力。
