The Youden index and the optimal cut-point corrected for measurement error
The Youden index and the optimal cut-point corrected for measurement error
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DOI：
10.1002/bimj.200410133
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发表时间：
2005-08-01
影响因子：
1.7
通讯作者：
Schisterman, EF
中科院分区：
文献类型：
--
作者：
Perkins, NJ;Schisterman, EF
随机测量误差可以削弱生物标志物区分患病和非患病群体的能力。生物标志物有效性的总体量度是约登指数，即灵敏度（正确分类患病个体的概率）与I特异性（错误分类健康个体的概率）之间的最大差异。我们提出了一种方法，用于估计约登指数和相关的最佳临界点的正态分布的生物标志物，校正正态分布的随机测量误差。我们还提供了这些校正估计的置信区间，使用三角洲方法和覆盖概率，通过模拟各种情况。将这些技术应用于生物标志物硫代巴比妥酸反应物质（TBARS），脂质过氧化的子产物的测量，已被提议作为心血管疾病的鉴别测量，在最佳临界点的诊断有效性增加了50%。这一结果可能会导致生物标志物，曾经天真地认为无效成为有用的诊断设备。
Random measurement error can attenuate a biomarker's ability to discriminate between diseased and non-diseased populations. A global measure of biomarker effectiveness is the Youden index, the maximum difference between sensitivity, the probability of correctly classifying diseased individuals, and I-specificity, the probability of incorrectly classifying health individuals. We present an approach for estimating the Youden index and associated optimal cut-point for a normally distributed biomarker that corrects for normally distributed random measurement error. We also provide confidence intervals for these corrected estimates using the delta method and coverage probability through simulation over a variety of situations. Applying these techniques to the biomarker thiobarbituric acid reaction substance (TBARS), a measure of sub-products of lipid peroxidation that has been proposed as a discriminating measurement for cardiovascular disease, yields a 50% increase in diagnostic effectiveness at the optimal cut-point. This result may lead to biomarkers that were once naively considered ineffective becoming useful diagnostic devices.