Generalized ROC curve inference for a biomarker subject to a limit of detection and measurement error.
Generalized ROC curve inference for a biomarker subject to a limit of detection and measurement error.
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DOI：
10.1002/sim.3575
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发表时间：
2009-06-15
影响因子：
2
通讯作者：
Vexler, Albert
中科院分区：
文献类型：
--
作者：
Perkins, Neil J.;Schisterman, Enrique F.;Vexler, Albert
受试者工作特征（ROC）曲线是一种常用的工具，用于评估生物标志物在疾病临床诊断中的效用，特别是在生物标志物开发研究期间。新兴的生物标志物通常以随机测量误差进行测量，并且受到检测极限的影响，这阻碍了它们的潜在效用或通过负偏ROC曲线的估计值和随后的曲线下面积来掩盖区分能力。已经开发了各种方法来校正这些类型的偏倚来源的ROC曲线，但在这里，我们开发了一种方法，通过该方法同时通过重复测量和最大似然来校正ROC曲线。我们的方法进行评估，通过模拟研究和应用到两个潜在的歧视妇女和没有先兆子痫。
The receiver operating characteristic (ROC) curve is a tool commonly used to evaluate biomarker utility in clinical diagnosis of disease, especially during biomarker development research. Emerging biomarkers are often measured with random measurement error and subject to limits of detection that hinder their potential utility or mask an ability to discriminate by negatively biasing the estimates of ROC curves and subsequent area under the curve. Methods have been developed to correct the ROC curve for each of these types of sources of bias but here we develop a method by which the ROC curve is corrected for both simultaneously through replicate measures and maximum likelihood. Our method is evaluated via simulation study and applied to two potential discriminators of women with and without preeclampsia.
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影响因子：
1.7
作者：
Perkins, NJ;Schisterman, EF
通讯作者：
Schisterman, EF
影响因子：
5
作者：
Schisterman, EF;Faraggi, D;Trevisan, M
通讯作者：
Trevisan, M
影响因子：
--
作者：
COHEN, AC
通讯作者：
COHEN, AC
影响因子：
1.9
作者：
Vexler, Albert;Liu, Aiyi;Schisterman, Enrique F.
通讯作者：
Schisterman, Enrique F.
影响因子：
5
作者：
Perkins, Neil J.;Schisterman, Enrique F.;Vexler, Albert
通讯作者：
Vexler, Albert