Empirical likelihood inference for the area under the ROC curve
Empirical likelihood inference for the area under the ROC curve
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DOI：
10.1111/j.1541-0420.2005.00453.x
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发表时间：
2006-06-01
期刊：
影响因子：
1.9
通讯作者：
Zhou, XH
中科院分区：
文献类型：
--
作者：
Qin, GS;Zhou, XH
对于连续规模的诊断测试，最常用的受试者工作特征曲线（ROC）的汇总指标是衡量诊断测试准确性的曲线下面积（AUC）。在这篇文章中，我们提出了一个经验似然（EL）的方法推断的AUC。首先，我们定义了一个EL比的AUC，并表明其极限分布是一个缩放的卡方分布。然后，我们使用缩放卡方分布获得AUC的基于EL的置信区间。AUC的这种EL推断可以扩展到分层样本，并且所得的极限分布是独立卡方分布的加权和。此外，我们还进行了模拟研究，以比较拟议的基于EL的AUC区间与现有的基于正常近似的AUC区间和自举区间的相对性能。
For a continuous-scale diagnostic test, the most commonly used summary index of the receiver operating characteristic curve (ROC) is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this article, we propose an empirical likelihood (EL) approach for the inference on the AUC. First we define an EL ratio for the AUC and show that its limiting distribution is a scaled chisquare distribution. We then obtain an EL-based confidence interval for the AUC using the scaled chi-square distribution. This EL inference for the AUC can be extended to stratified samples, and the resulting limiting distribution is a weighted sum of independent chi-square distributions. Additionally we conduct simulation studies to compare the relative performance of the proposed EL-based interval with the existing normal approximation-based intervals and bootstrap intervals for the AUC.