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Estimating the Area under the ROC Curve with Modified Profile Likelihoods

机译：估计ROC曲线下的区域，具有修改的简档可能性

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摘要

Receiver operating characteristic (ROC) curves are a frequent tool to study the discriminating ability of a certain characteristic. The area under the ROC curve (AUC) is a widely used measure of statistical accuracy of continuous markers for diagnostic tests, and has the advantage of providing a single summary? index of overall performance of the test. Recent studies have shown some critical issues related to traditional point and interval estimates for the AUC, especially for small samples, more complex models, unbalanced samples or values near the boundary of the parameter space, i.e., when the AUC approaches the values 0.5 or 1.Parametric models for the AUC have shown to be powerful when the underlying distributional assumptions are not misspecified. However, in the above circumstances parametric inference may be not accurate, sometimes yielding? misleading conclusions. The objective of the paper is to propose an alternative inferential approach based on modified profile likelihoods, which provides more accurate statistical results in any parametric settings, including the above circumstances. The proposed method is illustrated for the binormal model, but can potentially be used in any other complex model and for any other parametric distribution. We report simulation studies to show the improved performance of the proposed approach, when compared to classical first-order likelihood theory. An? application to real-life data in a small sample setting is also discussed, to provide practical guidelines.
机译：接收器操作特征（ROC）曲线是研究某种特征的辨别能力的频繁工具。 ROC曲线（AUC）下的区域是诊断测试连续标记的统计精度的广泛使用量度，并且具有提供单一摘要的优点？测试的整体性能指数。最近的研究表明了与AUC的传统点和间隔估计有关的一些关键问题，特别是对于参数空间边界附近的小样本，更复杂的模型，不平衡样本或值，即，当AUC接近值0.5或1时。当潜在的分布假设不被遗漏时，AUC的参数模型表明是强大的。但是，在上面的情况下，参数推断可能不准确，有时会屈服？误导的结论。本文的目的是提出基于修改的简档可能性的替代推理方法，该方法在任何参数设置中提供更准确的统计结果，包括上述情况。所提出的方法被示出为双重模型，但是可能在任何其他复杂模型中使用和任何其他参数分布。我们报告了模拟研究，以显示与古典一阶似然理论相比的提出方法的提高性能。一个？还讨论了在小样本设置中的实际数据应用，以提供实用的指导。

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