Doubly robust estimation of the area under the receiver-operating characteristic curve in the presence of verification bias
Doubly robust estimation of the area under the receiver-operating characteristic curve in the presence of verification bias
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DOI：
10.1198/016214505000001339
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发表时间：
2006-09-01
影响因子：
3.7
通讯作者：
Schisterman, Enrique
中科院分区：
文献类型：
--
作者：
Rotnitzky, Andrea;Faraggi, David;Schisterman, Enrique
接受者工作特征曲线下的面积（AUC）是医学诊断测试区分健康和患病受试者的功效的一种流行的汇总测量。在评估新诊断测试的研究中经常遇到的一个问题是，并非所有患者都接受疾病验证，因为验证测试昂贵或有创性，或两者兼而有之。此外，将患者送去验证的决定往往取决于新的检测方法和其他真实疾病状态的预测指标。在这种情况下，通常仅基于验证患者的AUC估计是有偏差的。在本文中，我们开发了在任何尺度上测量的标记的AUC估计器，该估计器可以根据选择进行调整以进行验证。这些估计值根据测量的患者协变量和诊断测试结果以及假设的残余选择偏差程度进行调整。然后，它们可以用于敏感性分析，以检查假设不同似是而非的残差关联程度时AUC估计是如何变化的。与其他数据缺失问题一样，由于维度的缺陷，当标记和/或测量的协变量是连续的时，需要一个疾病模型或选择模型来获得AUC的良好估计。我们描述了一个双鲁棒估计量，它具有一致性和渐近正态的吸引特征，如果疾病或选择模型（但不一定是两者）是正确的。
The area under the receiver operating characteristic curve (AUC) is a popular summary measure of the efficacy of a medical diagnostic test to discriminate between healthy and diseased subjects. A frequently encountered problem in studies that evaluate a new diagnostic test is that not all patients undergo disease verification because the verification test is expensive, invasive, or both. Furthermore, the decision to send patients to verification often depends on the new test and on other predictors of true disease status. In such cases, usual estimators of the AUC based on verified patients only are biased. In this article we develop estimators of the AUC of markers measured on any scale that adjust for selection to verification. These estimators adjust for measured patient covariates and diagnostic test results and also for an assumed degree of residual selection bias. They can then be used in a sensitivity analysis to examine how the AUC estimates change when different plausible degrees of residual association are assumed. As with other missing-data problems, due to the curse of dimensionality, a model for disease or a model for selection is needed to obtain well-behaved estimators of the AUC when the marker and/or the measured covariates are continuous. We describe a doubly robust estimator that has the attractive feature of being consistent and asymptotically normal if either the disease or the selection model (but not necessarily both) is correct.