Obuchowski N A
Department of Biostatistics and Epidemiology, Cleveland Clinic Foundation, Ohio 44195-5196, USA.
Biometrics. 1997 Jun;53(2):567-78.
Current methods for estimating the accuracy of diagnostic tests require independence of the test results in the sample. However, cases in which there are multiple test results from the same patient are quite common. In such cases, estimation and inference of the accuracy of diagnostic tests must account for intracluster correlation. In the present paper, the structural components method of DeLong, DeLong, and Clarke-Pearson (1988, Biometrics 44, 837-844) is extended to the estimation of the Receiver Operating Characteristics (ROC) curve area for clustered data, incorporating the concepts of design effect and effective sample size used by Rao and Scott (1992, Biometrics 48, 577-585) for clustered binary data. Results of a Monte Carlo simulation study indicate that the size of statistical tests that assume independence is inflated in the presence of intracluster correlation. The proposed method, on the other hand, appropriately handles a wide variety of intracluster correlations, e.g., correlations between true disease statuses and between test results. In addition, the method can be applied to both continuous and ordinal test results. A strategy for estimating sample size requirements for future studies using clustered data is discussed.
当前用于估计诊断测试准确性的方法要求样本中的测试结果相互独立。然而，同一患者有多个测试结果的情况相当常见。在这种情况下，诊断测试准确性的估计和推断必须考虑聚类内相关性。在本文中，将DeLong、DeLong和Clarke-Pearson（1988年，《生物统计学》44卷，837 - 844页）的结构成分法扩展到用于聚类数据的受试者工作特征（ROC）曲线面积估计，纳入了Rao和Scott（1992年，《生物统计学》48卷，577 - 585页）用于聚类二元数据的设计效应和有效样本量的概念。蒙特卡罗模拟研究结果表明，在存在聚类内相关性的情况下，假设独立性的统计检验规模会膨胀。另一方面，所提出的方法能够适当地处理各种聚类内相关性，例如真实疾病状态之间以及测试结果之间的相关性。此外，该方法可应用于连续和有序的测试结果。还讨论了使用聚类数据进行未来研究时估计样本量要求的策略。
