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A Marginal Model Approach for Analysis of Multi-reader Multi-test Receiver Operating Characteristic (ROC) Data

机译:分析多读取器,多测试接收器工作特征(ROC)数据的边际模型方法

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摘要

The receiver operating characteristic (ROC) curve is a popular tool to characterize the capabilities of diagnostic tests with continuous or ordinal responses. One common design for assessing the accuracy of diagnostic tests is to have each patient examined by multiple readers with multiple tests; this design is most commonly used in a radiology setting, where the results of diagnostic tests depend on a radiologistu27s subjective interpretation. The most widely used approach for analyzing data from such a study is the Dorfman-Berbaum-Metz (DBM) method (Dorfman, Berbaum and Metz, 1992) which utilizes a standard analysis of variance (ANOVA) model for the jackknife pseudovalues of the AUCs. Although the DBM method performs well in previous simulation studies, there is no clear theoretical basis for this approach. In this paper, focusing on continuous outcomes, we investigate the theoretical basis of this approach. Our result indicates that the DBM method does not satisfy the regular assumptions for standard ANOVA models, and thus might lead to erroneous inference. We then propose a marginal model approach based on the AUCs which can adjust for covariates as well. We derive consistent and asymptotically normal estimators for the regression coe±cients. We compare our approach with the DBM method via simulation and by an application to data from a breast cancer study. The simulation results show that both our new method and the DBM method perform well when the accuracy of tests under the study is the same and that our new method outperforms the DBM method when the accuracy of tests is not the same. The marginal model approach can be easily extended to ordinal outcomes.
机译:接收器工作特性(ROC)曲线是一种流行的工具,用于以连续或顺序响应来表征诊断测试的功能。评估诊断测试准确性的一种常见设计是让每位患者由多名读者进行多次测试。此设计最常用于放射学环境,在该环境中,诊断测试的结果取决于放射科医生的主观解释。分析此类研究数据的最广泛使用的方法是Dorfman-Berbaum-Metz(DBM)方法(Dorfman,Berbaum和Metz,1992年),该方法利用标准方差分析(ANOVA)模型计算AUC的折刀假值。 。尽管DBM方法在以前的模拟研究中表现良好,但这种方法尚无明确的理论基础。在本文中,着眼于连续的结果,我们研究了这种方法的理论基础。我们的结果表明,DBM方法不满足标准ANOVA模型的常规假设,因此可能导致错误的推断。然后,我们提出基于AUC的边际模型方法,该方法也可以针对协变量进行调整。我们得出回归系数的一致且渐近正态估计。我们通过仿真以及将其应用于乳腺癌研究的数据,将我们的方法与DBM方法进行了比较。仿真结果表明,在所研究的测试精度相同的情况下,我们的新方法和DBM方法均表现良好;在测试精度不相同的情况下,我们的新方法优于DBM方法。边际模型方法可以轻松地扩展到序数结果。

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    Song Xiao; Zhou Xiao-Hua;

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  • 年度 2004
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  • 入库时间 2022-08-20 20:25:23

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