Relationship between Obuchowski-Rockette-Hillis and Gallas methods for analyzing multi-reader diagnostic imaging data with empirical AUC as the reader performance measure.
Relationship between Obuchowski-Rockette-Hillis and Gallas methods for analyzing multi-reader diagnostic imaging data with empirical AUC as the reader performance measure.
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Obuchowski-Rockette-Hillis 和 Gallas 方法之间的关系，以经验 AUC 作为读取器性能测量来分析多读取器诊断成像数据。
DOI：
10.1080/24709360.2022.2062115
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发表时间：
2023
影响因子：
--
通讯作者：
Hillis,StephenL
中科院分区：
文献类型：
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作者：
Hillis,StephenL
当感兴趣的阅片人性能指标是受试者工作特征曲线下面积（AUC）时，为了分析多阅片人多病例（MRMC）诊断成像数据，两种流行的分析方法允许将结论推广到阅片人和病例人群，这两种方法是Obuchowski、Rocaland和Hillis（ORH）开发的方法和Gallas（Gallas）主要开发的方法。虽然ORH方法是适用于大多数读者性能指标的通用方法，但Gallas方法仅限于存在无偏方差估计的那些指标。以前，它是不知道的ORH方法是否可以适应，以产生相同的方差估计的加拉方法。在本文中，我表明，最近提出的版本的OR方法产生相同的无约束方差统计的加拉方法。然而，这两种方法在约束方差估计为非负的方法和自由度估计方面有所不同。这两个差异进行了讨论和建议。此外，ORH方法的发展作出了一些贡献，包括确定无偏方差估计的充分条件和ORH方差约束和协方差估计方法的理由。
For analyzing multireader multicase (MRMC) diagnostic imaging data when the reader performance measure of interest is the area under the receiver-operating-characteristic curve (AUC), two popular methods of analysis that allow conclusions to generalize to both the reader and case populations are the method developed by Obuchowski, Rockette and Hillis (ORH) and the method primarily developed by Gallas (Gallas). While the ORH method is a general method that is applicable to most reader performance metrics, the Gallas method is limited to those metrics for which an unbiased variance estimate exists. Previously it was not known if the ORH method could be adapted so as to produce the same variance estimate as the Gallas method. In this paper, I show that a recently proposed version of the OR method produces the same unconstrained variance statistic as the Gallas method. However, the two methods differ in their approaches to constraining the variance estimate to be nonnegative and in their degrees-of-freedom estimates. These two differences are discussed and recommendations given. In addition, several contributions to the development of the ORH method are made, including determining sufficient conditions for unbiased variance estimates and providing justification for the ORH variance constraints and covariance estimation method.