Departments of Radiology and Biostatistics, The University of Iowa, 3710 Medical Laboratories, 200 Hawkins Drive, Iowa City, IA 52242-1077, U.S.A.; Comprehensive Access and Delivery Research and Evaluation (CADRE) Center, Iowa City VA Health Care System, IA 52242-1077, U.S.A.
Stat Med. 2014 Jan 30;33(2):330-60. doi: 10.1002/sim.5926. Epub 2013 Aug 23.
The correlated-error ANOVA method proposed by Obuchowski and Rockette (OR) has been a useful procedure for analyzing reader-performance outcomes, such as the area under the receiver-operating-characteristic curve, resulting from multireader multicase radiological imaging data. This approach, however, has only been formally derived for the test-by-reader-by-case factorial study design. In this paper, I show that the OR model can be viewed as a marginal-mean ANOVA model. Viewing the OR model within this marginal-mean ANOVA framework is the basis for the marginal-mean ANOVA approach, the topic of this paper. This approach (1) provides an intuitive motivation for the OR model, including its covariance-parameter constraints; (2) provides easy derivations of OR test statistics and parameter estimates, as well as their distributions and confidence intervals; and (3) allows for easy generalization of the OR procedure to other study designs. In particular, I show how one can easily derive OR-type analysis formulas for any balanced study design by following an algorithm that only requires an understanding of conventional ANOVA methods.
Obuchowski 和 Rockette（OR）提出的相关误差方差分析方法（ANOVA）一直是一种用于分析读者绩效结果的有用方法，例如多读者多病例放射影像学数据的受试者工作特征曲线下面积。然而，这种方法仅针对测试者-读者-病例析因研究设计进行了正式推导。在本文中，我表明 OR 模型可以被视为边缘均值方差分析模型。在这个边缘均值方差分析框架内观察 OR 模型是边缘均值方差分析方法的基础，也是本文的主题。这种方法：（1）为 OR 模型提供了直观的动机，包括其协方差参数约束；（2）提供了 OR 检验统计量和参数估计的简便推导，以及它们的分布和置信区间；（3）允许将 OR 程序轻松推广到其他研究设计。具体来说，我通过遵循仅需要对传统方差分析方法有一定了解的算法，展示了如何轻松地为任何平衡研究设计推导出 OR 型分析公式。
