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A Regression Model for the AUC of Clustered Ordinal Test Results and Working Independent Optimal Weights

机译:聚类序数检验结果和独立工作的最佳权重的AUC回归模型

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We study a regression model on the area under the receiver operating characteristic curves (A L/C) for clustered (or repeatedly measured) test results. To account for cluster information, we consider a weighted estimating equation for Dodd and Pepe (2003)'s regression model with working independence weights. We find the optimal weight in the given class of working independence weights to minimize the variance (or MSE) of regression estimators. We apply the proposed procedure to analyzing our recent experiment on diagnosing a liver disorder. In this experiment, we investigated MR1 images of patients having symptoms of potential liver disorder to compare the performance of different MRI picturing methods in testing for liver disorders.
机译:我们针对接收器工作特性曲线(A L / C)下的区域研究了一个回归模型,以进行聚类(或反复测量)测试结果。为了说明群集信息,我们考虑了具有工作独立权重的Dodd和Pepe(2003)回归模型的加权估计方程。我们在给定的工作独立权重类别中找到最佳权重,以最小化回归估计量的方差(或MSE)。我们将提出的程序用于分析我们最近诊断肝脏疾病的实验。在此实验中,我们调查了具有潜在肝病症状的患者的MR1图像,以比较不同MRI成像方法在测试肝病中的表现。

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