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A Linear Regression Framework for Receiver Operating Characteristic(ROC) Curve Analysis

机译:接收机工作特性(ROC)曲线分析的线性回归框架

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摘要

In the field of medical diagnostic testing, the receiver operating characteristics(ROC) curve has long been used as a standard statistical tool to assess the accuracy of tests that yield continuous results. Although previous research in this area focused mostly on estimating the ROC curve, recently it has been recognized that the accuracy of a given test may fluctuate depending on certain factors, which motivates modelling covariate effects on the ROC curve. Comparing the corresponding ROC curves between two or more tests is a special case of covariate effect modelling. In this manuscript, we introduce a linear regression framework to model covariate effect on the ROC curve. We assumes the ROC curve takes a specific parametric form for each covariate level and the covariate effect reflects on the parameters of the curves. The new method provides an unified approach for the ROC curve analysis and it is intuitive and easy to apply. Two real data sets are used to illustrate the new approach.
机译:在医学诊断测试领域,接收器工作特性(ROC)曲线长期以来一直用作评估产生连续结果的测试准确性的标准统计工具。尽管以前在该领域的研究主要集中在估计ROC曲线上,但是最近人们已经认识到,给定测试的准确性可能会根据某些因素而波动,这会激发对ROC曲线建模协变量的影响。比较两个或多个测试之间的相应ROC曲线是协变量效应建模的一种特殊情况。在此手稿中,我们引入了线性回归框架来建模对ROC曲线的协变量影响。我们假设ROC曲线对每个协变量水平采用特定的参数形式,并且协变量效应反映在曲线的参数上。新方法为ROC曲线分析提供了统一的方法,并且直观,易于应用。使用两个实际数据集来说明新方法。

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