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首页> 外文期刊>Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology >Three approaches to regression analysis of receiver operating characteristic curves for continuous test results
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Three approaches to regression analysis of receiver operating characteristic curves for continuous test results

机译:连续工作结果的接收机工作特性曲线回归分析的三种方法

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摘要

The accuracy of a medical diagnostic test is typically summarized by the sensitivity and specificity when the test result is dichotomous. Receiver operating characteristic (ROC) curves are measures of test accuracy that are used when test results are continuous and are considered the analogs of sensitivity and specificity for continuous tests. ROC regression analysis allows one to evaluate effects of factors that may influence test accuracy. Such factors might include characteristics of study subjects or operating conditions for the test. Unfortunately, regression analysis methods for ROC curves are not well developed and methods that do exist have received little use to date. In this paper, we propose and compare three very different regression analysis methods. Two are modifications of methods previously proposed for radiology settings. The third is a special case of a general method recently proposed by us. The three approaches are compared with regard to settings in which they can be applied and distributional assumptions they require. In the setting where test results are normally distributed, we elucidate the correspondence between regression parameters in the different models. The methods are applied to simulated data and to data from a study of a new diagnostic test for hearing impairment. It is hoped that the presentation in this paper will both encourage the use of regression analysis for evaluating diagnostic tests and help guide the choice of the most appropriate regression analysis approach in applications. [References: 17]
机译:当诊断结果二分时,通常通过敏感性和特异性来概括医学诊断测试的准确性。接收器工作特性(ROC)曲线是在测试结果连续时使用的测试准确性的度量,被视为连续测试的灵敏度和特异性的类似物。 ROC回归分析可以评估可能影响测试准确性的因素的影响。这些因素可能包括研究对象的特征或测试的操作条件。不幸的是,ROC曲线的回归分析方法还没有得到很好的开发,并且迄今为止,确实存在的方法使用很少。在本文中,我们提出并比较了三种非常不同的回归分析方法。二是对先前为放射学设置提出的方法的修改。第三是我们最近提出的一种通用方法的特例。比较了这三种方法的适用环境和所需的分布假设。在测试结果呈正态分布的环境中,我们阐明了不同模型中回归参数之间的对应关系。该方法适用于模拟数据和来自新的听力障碍诊断测试研究的数据。希望本文中的介绍会鼓励使用回归分析来评估诊断测试,并有助于指导在应用程序中选择最合适的回归分析方法。 [参考:17]

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