A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing
A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing
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DOI：
10.1093/biomet/84.3.595
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发表时间：
1997-09-01
期刊：
影响因子：
2.7
通讯作者：
Pepe, MS
中科院分区：
文献类型：
--
作者：
Pepe, MS
受试者工作特征曲线（ROC）用于评估诊断测试时，测试结果不是二元的。它们描述了该测试区分真正患病和未患病受试者的固有能力。虽然用于估计和比较ROC的方法已经很成熟，但迄今为止还没有一个通用的框架来评估协变量对ROC的影响。我们制定了一个通用的回归模型，它允许协变量对测试精度的影响被简洁地总结。此类协变量可能包括，例如，患者或测试环境的特征、测试类型或疾病的严重程度。回归模型是自然产生的一些经典模型的连续或有序的测试数据。使用估计方程方法拟合回归参数。该方法说明了从多格式的摄影图像的研究数据用于照相术。
Receiver operating characteristic curves (ROC's) are used to evaluate diagnostic tests when test results are not binary. They describe the inherent capacity of the test for distinguishing between truly diseased and nondiseased subjects. Although methodology for estimating and for comparing Roc's is well developed, to date no general framework exists for evaluating covariate effects on ROC's. We formulate a general regression model which allows the effects of covariates on test accuracy to be succinctly summarised. Such covariates might include, for example, characteristics of the patient or test environment, test type or severity of disease. The regression models are shown to arise naturally from some classic models for continuous or ordinal test data. Regression parameters are fitted using an estimating equation approach. The method is illustrated on data from a study of multiformat photographic images used for scintigraphy.