Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests
Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests
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DOI：
10.1016/s0167-5877(00)00115-x
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发表时间：
2000-05-30
影响因子：
2.6
通讯作者：
Smith, RD
中科院分区：
文献类型：
--
作者：
Greiner, M;Pfeiffer, D;Smith, RD
本文综述了受试者工作特征（ROC）分析在诊断试验中的原理和实际应用。ROC分析可用于诊断测试，其结果以顺序、间隔或比率量表测量。诊断灵敏度和特异性对所选临界值的依赖性必须在全面试验评价和试验比较中加以考虑。通过改变测试的截止值可以实现的所有可能的灵敏度和特异性组合可以使用单个参数进行总结; ROC曲线下面积。ROC技术也可用于优化目标人群中给定患病率的截止值以及假阳性和假阴性结果的成本比。然而，针对所选临界值的优化参数图为临界值选择提供了更直接的方法。这种优化参数的候选者是灵敏度和特异性的线性组合（选择权重以反映决策情况）、比值比、关联的机会校正测量（例如kappa）和似然比。我们讨论了ROC分析的一些最新进展，包括诊断测试的荟萃分析，相关ROC曲线（配对样本设计）和机会和患病率校正的ROC曲线。(C)2000 Elsevier Science B. V.保留所有权利。
We review the principles and practical application of receiver-operating characteristic (ROC) analysis for diagnostic tests. ROC analysis can be used for diagnostic tests with outcomes measured on ordinal, interval or ratio scales. The dependence of the diagnostic sensitivity and specificity on the selected cut-off value must be considered for a full test evaluation and for test comparison. All possible combinations of sensitivity and specificity that can be achieved by changing the test's cutoff value can be summarised using a single parameter; the area under the ROC curve. The ROC technique can also be used to optimise cut-off values with regard to a given prevalence in the target population and cost ratio of false-positive and false-negative results. However, plots of optimisation parameters against the selected cut-off value provide a more-direct method for cut-off selection. Candidates for such optimisation parameters are linear combinations of sensitivity and specificity (with weights selected to reflect the decision-making situation), odds ratio, chance-corrected measures of association (e.g. kappa) and likelihood ratios. We discuss some recent developments in ROC analysis, including meta-analysis of diagnostic tests, correlated ROC curves (paired-sample design) and chance- and prevalence-corrected ROC curves. (C) 2000 Elsevier Science B.V. All rights reserved.