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[医学决策在大规模筛查中的应用:ROC分析的基本原则]

[The application of medical decision making to the mass screening: the basic principles on ROC analysis].

作者信息

Tsji I

机构信息

Department of Public Health, Tohoku University School of Medicine, Sendai.

出版信息

Rinsho Byori. 1990 May;38(5):597-600.

PMID:2199711
Abstract

The basic principles on receiver-operating characteristic (ROC) analysis were discussed. ROC curves showed the discriminative ability of a test by the position of the full curve in a graph plotting the relation between the true positive rate (TPR) and the false positive rate (FPR) over a wide range of cut-off points of a test. The increase in the area under the ROC curve, or the shift of the curve upward and to the left in the diagram means that the test has better discriminative ability. A manual was given to conduct the ROC analysis with special reference to calculation of TPR, FPR, and the area under the ROC curve. Also discussed was the method to decide the best or optimal cut-off point as the positivity criterion of a test, based on the ROC analysis. Attention was paid to balance the risk of false negatives and false positives. We made an equation to decide the best cut-off point, which showed us the variables to be considered in the analysis of cut-off problems: the prevalence of disease and the outcome associated with each state classified by the test, i.e., true positive, false positive, true negative, and false negative.

摘要

讨论了接受者操作特征(ROC)分析的基本原理。ROC曲线通过在绘制真阳性率(TPR)与假阳性率(FPR)关系的图表中全曲线的位置,展示了一项检测的辨别能力,该关系涵盖了检测的广泛截断点范围。ROC曲线下面积的增加,或者在图表中曲线向上和向左的移动,意味着该检测具有更好的辨别能力。给出了一份进行ROC分析的手册,特别提及了TPR、FPR以及ROC曲线下面积的计算。还讨论了基于ROC分析确定最佳或最优截断点作为检测阳性标准的方法。注意要平衡假阴性和假阳性的风险。我们制定了一个确定最佳截断点的方程,该方程向我们展示了在截断问题分析中要考虑的变量:疾病的患病率以及检测所分类的每种状态(即真阳性、假阳性、真阴性和假阴性)相关的结果。

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