ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves
ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves
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DOI：
10.1016/j.surg.2015.12.029
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发表时间：
2016-06-01
期刊：
影响因子：
3.8
通讯作者：
Galandiuk, Susan
中科院分区：
文献类型：
--
作者：
Carter, Jane V.;Pan, Jiamnin;Galandiuk, Susan
背景临床医生正确理解和解释临床研究中使用的医学统计数据至关重要。在这篇综述中，我们解决了当前的问题，并专注于提供一个简单而全面的解释，共同的研究方法，涉及受试者工作特征（ROC）曲线。ROC曲线在医学中最常用作评估诊断测试的手段。来自用于诊断结直肠癌的血浆测试的样本数据用于生成预测模型。这些是用于描述灵敏度、特异性、阳性预测值和阴性预测值以及准确度计算的实际未发表数据。生成ROC曲线以确定该血浆测试的准确性。这些曲线是通过在y轴上绘制灵敏度（真阳性率）和在x轴上绘制1 -特异性（假阳性率）来生成的。最接近坐标（x = 0，y = 1）的曲线具有更高的预测性，而接近相等线的ROC曲线表明结果并不比偶然获得的结果更好。最佳灵敏度和特异性可以从图中确定为最小距离线与ROC曲线相交的点。这一点对应于约登指数（J），这是一个通常用于评估诊断测试的灵敏度和特异性的函数。曲线下面积用于量化测试区分2种结局的总体能力。结论。通过遵循这些简单的指导方针，ROC曲线的解释将变得不那么困难，并且在撰写、审查或分析科学论文时可以更可靠地解释它们。
Background. It is vital for clinicians to understand and interpret correctly medical statistics as used in clinical studies. In this review, we address current issues and focus on delivering a simple, yet comprehensive, explanation of common research methodology involving receiver operating characteristic (ROC) curves. ROC curves are used most commonly in medicine as a means of evaluating diagnostic tests.Methods. Sample data from a plasma test for the diagnosis of colorectal cancer were used to generate a prediction model. These are actual, unpublished data that have been used to describe the calculation of sensitivity, specificity, positive predictive and negative predictive values, and accuracy. The ROC curves were generated to determine the accuracy of this plasma test. These curves are generated by plotting the sensitivity (true-positive rate) on the y axis and 1 - specificity (false-positive rate) on the x axis.Results. Curves that approach closest to the coordinate (x = 0, y = 1) are more highly predictive, whereas ROC curves that lie close to the line of equality indicate that the result is no better than that obtained by chance. The optimum sensitivity and specificity can be determined from the graph as the point where the minimum distance line crosses the ROC curve. This point corresponds to the Youden index (J), a function of sensitivity and specificity used commonly to rate diagnostic tests. The area under the curve is used to quantify the overall ability of a test to discriminate between 2 outcomes.Conclusion. By following these simple guidelines, interpretation of ROC curves will be less difficult and they can then be interpreted more reliably when writing, reviewing, or analyzing scientific papers.