Reflection on modern methods: Revisiting the area under the ROC Curve
Reflection on modern methods: Revisiting the area under the ROC Curve
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DOI：
10.1093/ije/dyz274
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发表时间：
2020-08-01
影响因子：
7.7
通讯作者：
Martens, Forike K.
中科院分区：
文献类型：
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作者：
Janssens, A. Cecile J. W.;Martens, Forike K.
受试者工作特征(ROC)曲线下面积(AUC)通常被用来评估预测模型的判别能力，尽管该指标被批评为临床无关和缺乏直观的解释。每本教程都解释了ROC曲线的坐标是如何从患病和非患病个体的风险分布中获得的，但没有成为常识，即ROC曲线图只是表示这些风险分布的另一种方式。我们展示了ROC曲线是如何替代表示患病和未患病个体的风险分布的方法，以及ROC曲线的形状如何告知风险分布的重叠。例如，当预测模型包括对疾病风险具有类似影响的变量时，ROC曲线是四舍五入的；当例如一个二元风险因素具有更强的影响时，ROC曲线是有角度的；当预测模型基于相对较小的类别预测者集时，当样本大小或发病率较低时，ROC曲线是阶梯式的，而不是平滑的。这种关于ROC情节的另一种视角使AUC的大多数声称的局限性无效，并将其他因素归因于潜在的风险分布。AUC是衡量预测模型辨别能力的指标。预测模型的评估应与其他指标相补充，以评估其临床实用性。
The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions. For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions. AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility.