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.

中文翻译：

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对现代方法的反思：重新审视 ROC 曲线下的区域。

接受者操作特征 (ROC) 曲线 (AUC) 下的面积通常用于评估预测模型的判别能力，尽管该测量被批评为与临床无关且缺乏直观的解释。每个教程都解释了如何从患病和未患病个体的风险分布中获得 ROC 曲线的坐标，但 ROC 图只是呈现这些风险分布的另一种方式并没有成为常识。我们展示了 ROC 曲线如何成为呈现患病和未患病个体风险分布的替代方法，以及 ROC 曲线的形状如何告知风险分布的重叠。例如，当预测模型包含对疾病风险具有相似影响的变量并且在例如一个二元风险因素具有更强影响时具有角度时，ROC 曲线是圆整的；当样本量或发生率较低时，当预测模型基于一组相对较小的分类预测变量时，ROC 曲线是阶梯式的而不是平滑的。ROC 图的这种替代观点使大多数声称的 AUC 限制无效，并将其他限制归因于潜在的风险分布。AUC 是衡量预测模型判别能力的指标。预测模型的评估应辅以其他指标，以评估其临床效用。当样本量或发生率较低时，当预测模型基于一组相对较小的分类预测变量时，ROC 曲线是阶梯式的而不是平滑的。ROC 图的这种替代观点使大多数声称的 AUC 限制无效，并将其他限制归因于潜在的风险分布。AUC 是衡量预测模型判别能力的指标。预测模型的评估应辅以其他指标，以评估其临床效用。当样本量或发生率较低时，当预测模型基于一组相对较小的分类预测变量时，ROC 曲线是阶梯式的而不是平滑的。ROC 图的这种替代观点使大多数声称的 AUC 限制无效，并将其他限制归因于潜在的风险分布。AUC 是衡量预测模型判别能力的指标。预测模型的评估应辅以其他指标，以评估其临床效用。