National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
PLoS One. 2019 Aug 30;14(8):e0221433. doi: 10.1371/journal.pone.0221433. eCollection 2019.
Cumulative receiver operator characteristic (ROC) curve analysis extends classic ROC curve analysis to discriminate three or more ordinal outcome levels on a shared continuous scale. The procedure combines cumulative logit regression with a cumulative extension to the ROC curve and performs as expected with ternary (three-level) ordinal outcomes under a variety of simulated conditions (unbalanced data, proportional and non-proportional odds, areas under the ROC curve [AUCs] from 0.70 to 0.95). Simulations also compared several criteria for selecting cutpoints to discriminate outcome levels: the Youden Index, Matthews Correlation Coefficient, Total Accuracy, and Markedness. Total Accuracy demonstrated the least absolute percent-bias. Cutpoints computed from maximum likelihood regression parameters demonstrated bias that was often negligible. The procedure was also applied to publicly available data related to computer imaging and biomarker exposure science, yielding good to excellent AUCs, as well as cutpoints with sensitivities and specificities of commensurate quality. Implementation of cumulative ROC curve analysis and extension to more than three outcome levels are straightforward. The author's programs for ternary ordinal outcomes are publicly available.
累积受试者工作特征（ROC）曲线分析将经典 ROC 曲线分析扩展到在共享的连续尺度上区分三个或更多有序结局水平。该方法将累积对数回归与 ROC 曲线的累积扩展相结合，在各种模拟条件下（不平衡数据、比例和非比例优势、ROC 曲线下面积 [AUC] 从 0.70 到 0.95）对三分类（三级）有序结局表现良好。模拟还比较了几种选择切点来区分结局水平的标准：Youden 指数、马修斯相关系数、总准确率和显著性。总准确率表现出最小的绝对百分比偏差。从最大似然回归参数计算的切点显示出的偏差通常可以忽略不计。该方法还应用于与计算机成像和生物标志物暴露科学相关的公开可用数据，得出了良好到优秀的 AUC，以及具有相当质量的敏感性和特异性的切点。累积 ROC 曲线分析的实施和扩展到超过三个结局水平非常简单。作者的三分类有序结局程序可公开获取。
