The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for evaluation using metrics defined conditionally on the outcome of the forecast rather than metrics defined conditionally on the actual disease status. Starting from the binormal ROC curve formulation, an overview of some previously published binormal PROC curves is presented in order to place the PROC curve in the context of other methods used in statistical evaluation of probabilistic disease forecasts based on the analysis of predictive values; in particular, the index of separation (PSEP) and the leaf plot. An information theoretic perspective on evaluation is also outlined. Five straightforward recommendations are made with a view to aiding understanding and interpretation of the sometimes-complex patterns generated by PROC curve analysis. The PROC curve and related analyses augment the perspective provided by traditional ROC curve analysis. Here, the binormal ROC model provides the exemplar for investigation of the PROC curve, but potential application extends to analysis based on other distributional models as well as to empirical analysis.

中文翻译：

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联合评估正负预测值的双正态预测接受者操作特征曲线


预测接受者操作特征 (PROC) 曲线是一种图表格式，应用于概率疾病预测的统计评估。 PROC 曲线与更广为人知的受试者工作特征 (ROC) 曲线不同，它提供了使用根据预测结果有条件定义的指标（而不是根据实际疾病状态有条件定义的指标）进行评估的基础。从双正态 ROC 曲线公式开始，概述了一些先前发表的双正态 PROC 曲线，以便将 PROC 曲线置于基于预测值分析的概率疾病预测统计评估中使用的其他方法的背景下；特别是分离指数 (PSEP) 和叶图。还概述了评估的信息论视角。提出了五个简单的建议，旨在帮助理解和解释 PROC 曲线分析生成的有时复杂的模式。 PROC 曲线和相关分析增强了传统 ROC 曲线分析提供的视角。在这里，双正态 ROC 模型提供了 PROC 曲线研究的范例，但潜在的应用扩展到基于其他分布模型的分析以及实证分析。