Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three different ways of using covariate information. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (i.e. the ROC curve), we describe methods for modelling the ROC curve as a function of covariates. Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and ask how much discriminatory accuracy improves with the addition of the marker to the covariates (incremental value). These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution.

中文翻译：

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在 ROC 分析中适应协变量。

分类准确性是标记或诊断测试区分两组个体、病例和对照的能力，通常使用受试者工作特征 (ROC) 曲线进行总结。在分类准确性的研究中，经常有协变量应该被纳入 ROC 分析。我们描述了使用协变量信息的三种不同方式。对于影响控件之间标记观察的因素，我们提出了一种协变量调整方法。对于影响区分的因素（即 ROC 曲线），我们描述了将 ROC 曲线建模为协变量函数的方法。最后，对于导致歧视的因素，我们建议结合标记和协变量信息，并询问将标记添加到协变量（增量值）后，判别准确度提高了多少。当将 ROC 曲线表示为病例标记观察值分布的摘要时，这些方法自然会遵循这些方法，这些方法相对于对照分布进行了标准化。