校正协变量的ROC曲线在评价标志物预测分类准确性中的应用

Application of covariate-adjusted ROC curves to evaluate the prediction and classification accuracy of markers

本文介绍校正协变量的ROC曲线的原理、相对优势、应用场景及R软件实现方法。相较于传统的单变量ROC曲线,校正协变量的ROC曲线具有明显的方法学优势和更广阔的应用场景,能帮助更科学地评价标志物预测分类结局指标的能力,应在实践研究中推广应用。

This paper introduced the fundamental theory,method advantages,application scenario and R software implementation method of the covariate-adjusted receiver operating characteristic(ROC)curve.Compared with the traditional univariate ROC curve,the covariate-adjusted ROC curve has distinct methodological advantages and wider application scenarios,which can help to evaluate the ability of markers to predict the targeted outcome more scientifically.It merits more widespread and prior adoption in practical research.