In this paper, we present three estimators of the ROC curve when missing observations arise among the biomarkers. Two of the procedures assume that we have covariates that allow to estimate the propensity and the estimators are obtained using an inverse probability weighting method or a smoothed version of it. The other one assumes that the covariates are related to the biomarkers through a regression model which enables us to construct convolution--based estimators of the distribution and quantile functions. Consistency results are obtained under mild conditions. Through a numerical study we evaluate the finite sample performance of the different proposals. A real data set is also analysed.

翻译：在本文中,当生物标志物之间出现缺失的观测时,我们提出三个ROC曲线估计符。其中两个程序假设我们有共同变量,可以对倾向性作出估计,估计符是用反概率加权法或平滑的版本获得的。另一个假设共变量通过回归模型与生物标志有关,该模型使我们能够构建基于革命的分布和量化函数估计符。在温和条件下取得一致性结果。通过数字研究,我们评估了不同提案的有限样本性能。还分析了真实的数据集。