This article considers the receiver operating characteristic (ROC) curve analysis for medical data with non-ignorable missingness in the disease status. In the framework of the logistic regression models for both the disease status and the verification status, we first establish the identifiability of model parameters, and then propose a likelihood method to estimate the model parameters, the ROC curve, and the area under the ROC curve (AUC) for the biomarker. The asymptotic distributions of these estimators are established. Via extensive simulation studies, we compare our method with competing methods in the point estimation and assess the accuracy of confidence interval estimation under various scenarios. To illustrate the application of the proposed method in practical data, we apply our method to the National Alzheimer's Coordinating Center data set.

翻译：本文研究了在疾病状态存在不可忽略缺失情况下的医学数据受试者工作特征（ROC）曲线分析。在分别针对疾病状态与验证状态建立逻辑回归模型的框架下，我们首先证明了模型参数的可识别性，继而提出通过似然方法估计生物标志物的模型参数、ROC曲线及曲线下面积（AUC）。建立了这些估计量的渐近分布。通过大量模拟研究，我们在点估计层面将本方法与现有方法进行比较，并评估了不同情境下置信区间估计的准确性。为说明所提方法在实际数据中的应用，我们将该方法应用于国家阿尔茨海默病协调中心数据集。