Receiver operating characteristic curve analysis with non-ignorable missing disease status

Abstract

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 for the biomarker. The asymptotic distributions of these estimators are established. Via extensive simulation studies, we compare our method with competing methods of point estimation and assess the accuracy of confidence interval estimation under various scenarios. To illustrate the use of our proposed approach in a practical setting, we apply our method to the Alzheimer's disease dataset from the National Alzheimer's Coordinating Center.