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

TL;DR

This paper develops a likelihood-based method for ROC curve analysis in medical data with non-ignorable missing disease status, ensuring accurate estimation of diagnostic performance metrics.

Contribution

It introduces a new likelihood approach for ROC analysis under non-ignorable missingness, with proven identifiability and asymptotic properties.

Findings

The proposed method outperforms existing methods in simulation studies.

It provides accurate confidence intervals for ROC and AUC estimates.

Application to Alzheimer's data demonstrates practical utility.

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 (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.