Statistical inference for the two-sample problem under likelihood ratio ordering, with application to the ROC curve estimation

The receiver operating characteristic (ROC) curve is a powerful statistical tool and has been widely applied in medical research. In the ROC curve estimation, a commonly used assumption is that larger the biomarker value, greater severity the disease. In this article, we mathematically interpret greater severity of the disease as larger probability of being diseased. This in turn is equivalent to assume the likelihood ratio ordering of the biomarker between the diseased and healthy individuals. With this assumption, we first propose a Bernstein polynomial method to model the distributions of both samples; we then estimate the distributions by the maximum empirical likelihood principle. The ROC curve estimate and the associated summary statistics are obtained subsequently. Theoretically, we establish the asymptotic consistency of our estimators. Via extensive numerical studies, we compare the performance of our method with competitive methods. The application of our method is illustrated by a real-data example.

Automated summary

This article mathematically interprets the application of the receiver operating characteristic (ROC) curve in medical research and proposes a method to estimate the ROC curve and associated summary statistics. The performance of the method is compared with competitive methods through numerical studies and illustrated with a real-data example.