Resilience family of receiver operating characteristic curves

TL;DR

This paper introduces a new semiparametric ROC curve model based on the resilience family, offering simple analytic forms and estimation methods, with validation through simulations and real-world data analysis.

Contribution

It proposes a novel resilience family model for ROC curves, providing new estimation techniques and demonstrating its effectiveness on diverse datasets.

Findings

The model yields simple analytic forms for ROC and summary indices.

Estimation methods based on Mann-Whitney and Rojo approaches are effective.

The model performs well even under misspecification scenarios.

Abstract

A new semiparametric model of the ROC curve based on the resilience family or proportional reversed hazard family is proposed which is an alternative to the existing models. The resulting ROC curve and its summary indices (such as area under the curve (AUC) and Youden index) have simple analytic forms. The partial likelihood method is applied to estimate the ROC curve. Moreover, the estimation methodologies of the resilience family of the ROC curve have been developed based on AUC estimators exploiting Mann-Whitney statistics and the Rojo approach. A simulation study has been carried out to assess the performance of all considered estimators. Real data from the American National Health and Nutrition Examination Survey (NHANES) has been analysed in detail based on the proposed model and the usual binormal model prevalent in the literature. Real data in the context of brain injury-related biomarkers is also analysed in order to compare our model with the Lehmann family of the ROC curves. Finally, we show that the proposed model may be applicable in the misspecification scenario through a Ducheme muscular dystrophy data.