Interval estimation in three-class ROC analysis: a fairly general approach based on the empirical likelihood

TL;DR

This paper introduces a flexible empirical likelihood-based method for interval estimation in three-class ROC analysis, improving inference for diagnostic tests with complex distributions.

Contribution

It develops novel theoretical results and techniques for empirical likelihood interval estimation in three-class ROC analysis, accommodating flexible population distributions.

Findings

Proposed methods outperform existing competitors in simulations.

New techniques are highly flexible and adaptable to various distributions.

Application demonstrated on real diagnostic data.

Abstract

The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart -- the parametric likelihood -- preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on interval estimation in a three-class ROC analysis, where a variety of inferential tasks could be of interest. We present novel theoretical results and tailored techniques studied to efficiently solve some of such tasks. Extensive simulation experiments are provided in a supporting role, with our novel proposals compared to existing competitors, when possible. It emerges that our new proposals are extremely flexible, being able to compete with contestants and being the most suited to accommodating flexible distributions for target populations. We illustrate the application of the novel proposals with a real data example. The article ends with a discussion and a presentation of some directions for future research.