Bayesian Bootstrap Inference for the ROC Surface

TL;DR

This paper introduces a Bayesian bootstrap method for nonparametric inference of the ROC surface, effectively evaluating diagnostic tests across three disease classes with demonstrated accuracy in simulations and real data application.

Contribution

It presents a novel Bayesian bootstrap approach for ROC surface inference, extending nonparametric methods to three-class diagnostic evaluation.

Findings

Successfully recovers true ROC surface in simulations

Produces valid inferences in complex scenarios

Effectively applied to Parkinson's disease data

Abstract

Accurate diagnosis of disease is of great importance in clinical practice and medical research. The receiver operating characteristic (ROC) surface is a popular tool for evaluating the discriminatory ability of continuous diagnostic test outcomes when there exist three ordered disease classes (e.g., no disease, mild disease, advanced disease). We propose the Bayesian bootstrap, a fully nonparametric method, for conducting inference about the ROC surface and its functionals, such as the volume under the surface. The proposed method is based on a simple, yet interesting, representation of the ROC surface in terms of placement variables. Results from a simulation study demonstrate the ability of our method to successfully recover the true ROC surface and to produce valid inferences in a variety of complex scenarios. An application to data from the Trail Making Test to assess cognitive impairment in Parkinson's disease patients is provided.