A robust approach for ROC curves with covariates

TL;DR

This paper introduces a robust semiparametric method for estimating ROC curves with covariates, effectively handling outliers and improving diagnostic accuracy assessment.

Contribution

It develops a novel robust estimation procedure for ROC curves incorporating covariates using a location-scale regression model and adaptive weighting, with proven consistency.

Findings

Robust estimators outperform classical ones in contaminated data scenarios.

The method maintains consistency under mild assumptions.

Application to real data demonstrates practical effectiveness.

Abstract

The Receiver Operating Characteristic (ROC) curve is a useful tool that measures the discriminating power of a continuous variable or the accuracy of a pharmaceutical or medical test to distinguish between two conditions or classes. In certain situations, the practitioner may be able to measure some covariates related to the diagnostic variable which can increase the discriminating power of the ROC curve. To protect against the existence of atypical data among the observations, a procedure to obtain robust estimators for the ROC curve in presence of covariates is introduced. The considered proposal focusses on a semiparametric approach which fits a location-scale regression model to the diagnostic variable and considers empirical estimators of the regression residuals distributions. Robust parametric estimators are combined with adaptive weighted empirical distribution estimators to down-weight the influence of outliers. The uniform consistency of the proposal is derived under mild assumptions. A Monte Carlo study is carried out to compare the performance of the robust proposed estimators with the classical ones both, in clean and contaminated samples. A real data set is also analysed.