Nonparametric Covariate Adjustment for Receiver Operating Characteristic Curves

TL;DR

This paper introduces nonparametric covariate adjustment methods for ROC curve analysis, specifically for AUC estimation, accommodating both normal and non-normal error models, with proven asymptotic properties and demonstrated effectiveness.

Contribution

It develops a covariate-adjusted Mann-Whitney estimator for AUC that accounts for covariate effects, extending traditional ROC analysis methods.

Findings

The proposed estimator is asymptotically normal.

It achieves optimal convergence rates.

Demonstrated effectiveness on simulated and real data.

Abstract

The accuracy of a diagnostic test is typically characterised using the receiver operating characteristic (ROC) curve. Summarising indexes such as the area under the ROC curve (AUC) are used to compare different tests as well as to measure the difference between two populations. Often additional information is available on some of the covariates which are known to influence the accuracy of such measures. We propose nonparametric methods for covariate adjustment of the AUC. Models with normal errors and non-normal errors are discussed and analysed separately. Nonparametric regression is used for estimating mean and variance functions in both scenarios. In the general noise case we propose a covariate-adjusted Mann-Whitney estimator for AUC estimation which effectively uses available data to construct working samples at any covariate value of interest and is computationally efficient for implementation. This provides a generalisation of the Mann-Whitney approach for comparing two populations by taking covariate effects into account. We derive asymptotic properties for the AUC estimators in both settings, including asymptotic normality, optimal strong uniform convergence rates and MSE consistency. The usefulness of the proposed methods is demonstrated through simulated and real data examples.