A new test for assessing the covariate effect in ROC curves

TL;DR

This paper introduces a new statistical test to compare covariate-adjusted and pooled ROC curves, helping determine whether covariates influence diagnostic accuracy assessments.

Contribution

The paper proposes a novel test for assessing the impact of covariates on ROC curves, enhancing the analysis of diagnostic test performance.

Findings

The new test effectively distinguishes covariate effects on ROC curves.

Application to real data demonstrates practical utility.

Guidelines for including covariates in ROC analysis.

Abstract

The ROC curve is a statistical tool that analyses the accuracy of a diagnostic test in which a variable is used to decide whether an individual is healthy or not. Along with that diagnostic variable it is usual to have information of some other covariates. In some situations it is advisable to incorporate that information into the study, as the performance of the ROC curves can be affected by them. Using the covariate-adjusted, the covariate-specific or the pooled ROC curves we discuss how to decide if we can exclude the covariates from our study or not, and the implications this may have in further analyses of the ROC curve. A new test for comparing the covariate-adjusted and the pooled ROC curve is proposed, and the problem is illustrated by analysing a real database.