Confidence intervals of the difference between areas under two ROC curves in matched‐pair experiments. (10th May 2020)

• Record Type: Journal Article
• Authors: Zhang, Yunqi; Tang, Niansheng
• Is Part Of: Statistics in medicine. Volume 39:Number 20(2020)
• Page Start: 2621
• Page End: 2638
• Publication Date: 2020-05-10
• Subjects: bootstrap‐resampling method -- confidence interval -- difference between two correlated AUCs -- hybrid method -- matched‐pair design; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8562
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In a matched‐pair study, when outcomes of two diagnostic tests are ordinal/continuous, the difference between two correlated areas under ROC curves (AUCs) is usually used to compare the overall discriminatory ability of two diagnostic tests. This article considers confidence interval (CI) construction problems of difference between two correlated AUCs in a matched‐pair experiment, and proposes 13 hybrid CIs based on variance estimates recovery with the maximum likelihood estimation, Delong's statistic, Wilson score statistic (WS) and WS with continuity correction, the modified Wald statistic (MW) and MW with continuity correction and Agresti‐Coull statistic, and three Bootstrap‐resampling‐based CIs. For comparison, we present traditional parametric and nonparametric CIs. Simulation studies are conducted to assess the performance of the proposed CIs in terms of empirical coverage probabilities, empirical interval widths, and ratios of the mesial noncoverage probabilities to the noncoverage probabilities. Two examples from clinical studies are illustrated by the proposed methodologies. Empirical results evidence that the hybrid Agresti‐Coull CI with the empirical estimation (EAC) behaved most satisfactorily because its coverage probability was quite close to the prespecified confidence level with short interval width. Hence, we recommend the usage of the EAC CI in applications.