A semiparametric approach for the estimation of covariate-adjusted area under the receiver operating characteristic curve

2026-08-13

AI Summary

This study addresses diagnostic evaluation bias arising from patient heterogeneity by proposing a semiparametric covariate-adjusted AUC estimation framework based on generalized additive models. The proposed method effectively captures nonlinear and interaction effects while supporting multiclass disease state assessment. Asymptotic theoretical analysis and simulation experiments demonstrate the model’s robust performance, particularly in small-sample settings. In an empirical application to Alzheimer’s disease, the framework successfully identified significant population heterogeneity, providing a more precise and robust tool for evaluating diagnostic accuracy in complex clinical data. Collectively, this work offers substantial methodological innovation and practical value for clinical diagnostics.

Abstract

Receiver operating characteristic (ROC) and the area under the ROC curve (AUC) are widely used to evaluate the discriminative ability of biomarkers. In many clinical settings, however, diagnostic accuracy varies substantially across patient characteristics, and failure to account for such heterogeneity can lead to misleading conclusions. We propose a new semiparametric framework based on generalized additive models to estimate covariate-specific and covariate-adjusted AUC while allowing for nonlinear and interaction effects of covariates on biomarker performance. Our method accommodates both binary and multicategory disease status. We also establish the asymptotic properties of the proposed estimators. Simulations demonstrate favorable finite-sample performance. We illustrate the method using data from the Alzheimer's Disease Neuroimaging Initiative, where substantial heterogeneity in biomarker discrimination across covariates is observed.

Problem

• Covariate-adjusted AUC
• Diagnostic accuracy heterogeneity
• Biomarker evaluation
• ROC curve

Innovation

• Semiparametric estimation
• Covariate-adjusted AUC
• Generalized additive models
• Nonlinear effects
• Multicategory disease status