Nonparametric Inference for the Covariate‐Adjusted Youden Index and Associated Cut‐Off Points for Three Ordinal Diagnostic Groups

In this paper, we propose point estimators and confidence intervals for the Youden index and optimal cut-off points in the context of three ordinal diagnostic groups, accounting for the presence of covariates. Using heteroscedastic regression models, we introduce two point estimators based on different assumptions and examine their asymptotic properties. Additionally, we present confidence intervals for the covariate-adjusted Youden index and its corresponding optimal cut-off points. The performance of the proposed estimators and confidence intervals is evaluated through a Monte Carlo simulation study. Finally, we demonstrate the applicability of our methods to an Alzheimer's disease dataset.