Linear regression models for analyzing the covariate-adjusted Youden index and associated cut-off points in three diagnostic groups

In medical diagnostic studies involving a transitional intermediate stage of disease progression, the Youden index offers a valuable summary measure for evaluating test accuracy across three diagnostic groups. However, ignoring covariate effects may lead to misleading assessments. To address this, we incorporate covariate information using linear regression models with normally distributed errors, enabling maximum likelihood estimation of the covariate-adjusted Youden index and its corresponding optimal cut-off points. We further develop several types of confidence intervals for these parameters, including generalized confidence intervals, Bayesian credible intervals, and bootstrap-based intervals. The finite-sample performance of the proposed estimators and interval procedures is evaluated via Monte Carlo simulations. Finally, we apply our methods to a diabetic dataset to illustrate their practical utility.