LAUSR

ABSTRACT In medical diagnostic studies, the Youden index is a summary measure widely used in the evaluation of the diagnostic accuracy of a medical test. When covariates are not considered, the diagnostic accuracy of the test can be biased or misleading. By incorporating information from covariates using linear regression models, we propose generalized confidence intervals for the covariate-adjusted Youden index and its optimal cut-off point. Furthermore, under heteroscedastic regression models, we propose various confidence intervals for the covariate-adjusted Youden index and its optimal cut-off point. Extensive simulation studies are conducted to evaluate the finite sample performance of various confidence intervals for the Youden index and its optimal cut-off point in the presence of covariates. To illustrate the application of our recommended methods, we apply the methods to a dataset on postprandial blood glucose measurements.