Extension of overlap measures for Multi-Class Biomarker Evaluation in Alzheimer's Disease

In a progressive disease such as the Alzheimer's disease (AD), the availability of suitable biomarkers tracking the stages of its progression could markedly accelerate drug development by providing an earlier indication of drug efficacy. Investigators use diagnostic tests to classify disease stages into probable Alzheimer's disease, mild cognitive impairment (MCI), and normal cognitive aging. In this paper, we focus on developing a proper statistical overlap measure-based method to evaluate the diagnostic accuracy of tests with three diagnostic categories. Parametric and non-parametric approaches for the estimation of the overlap measure (OVL) are presented as well as their bootstrap confidence intervals (CIs). The performance of these estimations and its CIs are evaluated through simulations. Furthermore, it is compared with the Volume Under the ROC Surface (VUS), the most common measure to assess the accuracy of tests with three ordinal diagnostic categories. A neuropsychological data set from a longitudinal cohort study for the detection of biomarkers for identifying stages of Alzheimer's disease is discussed.