Measuring Diagnostic Accuracy and Selecting Optimal Cutpoints for K-class Diseases Based on Concordance and Discordance with Application

Presentation given at the ICSA Applied Statistics Symposium. Program An essential aspect of medical diagnostic testing using biomarkers is to find an optimal cut-point that categorizes a patient as diseased or healthy. This aspect can be extended to the diseases which can be classified into more than two classes. For diseases with general k (k\u3e2) classes, well-established measures include hypervolume under the manifold and the generalized Youden Index. Another two diagnostic accuracy measures, maximum absolute determinant (MADET) and Kullback-Leibler divergence measure (KL) are recently proposed.This research proposes a new measure of diagnostic accuracy based on concordance and discordance (CD) for diseases with k (k\u3e2) classes and uses it as a cut-points selection criterion. The CD measure utilizes all the classification information and provides more balanced class probabilities. Power studies and simulations show that the optimal cut-points selected with CD measure may be more accurate for early-stage detection in some scenarios compared with other available measures. As well, an example of an actual dataset from the medical field will be provided using the proposed CD measure