A New Measure of Diagnostic Accuracy with Cut-point Selection Criterion for K-stage Diseases Using Concordance and Discordance
Abstract or Description
Presented at the ENAR Conference
An essential aspect for medical diagnostic testing using biomarkers is to find an optimal cut-point which 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>2) stages, 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>2) stages and uses it as a cut-points selection criterion. The CD measure utilizes all the classification information and provides more balanced class probabilities in some scenarios. Power studies and simulations will be carried out to compare the performance of available measures. As well, an example of an actual dataset from the medical field will be provided using the proposed CD measure.
Eastern North American Region International Biometric Society Conference
Kersey, Jing, Hani Samawi, Jingjing Yin, Haresh Rochani, Xinyan Zhang, Chen Mo.
"A New Measure of Diagnostic Accuracy with Cut-point Selection Criterion for K-stage Diseases Using Concordance and Discordance."
Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Presentations.