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

Research output: Contribution to conferencePresentation

Abstract

<p> Presentation given at the ICSA Applied Statistics Symposium.</p><p> <a href="https://symposium2020.icsa.org/wp-content/uploads/2020/12/ICSA2020ProgramBook-12-14-10am.pdf" target="_blank"> Program </a></p><p> 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&gt;2) 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&gt;2) 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.</p>
Original languageAmerican English
StatePublished - Dec 15 2020
EventICSA Applied Statistics Symposium -
Duration: Dec 15 2020 → …

Conference

ConferenceICSA Applied Statistics Symposium
Period12/15/20 → …

DC Disciplines

  • Biostatistics
  • Environmental Public Health
  • Epidemiology

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