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>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>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 language | American English |
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State | Published - Dec 15 2020 |
Event | ICSA Applied Statistics Symposium - Duration: Dec 15 2020 → … |
Conference
Conference | ICSA Applied Statistics Symposium |
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Period | 12/15/20 → … |
DC Disciplines
- Biostatistics
- Environmental Public Health
- Epidemiology