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Nonparametric predictive comparison of two diagnostic tests based on total numbers of correctly diagnosed individuals

Alabdulhadi, M.H.; Coolen, F.P.A.; Coolen-Maturi, T.

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Authors

M.H. Alabdulhadi



Abstract

In clinical applications, it is important to compare and study the ability of diagnostic tests to discriminate between individuals with and without the disease. In this paper, comparison of two diagnostic tests is presented and discussed using nonparametric predictive inference (NPI). We compare the two tests by considering the total numbers of correct diagnoses for specific numbers of future healthy individuals and future patients. This NPI approach for comparison of diagnostic tests is also generalized by the use of weighted sums for the healthy and patients groups, reflecting possibly different importance of correct diagnoses. Examples are provided to illustrate the new method.

Citation

Alabdulhadi, M., Coolen, F., & Coolen-Maturi, T. (2019). Nonparametric predictive comparison of two diagnostic tests based on total numbers of correctly diagnosed individuals. Journal of statistical theory and practice, 13, Article 38. https://doi.org/10.1007/s42519-019-0039-6

Journal Article Type Article
Acceptance Date Feb 20, 2019
Online Publication Date Mar 14, 2019
Publication Date Sep 30, 2019
Deposit Date Feb 22, 2019
Publicly Available Date Mar 15, 2019
Journal Journal of Statistical Theory and Practice
Electronic ISSN 1559-8616
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 13
Article Number 38
DOI https://doi.org/10.1007/s42519-019-0039-6
Public URL https://durham-repository.worktribe.com/output/1307299
Publisher URL https://link.springer.com/journal/volumesAndIssues/42519

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Published Journal Article (778 Kb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.






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