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Optimal Decision Theory for Diagnostic Testing: Minimizing Indeterminate Classes with Applications to Saliva-Based SARS-CoV-2 Antibody Assays

Authors :
Paul N. Patrone
Prajakta Bedekar
Nora Pisanic
Yukari C. Manabe
David L. Thomas
Christopher D. Heaney
Anthony J. Kearsley
Source :
ArXiv, article-version (number) 1, article-version (status) pre
Publication Year :
2022

Abstract

In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing less efficient and must be balanced against overall assay performance. We address this problem by reformulating data classification in terms of a constrained optimization problem that (i) minimizes the probability of labeling samples as indeterminate while (ii) ensuring that the remaining ones are classified with an average target accuracy X. We show that the solution to this problem is expressed in terms of a bathtub-type principle that holds out those samples with the lowest local accuracy up to an X-dependent threshold. To illustrate the usefulness of this analysis, we apply it to a multiplex, saliva-based SARS-CoV-2 antibody assay and demonstrate up to a 30 % reduction in the number of indeterminate samples relative to more traditional approaches.

Details

ISSN :
23318422
Database :
OpenAIRE
Journal :
ArXiv
Accession number :
edsair.doi.dedup.....2d1aec5042d592441f59d1ab420ee0aa