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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Optimal decision theory for diagnostic testing: Minimizing indeterminate classes with applications to saliva-based SARS-CoV-2 antibody assays
Mathematical Biosciences, Volume 351, Article 108858, Year 2022
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Description
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. © 2022
Authors & Co-Authors
Pisanic, Nora
United States, Baltimore
Johns Hopkins University
Manabe, Yukari C.
United States, Baltimore
Johns Hopkins University
Thomas, David L.
United States, Baltimore
Johns Hopkins University
Statistics
Citations: 3
Authors: 3
Affiliations: 2
Identifiers
Doi:
10.1016/j.mbs.2022.108858
ISSN:
00255564