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Optimal Decision Theory for Diagnostic Testing: Minimizing Indeterminate Classes with Applications to Saliva-Based SARS-CoV-2 Antibody Assays
- 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.
- Subjects :
- FOS: Computer and information sciences
Statistics and Probability
FOS: Physical sciences
Antibodies, Viral
Quantitative Biology - Quantitative Methods
Article
General Biochemistry, Genetics and Molecular Biology
Methodology (stat.ME)
COVID-19 Testing
Decision Theory
FOS: Mathematics
Humans
Physics - Biological Physics
Saliva
Mathematics - Optimization and Control
Quantitative Methods (q-bio.QM)
Statistics - Methodology
General Immunology and Microbiology
SARS-CoV-2
Applied Mathematics
Probability (math.PR)
COVID-19
General Medicine
Optimization and Control (math.OC)
Biological Physics (physics.bio-ph)
FOS: Biological sciences
Modeling and Simulation
General Agricultural and Biological Sciences
Mathematics - Probability
Subjects
Details
- ISSN :
- 23318422
- Database :
- OpenAIRE
- Journal :
- ArXiv
- Accession number :
- edsair.doi.dedup.....2d1aec5042d592441f59d1ab420ee0aa