ArticleMathematical medicine and biology : a journal of the IMA2021
Classification under uncertainty: data analysis for diagnostic antibody testing.
Article in Mathematical medicine and biology : a journal of the IMA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 1 citations in OpenAlex.
- Potential for Bias in Prevalence Estimates when Not Accounting for Test Sensitivity and Specificity: A Systematic Review of COVID-19 Seroprevalence Studies.International journal of public health · 2025Pooled it
- A Probabilistic Modeling Analysis of the Longitudinal Immune Response to Infection and Vaccination Across Demographic Groups and Pulmonary Symptoms.Spora : a journal of biomathematics · 2026Article
- Probabilistic Modeling of Antibody Kinetics Post Infection and Vaccination: A Markov Chain Approach.ArXiv · 2025Article
- Prevalence Estimation Methods for Time-Dependent Antibody Kinetics of Infected and Vaccinated Individuals: A Markov Chain Approach.Bulletin of mathematical biology · 2025Article
- Aggregating multiple test results to improve medical decision-making.PLoS computational biology · 2025Article
- Reproducibility in cytometry: Signals analysis and its connection to uncertainty quantification.PloS one · 2023Article
- Article
- Optimal decision theory for diagnostic testing: Minimizing indeterminate classes with applications to saliva-based SARS-CoV-2 antibody assays.Mathematical biosciences · 2022Article
- Article
- Article
- A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors.medRxiv : the preprint server for health sciences · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevalence and uncertainty therein can lead to significant classification errors. We present a novel method that leverages optimal decision theory to address this problem. As a preliminary step, we develop an analysis that uses an assumed prevalence and conditional probability models of diagnostic measurement outcomes to define optimal (in the sense of minimizing rates of false positives and false negatives) classification domains. Critically, we demonstrate how this strategy can be generalized to a setting in which the prevalence is unknown by either (i) defining a third class of hold-out samples that require further testing or (ii) using an adaptive algorithm to estimate prevalence prior to defining classification domains. We also provide examples for a recently published SARS-CoV-2 serology test and discuss how measurement uncertainty (e.g. associated with instrumentation) can be incorporated into the analysis. We find that our new strategy decreases classification error by up to a decade relative to more traditional methods based on confidence intervals. Moreover, it establishes a theoretical foundation for generalizing techniques such as receiver operating characteristics by connecting them to the broader field of optimization.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.