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Capturing the pool dilution effect in group testing regression: A Bayesian approach

Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine...

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Autores principales: Self, Stella, McMahan, Christopher, Mokalled, Stefani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9489666/
https://www.ncbi.nlm.nih.gov/pubmed/35879887
http://dx.doi.org/10.1002/sim.9532
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author Self, Stella
McMahan, Christopher
Mokalled, Stefani
author_facet Self, Stella
McMahan, Christopher
Mokalled, Stefani
author_sort Self, Stella
collection PubMed
description Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine) collected on individuals into pooled specimens which are tested for an infection of interest. Through testing these pooled specimens, group testing methods reduce the cost of diagnosing all individuals under study by reducing the number of tests performed. Even though group testing offers substantial cost reductions, some practitioners are hesitant to adopt group testing methods due to the so‐called dilution effect. The dilution effect describes the phenomenon in which biomaterial from negative individuals dilute the contributions from positive individuals to such a degree that a pool is incorrectly classified. Ignoring the dilution effect can reduce classification accuracy and lead to bias in parameter estimates and inaccurate inference. To circumvent these issues, we propose a Bayesian regression methodology which directly acknowledges the dilution effect while accommodating data that arises from any group testing protocol. As a part of our estimation strategy, we are able to identify pool specific optimal classification thresholds which are aimed at maximizing the classification accuracy of the group testing protocol being implemented. These two features working in concert effectively alleviate the primary concerns raised by practitioners regarding group testing. The performance of our methodology is illustrated via an extensive simulation study and by being applied to Hepatitis B data collected on Irish prisoners.
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spelling pubmed-94896662023-01-04 Capturing the pool dilution effect in group testing regression: A Bayesian approach Self, Stella McMahan, Christopher Mokalled, Stefani Stat Med Research Articles Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine) collected on individuals into pooled specimens which are tested for an infection of interest. Through testing these pooled specimens, group testing methods reduce the cost of diagnosing all individuals under study by reducing the number of tests performed. Even though group testing offers substantial cost reductions, some practitioners are hesitant to adopt group testing methods due to the so‐called dilution effect. The dilution effect describes the phenomenon in which biomaterial from negative individuals dilute the contributions from positive individuals to such a degree that a pool is incorrectly classified. Ignoring the dilution effect can reduce classification accuracy and lead to bias in parameter estimates and inaccurate inference. To circumvent these issues, we propose a Bayesian regression methodology which directly acknowledges the dilution effect while accommodating data that arises from any group testing protocol. As a part of our estimation strategy, we are able to identify pool specific optimal classification thresholds which are aimed at maximizing the classification accuracy of the group testing protocol being implemented. These two features working in concert effectively alleviate the primary concerns raised by practitioners regarding group testing. The performance of our methodology is illustrated via an extensive simulation study and by being applied to Hepatitis B data collected on Irish prisoners. John Wiley & Sons, Inc. 2022-07-25 2022-10-15 /pmc/articles/PMC9489666/ /pubmed/35879887 http://dx.doi.org/10.1002/sim.9532 Text en © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Self, Stella
McMahan, Christopher
Mokalled, Stefani
Capturing the pool dilution effect in group testing regression: A Bayesian approach
title Capturing the pool dilution effect in group testing regression: A Bayesian approach
title_full Capturing the pool dilution effect in group testing regression: A Bayesian approach
title_fullStr Capturing the pool dilution effect in group testing regression: A Bayesian approach
title_full_unstemmed Capturing the pool dilution effect in group testing regression: A Bayesian approach
title_short Capturing the pool dilution effect in group testing regression: A Bayesian approach
title_sort capturing the pool dilution effect in group testing regression: a bayesian approach
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9489666/
https://www.ncbi.nlm.nih.gov/pubmed/35879887
http://dx.doi.org/10.1002/sim.9532
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