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Jointly modeling prevalence, sensitivity and specificity for optimal sample allocation

View ORCID ProfileDaniel B. Larremore, View ORCID ProfileBailey K. Fosdick, View ORCID ProfileSam Zhang, View ORCID ProfileYonatan H. Grad
doi: https://doi.org/10.1101/2020.05.23.112649
Daniel B. Larremore
1Department of Computer Science, University of Colorado Boulder, Boulder, CO, 80309, USA
2BioFrontiers Institute, University of Colorado at Boulder, Boulder, CO, 80303, USA
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  • For correspondence: larremor@colorado.edu
Bailey K. Fosdick
3Department of Statistics, Colorado State University, Fort Collins, CO, 80523, USA
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Sam Zhang
4Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, 80309, USA
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Yonatan H. Grad
5Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
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Abstract

The design and interpretation of prevalence studies rely on point estimates of the performance characteristics of the diagnostic test used. When the test characteristics are not well defined and a limited number of tests are available, such as during an outbreak of a novel pathogen, tests can be used either for the field study itself or for additional validation to reduce uncertainty in the test characteristics. Because field data and validation data are based on finite samples, inferences drawn from these data carry uncertainty. In the absence of a framework to balance those uncertainties during study design, it is unclear how best to distribute tests to improve study estimates. Here, we address this gap by introducing a joint Bayesian model to simultaneously analyze lab validation and field survey data. In many scenarios, prevalence estimates can be most improved by apportioning additional effort towards validation rather than to the field. We show that a joint model provides superior estimation of prevalence, as well as sensitivity and specificity, compared with typical analyses that model lab and field data separately.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/LarremoreLab/bayesian-joint-prev-se-sp

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 26, 2020.
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Jointly modeling prevalence, sensitivity and specificity for optimal sample allocation
Daniel B. Larremore, Bailey K. Fosdick, Sam Zhang, Yonatan H. Grad
bioRxiv 2020.05.23.112649; doi: https://doi.org/10.1101/2020.05.23.112649
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Jointly modeling prevalence, sensitivity and specificity for optimal sample allocation
Daniel B. Larremore, Bailey K. Fosdick, Sam Zhang, Yonatan H. Grad
bioRxiv 2020.05.23.112649; doi: https://doi.org/10.1101/2020.05.23.112649

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