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Correcting prevalence estimation for biased sampling with testing errors
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Number of Authors: 52023 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 42, no 26, p. 4713-4737Article in journal (Refereed) Published
Abstract [en]

Sampling for prevalence estimation of infection is subject to bias by both oversampling of symptomatic individuals and error-prone tests. This results in naïve estimators of prevalence (ie, proportion of observed infected individuals in the sample) that can be very far from the true proportion of infected. In this work, we present a method of prevalence estimation that reduces both the effect of bias due to testing errors and oversampling of symptomatic individuals, eliminating it altogether in some scenarios. Moreover, this procedure considers stratified errors in which tests have different error rate profiles for symptomatic and asymptomatic individuals. This results in easily implementable algorithms, for which code is provided, that produce better prevalence estimates than other methods (in terms of reducing and/or removing bias), as demonstrated by formal results, simulations, and on COVID-19 data from the Israeli Ministry of Health.

Place, publisher, year, edition, pages
2023. Vol. 42, no 26, p. 4713-4737
Keywords [en]
active information, bias correction, COVID-19, maximum entropy, prevalence, sampling, sampling bias, testing errors
National Category
Probability Theory and Statistics Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:su:diva-225644DOI: 10.1002/sim.9885ISI: 001122028600001PubMedID: 37655557Scopus ID: 2-s2.0-85169446081OAI: oai:DiVA.org:su-225644DiVA, id: diva2:1832960
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-02-22Bibliographically approved

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Hössjer, Ola

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