Correcting prevalence estimation for biased sampling with testing errors

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 in...

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Published inStatistics in medicine Vol. 42; no. 26; pp. 4713 - 4737
Main Authors Zhou, Lili, Díaz‐Pachón, Daniel Andrés, Zhao, Chen, Rao, J. Sunil, Hössjer, Ola
Format Journal Article
LanguageEnglish
Published New York Wiley Subscription Services, Inc 20.11.2023
Subjects
active information
bias correction
COVID-19
maximum entropy
prevalence
sampling
sampling bias
testing errors
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Summary: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.
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.9885