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Exposure misclassification bias in the estimation of vaccine effectiveness

In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article...

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Autores principales: Baum, Ulrike, Kulathinal, Sangita, Auranen, Kari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118540/
https://www.ncbi.nlm.nih.gov/pubmed/33984065
http://dx.doi.org/10.1371/journal.pone.0251622
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author Baum, Ulrike
Kulathinal, Sangita
Auranen, Kari
author_facet Baum, Ulrike
Kulathinal, Sangita
Auranen, Kari
author_sort Baum, Ulrike
collection PubMed
description In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naïve estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial.
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spelling pubmed-81185402021-05-24 Exposure misclassification bias in the estimation of vaccine effectiveness Baum, Ulrike Kulathinal, Sangita Auranen, Kari PLoS One Research Article In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naïve estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial. Public Library of Science 2021-05-13 /pmc/articles/PMC8118540/ /pubmed/33984065 http://dx.doi.org/10.1371/journal.pone.0251622 Text en © 2021 Baum et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Baum, Ulrike
Kulathinal, Sangita
Auranen, Kari
Exposure misclassification bias in the estimation of vaccine effectiveness
title Exposure misclassification bias in the estimation of vaccine effectiveness
title_full Exposure misclassification bias in the estimation of vaccine effectiveness
title_fullStr Exposure misclassification bias in the estimation of vaccine effectiveness
title_full_unstemmed Exposure misclassification bias in the estimation of vaccine effectiveness
title_short Exposure misclassification bias in the estimation of vaccine effectiveness
title_sort exposure misclassification bias in the estimation of vaccine effectiveness
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118540/
https://www.ncbi.nlm.nih.gov/pubmed/33984065
http://dx.doi.org/10.1371/journal.pone.0251622
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