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:	Ulrike Baum, Sangita Kulathinal, Kari Auranen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ceef52cda0394f83852875c31cde96f4
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spelling	oai:doaj.org-article:ceef52cda0394f83852875c31cde96f42021-12-02T20:04:03ZExposure misclassification bias in the estimation of vaccine effectiveness.1932-620310.1371/journal.pone.0251622https://doaj.org/article/ceef52cda0394f83852875c31cde96f42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251622https://doaj.org/toc/1932-6203In 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.Ulrike BaumSangita KulathinalKari AuranenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251622 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ulrike Baum Sangita Kulathinal Kari Auranen Exposure misclassification bias in the estimation of vaccine effectiveness.
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.
format	article
author	Ulrike Baum Sangita Kulathinal Kari Auranen
author_facet	Ulrike Baum Sangita Kulathinal Kari Auranen
author_sort	Ulrike Baum
title	Exposure misclassification bias in the estimation of vaccine effectiveness.
title_short	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_sort	exposure misclassification bias in the estimation of vaccine effectiveness.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ceef52cda0394f83852875c31cde96f4
work_keys_str_mv	AT ulrikebaum exposuremisclassificationbiasintheestimationofvaccineeffectiveness AT sangitakulathinal exposuremisclassificationbiasintheestimationofvaccineeffectiveness AT kariauranen exposuremisclassificationbiasintheestimationofvaccineeffectiveness
_version_	1718375629635715072

Exposure misclassification bias in the estimation of vaccine effectiveness.

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