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Exposure misclassification bias in the estimation of vaccine effectiveness. | LitMetric

Exposure misclassification bias in the estimation of vaccine effectiveness.

PLoS One

Department of Mathematics and Statistics, University of Turku, Turku, Finland.

Published: October 2021

AI Article Synopsis

  • Exposure misclassification can bias estimates of vaccine effectiveness by misrepresenting true exposure status and disease outcomes.
  • The bias is quantified by comparing naïve estimators (based on observed data) with true risk ratios, highlighting the difference between perceived vaccine effects and actual effectiveness.
  • The extent of this bias depends on factors like the real risks of disease in vaccinated/unvaccinated groups, the accuracy of exposure assessments, and vaccination coverage, necessitating careful analysis of conditional probabilities to correct for these inaccuracies.

Article Abstract

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118540PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251622PLOS

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