Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.

Autor: Eusebi P; Department of Medicine and Surgery, University of Perugia, Perugia, Italy. paoloeusebi@gmail.com.; Modus Outcomes, a division of THREAD, Lyon, France. paoloeusebi@gmail.com., Speybroeck N; Institute of Health and Society, Université catholique de Louvain, Brussels, Belgium., Hartnack S; Section of Epidemiology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland., Stærk-Østergaard J; Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark., Denwood MJ; Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark., Kostoulas P; Faculty of Public Health, University of Thessaly, Thessaly, Greece.
Jazyk: angličtina
Zdroj: BMC medical research methodology [BMC Med Res Methodol] 2023 Feb 27; Vol. 23 (1), pp. 55. Date of Electronic Publication: 2023 Feb 27.
DOI: 10.1186/s12874-023-01853-4
Abstrakt: Safe and effective vaccines are crucial for the control of Covid-19 and to protect individuals at higher risk of severe disease. The test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines. However, the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for diagnosing the SARS-Cov-2 infection. We propose a simple Bayesian modeling approach for estimating vaccine effectiveness that is robust even when the diagnostic test is imperfect. We use simulation studies to demonstrate the robustness of our method to misclassification bias and illustrate the utility of our approach using real-world examples.
(© 2023. The Author(s).)
Databáze: MEDLINE
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