Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.
BMC Med Res Methodol
; 23(1): 55, 2023 02 27.
Article
em En
| MEDLINE
| ID: mdl-36849911
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
COVID-19
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Med Res Methodol
Assunto da revista:
MEDICINA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Itália