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

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Standard

Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19. / Eusebi, Paolo; Speybroeck, Niko; Hartnack, Sonja; Stærk-Østergaard, Jacob; Denwood, Matthew J.; Kostoulas, Polychronis.

I: BMC Medical Research Methodology, Bind 23, 55, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Eusebi, P, Speybroeck, N, Hartnack, S, Stærk-Østergaard, J, Denwood, MJ & Kostoulas, P 2023, 'Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19', BMC Medical Research Methodology, bind 23, 55. https://doi.org/10.1186/s12874-023-01853-4

APA

Eusebi, P., Speybroeck, N., Hartnack, S., Stærk-Østergaard, J., Denwood, M. J., & Kostoulas, P. (2023). Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19. BMC Medical Research Methodology, 23, [55]. https://doi.org/10.1186/s12874-023-01853-4

Vancouver

Eusebi P, Speybroeck N, Hartnack S, Stærk-Østergaard J, Denwood MJ, Kostoulas P. Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19. BMC Medical Research Methodology. 2023;23. 55. https://doi.org/10.1186/s12874-023-01853-4

Author

Eusebi, Paolo ; Speybroeck, Niko ; Hartnack, Sonja ; Stærk-Østergaard, Jacob ; Denwood, Matthew J. ; Kostoulas, Polychronis. / Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19. I: BMC Medical Research Methodology. 2023 ; Bind 23.

Bibtex

@article{05a978a240d1441b9ad401d0edf68556,
title = "Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19",
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.",
keywords = "Covid-19, RT-PCR, Sensitivity, Specificity, Test-negative design",
author = "Paolo Eusebi and Niko Speybroeck and Sonja Hartnack and Jacob St{\ae}rk-{\O}stergaard and Denwood, {Matthew J.} and Polychronis Kostoulas",
note = "Funding Information: This work was funded by COST Action CA18208: HARMONY-Novel tools for test evaluation and disease prevalence estimation https://harmony-net.eu . Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
doi = "10.1186/s12874-023-01853-4",
language = "English",
volume = "23",
journal = "B M C Medical Research Methodology",
issn = "1471-2288",
publisher = "BioMed Central Ltd.",

}

RIS

TY - JOUR

T1 - Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19

AU - Eusebi, Paolo

AU - Speybroeck, Niko

AU - Hartnack, Sonja

AU - Stærk-Østergaard, Jacob

AU - Denwood, Matthew J.

AU - Kostoulas, Polychronis

N1 - Funding Information: This work was funded by COST Action CA18208: HARMONY-Novel tools for test evaluation and disease prevalence estimation https://harmony-net.eu . Publisher Copyright: © 2023, The Author(s).

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Covid-19

KW - RT-PCR

KW - Sensitivity

KW - Specificity

KW - Test-negative design

U2 - 10.1186/s12874-023-01853-4

DO - 10.1186/s12874-023-01853-4

M3 - Journal article

C2 - 36849911

AN - SCOPUS:85148966991

VL - 23

JO - B M C Medical Research Methodology

JF - B M C Medical Research Methodology

SN - 1471-2288

M1 - 55

ER -

ID: 339002946