Imperial College London
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MissLucyWilliams

Faculty of Medicine, School of Public Health

Research Postgraduate
 
 
 
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Contact

 

lucy.williams19 Website

 
 
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Location

 

Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Williams:2022:cid/ciab914,
author = {Williams, LR and Ferguson, NM and Donnelly, CA and Grassly, NC},
doi = {cid/ciab914},
journal = {Clinical Infectious Diseases},
pages = {e764--e773},
title = {Measuring vaccine efficacy against infection and disease in clinical trials: sources and magnitude of bias in COVID-19 vaccine efficacy estimates},
url = {http://dx.doi.org/10.1093/cid/ciab914},
volume = {75},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: Phase III trials have estimated COVID-19 vaccine efficacy (VE) against symptomatic and asymptomatic infection. We explore the direction and magnitude of potential biases in these estimates and their implications for vaccine protection against infection and against disease in breakthrough infections. METHODS: We developed a mathematical model that accounts for natural and vaccine-induced immunity, changes in serostatus and imperfect sensitivity and specificity of tests for infection and antibodies. We estimated expected biases in VE against symptomatic, asymptomatic and any SARSCoV2 infections and against disease following infection for a range of vaccine characteristics and measurement approaches, and the likely overall biases for published trial results that included asymptomatic infections. RESULTS: VE against asymptomatic infection measured by PCR or serology is expected to be low or negative for vaccines that prevent disease but not infection. VE against any infection is overestimated when asymptomatic infections are less likely to be detected than symptomatic infections and the vaccine protects against symptom development. A competing bias towards underestimation arises for estimates based on tests with imperfect specificity, especially when testing is performed frequently. Our model indicates considerable uncertainty in Oxford-AstraZeneca ChAdOx1 and Janssen Ad26.COV2.S VE against any infection, with slightly higher than published, bias-adjusted values of 59.0% (95% uncertainty interval [UI] 38.4 to 77.1) and 70.9% (95% UI 49.8 to 80.7) respectively. CONCLUSIONS: Multiple biases are likely to influence COVID-19 VE estimates, potentially explaining the observed difference between ChAdOx1 and Ad26.COV2.S vaccines. These biases should be considered when interpreting both efficacy and effectiveness study results.
AU  - Williams,LR
AU  - Ferguson,NM
AU  - Donnelly,CA
AU  - Grassly,NC
DO  - cid/ciab914
EP  - 773
PY  - 2022///
SN  - 1058-4838
SP  - 764
TI  - Measuring vaccine efficacy against infection and disease in clinical trials: sources and magnitude of bias in COVID-19 vaccine efficacy estimates
T2  - Clinical Infectious Diseases
UR  - http://dx.doi.org/10.1093/cid/ciab914
UR  - https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciab914/6410688
UR  - http://hdl.handle.net/10044/1/92427
VL  - 75
ER  -
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