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Evaluating the Efficacy of COVID-19 Vaccines
Dan-Yu Lin; Donglin Zeng; Devan V. Mehrotra; Lawrence Corey; Peter B. Gilbert.
Afiliación
  • Dan-Yu Lin; University of North Carolina, Chapel Hill
  • Donglin Zeng; University of North Carolina, Chapel Hill
  • Devan V. Mehrotra; Merck & Co., Inc.
  • Lawrence Corey; Fred Hutchinson Cancer Research Center
  • Peter B. Gilbert; Fred Hutchinson Cancer Research Center
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20205906
ABSTRACT
A large number of studies are being conducted to evaluate the efficacy and safety of candidate vaccines against novel coronavirus disease-2019 (COVID-19). Most Phase 3 trials have adopted virologically confirmed symptomatic COVID-19 disease as the primary efficacy endpoint, although laboratory-confirmed SARS-CoV-2 is also of interest. In addition, it is important to evaluate the effect of vaccination on disease severity. To provide a full picture of vaccine efficacy and make efficient use of available data, we propose using SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19 as dual or triple primary endpoints. We demonstrate the advantages of this strategy through realistic simulation studies. Finally, we show how this approach can provide rigorous interim monitoring of the trials and efficient assessment of the durability of vaccine efficacy. SummaryTo increase statistical power and meet vaccine success criteria, we propose to evaluate the efficacy of COVID-19 vaccines by using the dual or triple primary endpoints of SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19.
Licencia
cc_by_nc_nd
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Experimental_studies / Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Experimental_studies / Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint