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Evaluating the Long-Term Efficacy of COVID-19 Vaccines
Danyu Lin; Donglin Zeng; Peter Gilbert.
Affiliation
  • Danyu Lin; University of North Carolina
  • Donglin Zeng; University of North Carolina
  • Peter Gilbert; Fred Hutch
Preprint in English | medRxiv | ID: ppmedrxiv-21249779
ABSTRACT
Large-scale deployment of safe and durably effective vaccines can curtail the COVID-19 pandemic.1-3 However, the high vaccine efficacy (VE) reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about two months4-5 and thus does not pertain to long-term efficacy. To evaluate the duration of protection while allowing trial participants timely access to efficacious vaccine, investigators can sequentially cross participants over from the placebo arm to the vaccine arm according to priority groups. Here, we show how to estimate potentially time-varying placebo-controlled VE in this type of staggered vaccination of participants. In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term VE. Authors InformationDan-Yu Lin, Ph.D., is Dennis Gillings Distinguished Professor of Biostatistics, and Donglin Zeng, Ph.D., is Professor of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7420, USA. Peter B. Gilbert, Ph.D., is Member, Vaccine and Infectious Disease Division, Fred Hutch, Seattle, WA 98109-1024, USA. SummaryWe show how to estimate the potentially waning long-term efficacy of COVID-19 vaccines using data from randomized, placebo-controlled clinical trials with staggered enrollment of participants and sequential crossover of placebo recipients.
License
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Experimental_studies / Prognostic study / Rct Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Experimental_studies / Prognostic study / Rct Language: English Year: 2021 Document type: Preprint
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