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suck abstract from ncbi


10.1111/biom.13509

http://scihub22266oqcxt.onion/10.1111/biom.13509
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34174097!8444907!34174097
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suck abstract from ncbi

pmid34174097      Biometrics 2022 ; 78 (3): 825-838
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  • Estimating vaccine efficacy over time after a randomized study is unblinded #MMPMID34174097
  • Tsiatis AA; Davidian M
  • Biometrics 2022[Sep]; 78 (3): 825-838 PMID34174097show ga
  • The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired remarkable breakthroughs in the development of vaccines against the virus and the launch of several phase 3 vaccine trials in Summer 2020 to evaluate vaccine efficacy (VE). Trials of vaccine candidates using mRNA delivery systems developed by Pfizer-BioNTech and Moderna have shown substantial VEs of 94-95%, leading the US Food and Drug Administration to issue Emergency Use Authorizations and subsequent widespread administration of the vaccines. As the trials continue, a key issue is the possibility that VE may wane over time. Ethical considerations dictate that trial participants be unblinded and those randomized to placebo be offered study vaccine, leading to trial protocol amendments specifying unblinding strategies. Crossover of placebo subjects to vaccine complicates inference on waning of VE. We focus on the particular features of the Moderna trial and propose a statistical framework based on a potential outcomes formulation within which we develop methods for inference on potential waning of VE over time and estimation of VE at any postvaccination time. The framework clarifies assumptions made regarding individual- and population-level phenomena and acknowledges the possibility that subjects who are more or less likely to become infected may be crossed over to vaccine differentially over time. The principles of the framework can be adapted straightforwardly to other trials.
  • |*COVID-19 Vaccines[MESH]
  • |*COVID-19/prevention & control[MESH]
  • |Humans[MESH]
  • |Pandemics/prevention & control[MESH]
  • |Randomized Controlled Trials as Topic[MESH]
  • |Research Design[MESH]
  • |SARS-CoV-2[MESH]


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