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The design and analysis of vaccine trials for COVID ‐19 for the purpose of estimating efficacy
Author(s) -
Senn Stephen
Publication year - 2022
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.2226
Subject(s) - frequentist inference , covid-19 , bayesian probability , clinical trial , vaccine efficacy , medicine , medical physics , operations research , computer science , bayesian inference , virology , artificial intelligence , mathematics , outbreak , disease , infectious disease (medical specialty) , vaccination
After a preliminary explanation as to how I came to know Andy Grieve and some remarks about his career and mine and how they have intersected, I consider the design and analysis of trials of vaccines for COVID‐19 for the purpose of estimating efficacy. Five large trials, run by the sponsors Pfizer/BioNTech, AstraZeneca/Oxford University, Moderna, Novavax and J&J Janssen are considered briefly. Frequentist approaches to analysis were used for four of the trials but Pfizer/BioNTech nominated a Bayesian approach. The design and analysis of this trial is considered in some detail, in particular as regards the choice of prior distribution. I conclude by drawing some general lessons.