Borrowing historical information for non-inferiority trials on Covid-19 vaccines

Fulvio De Santis and Gubbiotti Stefania ()
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Fulvio De Santis: Dipartimento di Scienze Statistiche, Sapienza University of Rome, Roma, Italy
Gubbiotti Stefania: Dipartimento di Scienze Statistiche, Sapienza University of Rome, Roma, Italy

The International Journal of Biostatistics, 2023, vol. 19, issue 1, 177-189

Abstract: Non-inferiority vaccine trials compare new candidates to active controls that provide clinically significant protection against a disease. Bayesian statistics allows to exploit pre-experimental information available from previous studies to increase precision and reduce costs. Here, historical knowledge is incorporated into the analysis through a power prior that dynamically regulates the degree of information-borrowing. We examine non-inferiority tests based on credible intervals for the unknown effects-difference between two vaccines on the log odds ratio scale, with an application to new Covid-19 vaccines. We explore the frequentist properties of the method and we address the sample size determination problem.

Keywords: Bayesian analysis; dynamic power prior; Hellinger distance; sample size determination; SARS-CoV-2; type-I error (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2021-0120

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