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.
Borrowing historical information for non-inferiority trials on Covid-19 vaccines / DE SANTIS, Fulvio; Gubbiotti, Stefania. - In: THE INTERNATIONAL JOURNAL OF BIOSTATISTICS. - ISSN 1557-4679. - 1:19(2023), pp. 177-189. [10.1515/ijb-2021-0120]
Borrowing historical information for non-inferiority trials on Covid-19 vaccines
Fulvio De Santis;Stefania Gubbiotti
2023
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.File | Dimensione | Formato | |
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