Statistical Inference for Vaccine Efficacy: A Re-Randomization Procedure to Analyse Poisson Outcomes under Covariate-Adaptive Randomization

Re-randomization inference is used as an alternative approach to more traditional statistical methods. Free from parametric assumptions, its use is particularly suited for studies incorporating covariate-adaptive randomization, and can provide additional evaluation and confirmation of inferences dra...

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Bibliographic Details
Published inStatistics in biopharmaceutical research Vol. 16; no. 4; pp. 491 - 497
Main Authors Ovbude, Leroy Jide, Grassano, Luca, Cheuvart, Brigitte, Solmi, Francesca
Format Journal Article
LanguageEnglish
Published Taylor & Francis 01.10.2024
Subjects
Confidence intervals
Minimization
Poisson regression
Re-randomization
Vaccine efficacy
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Summary:Re-randomization inference is used as an alternative approach to more traditional statistical methods. Free from parametric assumptions, its use is particularly suited for studies incorporating covariate-adaptive randomization, and can provide additional evaluation and confirmation of inferences drawn from the original analyses, for example, as a sensitivity analysis. We discuss methodological and computational aspects in the context of a Poisson regression and describe an approach to re-randomization inference. This is tested in a simulation study and then illustrated in a case study in which we evaluate vaccine efficacy data from a previously published influenza vaccine study. Our simulations indicate that re-randomization inference corrects for model misspecification. The case study, which accounted for the minimization factors, shows that the p-value and confidence limits from re-randomization inference agree with the original analysis. In conclusion re-randomization inference is a useful method that can be used to support vaccine clinical development.
ISSN:1946-6315
1946-6315
DOI:10.1080/19466315.2023.2252150