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A Dynamic Power Prior for Bayesian Non-inferiority Trials

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Studies in Theoretical and Applied Statistics (SIS 2021)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 406))

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Abstract

Non-inferiority trials compare new experimental treatments to active controls. Previous information on the control treatments is often available and, as long as the past and the current experiments are sufficiently homogeneous, historical data may be useful to reserve resources to the new therapy’s arm and to improve accuracy of inference. In this article we propose a Bayesian method for exploiting historical information based on a dynamic power prior for the parameter of the control arm. The degree of information-borrowing is tuned by a quantity based on the Hellinger distance between the two posterior distributions of the control arm’s parameter, obtained respectively from the current and the historical experiments. Pre-posterior analysis for type-I error/power assessment and for sample size determination is also discussed.

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze Statistiche, Sapienza Universitá di Roma, Piazzale Aldo Moro N. 5, 00185, Roma, Italy

    Fulvio De Santis & Stefania Gubbiotti

Authors
  1. Fulvio De Santis
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  2. Stefania Gubbiotti
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Corresponding author

Correspondence to Stefania Gubbiotti .

Editor information

Editors and Affiliations

  1. Department of Economics and Management, University of Pisa, Pisa, Italy

    Nicola Salvati

  2. Department of Economics and Statistics, University of Salerno, Fisciano, Salerno, Italy

    Cira Perna

  3. Department of Economics and Management, University of Pisa, Pisa, Italy

    Stefano Marchetti

  4. School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia

    Raymond Chambers

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De Santis, F., Gubbiotti, S. (2022). A Dynamic Power Prior for Bayesian Non-inferiority Trials. In: Salvati, N., Perna, C., Marchetti, S., Chambers, R. (eds) Studies in Theoretical and Applied Statistics . SIS 2021. Springer Proceedings in Mathematics & Statistics, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-031-16609-9_2

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