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Designing Multi-arm Multi-stage Clinical Studies

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Developments in Statistical Evaluation of Clinical Trials

Abstract

In the early stages of drug development there often is uncertainty about the most promising among a set of different treatments, different doses of the same treatment or sets of combinations of treatments. An efficient solution to determine which intervention is most promising are multi-arm multi-stage clinical studies (MAMS). In this chapter we will discuss the general concept to designing MAMS studies within the group sequential framework and provide detailed solutions for multi-arm multi-stage studies with normally distributed endpoints in which all promising treatments are continued at the interim analyses. An approach to find optimal designs is discussed as well as asymptotic solutions for binary, ordinal and time-to event endpoints.

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Acknowledgements

This work is based on research arising from Dr. Jakis Career Development Fellowship (NIHR-CDF-2010-03-32) supported by the National Institute for Health Research and the MRC grant MR/J004979/1. The views expressed in this publication are those of the author and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. The author would also like to thank Dr. James Wason and Dominic Magirr for their helpful comments.

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Authors and Affiliations

  1. Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK

    Thomas Jaki

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  1. Thomas Jaki
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Correspondence to Thomas Jaki .

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Editors and Affiliations

  1. Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands

    Kees van Montfort

  2. Behavioural Science Institute, University of Nijmegen, Nijmegen, The Netherlands

    Johan Oud

  3. Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands

    Wendimagegn Ghidey

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Jaki, T. (2014). Designing Multi-arm Multi-stage Clinical Studies. In: van Montfort, K., Oud, J., Ghidey, W. (eds) Developments in Statistical Evaluation of Clinical Trials. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55345-5_3

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