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  • Paper - 30-12-14

    Rights statement: This is the pre-print pre reviewed version of the following article: Whitehead, J., Cleary, F. and Turner, A. (2015), Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control. Statist. Med., doi: 10.1002/sim.6469. which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.6469/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>30/05/2015
<mark>Journal</mark>Statistics in Medicine
Issue number12
Volume34
Number of pages14
Pages (from-to)2048-2061
Publication statusPublished
Early online date12/03/15
Original languageEnglish

Abstract

In this paper, a Bayesian approach is developed for simultaneously comparing multiple experimental treatments with a common control treatment in an exploratory clinical trial. The sample size is set to ensure that, at the end of the study, there will be at least one treatment for which the investigators have a strong belief that it is better than control, or else they have a strong belief that none of the experimental treatments are substantially better than control. This criterion bears a direct relationship with conventional frequentist power requirements, while allowing prior opinion to feature in the analysis with a consequent reduction in sample size. If it is concluded that at least one of the experimental treatments shows promise, then it is envisaged that one or more of these promising treatments will be developed further in a definitive phase III trial. The approach is developed in the context of normally distributed responses sharing a common standard deviation regardless of treatment. To begin with, the standard deviation will be assumed known when the sample size is calculated. The final analysis will not rely upon this assumption, although the intended properties of the design may not be achieved if the anticipated standard deviation turns out to be inappropriate. Methods that formally allow for uncertainty about the standard deviation, expressed in the form of a Bayesian prior, are then explored. Illustrations of the sample sizes computed from the new method are presented, and comparisons are made with frequentist methods devised for the same situation.

Bibliographic note

This is the pre-print pre reviewed version of the following article: Whitehead, J., Cleary, F. and Turner, A. (2015), Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control. Statist. Med., doi: 10.1002/sim.6469. which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.6469/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. 28 pages, 3 tables, 2 figures