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Optimal multistage designs for randomised clinical trials with continuous outcomes

Lookup NU author(s): Professor James WasonORCiD


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Multistage designs allow considerable reductions in the expected sample size of a trial. When stopping for futility or efficacy is allowed at each stage, the expected sample size under different possible true treatment effects (δ) is of interest. The δ-minimax design is the one for which the maximum expected sample size is minimised amongst all designs that meet the types I and II error constraints. Previous work has compared a two-stage δ-minimax design with other optimal two-stage designs. Applying the δ-minimax design to designs with more than two stages was not previously considered because of computational issues. In this paper, we identify the δ-minimax designs with more than two stages through use of a novel application of simulated annealing. We compare them with other optimal multistage designs and the triangular design. We show that, as for two-stage designs, the δ-minimax design has good expected sample size properties across a broad range of treatment effects but generally has a higher maximum sample size. To overcome this drawback, we use the concept of admissible designs to find trials which balance the maximum expected sample size and maximum sample size. We show that such designs have good expected sample size properties and a reasonable maximum sample size and, thus, are very appealing for use in clinical trials. © 2011 John Wiley & Sons, Ltd.

Publication metadata

Author(s): Wason JM, Mander AP, Thompson SG

Publication type: Article

Publication status: Published

Journal: Statistics in Medicine

Year: 2012

Volume: 31

Issue: 4

Pages: 301-312

Print publication date: 20/02/2012

Online publication date: 05/12/2011

ISSN (print): 0277-6715

ISSN (electronic): 1097-0258

Publisher: John Wiley & Sons Ltd


DOI: 10.1002/sim.4421

PubMed id: 22139822


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