Optimal multistage designs for randomised clinical trials with continuous outcomes

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...

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Main Authors:	Wason, James MS, Mander, Adrian P, Thompson, Simon G
Format:	Online
Language:	English
Published:	Blackwell Publishing Ltd 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499690/

id	pubmed-3499690
recordtype	oai_dc
spelling	pubmed-34996902012-11-20 Optimal multistage designs for randomised clinical trials with continuous outcomes Wason, James MS Mander, Adrian P Thompson, Simon G Research Articles 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. Copyright © 2011 John Wiley & Sons, Ltd. Blackwell Publishing Ltd 2012-02-20 2011-12-05 /pmc/articles/PMC3499690/ /pubmed/22139822 http://dx.doi.org/10.1002/sim.4421 Text en Copyright © 2012 John Wiley & Sons, Ltd. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Wason, James MS Mander, Adrian P Thompson, Simon G
spellingShingle	Wason, James MS Mander, Adrian P Thompson, Simon G Optimal multistage designs for randomised clinical trials with continuous outcomes
author_facet	Wason, James MS Mander, Adrian P Thompson, Simon G
author_sort	Wason, James MS
title	Optimal multistage designs for randomised clinical trials with continuous outcomes
title_short	Optimal multistage designs for randomised clinical trials with continuous outcomes
title_full	Optimal multistage designs for randomised clinical trials with continuous outcomes
title_fullStr	Optimal multistage designs for randomised clinical trials with continuous outcomes
title_full_unstemmed	Optimal multistage designs for randomised clinical trials with continuous outcomes
title_sort	optimal multistage designs for randomised clinical trials with continuous outcomes
description	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. Copyright © 2011 John Wiley & Sons, Ltd.
publisher	Blackwell Publishing Ltd
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499690/
_version_	1611924659259834368

Optimal multistage designs for randomised clinical trials with continuous outcomes

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