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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

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Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control. / Whitehead, John; Cleary, Faye; Turner, Amanda.
In: Statistics in Medicine, Vol. 34, No. 12, 30.05.2015, p. 2048-2061.

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Whitehead J, Cleary F, Turner A. Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control. Statistics in Medicine. 2015 May 30;34(12):2048-2061. Epub 2015 Mar 12. doi: 10.1002/sim.6469

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Bibtex

@article{a9a43823ce964d20a224ec36b4a7816a,
title = "Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control",
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.",
keywords = "math.ST, stat.ME, stat.TH, 62P10, Bayesian design, clinical trial, multiple treatments, phase II trial, sample size calculation",
author = "John Whitehead and Faye Cleary and Amanda Turner",
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",
year = "2015",
month = may,
day = "30",
doi = "10.1002/sim.6469",
language = "English",
volume = "34",
pages = "2048--2061",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "12",

}

RIS

TY - JOUR

T1 - Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control

AU - Whitehead, John

AU - Cleary, Faye

AU - Turner, Amanda

N1 - 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

PY - 2015/5/30

Y1 - 2015/5/30

N2 - 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.

AB - 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.

KW - math.ST

KW - stat.ME

KW - stat.TH

KW - 62P10

KW - Bayesian design

KW - clinical trial

KW - multiple treatments

KW - phase II trial

KW - sample size calculation

U2 - 10.1002/sim.6469

DO - 10.1002/sim.6469

M3 - Journal article

VL - 34

SP - 2048

EP - 2061

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 12

ER -