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Bayesian sample size for exploratory clinical trials incorporating historical data.

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Bayesian sample size for exploratory clinical trials incorporating historical data. / Whitehead, John; Valdés-Márquez, Elsa; Johnson, Patrick et al.
In: Statistics in Medicine, Vol. 27, No. 13, 06.2008, p. 2307-2327.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Whitehead, J, Valdés-Márquez, E, Johnson, P & Graham, G 2008, 'Bayesian sample size for exploratory clinical trials incorporating historical data.', Statistics in Medicine, vol. 27, no. 13, pp. 2307-2327. https://doi.org/10.1002/sim.3140

APA

Whitehead, J., Valdés-Márquez, E., Johnson, P., & Graham, G. (2008). Bayesian sample size for exploratory clinical trials incorporating historical data. Statistics in Medicine, 27(13), 2307-2327. https://doi.org/10.1002/sim.3140

Vancouver

Whitehead J, Valdés-Márquez E, Johnson P, Graham G. Bayesian sample size for exploratory clinical trials incorporating historical data. Statistics in Medicine. 2008 Jun;27(13):2307-2327. doi: 10.1002/sim.3140

Author

Whitehead, John ; Valdés-Márquez, Elsa ; Johnson, Patrick et al. / Bayesian sample size for exploratory clinical trials incorporating historical data. In: Statistics in Medicine. 2008 ; Vol. 27, No. 13. pp. 2307-2327.

Bibtex

@article{ff1bb154e8eb41a6b1bc54eaf1696d17,
title = "Bayesian sample size for exploratory clinical trials incorporating historical data.",
abstract = "This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence, either that an experimental treatment is better than a control, or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving “non-informative” prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p0. Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored.",
keywords = "Bayesian methods, clinical trial , phase II trial , proof-of-concept study , sample size , score statistic",
author = "John Whitehead and Elsa Vald{\'e}s-M{\'a}rquez and Patrick Johnson and Gordon Graham",
year = "2008",
month = jun,
doi = "10.1002/sim.3140",
language = "English",
volume = "27",
pages = "2307--2327",
journal = "Statistics in Medicine",
issn = "1097-0258",
publisher = "John Wiley and Sons Ltd",
number = "13",

}

RIS

TY - JOUR

T1 - Bayesian sample size for exploratory clinical trials incorporating historical data.

AU - Whitehead, John

AU - Valdés-Márquez, Elsa

AU - Johnson, Patrick

AU - Graham, Gordon

PY - 2008/6

Y1 - 2008/6

N2 - This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence, either that an experimental treatment is better than a control, or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving “non-informative” prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p0. Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored.

AB - This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence, either that an experimental treatment is better than a control, or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving “non-informative” prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p0. Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored.

KW - Bayesian methods

KW - clinical trial

KW - phase II trial

KW - proof-of-concept study

KW - sample size

KW - score statistic

U2 - 10.1002/sim.3140

DO - 10.1002/sim.3140

M3 - Journal article

VL - 27

SP - 2307

EP - 2327

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 1097-0258

IS - 13

ER -