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    Rights statement: © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Bayesian methods for the design and interpretation of clinical trials in very rare diseases

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Bayesian methods for the design and interpretation of clinical trials in very rare diseases. / Hampson, Lisa; Whitehead, John; Eleftheriou, Despina et al.

In: Statistics in Medicine, Vol. 33, No. 24, 30.10.2014, p. 4186-4201.

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Hampson L, Whitehead J, Eleftheriou D, Brogan P. Bayesian methods for the design and interpretation of clinical trials in very rare diseases. Statistics in Medicine. 2014 Oct 30;33(24):4186-4201. Epub 2014 Jun 23. doi: 10.1002/sim.6225

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Hampson, Lisa ; Whitehead, John ; Eleftheriou, Despina et al. / Bayesian methods for the design and interpretation of clinical trials in very rare diseases. In: Statistics in Medicine. 2014 ; Vol. 33, No. 24. pp. 4186-4201.

Bibtex

@article{c623fc49ee7e4dba847aca949521df99,
title = "Bayesian methods for the design and interpretation of clinical trials in very rare diseases",
abstract = "This paper considers the design and interpretation of clinical trials comparing treatments for conditions so rare that worldwide recruitment efforts are likely to yield total sample sizes of 50 or fewer, even when patients are recruited over several years. For such studies, the sample size needed to meet a conventional frequentist power requirement is clearly infeasible. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose a Bayesian approach for the conduct of rare disease trials comparing an experimental treatment with a control where patient responses are classified as a success or failure. A systematic elicitation from clinicians of their beliefs concerning treatment efficacy is used to establish Bayesian priors for unknown model parameters. The process of determining the prior is described, including the possibility of formally considering results from related trials. As sample sizes are small, it is possible to compute all possible posterior distributions of the two success rates. A number of allocation ratios between the two treatment groups can be considered with a view to maximising the prior probability that the trial concludes recommending the new treatment when in fact it is non-inferior to control. Consideration of the extent to whichopinion can be changed, even by data from the best feasible design, can help to determine whether such a trial is worthwhile.",
keywords = "allocation ratio, Bayesian model, expert opinion, prior elicitation, prior power, rare diseases",
author = "Lisa Hampson and John Whitehead and Despina Eleftheriou and Paul Brogan",
note = "{\textcopyright} 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.",
year = "2014",
month = oct,
day = "30",
doi = "10.1002/sim.6225",
language = "English",
volume = "33",
pages = "4186--4201",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "24",

}

RIS

TY - JOUR

T1 - Bayesian methods for the design and interpretation of clinical trials in very rare diseases

AU - Hampson, Lisa

AU - Whitehead, John

AU - Eleftheriou, Despina

AU - Brogan, Paul

N1 - © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

PY - 2014/10/30

Y1 - 2014/10/30

N2 - This paper considers the design and interpretation of clinical trials comparing treatments for conditions so rare that worldwide recruitment efforts are likely to yield total sample sizes of 50 or fewer, even when patients are recruited over several years. For such studies, the sample size needed to meet a conventional frequentist power requirement is clearly infeasible. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose a Bayesian approach for the conduct of rare disease trials comparing an experimental treatment with a control where patient responses are classified as a success or failure. A systematic elicitation from clinicians of their beliefs concerning treatment efficacy is used to establish Bayesian priors for unknown model parameters. The process of determining the prior is described, including the possibility of formally considering results from related trials. As sample sizes are small, it is possible to compute all possible posterior distributions of the two success rates. A number of allocation ratios between the two treatment groups can be considered with a view to maximising the prior probability that the trial concludes recommending the new treatment when in fact it is non-inferior to control. Consideration of the extent to whichopinion can be changed, even by data from the best feasible design, can help to determine whether such a trial is worthwhile.

AB - This paper considers the design and interpretation of clinical trials comparing treatments for conditions so rare that worldwide recruitment efforts are likely to yield total sample sizes of 50 or fewer, even when patients are recruited over several years. For such studies, the sample size needed to meet a conventional frequentist power requirement is clearly infeasible. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose a Bayesian approach for the conduct of rare disease trials comparing an experimental treatment with a control where patient responses are classified as a success or failure. A systematic elicitation from clinicians of their beliefs concerning treatment efficacy is used to establish Bayesian priors for unknown model parameters. The process of determining the prior is described, including the possibility of formally considering results from related trials. As sample sizes are small, it is possible to compute all possible posterior distributions of the two success rates. A number of allocation ratios between the two treatment groups can be considered with a view to maximising the prior probability that the trial concludes recommending the new treatment when in fact it is non-inferior to control. Consideration of the extent to whichopinion can be changed, even by data from the best feasible design, can help to determine whether such a trial is worthwhile.

KW - allocation ratio

KW - Bayesian model

KW - expert opinion

KW - prior elicitation

KW - prior power

KW - rare diseases

U2 - 10.1002/sim.6225

DO - 10.1002/sim.6225

M3 - Journal article

VL - 33

SP - 4186

EP - 4201

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 24

ER -