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

Hampson, Lisa and Whitehead, John and Eleftheriou, Despina and Brogan, Paul (2014) Bayesian methods for the design and interpretation of clinical trials in very rare diseases. Statistics in Medicine, 33 (24). pp. 4186-4201. ISSN 0277-6715

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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 which opinion can be changed, even by data from the best feasible design, can help to determine whether such a trial is worthwhile.

Item Type:
Journal Article
Journal or Publication Title:
Statistics in Medicine
Additional Information:
© 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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
71854
Deposited By:
Deposited On:
24 Nov 2014 09:55
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jul 2020 03:48