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Bayesian sample size for exploratory clinical trials incorporating historical data.

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>06/2008
<mark>Journal</mark>Statistics in Medicine
Number of pages21
<mark>Original language</mark>English


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.