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In this paper we introduce a stochastic optimization method based on
a mixed Bayesian/frequentist approach to a sample size determination
problem in a clinical trial. The data are assumed to come from a nor-
mal distribution for which both the mean and the variance are unknown.
In contrast to the usual Bayesian decision theoretic methodology, which
assumes a single decision maker, our method recognizes the existence of
three decision makers, namely: the company conducting the trial, which
decides on its size; the regulator, whose approval is necessary for the drug
to be licensed for sale; and the public at large, who determine ultimate
usage. Moreover, we model the subsequent usage by plausible assumptions
for actual behaviour. A Markov Chain Monte Carlo is applied to _nd the
maximum expected utility of conducting the trial.
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