A mixed Bayesian/Frequentist approach in sample size determination problem for clinical trials

Document Type: Original Research Papers


1 University of Tehran

2 University of Oxford


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 Monte Carlo Markov Chain is applied to nd the
maximum expected utility of conducting the trial.
Sample size determination problem is an important task in the planning of
trials. The problem may be formulated formally in statistical terms. The
most frequently used methods are based on the required size, and power of the
trial for a specifed treatment efect Several authors have
recognized the value of using prior distributions rather than point estimates
in sample size calculations.