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The choice of sample size: a mixed Bayesian / frequentist approach

Center of Excellence in Biomathematics and School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran, pezeshk{at}khayam.ut.ac.ir

Nader Nematollahi

Department of Statistics, Allameh Tabatabaie University, Tehran, Iran

Vahed Maroufy

Department of Statistics, Allameh Tabatabaie University, Tehran, Iran

John Gittins

Department of Statistics, University of Oxford, UK

Sample size computations are largely based on frequentist or classical methods. In the Bayesian approach the prior information on the unknown parameters is taken into account. In this work we consider a fully Bayesian approach to the sample size determination problem which was introduced by Grundy et al. and developed by Lindley. This approach treats the problem as a decision problem and employs a utility function to find the optimal sample size of a trial. Furthermore, we assume that a regulatory authority, which is deciding on whether or not to grant a licence to a new treatment, uses a frequentist approach. We then find the optimal sample size for the trial by maximising the expected net benefit, which is the expected benefit of subsequent use of the new treatment minus the cost of the trial.

This version was published on April 1, 2009

Statistical Methods in Medical Research, Vol. 18, No. 2, 183-194 (2009)
DOI: 10.1177/0962280208089298


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