A behavioral Bayes method to determine the sample size of a clinical trial considering efficacy and safety.
It is necessary for the calculation of sample size to achieve the best balance between the cost of a clinical trial and the possible benefits from a new treatment. Gittins and Pezeshk developed an innovative (behavioral Bayes) approach, which assumes that the number of users is an increasing function of the difference in performance between the new treatment and the standard treatment. The better a new treatment, the more the number of patients who want to switch to it. The optimal sample size is calculated in this framework. This BeBay approach takes account of three decision-makers, a pharmaceutical company, the health authority and medical advisers. Kikuchi, Pezeshk and Gittins generalized this approach by introducing a logistic benefit function, and by extending to the more usual unpaired case, and with unknown variance. The expected net benefit in this model is based on the efficacy of the new drug but does not take account of the incidence of adverse reactions. The present paper extends the model to include the costs of treating adverse reactions and focuses on societal cost-effectiveness as the criterion for determining sample size. The main application is likely to be to phase III clinical trials, for which the primary outcome is to compare the costs and benefits of a new drug with a standard drug in relation to national health-care.
Kikuchi
Takashi
T
Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, U K. kikuchi@stats.ox.ac.uk
Gittins
John
J
D001499:Bayes Theorem; D001699:Biometry; D002986:Clinical Trials as Topic / Q000191:economics / Q000706:statistics & numerical data*; D017327:Clinical Trials, Phase IV as Topic / Q000191:economics / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D018401:Sample Size