title: Sample sizes for phase II clinical trials derived from Bayesian decision theory.
abstract: In early phase clinical trials of a new medical treatment, patients are treated to decide whether there is sufficient promise to justify additional studies. A decision theoretic approach is proposed to help determine the number of patients that should be treated. The optimal sample size is obtained by maximizing a utility function which incorporates both the number of 'gained successes' and the costs of treatment. The method extends work of Sylvester and Staquet, and adopts a Bayesian formulation. Numbers of patients in later studies and in eventual routine use of the treatment are taken into account. We allow for the possibility that a later study might lead to an erroneous conclusion. The effects of these various influences on the recommended sampling plan for the early phase clinical trial are explored.
authors.affiliation: Department of Applied Statistics, The University, Reading, U.K.
authors.forename: H C
authors.initials: HC
authors.lastname: Brunier
authors.forename: J
authors.initials: J
authors.lastname: Whitehead
mesh_terms: D001499:Bayes Theorem; D016009:Chi-Square Distribution; D017322:Clinical Trials, Phase II as Topic / Q000191:economics / Q000706:statistics & numerical data*; D003365:Costs and Cost Analysis; D003662:Decision Theory; D006801:Humans; D018401:Sample Size; D016896:Treatment Outcome