SAMPLE SIZES FOR PHASE-II CLINICAL-TRIALS DERIVED FROM BAYESIAN DECISION-THEORY

In early phase clinical trials of a new medical treatment, patients ar e treated to decide whether there is sufficient promise to justify add itional studies. A decision theoretic approach is proposed to help det ermine the number of patients that should be treated. The optimal samp le size is obtained by maximizing a utility function which incorporate s both the number of 'gained successes' and the costs of treatment. Th e method extends work of Sylvester and Staquet, and adopts a Bayesian formulation. Numbers of patients in later studies and in eventual rout ine use of the treatment are taken into account. We allow for the poss ibility that a later study might lead to an erroneous conclusion. The effects of these various influences on the recommended sampling plan f or the early phase clinical trial are explored.