Approximately optimal designs for phase II clinical studies

There is no consensus on determination of sample size in phase II clinical trials. The use of Bayesian decision theory has been proposed by Stallard (1), among others. In this article, optimal three-stage designs are obtained using decision theory. These are compared with procedures proposed by Schoenfeld (2), Ensign et al. (3), and Chen et al. (4) and the sequential probability ratio test of Wald (5) and Barnard (6). The three-stage procedures are shown to be close to the true optimal test; the sequential probability ratio test is easier to obtain and only marginally inferior. Because optimality of the decision-theory approach depends on accurate specification of costs and profits, an assessment is made of the sensitivity of the proposed procedures to a range of gain function parameter values.