Bayesian optimal design for phase II screening trials

Most phase II screening designs available in the literature consider one treatment at a time. Each study is considered in isolation. We propose a more systematic decision-making approach to the phase II screening process. The sequential design allows for more efficiency and greater learning about treatments. The approach incorporates a Bayesian hierarchical model that allows combining information across several related studies in a formal way and improves estimation in small data sets by borrowing strength from other treatments. The design incorporates a utility function that includes sampling costs and possible future payoff. Computer simulations show that this method has high probability of discarding treatments with low success rates and moving treatments with high success rates to phase III trial.