Bayesian optimal design for phase II screening trials

Abstract

Rapid progress in biomedical research necessitates clinical evaluation that identifies promising innovations quickly and efficiently. Rapid evaluation is especially important if the number of innovations is large compared to the supply of suitable study patients. 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 study 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 underlying probability model is a hierarchical probit regression model that also allows for treatment-specific covariates. The design criterion is to maximize the utility for the new treatment, a sampling cost per patient, and the possible demonstration of a significant treatment benefit in a future randomized clinical trial. Computer simulations show that, this method has high probabilities of discarding treatments with low success rates and moving treatments with high success rates to phase III trial. Compared to the fully sequential design proposed by Wang and Leung's in 1998 Biometrics, this method provides a smaller number of patients required to screen out the first promising treatment and has better design characteristics