Premium
Bayesian Optimal Design for Phase II Screening Trials
Author(s) -
Ding Meichun,
Rosner Gary L.,
Müller Peter
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00951.x
Subject(s) - computer science , bayesian probability , bayesian experimental design , machine learning , phase (matter) , sampling (signal processing) , bayesian inference , optimal design , mathematical optimization , bayesian hierarchical modeling , artificial intelligence , data mining , econometrics , mathematics , chemistry , computer vision , organic chemistry , filter (signal processing)
Summary 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.