Abstract
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.
Original language | English (US) |
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Pages (from-to) | 886-894 |
Number of pages | 9 |
Journal | Biometrics |
Volume | 64 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2008 |
Externally published | Yes |
Keywords
- Backward induction
- Bayesian
- Decision theoretic
- Phase II screening trials
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics