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Bayesian Optimal Design for Phase II Screening Trials

Author

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  • Meichun Ding
  • Gary L. Rosner
  • Peter Müller

Abstract

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.

Suggested Citation

  • Meichun Ding & Gary L. Rosner & Peter Müller, 2008. "Bayesian Optimal Design for Phase II Screening Trials," Biometrics, The International Biometric Society, vol. 64(3), pages 886-894, September.
  • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:886-894
    DOI: 10.1111/j.1541-0420.2007.00951.x
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    References listed on IDEAS

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    1. Denis Heng‐Yan Leung & You‐Gan Wang, 2001. "Optimal Designs for Evaluating a Series of Treatments," Biometrics, The International Biometric Society, vol. 57(1), pages 168-171, March.
    2. Nigel Stallard & Peter F. Thall & John Whitehead, 1999. "Decision Theoretic Designs for Phase II Clinical Trials with Multiple Outcomes," Biometrics, The International Biometric Society, vol. 55(3), pages 971-977, September.
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    Cited by:

    1. Steffen Ventz & Lorenzo Trippa, 2015. "Bayesian designs and the control of frequentist characteristics: A practical solution," Biometrics, The International Biometric Society, vol. 71(1), pages 218-226, March.
    2. Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.

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