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A Bayesian Decision Approach for Sample Size Determination in Phase II Trials

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  • Denis Heng‐Yan Leung
  • You‐Gan Wang

Abstract

Summary. Stallard (1998, Biometrics54, 279–294) recently used Bayesian decision theory for sample‐size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics50, 337–349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one‐stage design becomes twice as efficient as Stallard's one‐stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.

Suggested Citation

  • Denis Heng‐Yan Leung & You‐Gan Wang, 2001. "A Bayesian Decision Approach for Sample Size Determination in Phase II Trials," Biometrics, The International Biometric Society, vol. 57(1), pages 309-312, March.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:309-312
    DOI: 10.1111/j.0006-341X.2001.00309.x
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    Cited by:

    1. Nigel Stallard, 2003. "Decision-Theoretic Designs for Phase II Clinical Trials Allowing for Competing Studies," Biometrics, The International Biometric Society, vol. 59(2), pages 402-409, June.
    2. Peter F. Thall & Hoang Q. Nguyen & Sarah Zohar & Pierre Maton, 2014. "Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 931-943, September.

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