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Optimal Designs for Evaluating a Series of Treatments

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  • Denis Heng‐Yan Leung
  • You‐Gan Wang

Abstract

Summary. Several articles in this journal have studied optimal designs for testing a series of treatments to identify promising ones for further study. These designs formulate testing as an ongoing process until a promising treatment is identified. This formulation is considered to be more realistic but substantially increases the computational complexity. In this article, we show that these new designs, which control the error rates for a series of treatments, can be reformulated as conventional designs that control the error rates for each individual treatment. This reformulation leads to a more meaningful interpretation of the error rates and hence easier specification of the error rates in practice. The reformulation also allows us to use conventional designs from published tables or standard computer programs to design trials for a series of treatments. We illustrate these using a study in soft tissue sarcoma.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:168-171
    DOI: 10.1111/j.0006-341X.2001.00168.x
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    Cited by:

    1. 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.

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