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Optimal designs for estimating the interesting part of a dose-effect curve

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  • Miller, Frank
  • Dette, Holger
  • Guilbaud, Olivier

Abstract

We consider a dose-finding trial in phase IIB of drug development. For choosing an appropriate design for this trial the specification of two points is critical: an appropriate model for describing the dose-effect relationship and the specification of the aims of the trial (objectives), which will be the focus in the present paper. For many practical situations it is essential to have a robust trial objective that has little risk of changing during the complete trial due to external information. An important and realistic objective of a dose-finding trial is to obtain precise information about the interesting part of the dose-effect curve. We reflect this goal in a statistical optimality criterion and derive efficient designs using optimal design theory. In particular we determine non-adaptive Bayesian optimal designs, i.e. designs which are not changed by information obtained from an interim analysis. Compared with a traditional balanced design for this trial it is shown that the optimal design is substantially more efficient. This implies either again in information or essential savings in sample size. Further, we investigate an adaptive Bayesian optimal design that uses two different optimal designs before and after an interim analysis, and we compare the adaptive with the non-adaptive Bayesian optimal design. The basic concept is illustrated using a modification of a recent AstraZeneca trial.

Suggested Citation

  • Miller, Frank & Dette, Holger & Guilbaud, Olivier, 2007. "Optimal designs for estimating the interesting part of a dose-effect curve," Technical Reports 2007,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200721
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    References listed on IDEAS

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    1. Dette, Holger & Bretz, Frank, 2007. "Optimal designs for dose finding studies," Technical Reports 2007,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. F. Bretz & J. C. Pinheiro & M. Branson, 2005. "Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies," Biometrics, The International Biometric Society, vol. 61(3), pages 738-748, September.
    3. Dette, Holger & Melas, Viatcheslav B. & Wong, Weng Kee, 2005. "Optimal Design for Goodness-of-Fit of the MichaelisMenten Enzyme Kinetic Function," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1370-1381, December.
    4. Holger Dette, 2004. "A comparison of sequential and non-sequential designs for discrimination between nested regression models," Biometrika, Biometrika Trust, vol. 91(1), pages 165-176, March.
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    Cited by:

    1. Hyun Seung Won & Wong Weng Kee, 2015. "Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 253-271, November.
    2. Bretz, Frank & Dette, Holger & Pinheiro, José, 2008. "Practical considerations for optimal designs in clinical dose finding studies," Technical Reports 2008,22, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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