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Optimal designs for dose finding studies

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  • Dette, Holger
  • Bretz, Frank

Abstract

Identifying the "right" dose is one of the most critical and difficult steps in the clinical development process of any medicinal drug. Its importance cannot be understated: selecting too high a dose can result in unacceptable toxicity and associated safety problems, while choosing too low a dose leads to smaller chances of showing sufficient efficacy in confirmatory trials, thus reducing the chance of approval for the drug. In this paper we investigate the problem of deriving e?cient designs for the estimation of the minimum effective dose (MED) by determining the appropriate number and actual levels of the doses to be administered to patients, as well as their relative sample size allocations. More specifically, we derive local optimal designs that minimize the asymptotic variance of the MED estimate under a particular dose response model. The small sample properties of these designs are investigated via simulation, together with their sensitivity to misspeciffication of the true parameter values and of the underlying dose response model. Finally, robust optimal designs are constructed, which take into account a set of potential dose response profiles within classes of models commonly used in practice.

Suggested Citation

  • Dette, Holger & Bretz, Frank, 2007. "Optimal designs for dose finding studies," Technical Reports 2007,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200701
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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.

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