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Covariate Adjusted Designs for Combining Efficiency, Ethics and Randomness in Normal Response Trials

  • Alessandro Baldi Antognini
  • Maroussa Zagoraiou
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

This paper deals with the problem of allocating patients to two competing treatments in the presence of covariates in order to achieve a good trade-off between efficiency, ethical concern and randomization. We propose a compound criterion that combines inferential precision and ethical gain by flexible weights depending on the unknown treatment effects. In the absence of treatment-covariate interactions, this criterion leads to a locally optimal allocation which does not depend on the covariates and can be targeted by a suitable implementation of the doubly-adaptive biased coin design aimed at balancing the roles of randomization, ethics and information. Some properties of the suggested procedure are described.

Keywords

Optimal Allocation Optimal Target Adaptive Design Allocation Proportion Ethical Criterion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

This research was partly supported by the Italian Ministry for Research (MIUR) PRIN 2007 “Statistical methods for learning in clinical research”.

Refeness

  1. Atkinson, A.C. (2002). The comparison of designs for sequential clinical trials with covariate information. Journal of the Royal Statistical Society, Series A 165, 349–373.MATHGoogle Scholar
  2. Atkinson, A.C. and A. Biswas (2005). Adaptive biased-coin designs for skewing the allocation proportion in clinical trials with normal responses. Statistics in Medicine 24, 2477–2492.CrossRefMathSciNetGoogle Scholar
  3. Bandyopadhyay, U. and A. Biswas (2001). Adaptive designs for normal responses with prognostic factors. Biometrika 88, 409–419.MATHCrossRefMathSciNetGoogle Scholar
  4. Eisele, J. (1994). The doubly adaptive biased coin design for sequential clinical trials. Journal of Statistical Planning and Inference 38, 249–262.MATHCrossRefMathSciNetGoogle Scholar
  5. Geraldes, M., V. Melfi, C. Page, and H. Zhang (2006). The doubly adaptive weighted difference design. Journal of Statistical Planning and Inference 136, 1923–1939.MATHCrossRefMathSciNetGoogle Scholar
  6. Rosenberger, W.F., N. Stallard, A. Ivanova, C.N. Harper and M.L. Ricks (2004). Optimal adaptive designs for binary response trials. Biometrics 57, 909–913.CrossRefMathSciNetGoogle Scholar
  7. Tymofyeyev, Y., W.F. Rosenberger and F. Hu (2007). Implementing optimal allocation in sequential binary response experiments. Journal of the American Statistical Association 102, 224–234.MATHCrossRefMathSciNetGoogle Scholar
  8. Zhang, L. and F. Hu (2009). A new family of covariate-adjusted response adaptive designs and their properties. Applied Mathematical Journal of Chinese Universities 24, 1–13.MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alessandro Baldi Antognini
    • 1
  • Maroussa Zagoraiou
    • 1
  1. 1.Department of Statistical SciencesUniversity of BolognaBolognaItaly

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