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Dose Finding Experiments: Responses of Mixed Type

  • Valerii V. Fedorov
  • Yuehui Wu
  • Rongmei Zhang
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

Multiple-endpoint models are widely used in drug development and other fields. It is common that the endpoints have different characteristics such as all continuous, all binary, or a mixture of them. This study investigates mixed responses, one continuous and one binary, correlated and observed simultaneously. It is an extension of our previous studies based on a bivariate probit model for two binary endpoints. We quantify the study goal with a utility function, construct locally two-stage D-optimal designs under the constraints, and use them as benchmarks for the two-stage designs with interim adjustment and fully adaptive designs. The simulation results suggest that the two-stage design is almost as efficient as the locally two-stage optimal design, as well as being logistically simpler than the fully adaptive design. We do not analyze asymptotic properties but confine ourselves to Monte-Carlo simulations to evaluate their properties for reasonable (practical) sample sizes.

Keywords

Optimal Design Penalty Function Design Point Information Matrix Single Observation 
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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References

  1. Dragalin, V., V. Fedorov, and Y. Wu (2008). Two-stage design for dose-finding that accounts for both efficacy and safety. Statistics in Medicine 27, 5156–5176.CrossRefGoogle Scholar
  2. Fedorov, V. (1972). Theory of Optimal Experiments. New York: Academic Press.Google Scholar
  3. Fedorov, V. and Y. Wu (2007). Dose finding designs for continuous responses and binary utility. Journal of Biopharmaceutical Statistics 17, 1085–1096.CrossRefMathSciNetGoogle Scholar
  4. Fedorov, V. V. and P. Hackl (1997). Model-oriented Design of Experiments. Berlin: Springer-Verlag.MATHGoogle Scholar
  5. Lai, T. L. (2001). Sequential analysis: Some classical problems and new challenges. Statistica Sinica 11, 303–351.MATHMathSciNetGoogle Scholar
  6. Lai, T. L. and H. Robbins (1978). Adaptive design in regression and control. Proceedings of the National Academy of Sciences of the United States of America 75, 586–587.MATHCrossRefMathSciNetGoogle Scholar
  7. Rao, C. R. (1973). Linear Statistical Inference and Its Applications. New York: Wiley.MATHCrossRefGoogle Scholar
  8. Rosenberger, W. F. and J. M. Hughes-Oliver (1999). Inference from a sequential design: Proof of a conjecture by Ford and Silvey. Statistics & Probability Letters 44, 177–180.MATHCrossRefMathSciNetGoogle Scholar
  9. Tate, R. (1955). The theory of correlation between two continuous variables when one is dichotomized. Biometrika 42, 205–216.MATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.GlaxoSmithKlineCollegevilleUSA
  2. 2.CCEB, University of PennsylvaniaPhiladlephiaUSA

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