Sample Size Reestimation

  • Wenping Wang
  • Andreas Krause


The number of subjects is an important design parameter in clinical trials. The key information when planning the sample size is the postulated effect and its variation. The effect size may come from prior trials, from literature review, or quite often from the best guess by the investigator.


Posterior Distribution Markov Chain Monte Carlo Gibbs Sampling Markov Chain Monte Carlo Method Royal Statistical Society 
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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  1. Berger, J. (1985). Statistical decision theory and Bayesian analysis. 2nd ed. Springer-Verlag, New York.zbMATHCrossRefGoogle Scholar
  2. Bernardo, J. and Smith, A. (1994). Bayesian Theory. Wiley, New York.Google Scholar
  3. Bernardo, J., Berger, J. Dawid, A., and Smith, A., eds. (1992). Bayesian Statistics 4. Oxford University Press.Google Scholar
  4. Casella, G., and George, E. (1992). Explaining the gibbs sampler. The American Statistician 46, 167–174.MathSciNetGoogle Scholar
  5. Chib, S., and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The American Statistician 49 (4), 327–35.Google Scholar
  6. Cowles, M., and Carlin, B. (1996). Markov chain monte carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 91 (434), 883–904.MathSciNetzbMATHCrossRefGoogle Scholar
  7. Dempster, A., Laird, N., and Rubin, D. (1977). Maximum likelihood from incomplete data via the em algorithm (with discussion). Journal of the Royal Statistical Society, Series A 132, 234–244.Google Scholar
  8. Diebolt, J., and Robert, C. (1994). Estimation of finite mixture distributions through bayesian sampling. Journal of the Royal Statistical Society, Series B 56, 363–375.MathSciNetzbMATHGoogle Scholar
  9. Draper, D. (1998). Bayesian hierarchical modeling. Manuscript preprint, 2nd version, available from: home.html.Google Scholar
  10. Escobar, M., and West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90 (2), 577–588.MathSciNetzbMATHCrossRefGoogle Scholar
  11. Gelfand, A., and Smith, A. (1990). Sampling—based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398–409.MathSciNetzbMATHCrossRefGoogle Scholar
  12. Gelman, A., and Rubin, D. (1992a). Inference from iterative simulation using multiple sequences (with discussion). Statistical Sciences 7 (4), 457–511.CrossRefGoogle Scholar
  13. Gelman, A., and Rubin, D. (1992b). A Single Series From the Gibbs Sampler Provides a False Sense of Security. In: Bernardo (1992).Google Scholar
  14. Gelman, A., Carlin, J., Stern, H., and Rubin, D. (1995). Bayesian Data Analysis. Chapman and Hall, London.Google Scholar
  15. Geman, S., and Geman, D. (1984). Stochastic relaxation, gibbs distributions and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741.zbMATHCrossRefGoogle Scholar
  16. Geyer, C. (1992). Practical markov chain monte carlo. Statistical Sciences 7 (4), 473–483.MathSciNetCrossRefGoogle Scholar
  17. Gilks, W., and Wild, P. (1992). Adaptive rejection sampling for gibbs sampling. Journal of the Royal Statistical Society, Series C 41, 337–348.zbMATHGoogle Scholar
  18. Gilks, W., Richardson, S., and Spiegelhalter, D., eds. (1996). Markov Chain Monte Carlo in practice. Chapman and Hall, London.zbMATHGoogle Scholar
  19. Gould, A., and Shih, W. (1992). Sample size re-estimation without un-blinding for normally distributed outcomes with unknown variance. Communications in Statisctics 21 (10), 2833–2853.zbMATHCrossRefGoogle Scholar
  20. Krause, A. (1994). Computerintensive statistische Methoden- Gibbs Sampling in Regressionsmodellen. Fischer, Stuttgart.Google Scholar
  21. Learner, E. (1978). Specification Searches. Wiley, New York.Google Scholar
  22. Meng, X., and Rubin, D. (1993). Maximum likelihood estimation via the ecm algorithm: A general framework. Biometrika 80 (2), 267–78.MathSciNetzbMATHCrossRefGoogle Scholar
  23. Peace, E., ed. (1992). Biopharmaceutical Sequential Statistical Applications. Marcel Dekker, New York.Google Scholar
  24. Raftery, A., and Lewis, S. (1992). How many iterations in the Gibbs Sampler?. In: Bernardo et al. ( 1992 ). pp. 763–773.Google Scholar
  25. Richardson, S., and Green, P. (1997). On bayesian analysis of mixtures with unknown number of components. Journal of the Royal Statistical Society, Series B 59, 731–792.Google Scholar
  26. Robert, C. (1996). Mixture of distribution: inference and estimation. In: Gilks (1996).Google Scholar
  27. Rubin, D. (1976). Inference and missing data. Biometrika 63, 581–592.MathSciNetzbMATHCrossRefGoogle Scholar
  28. Shih, W. (1992). Sample size reestimation in clinical trials. In: Peace ( 1992 ). pp. 285–301.Google Scholar
  29. Shih, W. (1993). Sample size reestimation for triple blind clinical trials. Drug Information Journal 27, 761–764.CrossRefGoogle Scholar
  30. Shih, W., and Gould, A. (1995). Re-evaluating design specifications of longitudinal clinical trials without unblinding when the key response is rate of change. Statistcs in Medicine 14, 2239–2248.CrossRefGoogle Scholar
  31. Spiegelhalter, D., Thomas, A., Best, N., and Wilks, W. (1996). The BUGS 0.5 Manual. Available from: ac. uk/bugs/welcome. shtml.Google Scholar
  32. Stephens, M. (1997). Bayesian methods for mixtures of normal distributions. Unpublished Ph.D. thesis.Google Scholar
  33. Wittes, J., and Brittan, E. (1990). The role of internal pilot studies in increasing the efficiency of clinical trials. Statistics in Medicine 9, 65–72.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Wenping Wang
    • 1
  • Andreas Krause
    • 2
  1. 1.Pharsight CorporationCaryUSA
  2. 2.Novartis Pharma AGBaselSwitzerland

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