Skip to main content
  • 507 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Berger, J. (1985). Statistical decision theory and Bayesian analysis. 2nd ed. Springer-Verlag, New York.

    Book  MATH  Google Scholar 

  • Bernardo, J. and Smith, A. (1994). Bayesian Theory. Wiley, New York.

    Google Scholar 

  • Bernardo, J., Berger, J. Dawid, A., and Smith, A., eds. (1992). Bayesian Statistics 4. Oxford University Press.

    Google Scholar 

  • Casella, G., and George, E. (1992). Explaining the gibbs sampler. The American Statistician 46, 167–174.

    MathSciNet  Google Scholar 

  • Chib, S., and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The American Statistician 49 (4), 327–35.

    Google Scholar 

  • 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.

    Article  MathSciNet  MATH  Google Scholar 

  • 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 

  • 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.

    MathSciNet  MATH  Google Scholar 

  • Draper, D. (1998). Bayesian hierarchical modeling. Manuscript preprint, 2nd version, available from: http://www.bath.ac.ukrmasdd/ home.html.

    Google Scholar 

  • Escobar, M., and West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90 (2), 577–588.

    Article  MathSciNet  MATH  Google Scholar 

  • Gelfand, A., and Smith, A. (1990). Sampling—based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398–409.

    Article  MathSciNet  MATH  Google Scholar 

  • Gelman, A., and Rubin, D. (1992a). Inference from iterative simulation using multiple sequences (with discussion). Statistical Sciences 7 (4), 457–511.

    Article  Google Scholar 

  • Gelman, A., and Rubin, D. (1992b). A Single Series From the Gibbs Sampler Provides a False Sense of Security. In: Bernardo et.al. (1992).

    Google Scholar 

  • Gelman, A., Carlin, J., Stern, H., and Rubin, D. (1995). Bayesian Data Analysis. Chapman and Hall, London.

    Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • Geyer, C. (1992). Practical markov chain monte carlo. Statistical Sciences 7 (4), 473–483.

    Article  MathSciNet  Google Scholar 

  • Gilks, W., and Wild, P. (1992). Adaptive rejection sampling for gibbs sampling. Journal of the Royal Statistical Society, Series C 41, 337–348.

    MATH  Google Scholar 

  • Gilks, W., Richardson, S., and Spiegelhalter, D., eds. (1996). Markov Chain Monte Carlo in practice. Chapman and Hall, London.

    MATH  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • Krause, A. (1994). Computerintensive statistische Methoden- Gibbs Sampling in Regressionsmodellen. Fischer, Stuttgart.

    Google Scholar 

  • Learner, E. (1978). Specification Searches. Wiley, New York.

    Google Scholar 

  • Meng, X., and Rubin, D. (1993). Maximum likelihood estimation via the ecm algorithm: A general framework. Biometrika 80 (2), 267–78.

    Article  MathSciNet  MATH  Google Scholar 

  • Peace, E., ed. (1992). Biopharmaceutical Sequential Statistical Applications. Marcel Dekker, New York.

    Google Scholar 

  • Raftery, A., and Lewis, S. (1992). How many iterations in the Gibbs Sampler?. In: Bernardo et al. ( 1992 ). pp. 763–773.

    Google Scholar 

  • 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 

  • Robert, C. (1996). Mixture of distribution: inference and estimation. In: Gilks et.al. (1996).

    Google Scholar 

  • Rubin, D. (1976). Inference and missing data. Biometrika 63, 581–592.

    Article  MathSciNet  MATH  Google Scholar 

  • Shih, W. (1992). Sample size reestimation in clinical trials. In: Peace ( 1992 ). pp. 285–301.

    Google Scholar 

  • Shih, W. (1993). Sample size reestimation for triple blind clinical trials. Drug Information Journal 27, 761–764.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Spiegelhalter, D., Thomas, A., Best, N., and Wilks, W. (1996). The BUGS 0.5 Manual. Available from: http://www.mrc-bsu.cam. ac. uk/bugs/welcome. shtml.

    Google Scholar 

  • Stephens, M. (1997). Bayesian methods for mixtures of normal distributions. Unpublished Ph.D. thesis.

    Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer Science+Business Media New York

About this chapter

Cite this chapter

Wang, W., Krause, A. (2001). Sample Size Reestimation. In: Millard, S.P., Krause, A. (eds) Applied Statistics in the Pharmaceutical Industry. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3466-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-3466-9_15

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-3166-5

  • Online ISBN: 978-1-4757-3466-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics