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Statistical Power and Bayesian Assurance in Clinical Trial Design

  • Ding-Geng Chen
  • Jenny K. Chen
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

In clinical trial design, statistical power is defined as the probability of rejecting the null hypothesis at a pre-specified true clinical treatment effect, which is conditioned on the true but actually unknown effect. In practice, however, this true effect is never a fixed value, but a range from previously held trials which would lead to underpowered or overpowered trials. In order to incorporate the uncertainties of this observed treatment effect, a Bayesian assurance has been proposed as an alternative to the conventional statistical power. This is defined as the unconditional probability of rejecting the null hypothesis. In this chapter, we will review the transition from conventional statistical power to Bayesian assurance and discuss the computations of Bayesian assurance using a Monte-Carlo simulation-based approach.

Keywords

Statistical power Sample size determination Assurance Prior distribution Conditional and unconditional probability Monte-Carlo simulation 

References

  1. Chen, D. G., & Ho, S. (2017). From statistical power to statistical assurance: It’s time for a paradigm change in clinical trial design. Communications in Statistics - Simulation and Computation., 46(10), 7957–7971.MathSciNetCrossRefGoogle Scholar
  2. Chen, D. G., Peace, K. E., & Zhang, P. (2017). Clinical trial data analysis using R and SAS. Boca Raton, FL: Chapman & Hall/CRC Biostatistics Series.Google Scholar
  3. Chuang-Stein, C. (2006). Sample size and the probability of a successful trial. Pharmaceutical Statistics, 5, 305–309.CrossRefGoogle Scholar
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  5. O’Hagan, A., Stevens, J. W., & Campbell, M. (2005). Assurance in clinical trial design. Pharmaceutical Statistics, 4, 187–201.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of BiostatisticsGillings School of Global Public Health, University of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of StatisticsUniversity of PretoriaPretoriaSouth Africa

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