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Sample Size Recalculation in Clinical Trials with Two Co-primary Endpoints

  • Toshimitsu Hamasaki
  • Koko Asakura
  • Scott R. Evans
  • Toshimitsu Ochiai
Chapter
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Abstract

Clinical trial design requires assumptions. Prior data often serve as the basis for these assumptions. However, prior data may be limited or an inaccurate indication of future data. This may result in trials that are over-/under-powered. Interim analyses provide opportunities to evaluate the accuracy of the design assumptions and potentially make design adjustments if the assumptions are markedly inaccurate. We discuss sample size recalculation based on the observed intervention’s effects during interim analyses with a focus on the control of statistical error rates.

Keywords

Conditional power Cui–Hung–Wang statistics Sample size recalculation Type I error 

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Toshimitsu Hamasaki
    • 1
  • Koko Asakura
    • 2
  • Scott R. Evans
    • 3
  • Toshimitsu Ochiai
    • 4
  1. 1.Department of Data ScienceNational Cerebral and Cardiovascular CenterSuitaJapan
  2. 2.Department of Data ScienceNational Cerebral and Cardiovascular CenterSuitaJapan
  3. 3.Department of Biostatistics and the Center for Biostatistics in AIDS ResearchHarvard T.H. Chan School of Public HealthBostonUSA
  4. 4.Biostatistics DepartmentShionogi & Co., Ltd.OsakaJapan

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