Future Developments

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


Chapters  1 6 focus on selected emerging statistical issues in clinical trials. This work provides a foundation for designing randomized trials with other design features. This includes clinical trials with more than two interventions (e.g., dose-selection clinical trials): trials with time-to-event endpoints and trials with targeted subgroups and enrichment clinical trial designs. In Chap.  7, we briefly discuss the issues in the design of these trials.


Endpoint selection Enrichment clinical trial designs Multiple-arm Subgroup analysis Time-to-event outcomes 


  1. Alosh M, Huque M (2009) A flexible strategy for testing subgroups and overall populations. Stat Med 28:2–23MathSciNetCrossRefGoogle Scholar
  2. Brannath W, Zuber E, Branson M, Bretz F, Gallo P, Posch M, Racine-Poon A (2009) Confirmatory adaptive designs with bayesian decision tools for a targeted therapy in oncology. Stat Med 28:1445–1463MathSciNetCrossRefGoogle Scholar
  3. Evans SR (2007) When and how can endpoints be changed after initiation of a randomized clinical trial? PLoS Clin Trials 2:e18CrossRefGoogle Scholar
  4. Evans SR, Li L, Wei LJ (2007) Data monitoring in clinical trials using prediction. Drug Inf J 41:733–742Google Scholar
  5. Fine JP, Jiang H, Chappell R (2001) On semi-competing risks data. Biometrika 88:907–919MathSciNetCrossRefzbMATHGoogle Scholar
  6. Follmann DA, Proschan MA, Geller NL (1994) Monitoring pairwise comparisons in multi-armed clinical trials. Biometrics 50:226–325MathSciNetCrossRefzbMATHGoogle Scholar
  7. Food and Drug Administration (2012) Guidance for industry: enrichment strategies for clinical trials to support approval of human drugs and biological products. U.S. Department of Health and Human Services Food and Drug Administration, Rockville, MD, USA. Available at: Accessed 25 Nov 2015
  8. Freidlin B, McShane LM, Korn EL (2013) Randomized clinical trials with biomarkers: design issues. J Natl Cancer Inst 102:152–160CrossRefGoogle Scholar
  9. Friede T, Parsons N, Stallard N (2012) A conditional error function approach for subgroup selection in adaptive clinical trials. Stat Med 31:4309–4320MathSciNetCrossRefGoogle Scholar
  10. Graf AC, Posch M, König F (2015) Adaptive designs for subpopulation analysis optimizing utility functions. Biometrical J 57:76–89MathSciNetCrossRefzbMATHGoogle Scholar
  11. Hamasaki T, Sugimoto T, Evans SR, Sozu T (2013) Sample size determination for clinical trials with co-primary outcomes: exponential event times. Pharm Stat 12:28–34CrossRefGoogle Scholar
  12. Hung HMJ, Wang SJ, Yang P, Jin K, Lawrence J, Kordzakhia G, Massie T (2015) Statistical challenges in regulatory review of cardiovascular and CNS clinical trials. J Biopharm Stat (First published online on 14 Sept 2015 as doi: 10.1080/10543406.2015.1092025)
  13. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) (1998) ICH harmonised tripartite guideline E9: statistical principles for clinical trials. February 1998. Available at: Accessed 25 Nov 2015
  14. Jenkins M, Stone A, Jennison C (2011) An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharm Stat 10:347–356CrossRefGoogle Scholar
  15. König F, Brannath W, Bretz F, Posch M (2008) Adaptive Dunnett tests for treatment selection. Stat Med 27:1612–1625MathSciNetCrossRefGoogle Scholar
  16. Li L, Evans SR, Uno H, Wei LJ (2009) Predicted interval plots: a graphical tool for data monitoring in clinical trials. Stat Biopharm Res 1:348–355CrossRefGoogle Scholar
  17. Magirr D, Jaki T, Whitehead J (2012) A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection. Biometrika 99:494–501MathSciNetCrossRefzbMATHGoogle Scholar
  18. Magnusson BP, Turnbull BW (2013) Group sequential enrichment design incorporating subgroup selection. Stat Med 32:2695–2714MathSciNetCrossRefGoogle Scholar
  19. Mandrekar SJ, Sargent DJ (2009a) Clinical trial designs for predictive biomarker validation: one size does not fit all. J Biopharm Stat 19:530–542MathSciNetCrossRefGoogle Scholar
  20. Mandrekar SJ, Sargent DJ (2009b) Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J Clin Oncol 27:4027–4034CrossRefGoogle Scholar
  21. Millen BA, Dmitrienko A, Ruberg S, Shen L (2012) A statistical framework for decision making in confirmatory multipopulation tailoring clinical trials. Drug Inf J 46:647–656Google Scholar
  22. Ondra T, Dmitrienko A, Friede T, Graf A, Miller F, Stallard N, Posch M (2015) Methods for identification and confirmation of targeted subgroups in clinical trials: a systematic review. J Biopharm Stat (First published online on 17 Sept 2015 as doi: 10.1080/10543406.2015.1092034)
  23. Song Y, Chi GYH (2007) A method for testing a prespecified subgroup in clinical trials. Stat Med 26:3535–3549MathSciNetCrossRefGoogle Scholar
  24. Stallard N, Todd S (2003) Sequential designs for phase III clinical trials incorporating treatment selection. Stat Med 22:689–703CrossRefGoogle Scholar
  25. Stallard N, Todd S (2008) A group-sequential design for clinical trials with treatment selection. Stat Med 27:6209–6227MathSciNetCrossRefGoogle Scholar
  26. Stallard N, Hamborg N, Parsons N, Friede T (2014) Adaptive designs for confirmatory clinical trials with subgroup selection. J Biopharm Stat 24:168–187MathSciNetCrossRefGoogle Scholar
  27. Sugimoto T, Sozu T, Hamasaki T, Evans SR (2013) A logrank test-based method for sizing clinical trials with two co-primary time-to-event endpoints. Biostatistics 14:409–421CrossRefGoogle Scholar
  28. Thall PF, Simon R, Ellenberg SS (1989) A two-stage design for choosing among several experimental treatments and a control in clinical trial. Biometrics 45:537–547MathSciNetCrossRefzbMATHGoogle Scholar
  29. Wang SJ, Hung HMJ (2014) A regulatory perspective on essential considerations in design and analysis of subgroups when correctly classified. J Biopharm Stat 24:19–41MathSciNetCrossRefGoogle Scholar
  30. Wang SJ, O’Neill RT, Hung HMJ (2007) Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm Stat 6:244–277Google Scholar
  31. Zelen M (1969) Play the winner rule and the controlled clinical trial. J Am Stat Assoc 64:131–146MathSciNetCrossRefGoogle Scholar

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

Personalised recommendations