Advertisement

Designing Multi-arm Multi-stage Clinical Studies

  • Thomas Jaki
Chapter

Abstract

In the early stages of drug development there often is uncertainty about the most promising among a set of different treatments, different doses of the same treatment or sets of combinations of treatments. An efficient solution to determine which intervention is most promising are multi-arm multi-stage clinical studies (MAMS). In this chapter we will discuss the general concept to designing MAMS studies within the group sequential framework and provide detailed solutions for multi-arm multi-stage studies with normally distributed endpoints in which all promising treatments are continued at the interim analyses. An approach to find optimal designs is discussed as well as asymptotic solutions for binary, ordinal and time-to event endpoints.

Keywords

Experimental Treatment Interim Analysis Equal Sample Size Maximum Sample Size Familywise Error Rate 
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.

Notes

Acknowledgements

This work is based on research arising from Dr. Jakis Career Development Fellowship (NIHR-CDF-2010-03-32) supported by the National Institute for Health Research and the MRC grant MR/J004979/1. The views expressed in this publication are those of the author and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. The author would also like to thank Dr. James Wason and Dominic Magirr for their helpful comments.

References

  1. 1.
    Antonijevic, Z., Pinhero, J., Fardipour, P., Lewis, R.J.: Impact of dose selection strategies used in phase II on the probability of success in phase III. Statistics in Biopharmaceutical Research 2, 469–486 (2010)Google Scholar
  2. 2.
    Barthel, F.M., Parmar, M.K.B., Royston, P.: How do multi-stage, multi-arm trials compare to the traditional two-arm parallel group design–a reanalysis of 4 trials. Trials 10:21 (2009)Google Scholar
  3. 3.
    Bauer, P., Kieser, M.: Combining different phases in the development of medical treatments within a single trial. Statistics in Medicine 18, 1833–1848 (1999)Google Scholar
  4. 4.
    Bauer, P., Köhne, K.: Evaluation of experiments with adaptive interim analyses. Biometrics 50, 1029–1041 (1994)Google Scholar
  5. 5.
    Bauer, P., König, F., Brannath, W., Posch, M.: Selection and bias – two hostile brothers. Statistics in Medicine 29, 1–13 (2010)Google Scholar
  6. 6.
    Bretz, F., König, F., Brannath, W., Glimm, E., Posch, M.: Adaptive designs for confirmatory clinical trials. Statistics in Medicine 28, 1181–1217 (2009)Google Scholar
  7. 7.
    DiMasi, J.A., Hansen, R.W., Grabowski, H.G.: The price of innovation: New estimates of drug development costs. Journal of Health Economics 22, 151–185 (2003)Google Scholar
  8. 8.
    Dunnett, C.W.: A multiple comparison procedure for comparing several treatments with a control. Journal of the American Statistical Association 50, 1096–1121 (1955)Google Scholar
  9. 9.
    Dunnett, C.W.: Selection of the best treatment in comparison to a control with an application to a medical trial. In: T.J. Santer, A.C. Tamhane (eds.) Design of Experiments: Ranking and Selection, pp. 47–66. Marcel Dekker: New York (1984)Google Scholar
  10. 10.
    Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics. Draft guidance. Food and Drug Administration (FDA) (2010). URL www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf. Last accessed 11 June 2012.
  11. 11.
    Jaki, T., Magirr, D.: MAMS: Designing Multi-arm Multi-stage Studies (2012). URL http://CRAN.R-project.org/package=MAMS. R package version 0.3
  12. 12.
    Jaki, T., Magirr, D.: Considerations on covariates and endpoints in multi-arm multi-stage clinical trials. Statistics in Medicine 32(7), 1150–1163 (2013)Google Scholar
  13. 13.
    Jennison, C., Turnbull, B.W.: Group Sequential Methods with Applications to Clinical Trials. Chapman and Hall (2000)Google Scholar
  14. 14.
    Kelly, P.J., Stallard, N., Todd, S.: An adaptive group sequential design for phase II/III clinical trials that involve treatment selection. Journal of Biopharmaceutical Statistics 15, 641–658 (2005)Google Scholar
  15. 15.
    Kola, I., Landis, J.: Can the pharmaceutical industry reduce attrition rates? Nature Reviews Drug Discovery 3, 711–715 (2004)Google Scholar
  16. 16.
    Lehmacher, W., Wassmer, G.: Adaptive sample size calculations in group sequential trials. Biometrics 55, 1286–1290 (1998)Google Scholar
  17. 17.
    Magirr, D., Jaki, T., Whitehead, J.: A generalized dunnett test for multiarm-multistage clinical studies with treatment selection. Biometrika 99(2), 494–501 (2012)Google Scholar
  18. 18.
    Magirr, D., Stallard, N., Jaki, T.: Flexible sequential designs for multi-arm clinical trials. Statistics in Medicine 33(19), 3269–3279 (2014)Google Scholar
  19. 19.
    Marcus, R., Peritz, E., Gabriel, K.R.: On closed testing procedures with special reference to ordered analysis of variance. Biometrika 63, 655–660 (1976)Google Scholar
  20. 20.
    McCullagh, P.: Regression models for ordinal data (with discussion). Journal of the Royal Statistical Society, Series B 42, 109–142 (1980)Google Scholar
  21. 21.
    Müller, H.H., Schäfer, H.: Adaptive group sequential designs for clinical trials: combining the advantages of the adaptive and of classical group sequential approaches. Biometrics 57, 886–891 (2001)Google Scholar
  22. 22.
    O’Brien, P., Fleming, T.: A multiple-testing procedure for clinical trials. Biometrics 35, 549–556 (1979)Google Scholar
  23. 23.
    Parsons, N.: asd: Simulations for adaptive seamless designs (2010). URL http://CRAN.R-project.org/package=asd. R package version 1.0
  24. 24.
    Pocock, S.J.: Group sequential methods in the design and analysis of clinical trials. Biometrika 64, 191–199 (1977)Google Scholar
  25. 25.
    Posch, M., König, F., Branson, M., Brannath, W., Dunger-Baldauf, C., Bauer, P.: Testing and estimation in flexible group sequential designs with adaptive treatment selection. Statistics in Medicine 24, 3697–3714 (2005)Google Scholar
  26. 26.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012). URL http://www.R-project.org/. ISBN 3-900051-07-0
  27. 27.
    Roberts, T.G., Lynch, T.J., Chabner, B.A.: The phase III trial in the era of targeted therapy: Unraveling the “go or no go” decision. Journal of Clinical Oncology 21, 3683–3695 (2003)Google Scholar
  28. 28.
    Royston, P., Parmar, M.K., Qian, W.: Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine 22, 2239–2256 (2003)Google Scholar
  29. 29.
    Stallard, N., Friede, T.: A group-sequential design for clinical trials with treatment selection. Statistics in Medicine 27, 6209–6227 (2008)Google Scholar
  30. 30.
    Stallard, N., Todd, S.: Sequential designs for phase III clinical trials incorporating treatment selection. Statistics in Medicine 22, 689–703 (2003)Google Scholar
  31. 31.
    Stallard, N., Todd, S.: Point estimates and confidence intervals for sequential trials involving selection. Journal of Planning and Statistical Inference 135, 402–419 (2005)Google Scholar
  32. 32.
    Sydes, M.R., Parmar, M.K., James, N.D., Clarke, N.W., Dearnaley, D.P., Mason, M.D., Morgan, R.C., Sanders, K., Royston, P.: Issues in applying multi-arm multi-phase methodology to a clinical trial in prostate cancer: the “MRC STAMPEDE” trial. Trials 10, 39 (2009)Google Scholar
  33. 33.
    Thall, P.F., Simon, R., Ellenberg, S.S.: Two-stage selection and testing designs for comparitive clinical trials. Biometrika 75, 303–310 (1988)Google Scholar
  34. 34.
    Wason, J.M.S., Jaki, T.: Optimal design of multi-arm multi-stage trials. Statistics in Medicine. 31(30), 4269–4279 (2012)Google Scholar
  35. 35.
    Wason, J.M.S., Jaki, T., Stallard, N.: Planning multi-arm screening studies within the context of a drug development programme. Statistics in Medicine 32(20), 3424–3435 (2013)Google Scholar
  36. 36.
    Wason, J.M.S., Magirr, D., Law, M., Jaki, T.: Some recommendations for multi-arm multi-stage trials. Statistical Methods in Medical Research. Published online ahead of print (2014). doi:10.1177/0962280212465498Google Scholar
  37. 37.
    Whitehead, J.: On the bias of maximum likelihood estimation following a sequential trial. Biometrika 73, 573–581 (1986)Google Scholar
  38. 38.
    Whitehead, J.: The Design and Analysis of Sequential Clinical Trials. Wiley: Chichester (1997)Google Scholar
  39. 39.
    Whitehead, J., Jaki, T.: One- and two-stage design proposals for a phase II trial comparing three active treatments with a control using an ordered categorical endpoint. Statistics in Medicine 28, 828–847 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and StatisticsLancaster UniversityLancasterUK

Personalised recommendations