Skip to main content
SpringerLink
Log in

Adaptive Clinical Trials

Overview of Early-Phase Designs and Challenges

  • Special Issue on Statistics
  • Published:
Therapeutic Innovation & Regulatory Science Aims and scope Submit manuscript

Abstract

In this paper, the authors describe developments in adaptive design methodology and discuss implementation strategies and operational challenges in early-phase adaptive clinical trials. The BATTLE trial—the first completed biomarker-based Bayesian adaptive randomized study in lung cancer—is presented as a case study to illustrate main ideas and share learnings.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ashby D. Bayesian statistics in medicine: a 25 year review. Stat Med. 2006;25(21):3589–3631.

    Article  PubMed  Google Scholar 

  2. Grieve AP. 25 years of Bayesian methods in the pharmaceutical industry: a personal, statistical bummel. Pharm Stat. 2007;6(4):261–281.

    Article  PubMed  Google Scholar 

  3. Chevret S. Bayesian adaptive clinical trials: a dream for statisticians only? Stat Med. 2012;31(11–12):1002–1013.

    Article  PubMed  Google Scholar 

  4. Lee JJ, Chu CT. Bayesian clinical trials in action. Stat Med. 2012;31(25):2955–2972.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zhou X, Liu S, Kim ES, Herbst RS, Lee JJ. Bayesian adaptive design for targeted therapy development in lung cancer: a step toward personalized medicine. Clin Trials. 2008;5(3):463–467.

    Google Scholar 

  6. Kim ES, Herbs RS, Wistuba II, et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 2011;1(1):44–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Barker AD, Sigman CC, Kelloff GJ, et al. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin Pharmacol Ther. 2009;86(1):97–100.

    Article  CAS  PubMed  Google Scholar 

  8. Gaydos B, Anderson K, Berry D, et al. Good practices for adaptive clinical trials in pharmaceutical product development. Drug Inf J. 2009;43:539–556.

    Article  Google Scholar 

  9. He W, Kuznetsova O, Harmer M, et al. Practical considerations and strategies for executing adaptive clinical trials. Drug Inf J. 2012;46:160–174.

    Article  Google Scholar 

  10. Dragalin V. Adaptive designs: terminology and classification. Drug Inf J. 2006;40:425–435.

    Article  Google Scholar 

  11. US Food and Drug Administration. Guidance for industry 2010: adaptive design clinical trials for drugs and biologics. https://www.fda.gov/downloads/DrugsGuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf.

  12. Hu F, Rosenberger W. The Theory of Response-Adaptive Randomization in Clinical Trials. New York, NY: Wiley; 2006.

    Book  Google Scholar 

  13. Rosenberger W, Lachin J. Randomization in Clinical Trials, Theory, and Practice. New York, NY: Wiley; 2002.

    Book  Google Scholar 

  14. Rosenberger W, Sverdlov O, Hu F. Adaptive randomization for clinical trials. J Biopharm Stat. 2012;22(4):719–736.

    Article  PubMed  Google Scholar 

  15. Albert JH, Chib S. Bayesian analysis of binary and polychotomous response data. J Am Stat Assoc. 1993;88:669–679.

    Article  Google Scholar 

  16. Berry D, Eick S. Adaptive assignment versus balanced randomization in clinical trials: a decision analysis. Stat Med. 1995;14:231–246.

    Article  CAS  PubMed  Google Scholar 

  17. Thall P, Wathen J. Practical Bayesian adaptive randomization in clinical trials. Eur J Cancer. 2007;43:859–866.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wathen J, Cook J. Power and Bias in Adaptively Randomized Clinical Trials. Houston, TX: Department of Biostatistics, MD Anderson Cancer Center; 2006.

    Google Scholar 

  19. Berry D, Carlin L, Lee JJ, Muller P. Bayesian Adaptive Methods for Clinical Trials. New York, NY: CRC Press; 2011.

    Google Scholar 

  20. Ghosh B, Sen PK. Handbook of Sequential Analysis. New York, NY: Marcel Dekker Inc; 1991.

    Google Scholar 

  21. Jennison C, Turnbull BW. Group Sequential Methods With Applications to Clinical Trials. New York, NY: CRC Press Inc; 2000.

    Google Scholar 

  22. Proshan MA, Lan KKG, Wittes JT. Statistical Monitoring of Clinical Trials: A Unified Approach. New York, NY: Springer; 2006.

    Google Scholar 

  23. Thall P, Simon R. Practical Bayesian guidelines for phase IIB clinical trials. Biometrics. 1994;50:337–349.

    Article  CAS  PubMed  Google Scholar 

  24. Thall P, Simon R, Estey E. Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes. Stat Med. 1995;14:357–379.

    Article  CAS  PubMed  Google Scholar 

  25. Lee J, Liu D. A predictive probability design for phase II cancer clinical trials. Clin Trials. 2008;5:93–106.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wetherill G. Sequential estimation of quantal response curves. J Royal Stat Soc B. 1963;25:1–48.

    Google Scholar 

  27. Lai TL, Robbins H. Adaptive design in regression and control. Proc Natl Acad Sci U S A. 1978;75:586–587.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. O’Quigley J, Pepe M, Fisher L. Continual reassessment method: A practical design for phase 1 clinical trials in cancer. Biometrics.1990;46:33–48.

    Article  PubMed  Google Scholar 

  29. Li Z, Durham SD, Flournoy N. An adaptive design for maximization of a contingent binary response. In Flournoy N, Rosenberger WF, eds. Adaptive Designs. Beachwood, OH: Institute of Mathematical Statistics; 1995:179–196.

    Chapter  Google Scholar 

  30. Thall PF, Cook JD. Dose-finding based on efficacy? Toxicity trade-offs. Biometrics. 2004;60(3):684–693.

    Article  PubMed  Google Scholar 

  31. Azriel D. A note on the robustness of the continual reassessment method. Stat Prob Lett. 2012;82:902–906.

    Article  Google Scholar 

  32. Azriel D, Mandel M, Rinott Y. The treatment versus experimentation dilemma in dose finding studies. J Stat Plan Infer. 2011;141(8):2759–2768.

    Article  Google Scholar 

  33. Bozin A, Zarrop M. Self-tuning extremum optimizer convergence and robustness. ECC. 1991;91:672–677.

    Google Scholar 

  34. Chang HH, Ying Z. Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests. Annals Stat. 2009;37(3):1466–1488.

    Article  Google Scholar 

  35. Ghosh M, Mukhopadhyay N, Sen PK. Sequential Estimation. New York, NY: Wiley; 1997.

    Book  Google Scholar 

  36. Lai TL, Robbins H. Iterated least squares in multiperiod control. Adv Appl Math. 1982;3(1):50–73.

    Article  Google Scholar 

  37. Oron AP, Azriel D, Hoff PD. Dose-finding designs: the role of convergence properties [published online October 27, 2011]. Int J Biostat.

  38. Pronzato L. Adaptive optimization and D-optimum experimental design. Annals Stat. 2000;28(6):1743–1761.

    Article  Google Scholar 

  39. Gooley TA, Martin PJ, Lloyd DF, Pettinger M. Simulation as a design tool for phase I/II clinical trials: an example from bone marrow transplantation. Control Clin Trials. 1994;15:450–460.

    Article  CAS  PubMed  Google Scholar 

  40. Fan SK, Chaloner K. Optimal designs and limiting optimal designs for a trinomial response. J Stat Plan Infer. 2004;126(1):347–360.

    Article  Google Scholar 

  41. Rabie H, Flournoy N. Optimal designs for contingent responses models. In: Di Bucchianico A, Luter H, Wynn HP, eds. mODa 7: Advances in Model-Oriented Design and Analysis. Heidelberg, germany: Physical-Verlag; 2004:133–142.

    Chapter  Google Scholar 

  42. Dragalin V, Fedorov V. Adaptive designs for dose-finding based on efficacy-toxicity response. J Stat Plan Infer. 2006;136:1800–1823.

    Article  Google Scholar 

  43. Fedorov V, Leonov S. Optimal Design for Nonlinear Response Models. New York, NY: CRC Press Inc; 2013.

    Book  Google Scholar 

  44. Thall PF. Bayesian models and decision algorithms for complex early phase clinical trials. Stat Sci. 2010;25(2):227–244.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Dragalin V, Fedorov V, Wu Y. Two-stage design for dose-finding that accounts for both efficacy and safety. Stat Med. 2008;27:5156–5176.

    Article  PubMed  Google Scholar 

  46. Fedorov V, Wu Y, Zhang R. Optimal dose-finding designs with correlated continuous and discrete responses. Stat Med. 2012;31:217–234.

    Article  PubMed  Google Scholar 

  47. Berry D. Statistics: A Bayesian Perspective. London, England: Duxbury Press; 1996.

    Google Scholar 

  48. Berry D. Bayesian clinical trials. Nat Rev Drug Discov. 2006;5:27–36.

    Article  CAS  PubMed  Google Scholar 

  49. Lin Y, Shih W. Statistical properties of the traditional algorithm-based designs for phase I cancer clinical trials. Biostatistics. 2001;2(2):203–215.

    Article  CAS  PubMed  Google Scholar 

  50. Babb J, Rogatko A, Zacks S. Cancer phase I clinical trials: efficient dose escalation with overdose control. Stat Med. 1998;17:1103–1120.

    Article  CAS  PubMed  Google Scholar 

  51. Chevret S. Statistical Methods for Dose-Finding Experiments. New York, NY: Wiley; 2006.

    Book  Google Scholar 

  52. Cheung YK. Dose Finding by the Continual Reassessment Method. New York, NY: Chapman. & Hall; 2011.

    Book  Google Scholar 

  53. Wang S, O’Neill R, Hung H. Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics. 2007;6:227–244.

    Article  PubMed  Google Scholar 

  54. Lipkovich I, Dmitrienko A, Denne J, Enas G. Subgroup identification based on differential effect search (SIDES): a recursive partitioning method for establishing response to treatment in patient subpopulations. Stat Med. 2011;30:2601–2621.

    PubMed  Google Scholar 

  55. Lipkovich I, Dmitrienko A. Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES. J Biopharm Stat. In press.

  56. Stallard N. A confirmatory seamless phase II/III clinical trial design incorporating short-term endpoint information. Stat Med. 2010;29:959–971.

    PubMed  Google Scholar 

  57. Jenkins M, Stone A, Jennison C. An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharm Stat. 2011;10:347–356.

    Article  PubMed  Google Scholar 

  58. Friede T, Parsons N, Stallard N. A conditional error function approach for subgroup selection in adaptive clinical trials. Stat Med. 2012;31(30):4309–4320.

    Article  CAS  PubMed  Google Scholar 

  59. Lara PN, Redman MW, Kelly KM, et al. Disease control rate at 8 weeks predicts clinical benefit in advanced non-small-cell lung cancer: results from Southwest Oncology Group randomized trials. J Clin Oncol. 2008;26(3):463–467.

    Article  PubMed  Google Scholar 

  60. Sequist LV, Muzikansky A, Engelman JA. A new BATTLE in the evolving war on cancer. Cancer Discov. 2011;1(1):14–16.

    Article  CAS  PubMed  Google Scholar 

  61. Rubin EH, Anderson KM, Gause CK. The BATTLE trial: a bold step toward improving the efficiency of biomarker-based drug development. Cancer Discov. 2011:1(1):17–20.

    Article  CAS  PubMed  Google Scholar 

  62. Tam AL, Kim ES, Lee JJ, et al. Feasibility of image-guided transthoracic core-needle biopsy in the BATTLE lung trial. J Thorac Oncol. 2013;8(4):436–442.

    Article  PubMed  Google Scholar 

  63. Lee JJ, Chen N, Yin G. Worth adapting? Revisiting the usefulness of outcome-adaptive randomization. Clin Cancer Res. 2012;18(17):4498–4507.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Lee JJ, Gu X, Liu S. Bayesian adaptive randomization designs for targeted agent development. Clin Trials. 2010;7(5):584–596.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Printz C. BATTLE to personalize lung cancer treatment: novel clinical trial design and tissue gathering procedures drive biomarker discovery. Cancer. 2010;116(14):3307–3308.

    Article  PubMed  Google Scholar 

  66. Gold KA, Kim ES, Lee JJ, Wistuba II, Farhangfar CJ, Hong WK. The BATTLE to personalize lung cancer prevention through reverse migration. Cancer Prev Res. 2011;4(7):962–972.

    Article  CAS  Google Scholar 

  67. Lai TL, Lavori PW, Shih MCI, Sikic BI. Clinical trial designs for testing biomarker-based personalized therapies. Clin Trials. 2012;9(2):141–154.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Rubin EH, Gilliland DG. Drug development and clinical trials: the path to an approved cancer drug. Nat Rev Clin Oncol. 2012;9(4):215–222.

    Article  CAS  PubMed  Google Scholar 

  69. Kelloff GJ, Sigman CC. Cancer biomarkers: selecting the right drug for the right patient. Nat Rev Drug Discov. 2012;11(3):201–214.

    Article  CAS  PubMed  Google Scholar 

  70. Berry DA, Herbst RS, Rubin EH. Reports from the 2010 clinical and translational cancer research think tank meeting: design strategies for personalized therapy trials. Clin Cancer Res. 2012;18(3):638–644.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Allison M. Reinventing clinical trials. Nat Biotechnol. 2012;30(1):41–49.

    Article  CAS  PubMed  Google Scholar 

  72. US Food and Drug Administration. Innovation/stagnation: challenge and opportunity on the critical path to new medical products. https://www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/ucm077262.htm. Published March 2004.

  73. Bornkamp B, Bretz F, Dmitrienko A, et al. Innovative approaches for designing and analyzing adaptive dose-ranging trials. J Biopharm Stat. 2007;17:965–995.

    Article  PubMed  Google Scholar 

  74. Pinheiro J, Sax R, Antonijevic Z, et al. Adaptive and model-based dose ranging trials: quantitative evaluation and recommendations. Stat Biopharm Res. 2010;2(4):435–454.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Olga Marchenko, Center for Statistics in Drug Development, Innovation, Quintiles Inc, 4820 Emperor Blvd, Durham, NC, 27703, USA

    Olga Marchenko PhD

  2. Predictive Analytics, Innovation, Quintiles Inc, Durham, NC, USA

    Valerii Fedorov PhD

  3. Department of Biostatistics, Division of Quantitative Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    J. Jack Lee PhD, DDS

  4. Innovation, Quintiles Inc, Durham, NC, USA

    Christy Nolan BSc, MT (ASCP)

  5. Quantitative Decision Strategies, Janssen Research & Development LLC, Raritan, NJ, USA

    José Pinheiro PhD

Authors
  1. Olga Marchenko PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valerii Fedorov PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Jack Lee PhD, DDS
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christy Nolan BSc, MT (ASCP)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José Pinheiro PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olga Marchenko PhD.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marchenko, O., Fedorov, V., Lee, J.J. et al. Adaptive Clinical Trials. Ther Innov Regul Sci 48, 20–30 (2014). https://doi.org/10.1177/2168479013513889

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1177/2168479013513889

Keywords

Search

Navigation