Skip to main content
SpringerLink
Log in

A Practical Guide to Data Monitoring Committees in Adaptive Trials

  • Biostatistics
  • Published:
Therapeutic Innovation & Regulatory Science Aims and scope Submit manuscript

Abstract

Adaptive clinical trials require access to interim data to carry out trial modification as allowed by a prespecified adaptation plan. A data monitoring committee (DMC) is a group of experts that is charged with monitoring accruing trial data to ensure the safety of trial participants and that in adaptive trials may also play a role in implementing a preplanned adaptation. In this paper, we summarize current practices and viewpoints and provide guidance on evolving issues related to the use of DMCs in adaptive trials. We describe the common types of adaptive designs and point out some DMC-related issues that are unique to this class of designs. We include 3 examples of DMCs in late-stage adaptive trials that have been implemented in practice. We advocate training opportunities for researchers who may be interested in serving on a DMC for an adaptive trial since qualified DMC members are fundamental to the successful execution of DMC responsibilities.

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. Ellenberg S, Fleming T, DeMets D. Data Monitoring Committees in Clinical Trials: A Practical Perspective. Chichester, United Kingdom: John Wiley and Sons; 2002.

    Google Scholar 

  2. Gallo P. Confidentiality and trial integrity issues for adaptive designs. Drug Inf J. 2006;40:445–450.

    Google Scholar 

  3. Herson J. Coordinating data monitoring committees and adaptive clinical trial designs. Drug Inf J. 2008;42:297–301.

    Google Scholar 

  4. Chow S, Corey R, Lin M. On the independence of data monitoring committee in adaptive design clinical trials. J Biopharm Stat. 2012;22(4):853–867.

    PubMed  Google Scholar 

  5. Hill AB. The clinical trial. N Engl J Med. 1952;247:113–119.

    CAS  PubMed  Google Scholar 

  6. Report from the Heart Special Project Committee to the National Advisory Heart Council, May 1967. Control Clin Trials. 1988;9:137–148.

    Google Scholar 

  7. Coronary Drug Project Research Group. Practical aspects of decision making in clinical trials: the Coronary Drug Project as a case study. Control Clin Trials. 1981;1:363–376.

    Google Scholar 

  8. US Food and Drug Administration. Guidance for Clinical Trial Sponsors on the Establishment and Operation of Clinical Trial Data Monitoring Committees. Rockville, Maryland: FDA; 2006.

    Google Scholar 

  9. US Food and Drug Administration. Draft Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics. Rockville, Maryland: FDA; 2010.

    Google Scholar 

  10. Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977;64:191–199.

    Google Scholar 

  11. O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979;35:549–556.

    PubMed  Google Scholar 

  12. Lan KKG, DeMets DL. Group sequential procedures: calendar versus information time. Stat Med. 1987;8:1191–1198.

    Google Scholar 

  13. Peto R, Pike MC, Armitage P, et al. Design and analysis of randomized clinical trials requiring prolonged observation of each patient, I: introduction and design. Br J Cancer. 1976;34:585–612.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang SK, Tsiatis AA. Approximately optimal one-parameter boundaries for sequential trials. Biometrics. 1987;43:193–200.

    CAS  PubMed  Google Scholar 

  15. Rosenberger WF, Stallard N, Ivanova A, Harper CN, Ricks ML. Optimal adaptive designs for binary response trials. Biometrics. 2001;57:909–913.

    CAS  PubMed  Google Scholar 

  16. Todd S. A 25-year review of sequential methodology in clinical studies. Stat Med. 2007;26:237–252.

    PubMed  Google Scholar 

  17. Friede T, Kieser M. A comparison of methods for adaptive sample size adjustment. Stat Med. 2001;20:3861–3874.

    CAS  PubMed  Google Scholar 

  18. Cui L, Hung HMJ, Wang SJ. Modification of sample size in group sequential trials. Biometrics. 1999;55:853–857.

    CAS  PubMed  Google Scholar 

  19. Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics. 1999;55:1286–1290.

    CAS  PubMed  Google Scholar 

  20. Bauer P, Köhne K. Evaluation of experiments with adaptive interim analyses. Biometrics. 1994;50:1029–1041.

    CAS  PubMed  Google Scholar 

  21. Müller HH, Schäfer H. Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and of classical group sequential approaches. Biometrics. 2001;57:886–891.

    PubMed  Google Scholar 

  22. Denne JS. Sample size recalculation using conditional power. Stat Med. 2001;20:2645–2660.

    CAS  PubMed  Google Scholar 

  23. Chen YHJ, DeMets DL, Lan KKG. Some drop-the-loser designs for monitoring multiple doses. Stat Med. 2010;29:1793–1807.

    Google Scholar 

  24. Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: a practical guide with examples. Stat Med. 2011;30:3267–3284.

    PubMed  Google Scholar 

  25. Chuang-Stein C, Anderson K, Gallo P, Collins S. Sample size reestimation: a review and recommendations. Drug Inf J. 2006;40:475–484.

    Google Scholar 

  26. Maca J, Bhattacharya S, Dragalin V, Gallo P, Krams M. Adaptive seamless phase II/III designs: background, operational aspects, and examples. Drug Inf J. 2006;40:463–474.

    Google Scholar 

  27. US Food and Drug Administration. Draft Guidance for Industry: Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products. Rockville, Maryland: FDA; 2012.

    Google Scholar 

  28. Kieser M, Bauer P, Lehmacher W. Inference on multiple endpoints in clinical trials with adaptive interim analyses. Biom J. 1999;41:261–277.

    Google Scholar 

  29. Brannath W, Zuber E, Branson M, et al. Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy on oncology. Stat Med. 2009;28:1445–1463.

    PubMed  Google Scholar 

  30. Wang SJ, O’Neill R, Hung J. Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm Stat. 2007;6:227–244.

    PubMed  Google Scholar 

  31. Wang SJ, Hung HMJ, O’Neill RT. Adaptive patient enrichment designs in therapeutic trials. Biom J. 2009;51(2):358–374.

    PubMed  Google Scholar 

  32. Freidlin B, Simon R. Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clin Cancer Res. 2005;11:7872–7878.

    CAS  PubMed  Google Scholar 

  33. Freidlin B, Jiang W, Simon R. The cross-validated adaptive signature design. Clin Cancer Res. 2010;16:691–698.

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  35. Pinheiro J, Sax F, Antonijevic Z, et al. Adaptive and model-based dose-ranging trials: quantitative evaluation and recommendations. White paper of the PhRMA Working Group on “Adaptive Dose-Ranging Studies” (with discussion). Stat Biopharm Res. 2010;2(4):435–454.

    Google Scholar 

  36. Dragalin V, Bornkamp B, Bretz F, et al. A simulation study to compare new adaptive dose-ranging designs. Stat Biopharm Res. 2010;2:487–512.

    Google Scholar 

  37. Antonijevic Z, Pinheiro J, Fardipour P, Lewis R. Impact of dose selection strategies used in phase II on the probability of success in phase III. Stat Biopharm Res. 2010;2:469–486.

    Google Scholar 

  38. Antonijevic Z, Gallo P, Chuang-Stein C, et al. Views on emerging issues pertaining to data monitoring committees for adaptive trials. Therapeutic Innovation & Regulatory Science. 2013;47:495–502.

    Google Scholar 

  39. DAMOCLES Study Group. A proposed charter for clinical trial data monitoring committees: helping them to do their job well. Lancet. 2005;365:711–722.

    Google Scholar 

  40. Cutlip D, Tcheng J, Budreau R, Fearnot N, Snyder S, Tacker W. Chartering a data safety monitoring board. Appl Clin Trials. 2008;17(7):58–60.

    Google Scholar 

  41. National Heart, Lung, and Blood Institute (NHLBI). Charter, data and safety (observational study) monitoring board template. August 2006. Available at: https://www.nhlbi.nih.gov/funding/policies/dsmpolicy.htm. Accessed June 15, 2012.

  42. Eastern Cooperative Oncology Group (ECOG). Data monitoring committee policy. Available at: https://www.ecog.org/general/monitoring.html. Accessed June 15, 2012.

  43. 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.

    Google Scholar 

  44. Geiger MJ, Skrivanek Z, Gaydos B, et al. An adaptive, dose-finding, seamless 2/3 study of long-acting GLP-1 analog (dulaglutide): trial design and baseline characteristics. J Diabetes Sci Technol. 2012;6(6):1319–1327.

    PubMed  PubMed Central  Google Scholar 

  45. Barnes PJ, Pocock SJ, Magnussen H, et al. Integrating indacaterol dose selection in a clinical study in COPD using an adaptive seamless design. Pulm Pharmacol Ther. 2010;23:165–171.

    CAS  PubMed  Google Scholar 

  46. Skrivanek Z, Berry S, Berry D, et al. Application of adaptive design methodology in development of a long-acting GLP-1 analog (dulaglutide): statistical design and simulations. J Diabetes Sci Technol. 2012;6(6):1305–1313.

    PubMed  PubMed Central  Google Scholar 

  47. Spencer K, Colvin K, Braunecker B, et al. Operational challenges and solutions with implementation of an adaptive seamless phase 2/3 study. J Diabetes Sci Technol. 2012;6(6):1296–1304.

    PubMed  PubMed Central  Google Scholar 

  48. Bhatt DL, Lincoff AM, Gibson CM, et al. Intravenous platelet blockade with cangrelor during PCI. N Engl J Med. 2009;361:2330–2341.

    CAS  PubMed  Google Scholar 

  49. Harrington RA, Stone GW, McNulty S, et al. Platelet inhibition with cangrelor in patients undergoing PCI. N Engl J Med. 2009;361:2318–2329.

    CAS  PubMed  Google Scholar 

  50. Mehta C, Gao P, Bhatt DL, Harrington RA, Skerjanec S, Ware JH. Optimizing trial design: sequential, adaptive and enrichment strategies. Circulation. 2009;119:597–605.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biostatistics and Data Management, Arena Pharmaceuticals Inc, 6154 Nancy Ridge Drive, San Diego, CA, 92121, USA

    Matilde Sanchez-Kam PhD

  2. Novartis Pharmaceuticals, East Hanover, NJ, USA

    Paul Gallo PhD

  3. Biostatistics, qPharmetra Inc, Winchester, MA, USA

    John Loewy PhD

  4. BioTx Statistics, Pfizer Inc, Cambridge, MA, USA

    Sandeep Menon PhD, MPH & Jared Christensen PhD

  5. Department of Biostatistics, Boston University, Boston, MA, USA

    Sandeep Menon PhD, MPH

  6. Strategic Consulting, Cytel Inc, Cambridge, MA, USA

    Zoran Antonijevic MS

  7. Statistical Research and Consulting Center, Pfizer Inc, Kalamazoo, MI, USA

    Christy Chuang-Stein PhD

  8. Regulatory Medical Writing and Product Development Consulting, Premier Research Group, Boston, MA, USA

    Thomas Laage MD, MPH

Authors
  1. Matilde Sanchez-Kam PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Gallo PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Loewy PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sandeep Menon PhD, MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zoran Antonijevic MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jared Christensen PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christy Chuang-Stein PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Thomas Laage MD, MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matilde Sanchez-Kam PhD.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanchez-Kam, M., Gallo, P., Loewy, J. et al. A Practical Guide to Data Monitoring Committees in Adaptive Trials. Ther Innov Regul Sci 48, 316–326 (2014). https://doi.org/10.1177/2168479013509805

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1177/2168479013509805

Keywords

Search

Navigation