Skip to main content

Advertisement

Log in

Statistical Considerations for an Adaptive Design for a Serious Rare Disease

  • Statistics: Original Research
  • Published:
Therapeutic Innovation & Regulatory Science Aims and scope Submit manuscript

Abstract

Background

Neuromyelitis optica spectrum disorder (NMOSD) is a rare, disabling autoimmune disorder of the central nervous system. Clinical trials in NMOSD present unique design and statistical challenges to adequately determine treatment effect and to minimize risk.

Methods

The N-MOmentum trial (NCT02200770) is evaluating the efficacy and safety of MEDI-551, an anti-CD 19 B-cell depleting monoclonal antibody, in patients with NMOSD and employs a number of unique design features. Patients are randomized (3:1) to receive MEDI-551 or placebo for up to 197 days. NMOSD attacks are evaluated by the investigator and confirmed by an independent adjudication committee. The primary endpoint is time to first relapse as determined by adjudication committee. Sample size re-estimation and futility analyses are planned interim analyses. Novel multiplicity adjustment methods are developed to control the study-wise type I error. Methods for assessing inter- and intrarater reliability are proposed.

Conclusions

The N-MOmentum study minimizes exposure to placebo for individual patients. The application of several statistical methods in the N-MOmentum trial is novel in NMOSD and aims to achieve a balance between minimizing risk and maintaining scientific integrity.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Sasinowski FJ, Panico EB, Valentine JE. Quantum of effectiveness in FDA’s approval of orphan drugs: update, July 2010 to June 2014. Ther Innov Regul Sci. 2015.

  2. Marrie RA, Gryba C. The incidence and prevalence of neuromyelitis optica: a systematic review. Int J MS Care. 2013;15:113–118.

    Article  Google Scholar 

  3. Pandit L, Asgari N, Apiwattanakul M, et al. Demographic and clinical features of neuromyelitis optica: A review. Mult Scler. 2015;21:845–853.

    Article  CAS  Google Scholar 

  4. Costanzi C, Matiello M, Lucchinetti CF, et al. Azathioprine: tolerability, efficacy, and predictors of benefit in neuromyelitis optica. Neurology. 2011;77:659–666.

    Article  CAS  Google Scholar 

  5. Jacob A, Matiello M, Weinshenker BG, et al. Treatment of neuromyelitis optica with mycophenolate mofetil: retrospective analysis of 24 patients. Arch Neurol. 2009;66:1128–1133.

    Article  Google Scholar 

  6. Bedi GS, Brown AD, Delgado SR, Usmani N, Lam BL, Sheremata WA. Impact of rituximab on relapse rate and disability in neuromyelitis optica. Mult Scler. 2011;17:1225–1230.

    Article  CAS  Google Scholar 

  7. Jacob A, Weinshenker BG, Violich I, et al. Treatment of neuromyelitis optica with rituximab: retrospective analysis of 25 patients. Arch Neurol. 2008;65:1443–1448.

    Article  Google Scholar 

  8. Kim SH, Kim W, Li XF, Jung IJ, Kim HJ. Repeated treatment with rituximab based on the assessment of peripheral circulating memory B cells in patients with relapsing neuromyelitis optica over 2 years. Arch Neurol. 2011;68:1412–1420.

    Article  Google Scholar 

  9. Wingerchuk DM. Neuromyelitis optica. Int MS J. 2006;13:42–50.

    CAS  PubMed  Google Scholar 

  10. Guidance for industry. Enrichment strategies for clinical trials to support approval of human drugs and biological products. Food and Drug Administration. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm332181.pdf. Accessed July 13, 2015.

  11. Pittock SJ, Lennon VA, McKeon A, et al. Eculizumab in AQP4-IgG-positive relapsing neuromyelitis optica spectrum disorders: an open-label pilot study. Lancet Neurol. 2013;12:554–562.

    Article  CAS  Google Scholar 

  12. Bernard-Valnet R, Liblau RS, Vukusic S, Marignier R. Neuromyelitis optica: a positive appraisal of seronegative cases. Eur J Neurol. 2015.

  13. Saville BR, Kim YS, Koch GG. Graphical displays for clarifying how allocation ratio affects total sample size for the two sample logrank test. Pharm Stat. 2011;10:40–44.

    Article  Google Scholar 

  14. Wittes J. Sample size calculations for randomized controlled trials. Epidemiol Rev. 2002;24:39–53.

    Article  Google Scholar 

  15. Cree B. Mycophenolate mofetil to treat neuromyelitis optica: is it time for a randomized trial? JAMA Neurol. 2014;71:1354–1357.

    Article  Google Scholar 

  16. Todd S, Valdes-Marquez E, West J. A practical comparison of blinded methods for sample size reviews in survival data clinical trials. Pharm Stat. 2012;11:141–148.

    Article  Google Scholar 

  17. Li G, Shih WJ, Wang Y. Two-stage adaptive design for clinical trials with survival data. J Biopharm Stat. 2005;15:707–718.

    Article  CAS  Google Scholar 

  18. Lan KKG, Hu P, Proschan MA. A conditional power approach to the evaluation of predictive power. Stat Biopharm Res. 2009;1:131–136.

    Article  Google Scholar 

  19. Bretz F, Maurer W, Brannath W, Posch M. A graphical approach to sequentially rejective multiple test procedures. Stat Med. 2009;28:586–604.

    Article  Google Scholar 

  20. Millen BA, Dmitrienko A. Chain procedures: a class of flexible closed testing procedures with clinical trial applications. Stat Biopharm Res. 2011;3:14–30.

    Article  Google Scholar 

  21. Regulatory workshop on clinical trials designs in neuromyelitis optica and spectrum disorders (NMOSD). European Medicines Agency. http://www.ema.europa.eu/docs/en_GB/document_library/Report/2015/06/WC500188313.pdf. Accessed July 13, 2015.

  22. Cree BAC. Placebo controlled trials in neuromyelisis optica are needed and ethical. Mult Scler Relat Disord. 2015; in press.

  23. Wei LJ, Lin DY, Weissfeld L. Regression analysis of multivariate incomplete failure time data by modeling marginal distribution. J Am Stat Assoc. 1989;84:1065–1073.

    Article  Google Scholar 

  24. Vangeneugden T, Molenberghs G, Laenen A, Alonso A, Geys H. Generalizability in nongaussian longitudinal clinical trial data based on generalized linear mixed models. J Biopharm Stat. 2008;18:691–712.

    Article  Google Scholar 

  25. Carrasco JL, Phillips BR, Puig-Martinez J, King TS, Chinchilli VM. Estimation of the concordance correlation coefficient for repeated measures using SAS and R. Comput Methods Programs Biomed. 2013;109:293–304.

    Article  Google Scholar 

  26. Uno H, Claggett B, Tian L, et al. Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol. 2014;32:2380–2385.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kaushik Patra PhD.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patra, K., Cree, B.A.C., Katz, E. et al. Statistical Considerations for an Adaptive Design for a Serious Rare Disease. Ther Innov Regul Sci 50, 375–384 (2016). https://doi.org/10.1177/2168479015619203

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1177/2168479015619203

Keywords

Navigation