CNS Drugs

, Volume 29, Issue 1, pp 71–81 | Cite as

Cost Effectiveness of Fingolimod, Teriflunomide, Dimethyl Fumarate and Intramuscular Interferon-β1a in Relapsing-Remitting Multiple Sclerosis

  • Xinke Zhang
  • Joel W. HayEmail author
  • Xiaoli Niu
Original Research Article



The aim of the study was to compare the cost effectiveness of fingolimod, teriflunomide, dimethyl fumarate, and intramuscular (IM) interferon (IFN)-β1a as first-line therapies in the treatment of patients with relapsing-remitting multiple sclerosis (RRMS).


A Markov model was developed to evaluate the cost effectiveness of disease-modifying drugs (DMDs) from a US societal perspective. The time horizon in the base case was 5 years. The primary outcome was incremental net monetary benefit (INMB), and the secondary outcome was incremental cost-effectiveness ratio (ICER). The base case INMB willingness-to-pay (WTP) threshold was assumed to be US$150,000 per quality-adjusted life year (QALY), and the costs were in 2012 US dollars. One-way sensitivity analyses and probabilistic sensitivity analysis were conducted to test the robustness of the model results.


Dimethyl fumarate dominated all other therapies over the range of WTPs, from US$0 to US$180,000. Compared with IM IFN-β1a, at a WTP of US$150,000, INMBs were estimated at US$36,567, US$49,780, and US$80,611 for fingolimod, teriflunomide, and dimethyl fumarate, respectively. The ICER of fingolimod versus teriflunomide was US$3,201,672. One-way sensitivity analyses demonstrated the model results were sensitive to the acquisition costs of DMDs and the time horizon, but in most scenarios, cost-effectiveness rankings remained stable. Probabilistic sensitivity analysis showed that for more than 90 % of the simulations, dimethyl fumarate was the optimal therapy across all WTP values.


The three oral therapies were favored in the cost-effectiveness analysis. Of the four DMDs, dimethyl fumarate was a dominant therapy to manage RRMS. Apart from dimethyl fumarate, teriflunomide was the most cost-effective therapy compared with IM IFN-β1a, with an ICER of US$7,115.


Multiple Sclerosis Expand Disability Status Scale Natalizumab Glatiramer Acetate Fingolimod 
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.



Xinke Zhang, Dr. Hay and Xiaoli Niu have no conflicts of interest to declare. No funding was received for the preparation of this manuscript.


  1. 1.
    Stavnitser A, Patel N, Miller, A, et al. Impact of new oral therapies on multiple sclerosis cost and utilization trends. Accessed 05 Aug 2013.
  2. 2.
    Lee S, Baxter DC, Limone B, et al. Cost-effectiveness of fingolimod versus interferon beta-1a for relapsing remitting multiple sclerosis in the United States. J Med Econ. 2012;15:1088–96.PubMedCrossRefGoogle Scholar
  3. 3.
    Cohen JA, Barkhof F, Comi G, et al. Oral fingolimod or intramuscular interferon for relapsing multiple sclerosis. N Engl J Med. 2010;362:402–15.PubMedCrossRefGoogle Scholar
  4. 4.
    O’Connor P, Wolinsky JS, Confavreux C, et al. Randomized trial of oral teriflunomide for relapsing multiple sclerosis. N Engl J Med. 2011;365:1293–303.PubMedCrossRefGoogle Scholar
  5. 5.
    Gold R, Kappos L, Arnold DL, et al. Placebo-controlled phase 3 study of oral BG-12 for relapsing multiple sclerosis. N Engl J Med. 2012;367:1098–107.PubMedCrossRefGoogle Scholar
  6. 6.
    Hay JW. Evaluation and review of pharmacoeconomic models. Expert Opin Pharmacother. 2004;5:1867–80.PubMedCrossRefGoogle Scholar
  7. 7.
    Scalfari A, Neuhaus A, Degenhardt A, et al. The natural history of multiple sclerosis, a geographically based study 10: relapses and long-term disability. Brain. 2010;133:1914–29.PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Drummond MF, Schulper MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press; 2005.Google Scholar
  9. 9.
    Glick HA, Doshi JA, Sonnad SS, Polsky D. Economic evaluation in clinical trials. New York: Oxford University Press; 2007.Google Scholar
  10. 10.
    World Health Organization. Cost-effectiveness thresholds, Accessed 19 Aug 2014.
  11. 11.
    The World Bank. GDP per capita, Accessed 01 June 2013.
  12. 12.
    Braithwaite SR, Meltzer D, King J, et al. What does the value of modern medicine say about the $50,000 per quality-adjusted life year decision rule? Med Care. 2008;46:349–56.PubMedCrossRefGoogle Scholar
  13. 13.
    Villacorta R, Hay JW, Messali A. Cost effectiveness of moderate to severe psoriasis therapy with etanercept and ustekinumab in the United States. PharmacoEconomics. 2013;31:823–39.PubMedCrossRefGoogle Scholar
  14. 14.
    Mehta D, Hay JW. Cost-effectiveness of adding bevacizumab to first line therapy for patients with advanced ovarian cancer. Gynecol Oncol. 2014;132:677–83.PubMedCrossRefGoogle Scholar
  15. 15.
    Messali A, Hay JW, Villacorta R. The cost-effectiveness of temozolomide in the adjuvant treatment of newly diagnosed glioblastoma in the United States: a literature review and Markov Model. Neuro Oncol. 2013;15:1532–42.PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Hay JW. Where’s the value in health care? Value Health. 2006;9:11–4.CrossRefGoogle Scholar
  17. 17.
    National Multiple Sclerosis Society. Treatments, Accessed 10 May 2013.
  18. 18.
    Kurtzke JF. Rating neurologic impairment in multiple sclerosis an expanded disability status scale (EDSS). Neurology. 1983;33:1444.PubMedCrossRefGoogle Scholar
  19. 19.
    Kobelt G, Berg J, Atherley D, et al. Costs and quality of life in multiple sclerosis: a cross-sectional study in the USA. Neurology. 2006;66:1696–702.PubMedCrossRefGoogle Scholar
  20. 20.
    Goodin DS, Cohen BA, O’Connor P, et al. Assessment: the use of natalizumab (Tysabri) for the treatment of multiple sclerosis (an evidence-based review): report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology. Neurology. 2008;71:766–73.PubMedCrossRefGoogle Scholar
  21. 21.
    Castillo-Trivino T, Mowry EM, Gajofatto A, et al. Switching multiple sclerosis patients with breakthrough disease to second-line therapy. PLoS One. 2011;6:e16664.PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Halpern R, Agarwal S, Borton L, et al. Adherence and persistence among multiple sclerosis patients after one immunomodulatory therapy failure: retrospective claims analysis. Adv Ther. 2011;28:761–75.PubMedCrossRefGoogle Scholar
  23. 23.
    Prosperini L, Gianni C, Leonardi L, et al. Escalation to natalizumab or switching among immunomodulators in relapsing multiple sclerosis. Mult Scler. 2012;18:64–71.PubMedCrossRefGoogle Scholar
  24. 24.
    Tremlett H, Zhao Y, Devonshire V. Natural history of secondary-progressive multiple sclerosis. Mult Scler. 2008;14:314–24.PubMedCrossRefGoogle Scholar
  25. 25.
    Bell C, Graham J, Earnshaw S, et al. Cost-effectiveness of four immunomodulatory therapies for relapsing-remitting multiple sclerosis: a Markov model based on long-term clinical data. J Manag Care Pharm. 2007;13:245–61.PubMedGoogle Scholar
  26. 26.
    Earnshaw SR, Graham J, Oleen-Burkey M, et al. Cost effectiveness of glatiramer acetate and natalizumab in relapsing-remitting multiple sclerosis. Appl Health Econ Health Policy. 2009;7:91–108.PubMedCrossRefGoogle Scholar
  27. 27.
    Jankovic SM, Kostic M, Radosavljevic M, et al. Cost-effectiveness of four immunomodulatory therapies for relapsing-remitting multiple sclerosis: a Markov model based on data a Balkan country in socioeconomic transition. Vojnosanit Pregl. 2009;66:556–62.PubMedCrossRefGoogle Scholar
  28. 28.
    Weinshenker BG, Bass B, Rice GP, et al. The natural history of multiple sclerosis: a geographically based study. I: clinical course and disability. Brain. 1989;112:133–46.PubMedCrossRefGoogle Scholar
  29. 29.
    Kremenchutzky M, Rice GP, Baskerville J, et al. The natural history of multiple sclerosis: a geographically based study 9: observations on the progressive phase of the disease. Brain. 2006;129:584–94.PubMedCrossRefGoogle Scholar
  30. 30.
    Scalfari A, Neuhaus A, Daumer M, et al. Early relapses, onset of progression, and late outcome in multiple sclerosis. JAMA Neurol. 2013;70:214–22.PubMedCrossRefGoogle Scholar
  31. 31.
    Guo S, Pelligra C, Thibault CSL, et al. Cost-effectiveness analyses in multiple sclerosis: a review of modelling approaches. Pharmacoeconomics. 2014;32:559–72.PubMedCrossRefGoogle Scholar
  32. 32.
    Eddy DM, Hollingworth W, Caro JJ, et al. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force-7. Value Health. 2012;15:843–50.PubMedCrossRefGoogle Scholar
  33. 33.
    Kappos L, Radue EW, O’Connor P, et al. A placebo-controlled trial of oral fingolimod in relapsing multiple sclerosis. N Engl J Med. 2010;362:387–401.PubMedCrossRefGoogle Scholar
  34. 34.
    Polman CH, O’Connor PW, Havrdova E, et al. A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. N Engl J Med. 2006;354:899–910.PubMedCrossRefGoogle Scholar
  35. 35.
    Beck JR, Kassirer JP, Pauker SG. A convenient approximation of life expectancy (the “DEALE”): I. Validation of the method. Am J Med. 1982;73:883–8.PubMedCrossRefGoogle Scholar
  36. 36.
    Beck JR, Pauker SG, Gottlieb JE, et al. A convenient approximation of life expectancy (the “DEALE”): II. Use in medical decision-making. Am J Med. 1982;73:889–97.PubMedCrossRefGoogle Scholar
  37. 37.
    Scalfari A, Neuhaus A, Daumer M, et al. Age and disability accumulation in multiple sclerosis. Neurology. 2011;77:1246–52.PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Redelings MD, McCoy L, Sorvillo F. Multiple sclerosis mortality and patterns of comorbidity in the United States from 1990 to 2001. Neuroepidemiology. 2006;26:102–7.PubMedCrossRefGoogle Scholar
  39. 39.
    Hoyert DL, Xu J. Deaths: preliminary data for 2011. Natl Vital Stat Rep. 2012;61:1–65.PubMedGoogle Scholar
  40. 40.
    Prosser LA, Kuntz KM, Bar-Or A, Weinstein MC. Patient and community preferences for treatments and health states in multiple sclerosis. Mult Scler. 2003;9:311–9.PubMedCrossRefGoogle Scholar
  41. 41.
    Torrance GW, Furlong W, Feeny D. Health utility estimation. Expert Rev Pharmacoecon Outcomes Res. 2002;2:99–108.PubMedCrossRefGoogle Scholar
  42. 42.
    Torrance GW. Measurement of health state utilities for economic appraisal. J Health Econ. 1986;5:1–30.PubMedCrossRefGoogle Scholar
  43. 43.
    Crayton H. Improved quality of life after therapy change to fingolimod. In: 27th Annual Meeting of the CMSC and the 5th Cooperative Meeting of the CMSC-ACTRIMS. Orlando, USA, 29 May-01 June 2013, Hackensack: CMSC.Google Scholar
  44. 44.
    Montalban X, Comi G, O’Connor P, et al. Oral fingolimod (FTY720) in relapsing multiple sclerosis: impact on health-related quality of life in a phase II study. Mult Scler. 2011;17:1341–50.PubMedCrossRefGoogle Scholar
  45. 45.
    Rudick RA, Miller D, Hutchinson M, et al. Health-related quality of life in multiple sclerosis: effects of natalizumab. Ann Neurol. 2007;62:335–46.PubMedCrossRefGoogle Scholar
  46. 46.
    O’Connor P, Briggs A, Carita P, et al. Impact on health-related quality of life of teriflunomide treatment by estimating utilities in patients with relapsing multiple sclerosis: results from TEMSO post hoc analysis. J Neurol. 2012;259:S107.Google Scholar
  47. 47.
    Kita M, Fox RJ, Phillips JT, et al. Effects of BG-12 (dimethyl fumarate) on health-related quality of life in patients with relapsing-remitting multiple sclerosis: findings from the CONFIRM study. Mult Scler. 2014;20:253–7.PubMedCrossRefGoogle Scholar
  48. 48.
    Kappos L, Gold R, Arnold DL. Quality of life outcomes with BG-12 (dimethyl fumarate) in patients with relapsing-remitting multiple sclerosis: the DEFINE study. Mult Scler. 2014;20:243–52.PubMedCrossRefGoogle Scholar
  49. 49.
    Gold MR, Siegel JE, Russell LB, et al. Cost-effectiveness in health and medicine: report of the panel on cost-effectiveness in health and medicine. New York: Oxford University Press; 1996. p. 250.Google Scholar
  50. 50.
    U.S. Department of Veterans Affairs. Drug pharmaceutical prices, Accessed 22 Aug 2013.
  51. 51.
    Oleen-Burkey M, Castelli-Haley J, Lage MJ, et al. Burden of a multiple sclerosis relapse: the patient’s perspective. Patient. 2012;5:57–69.PubMedCrossRefGoogle Scholar
  52. 52.
    Briggs A. Probabilistic analysis of cost-effectiveness models: statistical representation of parameter uncertainty. Value Health. 2005;8:1–2.PubMedCrossRefGoogle Scholar
  53. 53.
    Briggs A, Schulper MJ, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.Google Scholar
  54. 54.
    Fenwick E, Claxton K, Sculpher M. Representing uncertainty: the role of cost-effectiveness acceptability curves. Health Econ. 2001;10:779–87.PubMedCrossRefGoogle Scholar
  55. 55.
    O’Day K, Meyer K, Miller RM, et al. Cost-effectiveness of natalizumab versus fingolimod for the treatment of relapsing multiple sclerosis. J Med Econ. 2011;14:617–27.PubMedCrossRefGoogle Scholar
  56. 56.
    Pittock SJ, Mayr WT, McClelland RL, et al. Disability profile of MS did not change over 10 years in a population-based prevalence cohort. Neurology. 2004;62:601–6.PubMedCrossRefGoogle Scholar
  57. 57.
    Tremlett H, Paty D, Devonshire V. Disability progression in multiple sclerosis is slower than previously reported. Neurology. 2006;66:172–7.PubMedCrossRefGoogle Scholar
  58. 58.
    Goldberg LD, Edwards NC, Fincher C, et al. Comparing the cost-effectiveness of disease modifying drugs for the first-line treatment of relapsing-remitting multiple sclerosis. J Manag Care Pharm. 2009;15:543–55.PubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Clinical Pharmacy and Pharmaceutical Economics and PolicyLeonard D. Schaeffer Center for Health Policy and Economics, University of Southern CaliforniaLos AngelesUSA

Personalised recommendations