Journal of Neurology

, 256:554 | Cite as

Multiple sclerosis patients—benefit-risk preferences: Serious adverse event risks versus treatment efficacy

  • F. Reed Johnson
  • George Van Houtven
  • Semra Özdemir
  • Steve Hass
  • Jeff White
  • Gordon Francis
  • David W. Miller
  • J. Theodore Phillips
ORIGINAL COMMUNICATION

Abstract

Objective:

The aim of this study is to estimate the willingness of multiple sclerosis (MS) patients to accept life-threatening adverse event risks in exchange for improvements in their MS related health outcomes.

Methods:

MS patients completed a survey questionnaire that included a series of choice-format conjoint tradeoff tasks. Patients chose hypothetical treatments from pairs of treatment alternatives with varying levels of clinical efficacy and associated risks.

Results:

Among the 651 patients who completed the survey, delay in years to disability progression was the most important factor in treatment preferences. In return for decreases in relapse rates from 4 to 1 and increases in delay in progression from 3 to 5 years, patients were willing to accept a 0.38% annual risk of death or disability from PML, a 0.39% annual risk of death from liver failure or a 0.48% annual risk of death from leukemia.

Conclusions:

Medical interventions carry risks of adverse outcomes that must be evaluated against their clinical benefits. Most MS patients indicated they are willing to accept risks in exchange for clinical efficacy. Patient preferences for potential benefits and risks can assist in decision-making.

Key words

conjoint analysis benefit-risk analysis side-effect risk multiple sclerosis maximum acceptable risk 

References

  1. 1.
    Andrews CJ (2005) Lessons from the New Jersey Comparative Risk Project. In: Linkov I, Ramadan AB (eds) Comparative Risk Assessment and Environmental Decision Making. Netherlands: SpringerGoogle Scholar
  2. 2.
    Brassat D, Recher C, Waubant E, Le Page E, Rigal-Huguet F, Laurent G, Edan G, Clanet M (2002) Therapyrelated acute myeloblastic leukemia after mitoxantrone treatment in a patient with MS. Neurology 59(6):954–955PubMedGoogle Scholar
  3. 3.
    Bryan S, Martin B, Robert S, Alison G (1998) Magnetic resonance imaging for the investigation of knee injuries: an investigation of preferences. Health Economics 7:595–603CrossRefPubMedGoogle Scholar
  4. 4.
    Calfee, John E (2006) A representative survey of M.S. patients on attitudes toward the benefits and risks of drug therapy. AEI-Brookings Joint Center for Regulatory Studies. Related Publication 06–07 (March)Google Scholar
  5. 5.
    Corso PS, Hammitt JK, Graham JD (2001) Valuing mortality-risk reduction: Using visual aids to improve the validity of contingent valuation. The Journal of Risk and Uncertainty 23(2):165–184CrossRefGoogle Scholar
  6. 6.
    Covey J (2007) A meta-analysis of the effects of presenting treatment benefits in different formats. Medical Decision Making 27(5):638–654CrossRefPubMedGoogle Scholar
  7. 7.
    Edwards A, Gray J, Clarke A, et al. (2008) Interventions to improve risk communication in clinical genetics: Systematic review. Patient Education and Counseling 71(1):4–25CrossRefPubMedGoogle Scholar
  8. 8.
    European Medicines Agency, Committee for Medicinal Products for Human Use (CHMP) (2007) Report of the CHMP Working Group on Benefit-Risk Assessment Models and Methods (January)Google Scholar
  9. 9.
    Fischhoff B, Lichtenstein S, Slovic P, Derby SL, Keeney RL (1981) Acceptable risk. New York: Cambridge University PressGoogle Scholar
  10. 10.
    Francis G, Grumser Y, Alteri E, Micaleff A, O’Brien F, Alsop J, et al. (2003) Hepatic reactions during treatment of multiple sclerosis with Interferon-β-1a, incidence and clinical significance. Drug Safety 26(11):815–827CrossRefPubMedGoogle Scholar
  11. 11.
    Gan TJ, Lubarsky DA, Flood EM, Thanh T, Mauskopf J, Mayne T, et al. (2004) Patient preferences for acute pain treatment. British Journal of Anaesthesia 92(5):681–688CrossRefPubMedGoogle Scholar
  12. 12.
    Ghosh AK, Ghosh K (2005) Translating evidence-based information into effective risk communication: Current challenges and opportunities. J Lab Clin Med 145(4):171–180CrossRefPubMedGoogle Scholar
  13. 13.
    Gregory R, Mendelsohn R (1993) Perceived risk, dread, and benefits risk Analysis 13(3):259–264Google Scholar
  14. 14.
    Health Talk (2006) Available at: www.healthtalk.com. Last accessed 4/26/2006Google Scholar
  15. 15.
    Hensher DA, Rose JM, Greene WH (2005) Applied Choice Analysis: A Primer. Cambridge University Press: Cambridge, UKGoogle Scholar
  16. 16.
    Hughes TE, Larson LN (1991) Patient involvement in health care. A procedural justice viewpoint. Med Care 29:297–303CrossRefPubMedGoogle Scholar
  17. 17.
    Huber J, Zwerina K (1996) The importance of utility balance in efficient choice designs. Journal of Marketing Research 33:307–317CrossRefGoogle Scholar
  18. 18.
    Institute of Medicine Committee on the Assessment of the US Drug Safety System(2006) The Future of Drug Safety: Promoting and Protecting the Health of the Public. In: Alina Baciu, Kathleen Stratton, Sheila P. Burke (eds) National Academy of Sciences: Washington, D.C.Google Scholar
  19. 19.
    Johnson FR, Özdemir S, Hauber AB, Kauf TL (2007a) Women’s stated willingness to accept perceived risk for vasomotor symptom relief. Journal of Women’s Health 16(7):1028–1040CrossRefPubMedGoogle Scholar
  20. 20.
    Johnson FR, Özdemir S, Mansfield CA, Hass S, Miller DW, Siegel CA, Sands BE (2007b) Crohn’s disease patients’ benefit-risk preferences: Serious adverse event risks versus treatment efficacy. Gastroenterology 133(3):769–779CrossRefPubMedGoogle Scholar
  21. 21.
    Johnson FR, Özdemir S, Manjunath R, Burch SP, Hauber AB, Thompson TR (2007c) Factors that affect stated adherence to bipolar disorder treatments: A stated-preference approach. Med Care 45(6):545–552CrossRefPubMedGoogle Scholar
  22. 22.
    Johnson FR, Matthews KE (2001) Sources and effects of utility-theoretic inconsistencies in stated-preference surveys. American Journal of Agricultural Economics 83(5):1328–1333CrossRefGoogle Scholar
  23. 23.
    Johnson FR, Banzhaf M, Desvousges W (2000) Willingness to pay for improved respiratory and cardiovascular health: a multiple-format stated-preference approach. Health Economics 9:295–317CrossRefPubMedGoogle Scholar
  24. 24.
    Kanninen B (2002) Optimal design for multinomial choice experiments. Journal of Marketing Research 39:214–217CrossRefGoogle Scholar
  25. 25.
    Kobelt G, Berg J, Atherly D, Hadjimichael O (2006) Costs and quality of life in multiple sclerosis: A cross-sectional study in the United States. Neurology 66:1696–1702CrossRefPubMedGoogle Scholar
  26. 26.
    Kremer H, Ironson G, Schneiderman N, Hautzinger M (2007) “It’s my body” Does patient involvement in decision making reduce decisional conflict? Medical Decision Making 27:522–532CrossRefPubMedGoogle Scholar
  27. 27.
    Krupnick A, Alberini A, Cropper M, Simon N, O’Brien B, Goeree R, et al. (2002) Age, health and the willingness to pay for mortality risk reductions: a contingent valuation survey of Ontario residents. Journal of Risk Uncertainty 24(2):161–186CrossRefGoogle Scholar
  28. 28.
    Lipkus IM (2007) Numeric, verbal, and visual formats of conveying health risks: Suggested best practices and future recommendations. Medical Decision Making 27(5):696–713CrossRefPubMedGoogle Scholar
  29. 29.
    Mendeloff J (1995) Decision Analysis and FDA Drug Review: A Proposal for Shadow Advisory Committees. Risk (Summer):203–214Google Scholar
  30. 30.
    Patrick DL, Starks HE, Cain KC, Uhlmann RF, Pearlman RA (1994) Measuring preferences for health states worse than death. Medical Decision Making 14:9–18CrossRefPubMedGoogle Scholar
  31. 31.
    Polman C, O’Connor P, Havrdova E, Hutchinson M, Kappos L, Miller D, et al. (2006) A randomized placebo-controlled trial of Tysabri for relapsing multiple sclerosis. N Engl J Med 354(9):899–910CrossRefPubMedGoogle Scholar
  32. 32.
    Ryan M, McIntosh E, Shackley P (1998) Methodological issues in the application of conjoint analysis in health care. Health Economics 7:373–378CrossRefPubMedGoogle Scholar
  33. 33.
    Sandman PM, Weinstein ND, Miller P (1994) High risk or low: how location on a “Risk Ladder” affects perceived risk. Risk Analysis 14:35–45CrossRefPubMedGoogle Scholar
  34. 34.
    Slovic P (1987) The perception of risk. Science 236:280–285CrossRefPubMedGoogle Scholar
  35. 35.
    Train K (2003) Discrete choice methods with simulation. Cambridge University Press, CambridgeGoogle Scholar
  36. 36.
    Train K, Sonnier G (2005) Mixed logit with bounded distributions of correlated partworths. In: Scarpa R, Alberini A (eds) Applications of simulation methods in environmental and resource economics. Springer Publisher, DordrechtGoogle Scholar
  37. 37.
    Yousry TA, Habil DM, Major EO, Ryschkewitsch C, Fahle G, Fischer S, Hou J, Curfman B, Miszkiel K, Mueller-Lenke N, Sanchez E, Barkhof F, Radue EW, Jager HR, Clifford DB (2006) Evaluation of patients treated with natalizumab for progressive multifocal leukoencephalopathy. N Engl J Med 354:924–933CrossRefPubMedGoogle Scholar
  38. 38.
    Zwerina K, Huber J, Kuhfeld W (1996) A general method for constructing efficient choice designs. Durham: Fuqua School of Business, Duke UniversityGoogle Scholar

Copyright information

© Steinkopff-Verlag 2009

Authors and Affiliations

  • F. Reed Johnson
    • 1
  • George Van Houtven
    • 1
  • Semra Özdemir
    • 1
  • Steve Hass
    • 2
  • Jeff White
    • 2
  • Gordon Francis
    • 2
  • David W. Miller
    • 2
  • J. Theodore Phillips
    • 3
  1. 1.Research Triangle InstituteResearch Triangle ParkUSA
  2. 2.Elan PharmaceuticalsSan DiegoUSA
  3. 3.MS Center at Texas NeurologyDallasUSA

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