Advertisement

Applied Health Economics and Health Policy

, Volume 11, Issue 4, pp 319–329 | Cite as

Quantifying Benefit–Risk Preferences for Medical Interventions: An Overview of a Growing Empirical Literature

  • A. Brett HauberEmail author
  • Angelyn O. Fairchild
  • F. Reed Johnson
Review Article

Abstract

Decisions regarding the development, regulation, sale, and utilization of pharmaceutical and medical interventions require an evaluation of the balance between benefits and risks. Such evaluations are subject to two fundamental challenges—measuring the clinical effectiveness and harms associated with the treatment, and determining the relative importance of these different types of outcomes. In some ways, determining the willingness to accept treatment-related risks in exchange for treatment benefits is the greater challenge because it involves the individual subjective judgments of many decision makers, and these decision makers may draw different conclusions about the optimal balance between benefits and risks. In response to increasing demand for benefit–risk evaluations, researchers have applied a variety of existing welfare-theoretic preference methods for quantifying the tradeoffs decision makers are willing to accept among expected clinical benefits and risks. The methods used to elicit benefit–risk preferences have evolved from different theoretical backgrounds. To provide some structure to the literature that accommodates the range of approaches, we begin by describing a welfare-theoretic conceptual framework underlying the measurement of benefit–risk preferences in pharmaceutical and medical treatment decisions. We then review the major benefit–risk preference-elicitation methods in the empirical literature and provide a brief overview of the studies using each of these methods. The benefit–risk preference methods described in this overview fall into two broad categories: direct-elicitation methods and conjoint analysis. Rating scales (6 studies), threshold techniques (9 studies), and standard gamble (2 studies) are examples of direct elicitation methods. Conjoint analysis studies are categorized by the question format used in the study, including ranking (1 study), graded pairs (1 study), and discrete choice (21 studies). The number of studies reviewed here demonstrates that this body of research already is substantial, and it appears that the number of benefit–risk preference studies in the literature will continue to increase. In addition, benefit–risk preference-elicitation methods have been applied to a variety of healthcare decisions and medical interventions, including pharmaceuticals, medical devices, surgical and medical procedures, and diagnostics, as well as resource-allocation decisions such as facility placement. While preference-elicitation approaches may differ across studies, all of the studies described in this review can be used to provide quantitative measures of the tradeoffs patients and other decision makers are willing to make between benefits and risks of medical interventions. Eliciting and quantifying the preferences of decision makers allows for a formal, evidence-based consideration of decision-makers’ values that currently is lacking in regulatory decision making. Future research in this area should focus on two primary issues—developing best-practice standards for preference-elicitation studies and developing methods for combining stated preferences and clinical data in a manner that is both understandable and useful to regulatory agencies.

Keywords

Irritable Bowel Syndrome Risk Preference Conjoint Analysis Standard Gamble Question Format 
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.

Notes

Acknowledgments

The authors received no direct funding for this manuscript, and declare no financial conflicts of interest.

Author Contributions

A. Brett Hauber led all aspects of this study, including defining the objectives of the research, defining the search strategy, developing criteria for study inclusion, reviewing studies, and writing the manuscript. A. Brett Hauber also contributed to developing the conceptual model. Angelyn O. Fairchild conducted the literature search and contributed to defining the search strategy, developing criteria for study inclusion, reviewing studies, and writing the manuscript. F. Reed Johnson contributed to defining the objectives of the research and developing the conceptual model and writing the manuscript. A. Brett Hauber had access to all data and take full responsibility for the content of this manuscript.

References

  1. 1.
    Cioms, IV Working Group. Benefit–risk balance for marketed drugs: evaluating safety signals. Report of the CIOMS Working Group IV. Geneva: Council for International Organizations of Medical Sciences; 1998.Google Scholar
  2. 2.
    Lynd L, O’Brien BJ. Advances in risk-benefit evaluation using probabilistic simulation methods: an application to the prophylaxis of deep vein thrombosis. J Clin Epidemiol. 2004;57(8):795–803.PubMedCrossRefGoogle Scholar
  3. 3.
    Van Houtven G, Johnson FR, Kilambi V, Hauber AB. Eliciting benefit–risk preferences and probability-weighted utility using choice-format conjoint analysis. Med Decis Making. 2011;31(3):469–80.PubMedCrossRefGoogle Scholar
  4. 4.
    Ho M, Lavery B, Pullar T. The risk of treatment. A study of rheumatoid arthritis patients’ attitudes. Br J Rheumatol. 1998;37(4):459–60.PubMedCrossRefGoogle Scholar
  5. 5.
    Pullar T, Wright V, Feely M. What do patients and rheumatologists regard as an ‘acceptable’ risk in the treatment of rheumatic disease? Br J Rheumatol. 1990;29(3):215–8.PubMedCrossRefGoogle Scholar
  6. 6.
    Bremnes RM, Andersen K, Wist EA. Cancer patients, doctors and nurses vary in their willingness to undertake cancer chemotherapy. Eur J Cancer. 1995;31A(12):1955–9.PubMedCrossRefGoogle Scholar
  7. 7.
    Barker JH, Furr A, Cunningham M, Grossi F, Vasilic D, Storey B, et al. Investigation of risk acceptance in facial transplantation. Plast Reconstr Surg. 2006;118(3):663–70.PubMedCrossRefGoogle Scholar
  8. 8.
    Majzoub RK, Cunningham M, Grossi F, Maldonado C, Banis JC, Barker JH. Investigation of risk acceptance in hand transplantation. J Hand Surg Am. 2006;31(2):295–302.PubMedCrossRefGoogle Scholar
  9. 9.
    Reynolds CC, Martinez SA, Furr A, Cunningham M, Bumpous JM, Lentsch EJ, et al. Risk acceptance in laryngeal transplantation. Laryngoscope. 2006;116(10):1770–5.PubMedCrossRefGoogle Scholar
  10. 10.
    Devereaux PJ, Anderson DR, Gardner MJ, Putnam W, Flowerdew GJ, Brownell BF, et al. Differences between perspectives of physicians and patients on anticoagulation in patients with atrial fibrillation: observational study. BMJ. 2001;323(7323):1218–22.PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Kopec JA, Richardson CG, Llewellyn-Thomas H, Klinkhoff A, Carswell A, Chalmers A. Probabilistic threshold technique showed that patients’ preferences for specific trade-offs between pain relief and each side effect of treatment in osteoarthritis varied. J Clin Epidemiol. 2007;60(9):929–38.PubMedCrossRefGoogle Scholar
  12. 12.
    Richardson CG, Chalmers A, Llewellyn-Thomas HA, Klinkhoff A, Carswell A, Kopec JA. Pain relief in osteoarthritis: patients’ willingness to risk medication-induced gastrointestinal, cardiovascular, and cerebrovascular complications. J Rheumatol. 2007;34(7):1569–75.Google Scholar
  13. 13.
    Llewellyn-Thomas HA, Arshinoff R, Bell M, Williams JI, Naylor CD. In the queue for total joint replacement: patients’ perspectives on waiting times. Ontario Hip and Knee Replacement Project Team. J Eval Clin Pract. 1998;4(1):63–74.Google Scholar
  14. 14.
    Finlayson SR, Birkmeyer JD, Tosteson AN, Nease RF Jr. Patient preferences for location of care: implications for regionalization. Med Care. 1999;37(2):204–9.PubMedCrossRefGoogle Scholar
  15. 15.
    Palda VA, Llewellyn-Thomas HA, Mackenzie RG, Pritchard KI, Naylor CD. Breast cancer patients’ attitudes about rationing postlumpectomy radiation therapy: applicability of trade-off methods to policy-making. J Clin Oncol. 1997;15(10):3192–200.PubMedGoogle Scholar
  16. 16.
    Llewellyn-Thomas HA, Williams JI, Levy L, Naylor CD. Using a trade-off technique to assess patients’ treatment preferences for benign prostatic hyperplasia. Med Decis Making. 1996;16(3):262–82.PubMedCrossRefGoogle Scholar
  17. 17.
    Simes RJ, Coates AS. Patient preferences for adjuvant chemotherapy of early breast cancer: how much benefit is needed? J Natl Cancer Inst Monogr. 2001;30:146–52.PubMedCrossRefGoogle Scholar
  18. 18.
    Llewellyn-Thomas HA, Paterson JM, Carter JA, Basinski A, Myers MG, Hardacre GD, Dunn EV, D’Agostino RB, Naylor CD. Primary prevention drug therapy: can it meet patients’ requirements for reduced risk? Med Decision Making. 2002;22:326–39.CrossRefGoogle Scholar
  19. 19.
    O’Brien BJ, Elswood J, Calin A. Willingness to accept risk in the treatment of rheumatic disease. J Epidemiol Community Health. 1990;44(3):249–52.PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Thompson MS. Willingness to pay and accept risks to cure chronic disease. Am J Public Health. 1986;76(4):392–6.PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Fraenkel L, Bodardus S, Wittnik DR. Understanding patient preferences for the treatment of lupus nephritis with adaptive conjoint analysis. Med Care. 2001;39(11):1203–16.PubMedCrossRefGoogle Scholar
  22. 22.
    Sassi F, McKee M. Do clinicians always maximize patient outcomes? A conjoint analysis of preferences for carotid artery testing. J Health Serv Res Policy. 2008;13(2):61–6.PubMedCrossRefGoogle Scholar
  23. 23.
    Johnson FR, Ozdemir S, Hauber B, Kauf TL. Women’s willingness to accept perceived risks for vasomotor symptom relief. J Womens Health. 2007;16(7):1028–40.CrossRefGoogle Scholar
  24. 24.
    de Bekker-Grob EW, Essink-Bot ML, Meerding WJ, Pols HA, Koes BW, Steyerberg EW. Patients’ preferences for osteoporosis drug treatment: a discrete choice experiment. Osteoporos Int. 2008;19(7):1029–37.PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Eberth B, Watson V, Ryan M, Hughes J, Barnett G. Does one size fit all? Investigating heterogeneity in men’s preferences for benign prostatic hyperplasia treatment using mixed logit analysis. Med Decis Making. 2009;29(6):707–15.PubMedCrossRefGoogle Scholar
  26. 26.
    McTaggart-Cowan HM, Shi P, Fitzgerald JM, Anis AH, Kopec JA, Bai TR, et al. An evaluation of patients’ willingness to trade symptom-free days for asthma-related treatment risks: a discrete choice experiment. J Asthma. 2008;45(8):630–8.PubMedCrossRefGoogle Scholar
  27. 27.
    Salkeld G, Solomon M, Short L, Ryan M, Ward JE. Evidence-based consumer choice: a case study in colorectal cancer screening. Aust N Z J Public Health. 2003;27(4):449–55.PubMedCrossRefGoogle Scholar
  28. 28.
    Johnson FR, Ozdemir S, Mansfield C, Hass S, Miller DW, Siegel CA, et al. Crohn’s disease patients’ risk-benefit preferences: serious adverse event risks versus treatment efficacy. Gastroenterology. 2007;133(3):769–79.PubMedCrossRefGoogle Scholar
  29. 29.
    Fraenkel L, Gulanski B, Wittink D. Patient willingness to take teriparatide. Patient Educ Couns. 2007;65(2):237–44.PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Hauber AB, Johnson FR, Grotzinger KM, Ozdemir S. Patients’ benefit–risk preferences for chronic idiopathic thrombocytopenic purpura therapies. Ann Pharmacother. 2010;44(3):479–88.PubMedCrossRefGoogle Scholar
  31. 31.
    Mohamed AF, Johnson FR, Hauber AB, Lescrauwaet B, Masterson A. Physicians’ stated trade-off preferences for chronic hepatitis B treatment outcomes in Germany, France, Spain, Turkey, and Italy. Eur J Gastroenterol Hepatol. 2012;24(4):419–26.PubMedGoogle Scholar
  32. 32.
    Lewis SM, Cullinane FN, Bishop AJ, Chitty LS, Marteau TM, Halliday JL. A comparison of Australian and UK obstetricians’ and midwives’ preferences for screening tests for down syndrome. Prenat Diagn. 2006;26(1):60–6.PubMedCrossRefGoogle Scholar
  33. 33.
    Bridges JF, Mohamed AF, Finnern HW, Woehl A, Hauber AB. Patients’ preferences for treatment outcomes for advanced non-small cell lung cancer: a conjoint analysis. Lung Cancer. 2012;77(1):224–31.Google Scholar
  34. 34.
    Hauber AB, Johnson FR, Fillit H, Mohamed AF, Leibman C, Arrighi HM, Grundman M, Townsend RJ. Older Americans’ risk-benefit preferences for modifying the course of Alzheimer disease. Alzheimer Dis Assoc Disord. 2009;23(1):23–32.PubMedCrossRefGoogle Scholar
  35. 35.
    Johnson FR, Hauber AB, Ozdemir S, Lynd L. Quantifying women’s stated benefit–risk trade-off preferences for IBS treatment outcomes. Value Health. 2010;13(4):418–23.PubMedCrossRefGoogle Scholar
  36. 36.
    Johnson FR, Hauber B, Ozdemir S, Siegel CA, Hass S, Sands BE. Are gastroenterologists less tolerant of treatment risks than patients? Benefit–risk preferences in Crohn’s disease management. J Manag Care Pharm. 2010;16(8):616–28.PubMedGoogle Scholar
  37. 37.
    Johnson FR, Ozdemir S, Mansfield C, Hass S, Siegel CA, Sands BE. Are adult patients more tolerant of treatment risks than parents of juvenile patients? Risk Anal. 2009;29(1):121–36.PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Johnson FR, Van Houtven G, Ozdemir S, Hass S, White J, Francis G, et al. Multiple sclerosis patients’ benefit–risk preferences: serious adverse event risks versus treatment efficacy. J Neurol. 2009;256(4):554–62.PubMedCrossRefGoogle Scholar
  39. 39.
    Mohamed AF, Hauber AB, Neary MP. Patient benefit–risk preferences for targeted agents in the treatment of renal cell carcinoma. Pharmacoeconomics. 2011;29(11):977–88.PubMedCrossRefGoogle Scholar
  40. 40.
    Wong MK, Mohamed AF, Hauber AB, Yang JC, Liu Z, Rogerio J, Garay CA. Patients rank toxicity against progression free survival in second-line treatment of advanced renal cell carcinoma. J Med Econ. 2012;15(6):1139–48.PubMedCrossRefGoogle Scholar
  41. 41.
    Ratcliffe J, Buxton M. Patients’ preferences regarding the process and outcomes of life-saving technology. An application of conjoint analysis to liver transplantation. Int J Technol Assess Health Care. 1999;15(2):340–51.PubMedGoogle Scholar
  42. 42.
    Ratcliffe J, Buxton M, McGarry T, Sheldon R, Chancellor J. Patients’ preferences for characteristics associated with treatments for osteoarthritis. Rheumatology (Oxford). 2004;43(3):337–45.CrossRefGoogle Scholar
  43. 43.
    Arden NK, Hauber AB, Mohamed AF, Johnson FR, Peloso PM, Watson DJ, et al. How do physicians weigh benefits and risks associated with treatments in patients with osteoarthritis in the United Kingdom? J Rheumatol. 2012;39(5):1056–63.PubMedCrossRefGoogle Scholar
  44. 44.
    Hauber AB, Mohamed AF, Watson ME, Johnson FR, Hernandez JE. Benefits, risk, and uncertainty: preferences of antiretroviral-naive African Americans for HIV treatments. AIDS Patient Care STDS. 2009;23(1):29–34.PubMedCrossRefGoogle Scholar
  45. 45.
    European Medicines Agency (2010) Road map to 2015. http://www.ema.europa.eu/docs/en_GB/document_library/Report/2011/01/WC500101373.pdf. Accessed 15 April 2012.
  46. 46.
    Maxmen A. Law spurs regulator to heed patients’ priorities. Nature. 2012;487(7406):154. doi: 10.1038/487154a.Google Scholar
  47. 47.
    Holden WL. Benefit–risk analysis: a brief review and proposed quantitative approaches. Drug Saf. 2003;26(12):853–62.PubMedCrossRefGoogle Scholar
  48. 48.
    Lynd LD, Naiafzadeh M, Colley L, Byrne MF, Willan AR, Sculpher MJ, Johnson FR, Hauber AB. Using the incremental net benefit framework for quantitative benefit–risk analysis in regulatory decision making—a case study of alosetron in irritable bowel syndrome. Value Health. 2010;13(4):411–7.PubMedCrossRefGoogle Scholar
  49. 49.
    Glickman TS, Gough M. Readings in risk. Washington, DC: Resources for the Future; 1990.Google Scholar
  50. 50.
    Kahneman D, Tversky A. Choices, values, and frames. Oxford: Cambridge University Press; 2000.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • A. Brett Hauber
    • 1
    Email author
  • Angelyn O. Fairchild
    • 1
  • F. Reed Johnson
    • 1
  1. 1.RTI-Health SolutionsResearch Triangle ParkUSA

Personalised recommendations