4,363
Views
1
CrossRef citations to date
0
Altmetric
Original Research

Evaluating discrete choice experiment willingness to pay [DCE-WTP] analysis and relative social willingness to pay [RS-WTP] analysis in a health technology assessment of a treatment for an ultra-rare childhood disease [CLN2]

, , , , , , , , & show all
Pages 581-598 | Received 20 Nov 2020, Accepted 01 Dec 2021, Published online: 23 Feb 2022

References

  • Pearson I, Rothwell B, Olaye A, et al. Economic modeling considerations for rare diseases. Value Health. 2018;21(5):515–524. DOI:https://doi.org/10.1016/j.jval.2018.02.008.
  • Nestler-Parr S, Korchagina D, Toumi M, et al. Challenges in research and health technology assessment of rare disease technologies: report of the ISPOR rare disease special interest group. Value Health. 2018;21(5):493–500. DOI:https://doi.org/10.1016/j.jval.2018.03.004.
  • Richter T, Nestler-Parr S, Babela R, et al. Rare disease terminology and definitions-a systematic global review: report of the ISPOR rare disease special interest group. Value Health. 2015;18(6):906–914. DOI:https://doi.org/10.1016/j.jval.2015.05.008.
  • Schuller Y, Hollak CE, Biegstraaten M. The quality of economic evaluations of ultra-orphan drugs in Europe – a systematic review. Orphanet J Rare Dis. 2015;10(1):92.
  • Schlander M, Adarkwah CC, Gandjour A. Budget impact analysis of drugs for ultra-orphan non-oncological diseases in Europe. Expert Rev Pharmacoecon Outcomes Res. 2015;15(1):171–179.
  • Schlander M, Dintsios CM, Gandjour A. Budgetary impact and cost drivers of drugs for rare and ultrarare diseases. Value Health. 2018;21(5):525–531.
  • Williams RE. Appendix 1: NCL incidence and prevalence data. In: Mole SE, Williams RE, and Goebel HH, editors. the neuronal ceroid lipofuscinoses (Batten Disease). Oxford: Oxford University Press; 2011. p. 361–365.
  • Claussen M, Heim P, Knispel J, et al. Incidence of neuronal ceroid-lipofuscinoses in West Germany: variation of a method for studying autosomal recessive disorders. Am J Med Genet. 1992;42(4):536–538. DOI:https://doi.org/10.1002/ajmg.1320420422.
  • Teixeira C, Guimares A, Bessa C, et al. Clinicopathological and molecular characterization of neuronal ceroid lipofuscinosis in the Portuguese population. J Neurol. 2003;250(6):661–667. DOI:https://doi.org/10.1007/s00415-003-1050-z.
  • Moore SJ, Buckley DJ, MacMillan A, et al. The clinical and genetic epidemiology of neuronal ceroid lipofuscinosis in Newfoundland. Clin Genet. 2008;74(3):213–222. DOI:https://doi.org/10.1111/j.1399-0004.2008.01054.x.
  • Uvebrant P, Hagberg B. Neuronal ceroid lipofuscinoses in Scandinavia.Epidemiology and clinical pictures 1997;28(1):6–8 Neuropediatrics.
  • Bouslouk M. G-BA benefit assessment of new orphan drugs in Germany: the first five years. Expert Opin Orphan Drugs. 2016;4(5):453–455.
  • Janoudi G, Amegatse W, McIntosh B, et al. Health technology assessment of drugs for rare diseases: insights, trends, and reasons for negative recommendations from the CADTH common drug review. Orphanet J Rare Dis. 2016;11(1):164. DOI:https://doi.org/10.1186/s13023-016-0539-3.
  • Nicod E, Annemans L, Bucsics A, et al. HTA programme response to the challenges of dealing with orphan medicinal products: process evaluation in selected European countries. Health Policy. 2017;123(2):140–151. DOI:https://doi.org/10.1016/j.healthpol.2017.03.009.
  • Schlander M. Measures of efficiency in healthcare: qALMs about QALYs? Z Evid Fortbild Qual Gesundhwes. 2010;104(3):214–226.
  • Nord E, Richardson J, Street A, et al. Who cares about cost? Does economic analysis impose or reflect social values? Health Policy. 1995;34(2):79–94. DOI:https://doi.org/10.1016/0168-8510(95)00751-D.
  • Richardson J, Schlander M. Health technology assessment (HTA) and economic evaluation: efficiency or fairness first. J Mark Access Health Policy. 2019;7(1):1557981.
  • McCabe C, Claxton K, Tsuchiya A. Orphan drugs and the NHS: should we value rarity? BMJ. 2005;331(7523):1016–1019.
  • Gammie T, Lu CY, Babar ZU. Access to orphan drugs: a comprehensive review of legislations, regulations and policies in 35 countries. PLoS One. 2015;10(10):e0140002.
  • Schlander M, Garattini, S, Kolominsky-Rabas, P, et al. Determining the value of medical technologies to treat ultra-rare disorders: a consensus statement. J Mark Access Health Policy. 2016;4(1):33039 doi:https://doi.org/10.3402/jmahp.v4.33039.
  • Menon D, Stafinski, T, Dunn, A, et al. Developing a patient-directed policy framework for managing orphan and ultra-orphan drugs throughout their lifecycle. Patient. 2015;8(1):103–117. DOI:https://doi.org/10.1007/s40271-014-0108-6.
  • Farrugia A, O’Mahony B, Cassar J. Health technology assessment and haemophilia. Haemophilia. 2012;18(2):152–157.
  • Schlander M, Garattini S, Holm S, et al. Incremental cost per quality-adjusted life year gained? the need for alternative methods to evaluate medical interventions for ultra-rare disorders. J Comp Eff Res. 2014;3(4):399–422. DOI:https://doi.org/10.2217/cer.14.34.
  • Dolan P, Shaw R, Tsuchiya A, et al. QALY maximisation and people’s preferences: a methodological review of the literature. Health Econ. 2004;14(2):197–208. DOI:https://doi.org/10.1002/hec.924.
  • Smith RD, Richardson J. Can we estimate the ‘social’ value of a QALY? Four core issues to resolve. Health Policy. 2005;74(1):77–84.
  • Dolan P, Green C. Using the person trade-off approach to examine differences between individual and social values. Health Econ. 1998;7(4):307–312.
  • Weinstein MC, Stason WB. Foundations of cost-effectiveness analysis for health and medical practices. N Engl J Med. 1977;296(13):716–721.
  • Gray AM, Clarke PM, and Wolstenholme J, editors Chapter 5: measuring, valuing, and analysing health outcomes. Applied methods of cost-effectiveness analysis in healthcare. United States: Oxford University Press; 2011 83–118.
  • Ryan M, Gerard K. Inclusiveness in the health economic evaluation space. Soc Sci Med. 2014;108:248–251.
  • Clark M, Moro D, Szczepura A. Balancing patient preferences and clinical needs: community versus hospital based care for patients with suspected DVT. Health Policy. 2009;90(2–3):313–319.
  • Clark MD, Determann D, Petrou S, et al. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32(9):883–902. DOI:https://doi.org/10.1007/s40273-014-0170-x.
  • Richardson J, Iezzi A, Chen G, et al. Communal sharing and the provision of low-volume high-cost health services: results of a survey. Pharmacoecon Open. 2017;1(1):13–23. DOI:https://doi.org/10.1007/s41669-016-0002-3.
  • Richardson J, Iezzi A, Maxwell A. How important is severity for the evaluation of health services: new evidence using the relative social willingness to pay instrument. Eur J Health Econ. 2017;18(6):671–683.
  • Richardson J, Iezzi A, Maxwell A. Does a patient’s health potential affect the social valuation of health services? PLoS One. 2018;13(4):e0192585.
  • Richardson J, Iezzi A, Sinha K, et al. An instrument for measuring the social willingness to pay for health state improvement. Health Econ. 2014;23(7):792–805. DOI:https://doi.org/10.1002/hec.2950.
  • Dolan P, Olesen, JA, Menzel, P, et al. An inquiry into the different perspectives that can be used when eliciting preferences in health. Health Econ. 2003;12(7):545–551. DOI:https://doi.org/10.1002/hec.760.
  • Tsuchiya A, Watson V. Re-thinking ‘the different perspectives that can be used when eliciting preferences in health.’ Health Econ. 2017;26(12):e103–e107.
  • Richardson J, McKie J, Iezzi A, et al. Age weights for health services derived from the relative social willingness-to-pay instrument. Med Decis Making. 2017;37(3):239–251. DOI:https://doi.org/10.1177/0272989X16645576.
  • Damschroder LJ, Roberts TR, Goldstein CC, et al. Trading people versus trading time: what is the difference? Popul Health Metr. 2005;3(1):10. DOI:https://doi.org/10.1186/1478-7954-3-10.
  • Bourke SM, Plumpton CO, Hughes DA. societal preferences for funding orphan drugs in the United Kingdom: an application of person trade-off and discrete choice experiment methods. Value Health. 2018;21(5):538–546.
  • Karnon J, Partington A. Cost-value analysis and the SAVE: a work in progress, but an option for localised decision making? Pharmacoeconomics. 2015;33(12):1281–1288.
  • Dolan P, Tsuchiya A. The social welfare function and individual responsibility: some theoretical issues and empirical evidence. J Health Econ. 2009;28(1):210–220.
  • Smith RD, Sach TH. Contingent valuation: what needs to be done? Health Econ Policy Law. 2010;5(Pt 1):91–111.
  • Slothuus Skjoldborg U, Gyrd-Hansen D. Conjoint analysis. the cost variable: an achilles’ heel? Health Econ. 2003;12(6):479–491.
  • Bryan S, Dolan P. Discrete choice experiments in health economics. For better or for worse? Eur J Health Econ. 2004;5(3):199–202.
  • Richardson J, McKie J. Economic evaluation of services for a National Health scheme: the case for a fairness-based framework. J Health Econ. 2007;26(4):785–799.
  • NICE. Guide to methods of technology appraisal. London: HMSO; 2013.
  • de Bekker-grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145–172.
  • Harrison M, Milbers K, Hudson M, et al. Do patients and health care providers have discordant preferences about which aspects of treatments matter most? Evidence from a systematic review of discrete choice experiments. BMJ Open. 2017;7(5):e014719. DOI:https://doi.org/10.1136/bmjopen-2016-014719.
  • Clark MD, Leech D, Gumber A, et al. Who should be prioritized for renal transplantation?: analysis of key stakeholder preferences using discrete choice experiments. BMC Nephrol. 2012;13(1):152. DOI:https://doi.org/10.1186/1471-2369-13-152.
  • Ryan M, Skatum D, Major K. Using discrete choice experiments to go beyond clinical outcomes when evaluating clinical practice. In Ryan M, Gerard K, and Amaya-Amaya M, editors. Using Discrete Choice Experiments to Value Health and Health Care (Dordrecht: Springer). 2008;101–116 .
  • Tinelli M, Ryan M, Bond C. What, who and when? Incorporating a discrete choice experiment into an economic evaluation. Health Econ Rev. 2016;6(1):31.
  • Linley WG, Hughes DA. Societal views on NICE, cancer drugs fund and value-based pricing criteria for prioritising medicines: a cross-sectional survey of 4118 adults in Great Britain. Health economics. 2013;22(8):948–964.
  • Lloyd AJ, Gallop K, Ali S, et al. Social preference weights for treatments in Fabry disease in the UK: a discrete choice experiment. Curr Med Res Opin. 2017;33(1):23–29. DOI:https://doi.org/10.1080/03007995.2016.1232704.
  • Mentzakis E, Stefanowska P, Hurley J. A discrete choice experiment investigating preferences for funding drugs used to treat orphan diseases: an exploratory study. Health Econ Policy Law. 2011;6(3):405–433.
  • Morel T, Aymé S, Cassiman D, et al. Quantifying benefit-risk preferences for new medicines in rare disease patients and caregivers. Orphanet J Rare Dis. 2016;11(1):70. DOI:https://doi.org/10.1186/s13023-016-0444-9.
  • Muhlbacher AC, Nubling M. Analysis of physicians’ perspectives versus patients’ preferences: direct assessment and discrete choice experiments in the therapy of multiple myeloma. Eur J Health Econ. 2011;12(3):193–203.
  • Seanehia J, Treibich C, Holmberg C, et al. Quantifying population preferences around vaccination against severe but rare diseases: a conjoint analysis among French university students, 2016. Vaccine. 2017;35(20):2676–2684. DOI:https://doi.org/10.1016/j.vaccine.2017.03.086.
  • de Bekker-grob EW, Donkers B, Jonker MF, et al. sample size requirements for discrete-choice experiments in healthcare: a practical guide. The Patient - Patient-Centered Outcomes Research. 2015;8(5):373–384. DOI:https://doi.org/10.1007/s40271-015-0118-z.
  • Howard K, Salkeld G. Does attribute framing in discrete choice experiments influence willingness to pay? results from a discrete choice experiment in screening for colorectal cancer. Value Health. 2009;12(2):354–363.
  • Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics. 2008;26(8):661–677.
  • Mas-Colell A, Whinston M, Green J. Microeconomic theory. chapter 3 classical demand theory. New York: Oxford University Press; 1995. p. 40–96.
  • Lloyd AJ. Threats to the estimation of benefit: are preference elicitation methods accurate? Health Econ. 2003;12(5):393–402.
  • McCabe C, Claxton K, Culyer AJ. The NICE cost-effectiveness threshold: what it is and what that means. Pharmacoeconomics. 2008;26(9):733–744.
  • HMSO. Office of national statistics. [cited 2022 Jan 17]. https://www.ons.gov.uk/census/2011census.
  • Olsen JA, Donaldson C, Pereira J. The insensitivity of `willingness-to-pay’ to the size of the good: new evidence for health care. Journal of Economic Psychology. 2004;25(4):445–460.
  • Gigerenzer G, Todd PM, and the ABC Research Group. Simple heuristics that make us smart. New York: Oxford University Press; 1999.
  • Hole AR, Norman R, Viney R. Response patterns in health state valuation using endogenous attribute attendance and latent class analysis. Health Econ. 2016;25(2):212–224.
  • Spinks J, Mortimer D. Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments. BMC Med Inform Decis Mak. 2016;16(1):14.
  • Vass C, Rigby D, Tate K, et al. An exploratory application of eye-tracking methods in a discrete choice experiment. Med Decis Making. 2018;38(6):658–672. DOI:https://doi.org/10.1177/0272989X18782197.
  • Telser H, Becker K, Zweifel P. Validity and reliability of willingness-to-pay estimates: evidence from two overlapping discrete-choice experiments. Patient. 2008;1(4):283–298.
  • Johnson FR, Mohamed AF, Özdemir S, et al. How does cost matter in health-care discrete-choice experiments? Health Econ. 2011;20(3):323–330. DOI:https://doi.org/10.1002/hec.1591.
  • Gyrd-Hansen DS. US., the price proxy in discrete choice experiments: issues of relevance for future research. In: Ryan M, Gerard K, and M -A-A, editors. Using Discrete Choice Experiments to Value Health and Healthcare. Dordrecht: Springer; 2008;175–193.
  • Telser H, Zweifel P. Validity of discrete-choice experiments evidence for health risk reduction. Appl Econ. 2007;39(1):69–78.
  • Lancsar E, Louviere J. Deleting ‘irrational’ responses from discrete choice experiments: a case of investigating or imposing preferences? Health Econ. 2006;15(8):797–811.
  • Bryan S, Gold L, Sheldon R, et al. Preference measurement using conjoint methods: an empirical investigation of reliability. Health Econ. 2000;9(5):385–395. DOI:https://doi.org/10.1002/1099-1050(200007)9:5<385::AID-HEC533>3.0.CO;2-W.
  • Desser AS. Prioritizing treatment of rare diseases: a survey of preferences of Norwegian doctors. Soc Sci Med. 2013;94:56–62.
  • Desser AS, Gyrd-Hansen D, Olsen JA, et al. Societal views on orphan drugs: cross sectional survey of Norwegians aged 40 to 67. BMJ. 2010;341(sep22 3):c4715. DOI:https://doi.org/10.1136/bmj.c4715.
  • Desser AS, Olsen JA, Grepperud S. Eliciting preferences for prioritizing treatment of rare diseases: the role of opportunity costs and framing effects. Pharmacoeconomics. 2013;31(11):1051–1061.
  • Dragojlovic N, Rizzardo, S, Bansback, N, et al. Challenges in measuring the societal value of orphan drugs: insights from a Canadian stated preference survey. Patient. 2015;8(1):93–101. DOI:https://doi.org/10.1007/s40271-014-0109-5.
  • Gyrd-Hansen D. Using the stated preference technique for eliciting valuations: the role of the payment vehicle. Pharmacoeconomics. 2013;31(10):853–861.