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Preferences for antiviral therapy of chronic hepatitis C: a discrete choice experiment

Author

Listed:
  • Axel C. Mühlbacher

    (University of Applied Sciences Neubrandenburg
    Duke University)

  • John F. P. Bridges

    (Johns Hopkins Bloomberg School of Public Health)

  • Susanne Bethge

    (University of Applied Sciences Neubrandenburg)

  • Ch.-Markos Dintsios

    (Bayer Health Care
    Heinrich-Heine University)

  • Anja Schwalm

    (Institute for Quality and Efficiency in Health Care (IQWiG))

  • Andreas Gerber-Grote

    (Institute for Quality and Efficiency in Health Care (IQWiG))

  • Matthias Nübling

    (Empirical Consulting mbH (GEB mbH))

Abstract

Background The German Institute for Quality and Efficiency in Health Care (IQWiG) uses patient-relevant outcomes to inform decision-makers. Objective IQWiG conducted a pilot study to examine whether discrete choice experiments (DCEs) can be applied in health economic evaluations in Germany to identify, weight, and prioritize multiple patient-relevant outcomes, using the example of antiviral therapy for chronic hepatitis C (HCV). A further objective was to contribute to a more structured approach towards eliciting and comparing preferences across key stakeholders. Methods In autumn 2010, a DCE questionnaire was sent to patients with chronic HCV to estimate preferences across seven outcomes (“attributes”), including treatment efficacy [sustained viral response (SVR) at 6 months], adverse effects (flu-like symptoms, gastrointestinal symptoms, psychiatric symptoms, and skin symptoms/alopecia), and measures of treatment burden (duration of therapy, frequency of injections). A linear model and an effects coded full model were applied to assess the relative importance of the attributes. Results In total N = 326 patients were included. A clear preference for SVR was shown; frequency of injections and duration of therapy shared the second rank, while psychiatric symptoms ranked third. The duration of flu-like symptoms was the least important attribute. Conclusion Our findings indicate that it is possible to perform a DCE at the national level in a health technology assessment agency. The weighting of multiple outcomes allows an indication-specific and evidence-based measure to be used in health economic evaluations. In decision-making in health care, the approach generally allows for consideration of patient-relevant trade-offs regarding the benefits and harms of medical interventions.

Suggested Citation

  • Axel C. Mühlbacher & John F. P. Bridges & Susanne Bethge & Ch.-Markos Dintsios & Anja Schwalm & Andreas Gerber-Grote & Matthias Nübling, 2017. "Preferences for antiviral therapy of chronic hepatitis C: a discrete choice experiment," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(2), pages 155-165, March.
    • Handle: RePEc:spr:eujhec:v:18:y:2017:i:2:d:10.1007_s10198-016-0763-8
    DOI: 10.1007/s10198-016-0763-8
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    References listed on IDEAS

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    1. Emily Lancsar & Jordan Louviere, 2008. "Conducting Discrete Choice Experiments to Inform Healthcare Decision Making," PharmacoEconomics, Springer, vol. 26(8), pages 661-677, August.
    2. Teresa Kauf & Ateesha Mohamed & A. Hauber & Derek Fetzer & Atiya Ahmad, 2012. "Patients’ Willingness to Accept the Risks and Benefits of New Treatments for Chronic Hepatitis C Virus Infection," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(4), pages 265-278, December.
    3. Baltussen, Rob & Youngkong, Sitapon & Paolucci, Francesco & Niessen, Louis, 2010. "Multi-criteria decision analysis to prioritize health interventions: Capitalizing on first experiences," Health Policy, Elsevier, vol. 96(3), pages 262-264, August.
    4. P. Thokala & A. Duenas, 2012. "Multiple Criteria Decision Analysis for Health Technology Assessment," Post-Print hal-00800398, HAL.
    5. Mickael Bech & Trine Kjaer & Jørgen Lauridsen, 2011. "Does the number of choice sets matter? Results from a web survey applying a discrete choice experiment," Health Economics, John Wiley & Sons, Ltd., vol. 20(3), pages 273-286, March.
    6. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    7. Colin Green & Karen Gerard, 2009. "Exploring the social value of health‐care interventions: a stated preference discrete choice experiment," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 951-976, August.
    8. Martin, Douglas K. & Giacomini, Mita & Singer, Peter A., 2002. "Fairness, accountability for reasonableness, and the views of priority setting decision-makers," Health Policy, Elsevier, vol. 61(3), pages 279-290, September.
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    1. > Economics of Welfare > Health Economics

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    Cited by:

    1. Dudinskaya, Emilia Cubero & Naspetti, Simona & Zanoli, Raffaele, 2020. "Using eye-tracking as an aid to design on-screen choice experiments," Journal of choice modelling, Elsevier, vol. 36(C).
    2. Matthew Quaife & Fern Terris-Prestholt & Gian Luca Di Tanna & Peter Vickerman, 2018. "How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(8), pages 1053-1066, November.
    3. Matthias Herpers & Charalabos-Markos Dintsios, 2019. "Methodological problems in the method used by IQWiG within early benefit assessment of new pharmaceuticals in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(1), pages 45-57, February.
    4. Vikas Soekhai & Esther W. Bekker-Grob & Alan R. Ellis & Caroline M. Vass, 2019. "Discrete Choice Experiments in Health Economics: Past, Present and Future," PharmacoEconomics, Springer, vol. 37(2), pages 201-226, February.

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    More about this item

    Keywords

    Discrete choice experiment (DCE); Conjoint analysis (CA); Hepatitis C virus (HCV); Health technology assessment (HTA); Patient preferences; Priority setting;
    All these keywords.

    JEL classification:

    • I - Health, Education, and Welfare
    • H - Public Economics
    • I - Health, Education, and Welfare

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