IDEAS home Printed from https://ideas.repec.org/a/spr/eujhec/v15y2014i3p281-288.html
   My bibliography  Save this article

Eliciting preferences to the EQ-5D-5L health states: discrete choice experiment or multiprofile case of best–worst scaling?

Author

Listed:
  • Feng Xie
  • Eleanor Pullenayegum
  • Kathryn Gaebel
  • Mark Oppe
  • Paul Krabbe

Abstract

Choice-based methods have been used widely in assessing healthcare programs. This study compared the binary discrete choice experiment (DCE) and the multiprofile case of best–worst scaling (BWS) in eliciting preferences for the EQ-5D-5L. Forty-eight EQ-5D-5L health states were selected using a Bayesian efficient design and grouped into 24 pairs for the DCE tasks and 8 sets for the BWS tasks (each set has three health states). A total of 100 participants completed 12 pairs and 8 sets in a random order. A probit regression model and ranked order logistic regression model were used to estimate the latent utilities from the DCE and BWS, respectively. Both tasks were well understood by the majority of participants. The DCE tasks were relatively easier and took a shorter time to complete. The intraclass correlation coefficient (ICC) of the DCE was higher than that of the BWS. The variances associated with the latent utilities estimated from the DCE were larger than those from the BWS. The DCE is more feasible and reliable than the BWS in valuing the EQ-5D-5L. Future studies could focus on comparing the consistency and accuracy of these techniques in predicting the health utilities of the EQ-5D-5L. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Feng Xie & Eleanor Pullenayegum & Kathryn Gaebel & Mark Oppe & Paul Krabbe, 2014. "Eliciting preferences to the EQ-5D-5L health states: discrete choice experiment or multiprofile case of best–worst scaling?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(3), pages 281-288, April.
    • Handle: RePEc:spr:eujhec:v:15:y:2014:i:3:p:281-288
    DOI: 10.1007/s10198-013-0474-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10198-013-0474-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10198-013-0474-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nancy J. Devlin & Aki Tsuchiya & Ken Buckingham & Carl Tilling, 2011. "A uniform time trade off method for states better and worse than dead: feasibility study of the ‘lead time’ approach," Health Economics, John Wiley & Sons, Ltd., vol. 20(3), pages 348-361, March.
    2. Flynn, Terry N. & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2007. "Best-worst scaling: What it can do for health care research and how to do it," Journal of Health Economics, Elsevier, vol. 26(1), pages 171-189, January.
    3. Angela Robinson & Anne Spencer, 2006. "Exploring challenges to TTO utilities: valuing states worse than dead," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 393-402, April.
    4. Andrew Lloyd & Scott Doyle & Sarah Dewilde & Florian Turk, 2008. "Preferences and utilities for the symptoms of moderate to severe allergic asthma," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 9(3), pages 275-284, August.
    5. 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.
    6. Arthur E. Attema & Matthijs M. Versteegh & Mark Oppe & Werner B. F. Brouwer & Elly A. Stolk, 2013. "Lead Time Tto: Leading To Better Health State Valuations?," Health Economics, John Wiley & Sons, Ltd., vol. 22(4), pages 376-392, April.
    7. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D., 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, January.
    8. Marjon Pol & Larissa Roux, 2005. "Time preference bias in time trade-off," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(2), pages 107-111, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qinxin Guo & Junyi Shen, 2019. "An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice," Discussion Paper Series DP2019-14, Research Institute for Economics & Business Administration, Kobe University.
    2. Boxebeld, Sander, 2024. "Ordering effects in discrete choice experiments: A systematic literature review across domains," Journal of choice modelling, Elsevier, vol. 51(C).
    3. Samare P. I. Huls & Emily Lancsar & Bas Donkers & Jemimah Ride, 2022. "Two for the price of one: If moving beyond traditional single‐best discrete choice experiments, should we use best‐worst, best‐best or ranking for preference elicitation?," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2630-2647, December.
    4. Nicolas Krucien & Jonathan Sicsic & Mandy Ryan, 2019. "For better or worse? Investigating the validity of best–worst discrete choice experiments in health," Health Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 572-586, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alessandro Mengoni & Chiara Seghieri & Sabina Nuti, 2013. "The application of discrete choice experiments in health economics: a systematic review of the literature," Working Papers 201301, Scuola Superiore Sant'Anna of Pisa, Istituto di Management.
    2. Arthur E. Attema & Werner B.F. Brouwer, 2014. "Deriving Time Discounting Correction Factors For Tto Tariffs," Health Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 410-425, April.
    3. Arthur Attema & Yvette Edelaar-Peeters & Matthijs Versteegh & Elly Stolk, 2013. "Time trade-off: one methodology, different methods," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(1), pages 53-64, July.
    4. Spencer, Anne & Rivero-Arias, Oliver & Wong, Ruth & Tsuchiya, Aki & Bleichrodt, Han & Edwards, Rhiannon Tudor & Norman, Richard & Lloyd, Andrew & Clarke, Philip, 2022. "The QALY at 50: One story many voices," Social Science & Medicine, Elsevier, vol. 296(C).
    5. Nan Luo & Minghui Li & Elly Stolk & Nancy Devlin, 2013. "The effects of lead time and visual aids in TTO valuation: a study of the EQ-VT framework," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(1), pages 15-24, July.
    6. Matthijs Versteegh & Arthur Attema & Mark Oppe & Nancy Devlin & Elly Stolk, 2013. "Time to tweak the TTO: results from a comparison of alternative specifications of the TTO," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(1), pages 43-51, July.
    7. Jen-Yu Amy Chang & Chien-Ning Hsu & Juan Manuel Ramos-Goñi & Nan Luo & Hsiang-Wen Lin & Fang-Ju Lin, 2024. "Beyond 10-year lead-times in EQ-5D-5L: leveraging alternative lead-times in willingness-to-accept questions to capture preferences for worse-than-dead states and their implication," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(6), pages 1041-1055, August.
    8. Versteegh, MM & Attema, AE & Oppe, M & Devlin, NJ & Stolk, EA, 2012. "Time to tweak the TTO. But how?," MPRA Paper 37989, University Library of Munich, Germany.
    9. Lancsar, Emily & Louviere, Jordan & Flynn, Terry, 2007. "Several methods to investigate relative attribute impact in stated preference experiments," Social Science & Medicine, Elsevier, vol. 64(8), pages 1738-1753, April.
    10. Terry N. Flynn & Elisabeth Huynh & Tim J. Peters & Hareth Al‐Janabi & Sam Clemens & Alison Moody & Joanna Coast, 2015. "Scoring the Icecap‐a Capability Instrument. Estimation of a UK General Population Tariff," Health Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 258-269, March.
    11. Shah, Koonal K. & Tsuchiya, Aki & Wailoo, Allan J., 2015. "Valuing health at the end of life: A stated preference discrete choice experiment," Social Science & Medicine, Elsevier, vol. 124(C), pages 48-56.
    12. Simone Mueller & Larry Lockshin & Jordan Louviere, 2010. "What you see may not be what you get: Asking consumers what matters may not reflect what they choose," Marketing Letters, Springer, vol. 21(4), pages 335-350, December.
    13. Cooper, Bethany & Crase, Lin & Rose, John M., 2018. "Cost-reflective pricing: empirical insights into irrigators’ preferences for water tariffs," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(2), April.
    14. Eleanor Pullenayegum & Feng Xie, 2013. "Scoring the 5-Level EQ-5D," Medical Decision Making, , vol. 33(4), pages 567-578, May.
    15. Arthur E. Attema & Matthijs M. Versteegh, 2013. "Would You Rather Be Ill Now, Or Later?," Health Economics, John Wiley & Sons, Ltd., vol. 22(12), pages 1496-1506, December.
    16. Jeannette Winkelhage & Adele Diederich, 2012. "The Relevance of Personal Characteristics in Allocating Health Care Resources—Controversial Preferences of Laypersons with Different Educational Backgrounds," IJERPH, MDPI, vol. 9(1), pages 1-21, January.
    17. Soto, Jose R. & Adams, Damian C., 2012. "Estimating the Supply of Forest Carbon Offsets: A Comparison of Best- Worst and Discrete Choice Valuation Methods," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124830, Agricultural and Applied Economics Association.
    18. Alisa E White & David A Lutz & Richard B Howarth & José R Soto, 2018. "Small-scale forestry and carbon offset markets: An empirical study of Vermont Current Use forest landowner willingness to accept carbon credit programs," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
    19. T.N. Flynn & A.A.J. Marley, 2014. "Best-worst scaling: theory and methods," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 8, pages 178-201, Edward Elgar Publishing.
    20. KANEKO Shinji & KAWATA Keisuke & YIN Ting, 2019. "Estimating Family Preference for Home Elderly-care Services: Large-scale Conjoint Survey Experiment in Japan," Discussion papers 19092, Research Institute of Economy, Trade and Industry (RIETI).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eujhec:v:15:y:2014:i:3:p:281-288. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.