Advertisement

Applied Health Economics and Health Policy

, Volume 11, Issue 3, pp 287–298 | Cite as

A Pilot Discrete Choice Experiment to Explore Preferences for EQ-5D-5L Health States

  • Richard Norman
  • Paula Cronin
  • Rosalie Viney
Original Research Article

Abstract

Background

The EQ-5D-5L has recently been developed to improve the sensitivity of the widely used three-level version. Valuation studies are required before the use of this new instrument can be adopted. The use of discrete choice experiments (DCEs) in this area is a promising area of research.

Purpose

To test the plausibility and acceptability of estimating an Australian algorithm for the newly developed five-level version of the EQ-5D using a DCE.

Methods

A choice experiment was designed, consisting of 200 choice sets blocked such that each respondent answered 10 choice sets. Each choice set presented two health state–duration combinations, and an immediate death option. The experiment was implemented in an online Australian-representative sample. A random-effects probit model was estimated. To explore the feasibility of the approach, an indicative algorithm was developed. The algorithm is transformed to a 0 to 1 scale suitable for use to estimate quality-adjusted life-year weights for use in economic evaluation.

Results

A total of 973 respondents undertook the choice experiment. Respondents were slightly younger and better educated than the general Australian population. Of the 973 respondents, 932 (95.8 %) completed all ten choice sets, and a further 12 completed some of the choice sets. In choice sets in which one health state–duration combination dominated another, the dominant option was selected on 89.5 % of occasions. The mean and median completion times were 17.9 and 9.4 min, respectively, exhibiting a highly skewed distribution. The estimation results are broadly consistent with the monotonic nature of the EQ-5D-5L. Utility is increasing in life expectancy (i.e., respondents tend to prefer health profiles with longer life expectancy), and mainly decreases in higher levels in each dimension of the instrument. A high proportion of respondents found the task clear and relatively easy to complete.

Conclusions

DCEs are a feasible approach to the estimation of utility weights for more complex multi-attribute utility instruments such as the EQ-5D-5L.

Keywords

Discrete Choice Experiment Health Profile Full Health Death State Random Utility Theory 
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 fieldwork was funded through a grant from the Faculty of Business, University of Technology, Sydney. Other than funding, the Faculty had no input in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Conflict of interest

No authors have a conflict of interests regarding this study.

Contribution

RN led the design and administration of the survey, was primarily responsible for data analysis, and for the drafting of the article. He is the guarantor for the overall content. PC and RV provided guidance on the administration of the survey, and gave input into data analysis and drafting of the article.

References

  1. 1.
    Richardson J, McKie J, Bariola E. Review and critique of health related multi attribute utility instruments. Research Paper 64. Melbourne: Centre for Health Economics, Monash University; 2011.Google Scholar
  2. 2.
    Herdman M, Gudex C, Lloyd A, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727–36.PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Van Hout B, Janssen MF, Feng Y-S, et al. Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Health (in press).Google Scholar
  4. 4.
    Norman R, Cronin P, Viney R, King M, Street D, Ratcliffe J. International comparisons in valuing EQ-5D health states: a review and analysis. Value Health. 2009;12(8):1194–200.PubMedCrossRefGoogle Scholar
  5. 5.
    Dolan P. Modelling valuations for EuroQol health states. Med Care. 1997;35(11):1095–108.PubMedCrossRefGoogle Scholar
  6. 6.
    Tsuchiya A, Ikeda S, Ikegami N, et al. Estimating an EQ-5D population value set: the case of Japan. Health Econ. 2002;11(4):341–53.PubMedCrossRefGoogle Scholar
  7. 7.
    Tilling C, Devlin N, Tsuchiya A, Buckingham K. Protocols for time tradeoff valuations of health states worse than dead: a literature review. Med Decis Making. 2010;30(5):610–9.PubMedCrossRefGoogle Scholar
  8. 8.
    Robinson A, Spencer A. Exploring challenges to TTO utilities: valuing states worse than dead. Health Econ. 2006;15(4):393–402.PubMedCrossRefGoogle Scholar
  9. 9.
    Bansback N, Brazier J, Tsuchiya A, Anis A. Using a discrete choice experiment to estimate societal health state utility values. J Health Econ. 2012;31:306–18.PubMedCrossRefGoogle Scholar
  10. 10.
    Stolk EA, Oppe M, Scalone L, Krabbe PFM. Discrete choice modeling for the quantification of health states: the case of the EQ-5D. Value Health. 2010;13(8):1005–13.PubMedCrossRefGoogle Scholar
  11. 11.
    Norman R, Viney R, Brazier J, Cronin P, King M, Ratcliffe J, Street D. Valuing EQ-5D health states: the australian experience. 2012 EuroQol Group Scientific Plenary. Rotterdam, 13/14 Sep 2012.Google Scholar
  12. 12.
    Street DJ, Burgess L. The construction of optimal stated choice experiments: theory and methods. Hoboken: Wiley; 2007.CrossRefGoogle Scholar
  13. 13.
    McFadden D. Conditional logit analysis of qualitative choice behaviour. In: Zarembka P, editor. Frontiers in econometrics. New York: New York Academic Press; 1974. p. 105–142.Google Scholar
  14. 14.
    Thurstone LL. A law of comparative judgment. Psychol Rev. 1927;34:273–86.CrossRefGoogle Scholar
  15. 15.
    Viney R, Norman R, King MT, et al. Time trade-off derived EQ-5D weights for Australia. Value Health. 2011;14:928–36.PubMedCrossRefGoogle Scholar
  16. 16.
    Coast J, Flynn TN, Salisbury C, Louviere J, Peters TJ. Maximising responses to discrete choice experiments: a randomised trial. Appl Health Econ Health Policy. 2006;5(4):249–60.PubMedCrossRefGoogle Scholar
  17. 17.
    Bleichrodt N, Wakker P, Johannesson M. Characterizing QALYs by risk neutrality. J Risk Uncertain. 1997;15(2):107–14.CrossRefGoogle Scholar
  18. 18.
    Hole AR. A comparison of approaches to estimating confidence intervals for willingness to pay measures. Health Econ. 2007;16:827–40.PubMedCrossRefGoogle Scholar
  19. 19.
    Wittenberg E, Prosser LA. Ordering errors, objections and invariance in utility survey responses: a framework for understanding who, why and what to do. Appl Health Econ Health Policy. 2011;9(4):225–41.PubMedCrossRefGoogle Scholar
  20. 20.
    Bowling A. Mode of questionnaire administration can have serious effects on data quality. J Public Health (Oxford). 2005;27(3):281–91.CrossRefGoogle Scholar
  21. 21.
    Flynn TN, Louviere JJ, Peters TJ, Coast J. Best–worst scaling: what it can do for health care research and how to do it. J Health Econ. 2007;26(1):171–89.PubMedCrossRefGoogle Scholar
  22. 22.
    Greene WH, Hensher DA. Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models. Transportation. 2011;37(3):413–28.CrossRefGoogle Scholar
  23. 23.
    Rowen D, Brazier J, Van Hout B. A comparison of methods for converting DCE values onto the full health-dead QALY scale. HEDS Discussion Paper 11/15; 2011.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Centre for Health Economics Research and Evaluation (CHERE)University of TechnologySydneyAustralia

Personalised recommendations