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Quality of Life Research

, Volume 25, Issue 3, pp 637–649 | Cite as

Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format

  • R. Norman
  • R. Viney
  • N. K. Aaronson
  • J. E. Brazier
  • D. Cella
  • D. S. J. Costa
  • P. M. Fayers
  • G. Kemmler
  • S. Peacock
  • A. S. Pickard
  • D. Rowen
  • D. J. Street
  • G. Velikova
  • T. A. Young
  • M. T. King
Article

Abstract

Purpose

To assess the feasibility of using a discrete choice experiment (DCE) to value health states within the QLU-C10D, a utility instrument derived from the QLQ-C30, and to assess clarity, difficulty, and respondent preference between two presentation formats.

Methods

We ran a DCE valuation task in an online panel (N = 430). Respondents answered 16 choice pairs; in half of these, differences between dimensions were highlighted, and in the remainder, common dimensions were described in text and differing attributes were tabulated. To simplify the cognitive task, only four of the QLU-C10D’s ten dimensions differed per choice set. We assessed difficulty and clarity of the valuation task with Likert-type scales, and respondents were asked which format they preferred. We analysed the DCE data by format with a conditional logit model and used Chi-squared tests to compare other responses by format. Semi-structured telephone interviews (N = 8) explored respondents’ cognitive approaches to the valuation task.

Results

Four hundred and forty-nine individuals were recruited, 430 completed at least one choice set, and 422/449 (94 %) completed all 16 choice sets. Interviews revealed that respondents found ten domains difficult but manageable, many adopting simplifying heuristics. Results for clarity and difficulty were identical between formats, but the “highlight” format was preferred by 68 % of respondents. Conditional logit parameter estimates were monotonic within domains, suggesting respondents were able to complete the DCE sensibly, yielding valid results.

Conclusion

A DCE valuation task in which only four of the QLU-C10D’s ten dimensions differed in any choice set is feasible for deriving utility weights for the QLU-C10D.

Keywords

Quality of life Utility QLQ-C30 Discrete choice experiment Cancer 

Notes

Acknowledgments

The MAUCa Consortium, in addition to those named as authors, consists of the following members, all of whom made some contribution to the research reported in this paper: Helen McTaggart-Cowan, Peter Grimison, Monika Janda, and Julie Pallant. This research was supported by a National Health and Medical Research Council (Australia) Project Grant (632662). Associate Professor Janda was supported by a NHMRC Career Development Award 1045247. Dr. Norman was supported by a NHMRC early career research fellowship (1069732). Professor King was supported by the Australian Government through Cancer Australia.

Funding

This research was supported by a National Health and Medical Research Council (Australia) Project Grant (632662). Dr. Norman was supported by a NHMRC early career research fellowship (1069732). Professor King was supported by the Australian Government through Cancer Australia.

Compliance with ethical standards

Conflict of interest

The authors declare they do not have conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the University of Sydney Human Research Ethics Committee, Approval Number 2012/2444.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11136_2015_1115_MOESM1_ESM.docx (180 kb)
Supplementary material 1 (DOCX 179 kb)
11136_2015_1115_MOESM2_ESM.docx (14 kb)
Supplementary material 2 (DOCX 13 kb)

References

  1. 1.
    Bansback, N., Brazier, J., Tsuchiya, A., & Anis, A. (2012). Using a discrete choice experiment to estimate societal health state utility values. Journal of Health Economics, 31, 306–318.CrossRefPubMedGoogle Scholar
  2. 2.
    Norman, R., Cronin, P., & Viney, R. (2013). A pilot discrete choice experiment to explore preferences for EQ-5D-5L health states. Applied Health Economics and Health Policy, 11(3), 287–298.CrossRefPubMedGoogle Scholar
  3. 3.
    Norman, R., Viney, R., Brazier, J., Burgess, L., Cronin, P., King, M., et al. (2014). Valuing SF-6D health states using a discrete choice experiment. Medical Decision Making, 34(6), 773–786.CrossRefPubMedGoogle Scholar
  4. 4.
    Stolk, E. A., Oppe, M., Scalone, L., & Krabbe, P. F. M. (2010). Discrete choice modeling for the quantification of health states: The case of the EQ-5D. Value in Health, 13(8), 1005–1013.CrossRefPubMedGoogle Scholar
  5. 5.
    Viney, R., Norman, R., Brazier, J., Cronin, P., King, M. T., Ratcliffe, J., & Street, D. (2014). An Australian discrete choice experiment to value EQ-5D health states. Health Economics, 23(6), 729–742.CrossRefPubMedGoogle Scholar
  6. 6.
    Louviere, J., Carson, R. T., Burgess, L., Street, D., & Marley, A. A. (2013). Sequential preference question factors influencing completion rates and response times using an online panel. The Journal of Choice Modelling, 8, 19–31.CrossRefGoogle Scholar
  7. 7.
    Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271–292.CrossRefPubMedGoogle Scholar
  8. 8.
    Brazier, J., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42(9), 851–859.CrossRefPubMedGoogle Scholar
  9. 9.
    Rowen, D., Brazier, J., Young, T., Gaugris, S., Craig, B. M., King, M. T., & Velikova, G. (2011). Deriving a preference-based measure for cancer using the EORTC QLQ-C30. Value in Health, 14(5), 721–731.CrossRefPubMedGoogle Scholar
  10. 10.
    King, M. T., Costa, D. S. J., Aaronson, N. K., Brazier, J. E., Cella, D. F., Fayers, P. M., et al. (submitted). QLU-C10D: A health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30. Quality of Life Research (currently under review).Google Scholar
  11. 11.
    Ware, J. E., Jr., & Gandek, B. (1998). Overview of the SF-36 health survey and the international quality of life assessment (IQOLA) project. Journal of Clinical Epidemiology, 51(11), 903–912.CrossRefPubMedGoogle Scholar
  12. 12.
    Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organisation for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85(5), 365–376.CrossRefPubMedGoogle Scholar
  13. 13.
    Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S. L., et al. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32(6), 959–976.CrossRefPubMedGoogle Scholar
  14. 14.
    Herdman, M., Gudex, C., Lloyd, A., Janssen, M. F., Kind, P., Parkin, D., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Colbourn, C. J., & Dinitz, J. H. (2006). Handbook of Combinatorial designs. Boca Raton, FL: Taylor and Francis.CrossRefGoogle Scholar
  16. 16.
    Street, D. J., & Burgess, L. (2007). The construction of optimal stated choice experiments: Theory and methods. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  17. 17.
    Demirkale, F., Donovan, D., & Street, D. J. (2013). Constructing D-optimal symmetric stated preference discrete choice experiments. Journal of Statistical Planning and Inference, 143, 1380–1391.CrossRefGoogle Scholar
  18. 18.
    Bleichrodt, H., & Johannesson, M. (1997). The validity of QALYs: An experimental test of constant proportional tradeoff and utility independence. Medical Decision Making, 17(1), 21–32.CrossRefPubMedGoogle Scholar
  19. 19.
    Bleichrodt, N., Wakker, P., & Johannesson, M. (1997). Characterizing QALYs by risk neutrality. Journal of Risk and Uncertainty, 15(2), 107–114.CrossRefGoogle Scholar
  20. 20.
    Ritchie, J., & Spencer, L. (1994). Qualitative data analysis for applied policy research. In A. Bryman & R. Burgess (Eds.), Analyzing qualitative data (pp. 173–194). London: Routledge.CrossRefGoogle Scholar
  21. 21.
    Craig, B. M., Reeve, B. B., Brown, P. M., Cella, D., Hays, R. D., Lipscomb, J., et al. (2014). US valuation of health outcomes measured using the PROMIS-29. Value in Health, 17(8), 846–853.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Chrzan, K. (2010). Using partial profile choice experiments to handle large numbers of attributes. International Journal of Marketing Research, 52(6), 827–840.Google Scholar
  23. 23.
    Flynn, T. (2010). Using conjoint analysis to estimate health state values for cost-utility analysis: Issues to consider. Pharmacoeconomics, 28(9), 711–722.CrossRefPubMedGoogle Scholar
  24. 24.
    Vass, C., Rigby, D., Campbell, S., Tate, K., Stewart, A., & Payne, K. (2014). PS2-33 investigating the framing of risk attributes in a discrete choice experiment: An application of eye-tracking and think aloud. In Paper presented at the 36th meeting of the Society for Medical Decision Making, Miami, FL.Google Scholar
  25. 25.
    Krucien, N., Ryan, M., & Hermens, F. (2014). Using eye-tracking methods to inform decision making processes in discrete choice experiments, Health Economists’ Study Group (HESG). Glasgow Caledonian University.Google Scholar
  26. 26.
    Whitty, J. A., Ratcliffe, J., Chen, G., & Scuffham, P. A. (2014). Australian public preferences for the funding of new health technologies: A comparison of discrete choice and profile case best–worst scaling methods. Medical Decision Making, 34(5), 638–654.Google Scholar
  27. 27.
    van der Pol, M., Currie, G., Kromm, S., & Ryan, M. (2014). Specification of the utility function in discrete choice experiments. Value in Health, 17(2), 297–301.CrossRefPubMedGoogle Scholar
  28. 28.
    Mulhern, B., Bansback, N., Brazier, J., Buckingham, K., Cairns, J., Devlin, N., et al. (2014). Preparatory study for the revaluation of the EQ-5D tariff: Methodology report. Health Technology Assessment, 18(12), vii–xxvi, 1–191.Google Scholar
  29. 29.
    Bansback, N., Tsuchiya, A., Brazier, J., & Anis, A. (2012). Canadian valuation of EQ-5D health states: Preliminary value set and considerations for future valuation studies. PLoS One, 7(2), e31115.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • R. Norman
    • 1
    • 2
  • R. Viney
    • 2
  • N. K. Aaronson
    • 3
  • J. E. Brazier
    • 4
  • D. Cella
    • 5
  • D. S. J. Costa
    • 6
  • P. M. Fayers
    • 7
    • 8
  • G. Kemmler
    • 9
  • S. Peacock
    • 10
    • 11
    • 12
  • A. S. Pickard
    • 13
  • D. Rowen
    • 4
  • D. J. Street
    • 14
  • G. Velikova
    • 15
    • 16
  • T. A. Young
    • 4
  • M. T. King
    • 6
    • 17
  1. 1.School of Public HealthCurtin UniversityPerthAustralia
  2. 2.Centre for Health Economics Research and Evaluation (CHERE)University of Technology Sydney (UTS)SydneyAustralia
  3. 3.The Netherlands Cancer InstituteAmsterdamThe Netherlands
  4. 4.School of Health and Related ResearchUniversity of SheffieldSheffieldUK
  5. 5.Department of Medical Social Sciences, Feinberg School of MedicineNorthwestern UniversityChicagoUSA
  6. 6.Psycho-Oncology Cooperative Research Group (PoCoG)University of SydneySydneyAustralia
  7. 7.Institute of Applied Health SciencesUniversity of AberdeenAberdeenUK
  8. 8.Department of Cancer Research and Molecular MedicineNorwegian University of Science and Technology (NTNU)TrondheimNorway
  9. 9.Innsbruck Medical UniversityInnsbruckAustria
  10. 10.Faculty of Health SciencesSimon Fraser UniversityVancouverCanada
  11. 11.Canadian Centre for Applied Research in Cancer Control (ARCC)VancouverCanada
  12. 12.British Columbia Cancer AgencyVancouverCanada
  13. 13.Department of Pharmacy Systems, Outcomes and Policy, College of PharmacyUniversity of Illinois at ChicagoChicagoUSA
  14. 14.School of Mathematical and Physical SciencesUniversity of Technology SydneySydneyAustralia
  15. 15.Leeds Institute of Cancer and PathologyUniversity of LeedsLeedsUK
  16. 16.St James’s HospitalLeedsUK
  17. 17.Central Clinical School, Sydney Medical SchoolUniversity of SydneySydneyAustralia

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