Skip to main content
Log in

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

  • Published:
Quality of Life Research Aims and scope Submit manuscript

An Erratum to this article was published on 23 March 2017

An Erratum to this article was published on 08 April 2016

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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).

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Colbourn, C. J., & Dinitz, J. H. (2006). Handbook of Combinatorial designs. Boca Raton, FL: Taylor and Francis.

    Book  Google Scholar 

  16. Street, D. J., & Burgess, L. (2007). The construction of optimal stated choice experiments: Theory and methods. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  19. Bleichrodt, N., Wakker, P., & Johannesson, M. (1997). Characterizing QALYs by risk neutrality. Journal of Risk and Uncertainty, 15(2), 107–114.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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. Flynn, T. (2010). Using conjoint analysis to estimate health state values for cost-utility analysis: Issues to consider. Pharmacoeconomics, 28(9), 711–722.

    Article  PubMed  Google Scholar 

  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.

  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.

  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.

  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.

    Article  PubMed  Google Scholar 

  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.

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Norman.

Ethics declarations

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.

Additional information

On behalf of the MAUCa Consortium.

An erratum to this article is available at http://dx.doi.org/10.1007/s11136-017-1546-0.

An erratum to this article is available at http://dx.doi.org/10.1007/s11136-016-1289-3.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 179 kb)

Supplementary material 2 (DOCX 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Norman, R., Viney, R., Aaronson, N.K. et al. Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format. Qual Life Res 25, 637–649 (2016). https://doi.org/10.1007/s11136-015-1115-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-015-1115-3

Keywords

Navigation