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Accounting for Scale Heterogeneity in Healthcare-Related Discrete Choice Experiments when Comparing Stated Preferences: A Systematic Review

Author

Listed:
  • Stuart J. Wright

    (The University of Manchester)

  • Caroline M. Vass

    (The University of Manchester)

  • Gene Sim

    (The University of Manchester)

  • Michael Burton

    (University of Western Australia)

  • Denzil G. Fiebig

    (University of New South Wales)

  • Katherine Payne

    (The University of Manchester)

Abstract

Background Scale heterogeneity, or differences in the error variance of choices, may account for a significant amount of the observed variation in the results of discrete choice experiments (DCEs) when comparing preferences between different groups of respondents. Objective The aim of this study was to identify if, and how, scale heterogeneity has been addressed in healthcare DCEs that compare the preferences of different groups. Methods A systematic review identified all healthcare DCEs published between 1990 and February 2016. The full-text of each DCE was then screened to identify studies that compared preferences using data generated from multiple groups. Data were extracted and tabulated on year of publication, samples compared, tests for scale heterogeneity, and analytical methods to account for scale heterogeneity. Narrative analysis was used to describe if, and how, scale heterogeneity was accounted for when preferences were compared. Results A total of 626 healthcare DCEs were identified. Of these 199 (32%) aimed to compare the preferences of different groups specified at the design stage, while 79 (13%) compared the preferences of groups identified at the analysis stage. Of the 278 included papers, 49 (18%) discussed potential scale issues, 18 (7%) used a formal method of analysis to account for scale between groups, and 2 (1%) accounted for scale differences between preference groups at the analysis stage. Scale heterogeneity was present in 65% (n = 13) of studies that tested for it. Analytical methods to test for scale heterogeneity included coefficient plots (n = 5, 2%), heteroscedastic conditional logit models (n = 6, 2%), Swait and Louviere tests (n = 4, 1%), generalised multinomial logit models (n = 5, 2%), and scale-adjusted latent class analysis (n = 2, 1%). Conclusions Scale heterogeneity is a prevalent issue in healthcare DCEs. Despite this, few published DCEs have discussed such issues, and fewer still have used formal methods to identify and account for the impact of scale heterogeneity. The use of formal methods to test for scale heterogeneity should be used, otherwise the results of DCEs potentially risk producing biased and potentially misleading conclusions regarding preferences for aspects of healthcare.

Suggested Citation

  • Stuart J. Wright & Caroline M. Vass & Gene Sim & Michael Burton & Denzil G. Fiebig & Katherine Payne, 2018. "Accounting for Scale Heterogeneity in Healthcare-Related Discrete Choice Experiments when Comparing Stated Preferences: A Systematic Review," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(5), pages 475-488, October.
    • Handle: RePEc:spr:patien:v:11:y:2018:i:5:d:10.1007_s40271-018-0304-x
    DOI: 10.1007/s40271-018-0304-x
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    References listed on IDEAS

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    1. Debby van Helvoort‐Postulart & Benedict G. C. Dellaert & Trudy van der Weijden & Maarten F. von Meyenfeldt & Carmen D. Dirksen, 2009. "Discrete choice experiments for complex health‐care decisions: does hierarchical information integration offer a solution?," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 903-920, August.
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    4. Fiebig, Denzil G. & Haas, Marion & Hossain, Ishrat & Street, Deborah J. & Viney, Rosalie, 2009. "Decisions about Pap tests: What influences women and providers?," Social Science & Medicine, Elsevier, vol. 68(10), pages 1766-1774, May.
    5. Araña, Jorge E. & León, Carmelo J. & Quevedo, Jose L., 2006. "The effect of medical experience on the economic evaluation of health policies. A discrete choice experiment," Social Science & Medicine, Elsevier, vol. 63(2), pages 512-524, July.
    6. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    7. Hensher, David & Louviere, Jordan & Swait, Joffre, 1998. "Combining sources of preference data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 197-221, November.
    8. Burton, Michael & Davis, Katrina & Kragt, Marit Ellen, 2016. "Interpretation issues in heteroscedastic conditional logit models," Working Papers 235296, University of Western Australia, School of Agricultural and Resource Economics.
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    1. Buckell, John & Hess, Stephane, 2019. "Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data," Journal of Health Economics, Elsevier, vol. 65(C), pages 93-102.
    2. Catharina G. M. Groothuis-Oudshoorn & Terry N. Flynn & Hong Il Yoo & Jay Magidson & Mark Oppe, 2018. "Key Issues and Potential Solutions for Understanding Healthcare Preference Heterogeneity Free from Patient-Level Scale Confounds," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(5), pages 463-466, October.
    3. David J. Mott & Laura Ternent & Luke Vale, 2023. "Do preferences differ based on respondent experience of a health issue and its treatment? A case study using a public health intervention," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(3), pages 413-423, April.
    4. Caroline Vass & Dan Rigby & Kelly Tate & Andrew Stewart & Katherine Payne, 2018. "An Exploratory Application of Eye-Tracking Methods in a Discrete Choice Experiment," Medical Decision Making, , vol. 38(6), pages 658-672, August.
    5. Caroline M Vass & Anne Barton & Katherine Payne, 2022. "Towards Personalising the Use of Biologics in Rheumatoid Arthritis: A Discrete Choice Experiment," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 15(1), pages 109-119, January.
    6. Mihir Gandhi & Ravindran Kanesvaran & Mohamad Farid Bin Harunal Rashid & Dawn Qingqing Chong & Wen-Yee Chay & Rachel Lee-Yin Tan & Richard Norman & Madeleine T. King & Nan Luo, 2024. "Valuation of the EORTC Quality of Life Utility Core 10 Dimensions (QLU-C10D) in a Multi-ethnic Asian Setting: How Does Having Cancer Matter?," PharmacoEconomics, Springer, vol. 42(12), pages 1413-1425, December.
    7. 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.
    8. Julie Ratcliffe & Billingsley Kaambwa & Claire Hutchinson & Emily Lancsar, 2020. "Empirical Investigation of Ranking vs Best–Worst Scaling Generated Preferences for Attributes of Quality of Life: One and the Same or Differentiable?," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 13(3), pages 307-315, June.

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