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Comparison of health state utility values derived using time trade-off, rank and discrete choice data anchored on the full health-dead scale

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  • John Brazier
  • Donna Rowen
  • Yaling Yang
  • Aki Tsuchiya

Abstract

Recent years have seen increasing interest in the use of ordinal methods to elicit health state utility values as an alternative to conventional methods such as standard gamble and time trade-off (TTO). However, in order to use these ordinal methods to produce health state values for use in cost-effectiveness analysis using cost per quality adjusted life year (QALY) analysis, these values must be anchored on the full health-dead scale. The paper reports on two feasibility studies that use two approaches to anchor health state utility values derived from discrete choice data on the full health-dead scale: normalising using (1) the TTO value of the worst state and (2) the coefficient on the ‘dead’ dummy variable. Health state utility values obtained using rank and discrete choice data are compared to more commonly used TTO utility values for two condition-specific preference-based measures; asthma and overactive bladder. Ordinal methods were found to offer a promising alternative to conventional cardinal methods of standard gamble and TTO. There remains a large and important research agenda to address. Copyright Springer-Verlag 2012

Suggested Citation

  • John Brazier & Donna Rowen & Yaling Yang & Aki Tsuchiya, 2012. "Comparison of health state utility values derived using time trade-off, rank and discrete choice data anchored on the full health-dead scale," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(5), pages 575-587, October.
    • Handle: RePEc:spr:eujhec:v:13:y:2012:i:5:p:575-587
    DOI: 10.1007/s10198-011-0352-9
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    Cited by:

    1. Martijn S Visser & Sankha Amarakoon & Tom Missotten & Reinier Timman & Jan J Busschbach, 2017. "SF-6D utility values for the better- and worse-seeing eye for health states based on the Snellen equivalent in patients with age-related macular degeneration," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-9, February.
    2. Luciana Scalone & Peep Stalmeier & Silvano Milani & Paul Krabbe, 2015. "Values for health states with different life durations," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(9), pages 917-925, December.
    3. Matthew Franklin & Katherine Payne & Rachel A. Elliott, 2018. "Quantifying the Relationship between Capability and Health in Older People: Can’t Map, Won’t Map," Medical Decision Making, , vol. 38(1), pages 79-94, January.
    4. Mina Bahrampour & Joshua Byrnes & Richard Norman & Paul A. Scuffham & Martin Downes, 2020. "Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(7), pages 983-992, September.
    5. Osman, Ahmed M.Y. & Wu, Jing & He, Xiaoning & Chen, Gang, 2021. "Eliciting SF-6Dv2 health state utilities using an anchored best-worst scaling technique," Social Science & Medicine, Elsevier, vol. 279(C).

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    More about this item

    Keywords

    Ranking; Discrete choice experiment; Health state utility values; Preference-based measures; QALYs; I10 - health; General;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General

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