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The Role of Condition-Specific Preference-Based Measures in Health Technology Assessment

Author

Listed:
  • Donna Rowen

    (University of Sheffield)

  • John Brazier

    (University of Sheffield)

  • Roberta Ara

    (University of Sheffield)

  • Ismail Azzabi Zouraq

    (Takeda Pharmaceuticals International AG)

Abstract

A condition-specific preference-based measure (CSPBM) is a measure of health-related quality of life (HRQOL) that is specific to a certain condition or disease and that can be used to obtain the quality adjustment weight of the quality-adjusted life-year (QALY) for use in economic models. This article provides an overview of the role and the development of CSPBMs, and presents a description of existing CSPBMs in the literature. The article also provides an overview of the psychometric properties of CSPBMs in comparison with generic preference-based measures (generic PBMs), and considers the advantages and disadvantages of CSPBMs in comparison with generic PBMs. CSPBMs typically include dimensions that are important for that condition but may not be important across all patient groups. There are a large number of CSPBMs across a wide range of conditions, and these vary from covering a wide range of dimensions to more symptomatic or uni-dimensional measures. Psychometric evidence is limited but suggests that CSPBMs offer an advantage in more accurate measurement of milder health states. The mean change and standard deviation can differ for CSPBMs and generic PBMs, and this may impact on incremental cost-effectiveness ratios. CSPBMs have a useful role in HTA where a generic PBM is not appropriate, sensitive or responsive. However, due to issues of comparability across different patient groups and interventions, their usage in health technology assessment is often limited to conditions where it is inappropriate to use a generic PBM or sensitivity analyses.

Suggested Citation

  • Donna Rowen & John Brazier & Roberta Ara & Ismail Azzabi Zouraq, 2017. "The Role of Condition-Specific Preference-Based Measures in Health Technology Assessment," PharmacoEconomics, Springer, vol. 35(1), pages 33-41, December.
    • Handle: RePEc:spr:pharme:v:35:y:2017:i:1:d:10.1007_s40273-017-0546-9
    DOI: 10.1007/s40273-017-0546-9
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    References listed on IDEAS

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    1. Donna Rowen & Ismail Azzabi Zouraq & Helene Chevrou-Severac & Ben Hout, 2017. "International Regulations and Recommendations for Utility Data for Health Technology Assessment," PharmacoEconomics, Springer, vol. 35(1), pages 11-19, December.
    2. Julie Ratcliffe & John Brazier & Aki Tsuchiya & Tara Symonds & Martin Brown, 2009. "Using DCE and ranking data to estimate cardinal values for health states for deriving a preference‐based single index from the sexual quality of life questionnaire," Health Economics, John Wiley & Sons, Ltd., vol. 18(11), pages 1261-1276, November.
    3. John Brazier & Roberta Ara & Donna Rowen & Helene Chevrou-Severac, 2017. "A Review of Generic Preference-Based Measures for Use in Cost-Effectiveness Models," PharmacoEconomics, Springer, vol. 35(1), pages 21-31, December.
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    Cited by:

    1. Samer A. Kharroubi & Yara Beyh & Marwa Diab El Harake & Dalia Dawoud & Donna Rowen & John Brazier, 2020. "Examining the Feasibility and Acceptability of Valuing the Arabic Version of SF-6D in a Lebanese Population," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
    2. Madeleine T. King & Rosalie Viney & A. Simon Pickard & Donna Rowen & Neil K. Aaronson & John E. Brazier & David Cella & Daniel S. J. Costa & Peter M. Fayers & Georg Kemmler & Helen McTaggart-Cowen & R, 2018. "Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30," PharmacoEconomics, Springer, vol. 36(2), pages 225-238, February.
    3. Richard Huan Xu & Eliza Lai-yi Wong & Nan Luo & Richard Norman & Jens Lehmann & Bernhard Holzner & Madeleine T. King & Georg Kemmler, 2024. "The EORTC QLU-C10D: the Hong Kong valuation study," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(5), pages 889-901, July.
    4. Roberta Ara & John Brazier & Tessa Peasgood & Suzy Paisley, 2017. "The Identification, Review and Synthesis of Health State Utility Values from the Literature," PharmacoEconomics, Springer, vol. 35(1), pages 43-55, December.
    5. Samer A. Kharroubi & Donna Rowen, 2019. "Valuation of preference-based measures: can existing preference data be used to select a smaller sample of health states?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(2), pages 245-255, March.
    6. Koonal K. Shah & Bryan Bennett & Andrew Lenny & Louise Longworth & John E. Brazier & Mark Oppe & A. Simon Pickard & James W. Shaw, 2021. "Adapting preference-based utility measures to capture the impact of cancer treatment-related symptoms," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(8), pages 1301-1309, November.
    7. Richard Norman & Rebecca Mercieca‐Bebber & Donna Rowen & John E. Brazier & David Cella & A. Simon Pickard & Deborah J. Street & Rosalie Viney & Dennis Revicki & Madeleine T. King & On behalf of the Eu, 2019. "U.K. utility weights for the EORTC QLU‐C10D," Health Economics, John Wiley & Sons, Ltd., vol. 28(12), pages 1385-1401, December.
    8. Samer A. Kharroubi & Yara Beyh, 2021. "Bayesian modeling of health state preferences: could borrowing strength from existing countries’ valuations produce better estimates," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 773-788, July.

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