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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

Author

Listed:
  • Madeleine T. King

    (University of Sydney, Faculty of Science, School of Psychology, Psycho-Oncology Co-operative Research Group, Quality of Life Office
    University of Sydney, Faculty of Medicine, Sydney Medical School)

  • Rosalie Viney

    (University of Technology Sydney (UTS))

  • A. Simon Pickard

    (University of Illinois at Chicago)

  • Donna Rowen

    (University of Sheffield)

  • Neil K. Aaronson

    (The Netherlands Cancer Institute)

  • John E. Brazier

    (University of Sheffield)

  • David Cella

    (Northwestern University)

  • Daniel S. J. Costa

    (University of Sydney, Faculty of Science, School of Psychology, Psycho-Oncology Co-operative Research Group, Quality of Life Office
    University of Sydney, Faculty of Medicine, Sydney Medical School)

  • Peter M. Fayers

    (University of Aberdeen
    Norwegian University of Science and Technology)

  • Georg Kemmler

    (Innsbruck Medical University)

  • Helen McTaggart-Cowen

    (Canadian Centre for Applied Research in Cancer Control and British Columbia Cancer Agency)

  • Rebecca Mercieca-Bebber

    (University of Sydney, Faculty of Science, School of Psychology, Psycho-Oncology Co-operative Research Group, Quality of Life Office
    University of Sydney, Faculty of Medicine, Sydney Medical School)

  • Stuart Peacock

    (Canadian Centre for Applied Research in Cancer Control and British Columbia Cancer Agency)

  • Deborah J. Street

    (University of Technology Sydney (UTS))

  • Tracey A. Young

    (University of Sheffield)

  • Richard Norman

    (Curtin University)

Abstract

Background The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required. Objective The aim of this study was to provide Australian utility weights for the QLU-C10D. Methods An Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy. Results A total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death. Conclusions This study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:pharme:v:36:y:2018:i:2:d:10.1007_s40273-017-0582-5
    DOI: 10.1007/s40273-017-0582-5
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    References listed on IDEAS

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    1. 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.
    2. Richard Norman & Paula Cronin & Rosalie Viney, 2013. "A Pilot Discrete Choice Experiment to Explore Preferences for EQ-5D-5L Health States," Applied Health Economics and Health Policy, Springer, vol. 11(3), pages 287-298, June.
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    Cited by:

    1. Spencer, Anne & Rivero-Arias, Oliver & Wong, Ruth & Tsuchiya, Aki & Bleichrodt, Han & Edwards, Rhiannon Tudor & Norman, Richard & Lloyd, Andrew & Clarke, Philip, 2022. "The QALY at 50: One story many voices," Social Science & Medicine, Elsevier, vol. 296(C).
    2. 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.
    3. Moustapha Touré & Christian R. C. Kouakou & Thomas G. Poder, 2021. "Dimensions Used in Instruments for QALY Calculation: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-22, April.
    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.

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