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United States Utility Algorithm for the EORTC QLU-C10D, a Multiattribute Utility Instrument Based on a Cancer-Specific Quality-of-Life Instrument

Author

Listed:
  • Dennis A. Revicki

    (Patient-Centered Outcomes Research, Evidera, Bethesda, MD, USA)

  • Madeleine T. King

    (School of Psychology, Sydney, University of Sydney, New South Wales, Australia)

  • Rosalie Viney

    (Centre for Health Economics Research & Evaluation, UTS Business School, University of Technology Sydney, Sydney, New South Wales, Australia)

  • A. Simon Pickard

    (Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA)

  • Rebecca Mercieca-Bebber

    (School of Psychology, Sydney, University of Sydney, New South Wales, Australia
    NHMRC Clinical Trials Centre, Faculty of Medicine, University of Sydney, Sydney, New South Wales, Australia)

  • James W. Shaw

    (Patient-Reported Outcomes Assessment, Worldwide Health Economics and Outcomes Research, Bristol Myers Squibb, Lawrenceville, NJ, USA)

  • Fabiola Müller

    (School of Psychology, Sydney, University of Sydney, New South Wales, Australia
    NHMRC Clinical Trials Centre, Faculty of Medicine, University of Sydney, Sydney, New South Wales, Australia
    Department of Medical Psychology, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, the Netherlands)

  • Richard Norman

    (School of Population Health, Curtin University, Perth, WA, Australia)

Abstract

Background The EORTC QLU-C10D is a multiattribute utility measure derived from the cancer-specific quality-of-life questionnaire, the EORTC QLQ-C30. The QLU-C10D contains 10 dimensions (physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems). The objective of this study was to develop a United States value set for the QLU-C10D. Methods A US online panel was quota recruited to achieve a representative sample for sex, age (≥18 y), race, and ethnicity. Respondents undertook a discrete choice experiment, each completing 16 choice-pairs, randomly assigned from a total of 960 choice-pairs. Each pair included 2 QLU-C10D health states and duration. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy. Results A total of 2480 panel members opted in, 2333 (94%) completed at least 1 choice-pair, and 2273 (92%) completed all choice-pairs. Within dimensions, weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Cancer-specific dimensions, such as nausea and bowel problems, were associated with moderate utility decrements, as were general issues such as problems with emotional functioning and social functioning. Sleep problems and fatigue were associated with smaller utility decrements. The value of the worst health state was 0.032, which was slightly greater than 0 (equivalent to being dead). Conclusions This study provides the US-specific value set for the QLU-C10D. These estimated health state scores, based on responses to the EORTC QLQ-C30 questionnaire, can be used to evaluate the cost-utility of oncology treatments.

Suggested Citation

  • Dennis A. Revicki & Madeleine T. King & Rosalie Viney & A. Simon Pickard & Rebecca Mercieca-Bebber & James W. Shaw & Fabiola Müller & Richard Norman, 2021. "United States Utility Algorithm for the EORTC QLU-C10D, a Multiattribute Utility Instrument Based on a Cancer-Specific Quality-of-Life Instrument," Medical Decision Making, , vol. 41(4), pages 485-501, May.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:4:p:485-501
    DOI: 10.1177/0272989X211003569
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    References listed on IDEAS

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    1. Arne Risa Hole, 2007. "A comparison of approaches to estimating confidence intervals for willingness to pay measures," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 827-840, August.
    2. Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
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    4. 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.
    5. Rosalie Viney & Richard Norman & John Brazier & Paula Cronin & Madeleine T. King & Julie Ratcliffe & Deborah Street, 2014. "An Australian Discrete Choice Experiment To Value Eq‐5d Health States," Health Economics, John Wiley & Sons, Ltd., vol. 23(6), pages 729-742, June.
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    Cited by:

    1. Haode Wang & Donna L. Rowen & John E. Brazier & Litian Jiang, 2023. "Discrete Choice Experiments in Health State Valuation: A Systematic Review of Progress and New Trends," Applied Health Economics and Health Policy, Springer, vol. 21(3), pages 405-418, May.
    2. Micha J. Pilz & Simone Seyringer & Imad Al-Naesan & Madeleine T. King & Andrew Bottomley & Richard Norman & Lisa Schlosser & Tobias Hell & Eva Maria Gamper, 2024. "Cancer-Specific Health Utilities: Evaluation of Core Measurement Properties of the EORTC QLU-C10D in Lung Cancer Patients—Data from Four Multicentre LUX-Lung Trials, Applying Six Country Tariffs," PharmacoEconomics - Open, Springer, vol. 8(4), pages 627-640, July.
    3. 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.
    4. 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.
    5. Yiyin Cao & Haofei Li & Ling Jie Cheng & Madeleine T. King & Georg Kemmler & David Cella & Hongjuan Yu & Weidong Huang & Nan Luo, 2024. "A comparison of measurement properties between EORTC QLU-C10D and FACT-8D in patients with hematological malignances," Health Economics Review, Springer, vol. 14(1), pages 1-10, December.
    6. Chen-Wei Pan & Jun-Yi He & Yan-Bo Zhu & Chun-Hua Zhao & Nan Luo & Pei Wang, 2023. "Comparison of EQ-5D-5L and EORTC QLU-C10D utilities in gastric cancer patients," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(6), pages 885-893, August.
    7. Micha J. Pilz & Simon Seyringer & Lára R. Hallsson & Andrew Bottomley & Femke Jansen & Madeleine T. King & Richard Norman & Marianne J. Rutten & Irma M. Verdonck-de Leeuw & Peter D. Siersema & Eva Mar, 2024. "The EORTC QLU-C10D is a valid cancer-specific preference-based measure for cost-utility and health technology assessment in the Netherlands," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(9), pages 1539-1555, December.

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