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Using Linear Equating to Map PROMIS® Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3

Author

Listed:
  • Ron D. Hays

    (UCLA)

  • Dennis A. Revicki

    (Evidera)

  • David Feeny

    (McMaster University
    Health Utilities Incorporated)

  • Peter Fayers

    (University of Aberdeen
    Norwegian University of Science and Technology)

  • Karen L. Spritzer

    (UCLA)

  • David Cella

    (Northwestern University Feinberg School of Medicine)

Abstract

Background Preference-based health-related quality of life (HR-QOL) scores are useful as outcome measures in clinical studies, for monitoring the health of populations, and for estimating quality-adjusted life-years. Methods This was a secondary analysis of data collected in an internet survey as part of the Patient-Reported Outcomes Measurement Information System (PROMIS®) project. To estimate Health Utilities Index Mark 3 (HUI-3) preference scores, we used the ten PROMIS® global health items, the PROMIS-29 V2.0 single pain intensity item and seven multi-item scales (physical functioning, fatigue, pain interference, depressive symptoms, anxiety, ability to participate in social roles and activities, sleep disturbance), and the PROMIS-29 V2.0 items. Linear regression analyses were used to identify significant predictors, followed by simple linear equating to avoid regression to the mean. Results The regression models explained 48 % (global health items), 61 % (PROMIS-29 V2.0 scales), and 64 % (PROMIS-29 V2.0 items) of the variance in the HUI-3 preference score. Linear equated scores were similar to observed scores, although differences tended to be larger for older study participants. Conclusions HUI-3 preference scores can be estimated from the PROMIS® global health items or PROMIS-29 V2.0. The estimated HUI-3 scores from the PROMIS® health measures can be used for economic applications and as a measure of overall HR-QOL in research.

Suggested Citation

  • Ron D. Hays & Dennis A. Revicki & David Feeny & Peter Fayers & Karen L. Spritzer & David Cella, 2016. "Using Linear Equating to Map PROMIS® Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3," PharmacoEconomics, Springer, vol. 34(10), pages 1015-1022, October.
    • Handle: RePEc:spr:pharme:v:34:y:2016:i:10:d:10.1007_s40273-016-0408-x
    DOI: 10.1007/s40273-016-0408-x
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    Cited by:

    1. Admassu N. Lamu, 2020. "Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(6), pages 903-915, August.
    2. Steven P. Reise & Han Du & Emily F. Wong & Anne S. Hubbard & Mark G. Haviland, 2021. "Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 800-824, September.
    3. Alina Palimaru & Ron D. Hays, 2017. "Associations of Health-Related Quality of Life with Overall Quality of Life in the Patient Reported Outcomes Measurement Information System (PROMIS®) Project," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 12(2), pages 241-250, June.
    4. Chao Hsing Yeh & Jennifer Kawi & Lauren Grant & Xinran Huang & Hulin Wu & Robin L. Hardwicke & Paul J. Christo, 2022. "Self-Guided Smartphone Application to Manage Chronic Musculoskeletal Pain: A Randomized, Controlled Pilot Trial," IJERPH, MDPI, vol. 19(22), pages 1-16, November.
    5. Lan Gao & Wei Luo & Utsana Tonmukayakul & Marj Moodie & Gang Chen, 2021. "Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(2), pages 341-350, March.

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