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Estimation of a Preference-Based Summary Score for the Patient-Reported Outcomes Measurement Information System: The PROMIS®-Preference (PROPr) Scoring System

Author

Listed:
  • Barry Dewitt

    (Carnegie Mellon University, Department of Engineering and Public Policy, Pittsburgh, PA, USA)

  • David Feeny

    (McMaster University Faculty of Social Sciences, Hamilton, ON, Canada)

  • Baruch Fischhoff

    (Carnegie Mellon University, Department of Engineering and Public Policy, Pittsburgh, PA, USA)

  • David Cella

    (Northwestern University Feinberg School of Medicine, Chicago, IL, USA)

  • Ron D. Hays

    (University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA)

  • Rachel Hess

    (University of Utah, Salt Lake City, UT, USA)

  • Paul A. Pilkonis

    (University of Pittsburgh Medical Center, Pittsburgh, PA, USA)

  • Dennis A. Revicki

    (Evidera Inc, Bethesda, MD, USA)

  • Mark S. Roberts

    (University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA)

  • Joel Tsevat

    (University of Texas Health Science Center at San Antonio, San Antonio, TX, USA)

  • Lan Yu

    (University of Pittsburgh Medical Center, Pittsburgh, PA, USA)

  • Janel Hanmer

    (University of Pittsburgh Medical Center, Pittsburgh, PA, USA)

Abstract

Background . Health-related quality of life (HRQL) preference-based scores are used to assess the health of populations and patients and for cost-effectiveness analyses. The National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS ® ) consists of patient-reported outcome measures developed using item response theory. PROMIS is in need of a direct preference-based scoring system for assigning values to health states. Objective . To produce societal preference-based scores for 7 PROMIS domains: Cognitive Function–Abilities, Depression, Fatigue, Pain Interference, Physical Function, Sleep Disturbance, and Ability to Participate in Social Roles and Activities. Setting . Online survey of a US nationally representative sample ( n = 983). Methods . Preferences for PROMIS health states were elicited with the standard gamble to obtain both single-attribute scoring functions for each of the 7 PROMIS domains and a multiplicative multiattribute utility (scoring) function. Results . The 7 single-attribute scoring functions were fit using isotonic regression with linear interpolation. The multiplicative multiattribute summary function estimates utilities for PROMIS multiattribute health states on a scale where 0 is the utility of being dead and 1 the utility of “full health.†The lowest possible score is –0.022 (for a state viewed as worse than dead), and the highest possible score is 1. Limitations . The online survey systematically excludes some subgroups, such as the visually impaired and illiterate. Conclusions . A generic societal preference-based scoring system is now available for all studies using these 7 PROMIS health domains.

Suggested Citation

  • Barry Dewitt & David Feeny & Baruch Fischhoff & David Cella & Ron D. Hays & Rachel Hess & Paul A. Pilkonis & Dennis A. Revicki & Mark S. Roberts & Joel Tsevat & Lan Yu & Janel Hanmer, 2018. "Estimation of a Preference-Based Summary Score for the Patient-Reported Outcomes Measurement Information System: The PROMIS®-Preference (PROPr) Scoring System," Medical Decision Making, , vol. 38(6), pages 683-698, August.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:6:p:683-698
    DOI: 10.1177/0272989X18776637
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    Cited by:

    1. Janel Hanmer & Barry Dewitt & Lan Yu & Joel Tsevat & Mark Roberts & Dennis Revicki & Paul A Pilkonis & Rachel Hess & Ron D Hays & Baruch Fischhoff & David Feeny & David Condon & David Cella, 2018. "Cross-sectional validation of the PROMIS-Preference scoring system," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
    2. Ron D. Hays & Anthony Rodriguez & Nabeel Qureshi & Chengbo Zeng & Maria Orlando Edelen, 2024. "Support for a Single Underlying Dimension of Self-Reported Health in a Sample of Adults with Low Back Pain in the United States," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 19(5), pages 2213-2226, October.
    3. Mona Aghdaee & Yuanyuan Gu & Kompal Sinha & Bonny Parkinson & Rajan Sharma & Henry Cutler, 2023. "Mapping the Patient-Reported Outcomes Measurement Information System (PROMIS-29) to EQ-5D-5L," PharmacoEconomics, Springer, vol. 41(2), pages 187-198, February.
    4. Nicolas R. Thompson & Brittany R. Lapin & Irene L. Katzan, 2023. "Utilities Estimated from PROMIS Scales for Cost-Effectiveness Analyses in Stroke," Medical Decision Making, , vol. 43(6), pages 704-718, August.
    5. Tianxin Pan & Brendan Mulhern & Rosalie Viney & Richard Norman & Janel Hanmer & Nancy Devlin, 2022. "A Comparison of PROPr and EQ-5D-5L Value Sets," PharmacoEconomics, Springer, vol. 40(3), pages 297-307, March.

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