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Cross-sectional validation of the PROMIS-Preference scoring system

Author

Listed:
  • 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

Abstract

Objectives: The PROMIS-Preference (PROPr) score is a recently developed summary score for the Patient-Reported Outcomes Measurement Information System (PROMIS). PROPr is a preference-based scoring system for seven PROMIS domains created using multiplicative multi-attribute utility theory. It serves as a generic, societal, preference-based summary scoring system of health-related quality of life. This manuscript evaluates construct validity of PROPr in two large samples from the US general population. Methods: We utilized 2 online panel surveys, the PROPr Estimation Survey and the Profiles-Health Utilities Index (HUI) Survey. Both included the PROPr measure, patient demographic information, self-reported chronic conditions, and other preference-based summary scores: the EuroQol-5D (EQ-5D-5L) and HUI in the PROPr Estimation Survey and the HUI in the Profiles-HUI Survey. The HUI was scored as both the Mark 2 and the Mark 3. Known-groups validity was evaluated using age- and gender-stratified mean scores and health condition impact estimates. Condition impact estimates were created using ordinary least squares regression in which a summary score was regressed on age, gender, and a single health condition. The coefficient for the health condition is the estimated effect on the preference score of having a condition vs. not having it. Convergent validity was evaluated using Pearson correlations between PROPr and other summary scores. Results: The sample consisted of 983 respondents from the PROPr Estimation Survey and 3,000 from the Profiles-HUI survey. Age- and gender-stratified mean PROPr scores were lower than EQ-5D and HUI scores, with fewer subjects having scores corresponding to perfect health on the PROPr. In the PROPr Estimation survey, all 11 condition impact estimates were statistically significant using PROPr, 8 were statistically significant by the EQ-5D, 7 were statistically significant by HUI Mark 2, and 9 were statistically significant by HUI Mark 3. In the Profiles-HUI survey, all 21 condition impact estimates were statistically significant using summary scores from all three scoring systems. In these samples, the correlations between PROPr and the other summary measures ranged from 0.67 to 0.70. Conclusions: These results provide evidence of construct validity for PROPr using samples from the US general population.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0201093
    DOI: 10.1371/journal.pone.0201093
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    References listed on IDEAS

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    1. Janel Hanmer & Dasha Cherepanov & Mari Palta & Robert M. Kaplan & David Feeny & Dennis G. Fryback, 2016. "Health Condition Impacts in a Nationally Representative Cross-Sectional Survey Vary Substantially by Preference-Based Health Index," Medical Decision Making, , vol. 36(2), pages 264-274, February.
    2. David Feeny & Karen Spritzer & Ron D. Hays & Honghu Liu & Theodore G. Ganiats & Robert M. Kaplan & Mari Palta & Dennis G. Fryback, 2012. "Agreement about Identifying Patients Who Change over Time," Medical Decision Making, , vol. 32(2), pages 273-286, March.
    3. 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.
    4. Torrance, George W., 1986. "Measurement of health state utilities for economic appraisal : A review," Journal of Health Economics, Elsevier, vol. 5(1), pages 1-30, March.
    5. Janel Hanmer & William F. Lawrence & John P. Anderson & Robert M. Kaplan & Dennis G. Fryback, 2006. "Report of Nationally Representative Values for the Noninstitutionalized US Adult Population for 7 Health-Related Quality-of-Life Scores," Medical Decision Making, , vol. 26(4), pages 391-400, July.
    6. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884.
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    1. 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.

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