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PLoS One. 2018 Jul 31;13(7):e0201093. doi: 10.1371/journal.pone.0201093. eCollection 2018.

Cross-sectional validation of the PROMIS-Preference scoring system.

Author information

Department of General Internal Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America.
Department of Engineering and Public Policy and Institute for Politics and Strategy, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Department of Medicine, Long School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas and Department of Population Health, Dell Medical School, University of Texas at Austin, Austin, Texas, United States of America.
Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Outcomes Research, Evidera, Bethesda, Maryland, United States of America.
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
Division of Health System Innovation and Research, University of Utah Schools of the Health Sciences, Salt Lake City, Utah, United States of America.
Division of General Internal Medicine & Health Services Research, UCLA, Los Angeles, California, United States of America.
Department of Economics, McMaster University, Hamilton, Ontario, Canada.
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.



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.


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.


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.


These results provide evidence of construct validity for PROPr using samples from the US general population.

[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

David Feeny has a proprietary interest in Health Utilities Incorporated, Dundas, Ontario, Canada. HUInc. distributes copyrighted Health Utilities Index (HUI) materials and provides methodological advice on the use of the HUI. Dennis Revicki is employed by Evidera, Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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