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PharmacoEconomics

, Volume 34, Issue 10, pp 1015–1022 | Cite as

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

  • Ron D. Hays
  • Dennis A. Revicki
  • David Feeny
  • Peter Fayers
  • Karen L. Spritzer
  • David Cella
Original Research Article

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.

Keywords

Preference Score Item Bank Computerize Adaptive Testing Pain Interference Item Response Theory Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by a grant from National Cancer Institute (1U2-CCA186878-01) and a supplement to the PROMIS statistical center grant (3U54AR057951-04S4). Ron D. Hays, Dennis A. Revicki, Peter Fayers, Karen L. Spritzer, and David Cella declare no conflicts of interest. David Feeny has a proprietary interest in Health Utilities Incorporated, Dundas, Ontario, Canada.

Author Contributions

Ron D. Hays drafted the article and supervised the analyses of the data. All other authors provided edits to the draft article. David Feeny and Peter Fayers provided input on the statistical analyses. Karen L. Spritzer implemented the analyses.

Supplementary material

40273_2016_408_MOESM1_ESM.pdf (70 kb)
Supplementary material 1 (PDF 70 kb)
40273_2016_408_MOESM2_ESM.pdf (118 kb)
Supplementary material 2 (PDF 117 kb)

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ron D. Hays
    • 1
  • Dennis A. Revicki
    • 2
  • David Feeny
    • 3
    • 4
  • Peter Fayers
    • 5
    • 6
  • Karen L. Spritzer
    • 1
  • David Cella
    • 7
  1. 1.Division of General Internal Medicine, Department of MedicineUCLALos AngelesUSA
  2. 2.Outcomes ResearchEvideraBethesdaUSA
  3. 3.Department of EconomicsMcMaster UniversityHamiltonCanada
  4. 4.Health Utilities IncorporatedDundasCanada
  5. 5.Institute of Applied Health SciencesUniversity of AberdeenAberdeenUK
  6. 6.Department of Cancer Research and Molecular MedicineNorwegian University of Science and TechnologyTrondheimNorway
  7. 7.Department of Medical Social SciencesNorthwestern University Feinberg School of MedicineChicagoUSA

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