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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1055-1022 |
| Number of pages | 8 |
| Journal | Pharmacoeconomics |
| Volume | 34 |
| Issue number | 10 |
| Early online date | 26 Apr 2016 |
| DOIs | |
| Publication status | Published - Oct 2016 |
Bibliographical note
AcknowledgmentsThis 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.
Keywords
- Preference Score
- Item Bank
- Computerize Adaptive Testing
- Pain Inteference
- Item Response Theory Score