title: Predicting EuroQol (EQ-5D) scores from the patient-reported outcomes measurement information system (PROMIS) global items and domain item banks in a United States sample.
abstract: BACKGROUND
  Preference-based health index scores provide a single summary score assessing overall health-related quality of life and are useful as an outcome measure in clinical studies, for estimating quality-adjusted life years for economic evaluations, and for monitoring the health of populations. We predicted EuroQoL (EQ-5D) index scores from patient-reported outcomes measurement information system (PROMIS) global items and domain item banks.
  
  
  METHODS
  This was a secondary analysis of health outcome data collected in an internet survey as part of the PROMIS Wave 1 field testing. For this study, we included the 10 global items and the physical function, fatigue, pain impact, anxiety, and depression item banks. Linear regression analyses were used to predict EQ-5D index scores based on the global items and selected domain banks.
  
  
  RESULTS
  The regression models using eight of the PROMIS global items (quality of life, physical activities, mental health, emotional problems, social activities, pain, and fatigue and either general health or physical health items) explained 65% of the variance in the EQ-5D. When the PROMIS domain scores were included in a regression model, 57% of the variance was explained in EQ-5D scores. Comparisons of predicted to actual EQ-5D scores by age and gender groups showed that they were similar.
  
  
  CONCLUSIONS
  EQ-5D preference scores can be predicted accurately from either the PROMIS global items or selected domain banks. Application of the derived regression model allows the estimation of health preference scores from the PROMIS health measures for use in economic evaluations.
authors.affiliation: Center for Health Outcomes Research, United BioSource Corporation, Bethesda, MD 20814, USA. Dennis.Revicki@unitedbiosource.com
authors.forename: Dennis A
authors.initials: DA
authors.lastname: Revicki
authors.forename: Ariane K
authors.initials: AK
authors.lastname: Kawata
authors.forename: Neesha
authors.initials: N
authors.lastname: Harnam
authors.forename: Wen-Hung
authors.initials: WH
authors.lastname: Chen
authors.forename: Ron D
authors.initials: RD
authors.lastname: Hays
authors.forename: David
authors.initials: D
authors.lastname: Cella
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D019992:Databases as Topic; D005260:Female; D005544:Forecasting; D006304:Health Status; D006801:Humans; D007256:Information Systems; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D011788:Quality of Life; D012650:Self Disclosure; D014481:United States; D055815:Young Adult