title: Predicting preference-based utility values using partial proportional odds models.
abstract: BACKGROUND
  The majority of analyses on utility data have used ordinary least square (OLS) regressions to explore potential relationships. The aim of this paper is to explore the benefits of response mapping onto health dimension profiles to generate preference-based utility scores using partial proportional odds models (PPOM).
  
  
  METHODS
  Models are estimated using EQ-5D data collected in the Health Survey for England and the predicted utility scores are compared with those obtained using OLS regressions. Explanatory variables include age, acute illness, educational level, general health, deprivation and survey year. The expected EQ-5D scores for the PPOMs are obtained by weighting the predicted probabilities of scoring one, two or three for the five health dimensions by the corresponding preference-weights.
  
  
  RESULTS
  The EQ-5D scores obtained using the probabilities from the PPOMs characterise the actual distribution of EQ-5D preference-based utility scores more accurately than those obtained from the linear model. The mean absolute and mean squared errors in the individual predicted values are also reduced for the PPOM models.
  
  
  CONCLUSIONS
  The PPOM models characterise the underlying distributions of the EQ-5D data better than models obtained using OLS regressions. Additional research exploring the effect of modelling conditional responses and two part models could potentially improve the results further.
authors.affiliation: University of Sheffield, School of Health and Related Research, 30 Regent Street, Regent Court, Sheffield S1 4DA, UK. r.m.ara@sheffield.ac.uk.
authors.forename: Roberta
authors.initials: R
authors.lastname: Ara
authors.forename: Ben
authors.initials: B
authors.lastname: Kearns
authors.forename: Ben A
authors.initials: BA
authors.lastname: vanHout
authors.forename: John E
authors.initials: JE
authors.lastname: Brazier
mesh_terms: D000328:Adult; D004739:England; D005260:Female; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011594:Psychometrics / Q000706:statistics & numerical data*; D011788:Quality of Life / Q000523:psychology*; D019057:Quality-Adjusted Life Years; D011795:Surveys and Questionnaires