title: Modeling SF-6D Hong Kong standard gamble health state preference data using a nonparametric Bayesian method.
abstract: OBJECTIVE
  This article reports on the findings from applying a recently described approach to modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a nonparametric model to estimate a Bayesian six-dimensional health state short form (derived from short-form 36 health survey) health state valuation algorithm.
  
  
  METHODS
  A sample of 197 states defined by the six-dimensional health state short form (derived from short-form 36 health survey)has been valued by a representative sample of the Hong Kong general population by using standard gamble. The article reports the application of the nonparametric model and compares it to the original model estimated by using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance.
  
  
  RESULTS
  Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state (while ensuring that full health passes through unity). The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernible effect.
  
  
  CONCLUSIONS
  The nonparametric Bayesian model is argued to be more theoretically appropriate than previously used parametric models. Furthermore, it is more flexible to take into account the impact of covariates.
authors.affiliation: Department of Mathematics, University of York, York, UK. Electronic address: samer.kharroubi@york.ac.uk.
authors.forename: Samer A
authors.initials: SA
authors.lastname: Kharroubi
authors.forename: John E
authors.initials: JE
authors.lastname: Brazier
authors.forename: Sarah
authors.initials: S
authors.lastname: McGhee
mesh_terms: D000465:Algorithms; D001294:Attitude to Health; D001499:Bayes Theorem; D005260:Female; D006304:Health Status; D006723:Hong Kong; D006801:Humans; D008297:Male; D057240:Patient Preference; D011594:Psychometrics; D011788:Quality of Life / Q000523:psychology*; D011795:Surveys and Questionnaires
keywords: SF-6D; covariates; nonparametric Bayesian methods; preference-based health measure