title: Modelling SF-6D health state preference data using a nonparametric Bayesian method.
abstract: This paper reports on the findings from applying a new approach to modelling health state valuation data. The approach applies a nonparametric model to estimate SF-6D health state utility values using Bayesian methods. The data set is the UK SF-6D valuation study where a sample of 249 states defined by the SF-6D (a derivative of the SF-36) was valued by a representative sample of the UK general population using standard gamble. The paper presents the results from applying the nonparametric model and comparing it to the original model estimated using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. The paper discusses the implications of these results for future applications of the SF-6D and further work in this field.
authors.affiliation: Department of Mathematics, University of York, Heslington, York YO10 5DD, UK. sak503@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: Jennifer
authors.initials: J
authors.lastname: Roberts
authors.forename: Anthony
authors.initials: A
authors.lastname: O'Hagan
mesh_terms: D001499:Bayes Theorem; D006305:Health Status Indicators; D006801:Humans; D015233:Models, Statistical; D011788:Quality of Life; D018709:Statistics, Nonparametric; D011795:Surveys and Questionnaires; D006113:United Kingdom