University of Sheffield

Tony O'Hagan - Academic pages - Abstracts


Modelling covariates for the SF-6D standard gamble health state preference data using a nonparametric Bayesian method.

Kharroubi, S. A., Brazier, J. E. and O'Hagan, A.

Department of Probability and Statistics, University of Sheffield, Sheffield, England and School of Health And Related Research, University of Sheffield, Sheffield, England

Publication details: Social Science and Medicine, 64, 1242-1252, 2007.


It has long been recognised that respondent characteristics can impact on the values they give to health states. This paper reports on the findings from applying a nonparametric model to estimate the covariates in a model of SF-6D health state 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 sample of the UK general population using standard gamble. Advantages of the non-parametric model are that it can be used to predict scores in populations with different distributions of characteristics and that it allows for an impact to vary by health state (whilst ensuring that full health passes through unity). The results suggest an important age effect, with sex, class, education, employment and functioning probably having some effect, but the remaining covariates having no discernable effect. However, adjusting for covariates in the UK sample made little difference to mean health state values. The paper discusses the implications of these results for policy.

Keywords: Preference-based health measure; covariates; nonparametric Bayesian methods

Return to my publications page.
Updated: 22 February 2007
Maintained by: Tony O'Hagan