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Medical Decision Making
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Modeling HUI 2 Health State Preference Data Using a Nonparametric Bayesian Method

Samer A. Kharroubi

Department of Mathematics, University of York, York, UK, sak503{at}york.ac.uk

Christopher McCabe

Institute of Health Sciences, University of Leeds, Leeds, UK

This article reports the application of 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 Health Utilities Index Mark 2 (HUI 2) health state valuation algorithm. The data set is the UK HUI 2 valuation study where a sample of 51 states defined by the HUI 2 was valued by a sample of the UK general population using standard gamble. The article reports the application of the nonparametric model and compares it to the original model estimated using a conventional parametric random effects model. 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. The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernable effect. The article discusses the implications of these results for future applications of the HUI 2 and further work in this field.

Key Words: preference-based health measure • HUI 2 • covariates • nonparametric Bayesian methods.

This version was published on November 1, 2008

Medical Decision Making, Vol. 28, No. 6, 875-887 (2008)
DOI: 10.1177/0272989X08318460
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