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

Author

Listed:
  • Samer A. Kharroubi

    (Department of Mathematics, University of York, York, UK, sak503@york.ac.uk)

  • Christopher McCabe

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

Abstract

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.

Suggested Citation

  • Samer A. Kharroubi & Christopher McCabe, 2008. "Modeling HUI 2 Health State Preference Data Using a Nonparametric Bayesian Method," Medical Decision Making, , vol. 28(6), pages 875-887, November.
    • Handle: RePEc:sae:medema:v:28:y:2008:i:6:p:875-887
    DOI: 10.1177/0272989X08318460
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    Citations

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    Cited by:

    1. Samer A. Kharroubi & Yara Beyh, 2021. "Bayesian modeling of health state preferences: could borrowing strength from existing countries’ valuations produce better estimates," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 773-788, July.
    2. Musal, R. Muzaffer & Soyer, Refik & McCabe, Christopher & Kharroubi, Samer A., 2012. "Estimating the population utility function: A parametric Bayesian approach," European Journal of Operational Research, Elsevier, vol. 218(2), pages 538-547.
    3. Stevens, K, 2010. "Valuation of the Child Health Utility Index 9D (CHU9D)," MPRA Paper 29938, University Library of Munich, Germany.

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