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


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  • Kharroubi, Samer
  • Brazier, John E.
  • O'Hagan, Anthony
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    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 non-parametric approach 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 derivate of the SF-36) was valued by a sample of the UK general population using standard gamble. Advantages of the nonparametric 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 physical functioning probably having some effect, but the remaining covariates having no discernable effect. 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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Social Science & Medicine.

    Volume (Year): 64 (2007)
    Issue (Month): 6 (March)
    Pages: 1242-1252

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    Handle: RePEc:eee:socmed:v:64:y:2007:i:6:p:1242-1252

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    Keywords: Preference-based health measure Covariates Nonparametric Bayesian methods;


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    Cited by:
    1. Méndez, Ildefonso & Abellán Perpiñán, Jose M. & Sánchez Martínez, Fernando I. & Martínez Pérez, Jorge E., 2011. "Inverse probability weighted estimation of social tariffs: An illustration using the SF-6D value sets," Journal of Health Economics, Elsevier, vol. 30(6), pages 1280-1292.
    2. O'Hagan, A & Brazier, JE & Kharroubi, SA, 2007. "A comparison of United States and United Kingdom EQ-5D health states valuations using a nonparametric Bayesian method," MPRA Paper 29806, University Library of Munich, Germany.


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