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Statistical analysis of EQ-5D profiles: does the use of value sets bias inference?


  • Parkin, D.
  • Rice, N.
  • Devlin, N.


Health state profile data, such as those provided by the EQ-5D, are widely collected in clinical trials, population surveys and a growing range of other important health sector applications. However, these profile data are difficult to summarise to give an overall view of the health of a given population that can be analysed for differences between groups or within groups over time. A common way of short-cutting this problem is to transform profiles into a single number, or index, using sets of weights, often elicited from the general public in the form of values. Are there any problems with this procedure? In this paper we demonstrate the underlying effects of the use of value sets as a means of weighting profile data. We show that any set of weights introduces an exogenous source of variance to health profile data. These can distort findings about the significance of changes in health between groups or over time. No set of weights is neutral its effect. If a summary of patient reported outcomes is required, it may be better to use an instrument that yields this directly – such as the EQ VAS – along with the descriptive instrument. If this is not possible, researchers should have a clear rationale for their choice of weights; and be aware that those weighs may exert a non-trivial effect on their analysis. This paper focuses on the EQ-5D, but the arguments and their implications for statistical analysis are relevant to all health state descriptive systems.

Suggested Citation

  • Parkin, D. & Rice, N. & Devlin, N., 2008. "Statistical analysis of EQ-5D profiles: does the use of value sets bias inference?," Working Papers 08/10, Department of Economics, City University London.
  • Handle: RePEc:cty:dpaper:08/10

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    Cited by:

    1. Caitlyn T. Wilke & A. Simon Pickard & Surrey M. Walton & Joern Moock & Thomas Kohlmann & Todd A. Lee, 2010. "Statistical implications of utility weighted and equally weighted HRQL measures: an empirical study," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 101-110, January.
    2. Jeff Round & Annie Hawton, 2017. "Statistical Alchemy: Conceptual Validity and Mapping to Generate Health State Utility Values," PharmacoEconomics - Open, Springer, vol. 1(4), pages 233-239, December.
    3. Raymond Oppong & Billingsley Kaambwa & Jacqueline Nuttall & Kerenza Hood & Richard Smith & Joanna Coast, 2013. "The impact of using different tariffs to value EQ-5D health state descriptions: an example from a study of acute cough/lower respiratory tract infections in seven countries," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(2), pages 197-209, April.
    4. Feng, Y. & Parkin, D. & Devlin, N., 2012. "Assessing the Performance of the EQ-VAS in the NHS PROMs Programme," Research Papers 000165, Office of Health Economics.
    5. Parkin, D. & Devlin, N. & Feng, Y., 2014. "What Determines the Shape of an EQ-5D Index Distribution?," Research Papers 000530, Office of Health Economics.
    6. Devlin, N. & Shah, K.K & Buckingham, K., 2017. "What is the Normative Basis for Selecting the Measure of 'Average' Preferences for Use in Social Choices?," Research Papers 001798, Office of Health Economics.


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