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Statistical Analysis of EQ-5D Profiles: Does the Use of Value Sets Bias Inference?

Author

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  • David Parkin

    (NHS South East Coast, Horley, United Kingdom, City Health Economics Centre, City University, London, United Kingdom, david.parkin@southeastcoast.nhs.uk)

  • Nigel Rice

    (City Health Economics Centre, City University, London, United Kingdom)

  • Nancy Devlin

    (Centre for Health Economics, University of York, York, United Kingdom, Office for Health Economics, London, United Kingdom)

Abstract

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 summarize to give an overall view of the health of a given population that can be analyzed 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 article, the authors demonstrate the underlying effects of the use of value sets as a means of weighting profile data. They 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 in 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 weights may exert a nontrivial effect on their analysis. This article focuses on the EQ-5D, but the arguments and their implications for statistical analysis are relevant to all health state descriptive systems.

Suggested Citation

  • David Parkin & Nigel Rice & Nancy Devlin, 2010. "Statistical Analysis of EQ-5D Profiles: Does the Use of Value Sets Bias Inference?," Medical Decision Making, , vol. 30(5), pages 556-565, September.
    • Handle: RePEc:sae:medema:v:30:y:2010:i:5:p:556-565
    DOI: 10.1177/0272989X09357473
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    References listed on IDEAS

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    1. Xavier Badia & Montserrat Roset & Michael Herdman & Paul Kind, 2001. "A Comparison of United Kingdom and Spanish General Population Time Trade-off Values for EQ-5D Health States," Medical Decision Making, , vol. 21(1), pages 7-16, February.
    2. Duncan Mortimer & Leonie Segal, 2008. "Comparing the Incomparable? A Systematic Review of Competing Techniques for Converting Descriptive Measures of Health Status into QALY-Weights," Medical Decision Making, , vol. 28(1), pages 66-89, January.
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    Cited by:

    1. 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.
    2. 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.
    3. Franz Ombler & Michael Albert & Paul Hansen, 2018. "How Significant Are “High†Correlations Between EQ-5D Value Sets?," Medical Decision Making, , vol. 38(6), pages 635-645, August.
    4. Manuel B. Huber & Julia Felix & Martin Vogelmann & Reiner Leidl, 2017. "Health-Related Quality of Life of the General German Population in 2015: Results from the EQ-5D-5L," IJERPH, MDPI, vol. 14(4), pages 1-12, April.
    5. McCarthy, Ian M., 2016. "Eliminating composite bias in treatment effects estimates: Applications to quality of life assessment," Journal of Health Economics, Elsevier, vol. 50(C), pages 47-58.
    6. A. Enrique & J. Burke & D. Richards & L. Timulak, 2019. "Quality of Life Outcomes in Internet-Delivered (Space from Depression) Treatment for Depression," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 14(5), pages 1301-1313, November.
    7. Tianxin Pan & Brendan Mulhern & Rosalie Viney & Richard Norman & Janel Hanmer & Nancy Devlin, 2022. "A Comparison of PROPr and EQ-5D-5L Value Sets," PharmacoEconomics, Springer, vol. 40(3), pages 297-307, March.
    8. 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.
    9. Xiaojing Fan & Duolao Wang & Bruce Hellman & Mathieu F. Janssen & Gerben Bakker & Rupert Coghlan & Amelia Hursey & Helen Matthews & Ian Whetstone, 2018. "Assessment of Health-Related Quality of Life between People with Parkinson’s Disease and Non-Parkinson’s: Using Data Drawn from the ‘100 for Parkinson’s’ Smartphone-Based Prospective Study," IJERPH, MDPI, vol. 15(11), pages 1-12, November.

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