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A comparison of scoring weights for the EuroQol© derived from patients and the general public

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  • Daniel Polsky
  • Richard J. Willke
  • Karen Scott
  • Kevin A. Schulman
  • Henry A. Glick

Abstract

Objective: General health state classification systems, such as the EuroQol instrument, have been developed to improve the systematic measurement and comparability of health state preferences. In this paper we generate valuations for EuroQol health states using responses to this instrument's visual analogue scale made by patients enrolled in a randomized clinical trial evaluating tirilazad mesylate, a new drug used to treat subarachnoid haemorrhage. We then compare these valuations derived from patients with published valuations derived from responses made by a sample from the general public. Methods: The data were derived from two sources: (1) responses to the EuroQol instrument from 649 patients 3 months after enrollment in the clinical trial, and (2) from a published study reporting a scoring rule for the EuroQol instrument that was based upon responses made by the general public. We used a linear regression model to develop an additive scoring rule. This rule enables direct valuation of all 243 EuroQol health states using patients' scores for their own health states elicited using a visual analogue scale. We then compared predicted scores generated using our scoring rule with predicted scores derived from a sample from the general public. Results: The predicted scores derived using the additive scoring rules met convergent validity criteria and explained a substantial amount of the variation in visual analogue scale scores (R2=0.57). In the pairwise comparison of the predicted scores derived from the study sample with those derived from the general public, we found that the former set of scores were higher for 223 of the 243 states. Despite the low level of correspondence in the pairwise comparison, the overall correlation between the two sets of scores was 87%. Conclusions: The model presented in this paper demonstrated that scoring weights for the EuroQol instrument can be derived directly from patient responses from a clinical trial and that these weights can explain a substantial amount of variation in health valuations. Scoring weights based on patient responses are significantly higher than those derived from the general public. Further research is required to understand the source of these differences. Copyright © 2001 John Wiley & Sons, Ltd.

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  • Daniel Polsky & Richard J. Willke & Karen Scott & Kevin A. Schulman & Henry A. Glick, 2001. "A comparison of scoring weights for the EuroQol© derived from patients and the general public," Health Economics, John Wiley & Sons, Ltd., vol. 10(1), pages 27-37, January.
    • Handle: RePEc:wly:hlthec:v:10:y:2001:i:1:p:27-37
    DOI: 10.1002/1099-1050(200101)10:1<27::AID-HEC561>3.0.CO;2-R
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    1. Brooks, Richard G. & Jendteg, Stefan & Lindgren, Bjorn & Persson, Ulf & Bjork, Stefan, 1991. "EuroQol(c): health-related quality of life measurement. Results of the Swedish questionnaire exercise," Health Policy, Elsevier, vol. 18(1), pages 37-48, June.
    2. Henry A. Glick & Daniel Polsky & Richard J. Willke & Kevin A. Schulman, 1999. "A Comparison of Preference Assessment Instruments Used in a Clinical Trial," Medical Decision Making, , vol. 19(3), pages 265-275, August.
    3. James S. Dyer & Rakesh K. Sarin, 1979. "Measurable Multiattribute Value Functions," Operations Research, INFORMS, vol. 27(4), pages 810-822, August.
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    1. Eduardo Sánchez‐Iriso & Maria Errea Rodríguez & Juan Manuel Cabasés Hita, 2019. "Valuing health using EQ‐5D: The impact of chronic diseases on the stock of health," Health Economics, John Wiley & Sons, Ltd., vol. 28(12), pages 1402-1417, December.
    2. Joseph T. King Jr & Joel Tsevat & Mark S. Roberts, 2009. "Impact of the Scale Upper Anchor on Health State Preferences," Medical Decision Making, , vol. 29(2), pages 257-266, March.
    3. Burstrom, Kristina & Johannesson, Magnus & Diderichsen, Finn, 2006. "A comparison of individual and social time trade-off values for health states in the general population," Health Policy, Elsevier, vol. 76(3), pages 359-370, May.
    4. Damschroder, Laura J. & Zikmund-Fisher, Brian J. & Ubel, Peter A., 2005. "The impact of considering adaptation in health state valuation," Social Science & Medicine, Elsevier, vol. 61(2), pages 267-277, July.
    5. Christine McDonough & Anna Tosteson, 2007. "Measuring Preferences for Cost-Utility Analysis," PharmacoEconomics, Springer, vol. 25(2), pages 93-106, February.
    6. Julie Sturza, 2010. "A Review and Meta-Analysis of Utility Values for Lung Cancer," Medical Decision Making, , vol. 30(6), pages 685-693, November.
    7. Thomas Hammerschmidt & Hans-Peter Zeitler & Markus Gulich & Reiner Leidl, 2004. "A Comparison of Different Strategies to Collect Standard Gamble Utilities," Medical Decision Making, , vol. 24(5), pages 493-503, October.
    8. Yvette Peeters & Anne M. Stiggelbout, 2009. "Valuing Health: Does Enriching a Scenario Lead to Higher Utilities?," Medical Decision Making, , vol. 29(3), pages 334-342, May.
    9. Darrell J. Gaskin & Kevin D. Frick, 2008. "Race and Ethnic Disparities in Valuing Health," Medical Decision Making, , vol. 28(1), pages 12-20, January.

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