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A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index

Author

Listed:
  • Bernie J. O'Brien
  • Marian Spath
  • Gordon Blackhouse
  • J.L. Severens

    (Department of Health Organisation, Policy & Economics, University of Maastricht, Netherlands)

  • Paul Dorian

    (Department of Medicine, St. Michael's Hospital, Toronto, Canada)

  • John Brazier

    (Sheffield Health Economics Group, University of Sheffield, UK)

Abstract

Background: The SF-6D is a new health state classification and utility scoring system based on 6 dimensions ('6D') of the Short Form 36, and permits a “bridging” transformation between SF-36 responses and utilities. The Health Utilities Index, mark 3 (HUI3) is a valid and reliable multi-attribute health utility scale that is widely used. We assessed within-subject agreement between SF-6D utilities and those from HUI3. Methods: Patients at increased risk of sudden cardiac death and participating in a randomized trial of implantable defibrillator therapy completed both instruments at baseline. Score distributions were inspected by scatterplot and histogram and mean score differences compared by paired t-test. Pearson correlation was computed between instrument scores and also between dimension scores within instruments. Between-instrument agreement was by intra-class correlation coefficient (ICC). Results: SF-6D and HUI3 forms were available from 246 patients. Mean scores for HUI3 and SF-6D were 0.61 (95% CI 0.60-0.63) and 0.58 (95% CI 0.54-0.62) respectively; a difference of 0.03 (p<0.03). Score intervals for HUI3 and SF-6D were (-0.21 to 1.0) and (0.30-0.95). Correlation between the instrument scores was 0.58 (95% CI 0.48-0.68) and agreement by ICC was 0.42 (95% CI 0.31-0.52). Correlations between dimensions of SF-6D were higher than for HUI3. Conclusions: Our study casts doubt on the whether utilities and QALYs estimated via SF-6D are comparable with those from HUI3. Utility differences may be due to differences in underlying concepts of health being measured, or different measurement approaches, or both. No gold standard exists for utility measurement and the SF-6D is a valuable addition that permits SF-36 data to be transformed into utilities to estimate QALYs. The challenge is developing a better understanding as to why these classification-based utility instruments differ so markedly in their distributions and point estimates of derived utilities. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • Bernie J. O'Brien & Marian Spath & Gordon Blackhouse & J.L. Severens & Paul Dorian & John Brazier, 2003. "A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index," Health Economics, John Wiley & Sons, Ltd., vol. 12(11), pages 975-981.
  • Handle: RePEc:wly:hlthec:v:12:y:2003:i:11:p:975-981
    DOI: 10.1002/hec.789
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    File URL: http://hdl.handle.net/10.1002/hec.789
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    References listed on IDEAS

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    1. Brazier, John & Roberts, Jennifer & Deverill, Mark, 2002. "The estimation of a preference-based measure of health from the SF-36," Journal of Health Economics, Elsevier, vol. 21(2), pages 271-292, March.
    2. Keeney,Ralph L. & Raiffa,Howard, 1993. "Decisions with Multiple Objectives," Cambridge Books, Cambridge University Press, number 9780521438834, May.
    3. Torrance, George W., 1986. "Measurement of health state utilities for economic appraisal : A review," Journal of Health Economics, Elsevier, vol. 5(1), pages 1-30, March.
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    1. Rowen, D & Brazier, J & Tsuchiya, A & Hernández, M & Ibbotson, R, 2009. "The simultaneous valuation of states from multiple instruments using ranking and VAS data: methods and preliminary results," MPRA Paper 29841, University Library of Munich, Germany.
    2. Christine McDonough & Anna Tosteson, 2007. "Measuring Preferences for Cost-Utility Analysis," PharmacoEconomics, Springer, vol. 25(2), pages 93-106, February.
    3. repec:spr:pharme:v:35:y:2017:i:1:d:10.1007_s40273-017-0549-6 is not listed on IDEAS
    4. Cecilia Quercioli & Gabriele Messina & Emanuela Barbini & Giovanni Carriero & Mara Fanì & Nicola Nante, 2009. "Importance of sociodemographic and morbidity aspects in measuring health-related quality of life: performances of three tools," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 10(4), pages 389-397, October.
    5. Kontodimopoulos, Nick & Niakas, Dimitris, 2008. "An estimate of lifelong costs and QALYs in renal replacement therapy based on patients' life expectancy," Health Policy, Elsevier, vol. 86(1), pages 85-96, April.
    6. David Feeny, 2012. "The Multi-attribute Utility Approach to Assessing Health-related Quality of Life," Chapters,in: The Elgar Companion to Health Economics, Second Edition, chapter 36 Edward Elgar Publishing.
    7. Janelle Seymour & Paul McNamee & Anthony Scott & Michela Tinelli, 2010. "Shedding new light onto the ceiling and floor? A quantile regression approach to compare EQ-5D and SF-6D responses," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 683-696.
    8. Tubeuf, S, 2008. "Income-related inequalities in self-assessed health: comparisons of alternative measurements of health," Health, Econometrics and Data Group (HEDG) Working Papers 08/04, HEDG, c/o Department of Economics, University of York.
    9. Brazier, JE & Yang, Y & Tsuchiya, A, 2008. "A review of studies mapping (or cross walking) from non-preference based measures of health to generic preference-based measures," MPRA Paper 29808, University Library of Munich, Germany.
    10. Silvia Garrido & Ildefonso Méndez & José-María Abellán, 2013. "Analysing the Simultaneous Relationship Between Life Satisfaction and Health-Related Quality of Life," Journal of Happiness Studies, Springer, vol. 14(6), pages 1813-1838, December.
    11. Stavros Petrou & Christine Hockley, 2005. "An investigation into the empirical validity of the EQ-5D and SF-6D based on hypothetical preferences in a general population," Health Economics, John Wiley & Sons, Ltd., vol. 14(11), pages 1169-1189.

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