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

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  • Bernie J. O'Brien
  • Marian Spath
  • Gordon Blackhouse
  • J.L. Severens
  • Paul Dorian
  • John Brazier

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

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  • 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, November.
    • Handle: RePEc:wly:hlthec:v:12:y:2003:i:11:p:975-981
    DOI: 10.1002/hec.789
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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. Roberta Ara & John Brazier & Tracey Young, 2017. "Recommended Methods for the Collection of Health State Utility Value Evidence in Clinical Studies," PharmacoEconomics, Springer, vol. 35(1), pages 67-75, December.
    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. Mónica Hernández Alava & John Brazier & Donna Rowen & Aki Tsuchiya, 2013. "Common Scale Valuations across Different Preference-Based Measures," Medical Decision Making, , vol. 33(6), pages 839-852, August.
    6. 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.
    7. David Feeny, 2012. "The Multi-attribute Utility Approach to Assessing Health-related Quality of Life," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 36, Edward Elgar Publishing.
    8. 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, June.
    9. 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.
    10. Donna Rowen & John Brazier & Aki Tsuchiya & Mónica Hernández Alava, 2012. "Valuing states from multiple measures on the same visual analogue sale: a feasibility study," Health Economics, John Wiley & Sons, Ltd., vol. 21(6), pages 715-729, June.
    11. 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.
    12. Carmen Selva-Sevilla & Paula Ferrara & Manuel Gerónimo-Pardo, 2020. "Interchangeability of the EQ-5D and the SF-6D, and comparison of their psychometric properties in a spinal postoperative Spanish population," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(4), pages 649-662, June.
    13. 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.
    14. 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, November.

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