The classification systems of the EQ-5D, the HUI II and the SF-6D: what do they have in common?
EQ-5D, HUI II and SF-6D often produce very different valuations for the same health state. This paper aims at clarifying to what extent this might be caused by differences between the multi-attribute classification systems belonging to these instruments.
Subjects were 264 patients of rehabilitation clinics in Mecklenburg-Western Pomerania (44.3% female; mean age 49.1) who completed the EQ-5D, the HUI II and the SF-36 (the basis of the SF-6D). After scaling with principal component analyses for categorical data, each attribute of each classification system was regressed on the classification systems of the other two instruments, and all attributes together were subjected to ordinary principal component analysis with varimax rotation.
Adjusted multiple R 2 for regression analyses ranged from 0.01 to 0.57. The HUI II attribute ‘sensation’ and the SF-6D attribute ‘role limitation’ are virtually unrelated to the remainder. All other attributes of all three instruments can be predicted by each other. EQ-5D and HUI II focus distinctly more on physical functioning than SF-6D.
Although all three classification systems have a lot in common, they differ so much that EQ-5D, HUI II and SF-6D would produce different valuations even if these valuations were determined according to the same principle.
KeywordsQuality of life Measurement Classification Health
Euro-Qol 5 Dimensions
Health-Related Quality of Life
Health Utility Index
- HUI II
Health Utility Index, Mark II
- HUI III
Health Utility Index, Mark III
Principal Component Analysis for Categorical Data
Principal Component Analysis for Categorical Data
Adjusted squared multiple correlation coefficient
Standardised estimated quantification
Standardised estimated quantifications
Short Form 36 items
Short Form 6 Dimensions
Statistical Package for the Social Sciences
- SPSS 15.0
Statistical Package for the Social Sciences, Version 15.0
This project was funded by a grant (no. 01GD0106) from the German Federal Ministry of Education and Research within the North German Network for Rehabilitation Research (NVRF). The analyses presented here were financially supported by an additional grant from the German Federal Ministry of Education and Research (grant no. 01ZZ0403). We would like to thank Kathrin Bezold for her support in performing the study, three anonymous reviewers for critically discussing a former version of the manuscript, and Peter Bereza for correcting our English.
- 7.Ware, J. E., Kosinski, M., & Dewey, J. E. (2000). How to score version two of the SF-36 health survey. Lincoln, RI: QualityMetric, Incorporated.Google Scholar
- 10.Teng, Y. K., Verburg, R. J., Sont, J. K., van den Hout, W. B., Breedveld, F. C., & van Laar, J. M. (2005). Long-term followup of health status in patients with severe rheumatoid arthritis after high-dose chemotherapy followed by autologous hematopoietic stem cell transplantation. Arthritis and Rheumatism, 52(8), 2272–2276.PubMedCrossRefGoogle Scholar
- 11.van den Hout, W. B., de Jong, Z., Munneke, M., Hazes, J. M., Breedveld, F. C., & Vliet Vlieland, T. P. (2005). Cost-utility and cost-effectiveness analyses of a long-term, high-intensity exercise program compared with conventional physical therapy in patients with rheumatoid arthritis. Arthritis and Rheumatism, 53(1), 39–47.PubMedCrossRefGoogle Scholar
- 18.Franks, P., Hanmer, J., & Fryback, D. G. (2006). Relative disutilities of 47 risk factors and conditions assessed with seven preference-based health status measures in a national U.S. sample: toward consistency in cost-effectiveness analyses. Medical Care, 44(5), 478–485.PubMedCrossRefGoogle Scholar
- 23.McDonough, C. M., Grove, M. R., Tosteson, T. D., Lurie, J. D., Hilibrand, A. S., & Tosteson, A. N. (2005). Comparison of EQ-5D, HUI, and SF-36-derived societal health state values among spine patient outcomes research trial (SPORT) participants. Quality of Life Research, 14(5), 1321–1332.PubMedCrossRefGoogle Scholar
- 31.Thomas K.J., MacPherson H., Ratcliffe J., Thorpe L., Brazier J., Campbell M., Fitter M., Roman M., Walters S., Nicholl J. P. (2005). Longer term clinical and economic benefits of offering acupuncture care to patients with chronic low back pain. Health Technology Assessment, 9(32), iii-iv, ix-x, 1–109.Google Scholar
- 32.van Stel H.F., Buskens E. (2006). Comparison of the SF-6D and the EQ-5D in patients with coronary heart disease. Health and Quality of Life Outcomes, 25, 4:20.Google Scholar
- 52.Ware, J. E., Jr, Gandek, B., Kosinski, M., Aaronson, N. K., Apolone, G., Brazier, J., et al. (1998). The equivalence of SF-36 summary health scores estimated using standard and country-specific algorithms in 10 countries: results from the IQOLA Project. International Quality of Life Assessment. Journal of Clinical Epidemiology, 51(11), 1167–1170.PubMedCrossRefGoogle Scholar
- 53.Ware, J. E., Jr, Kosinski, M., Gandek, B., Aaronson, N. K., Apolone, G., Bech, P., et al. (1998). The factor structure of the SF-36 Health Survey in 10 countries: results from the IQOLA Project. International Quality of Life Assessment. Journal of Clinical Epidemiology, 51(11), 1159–1165.PubMedCrossRefGoogle Scholar
- 55.Essink-Bot, M. L., Krabbe, P. F., Bonsel, G. J., & Aaronson, N. K. (1997). An empirical comparison of four generic health status measures. The Nottingham Health Profile, the Medical Outcomes Study 36-item Short-Form Health Survey, the COOP/WONCA charts, and the EuroQol instrument. Medical Care, 35(5), 522–537.PubMedCrossRefGoogle Scholar
- 59.Furlong, W. J., Feeny, D. H., & Torrance, G. W. (2002). Health utilities index (HUI) procedures manual. Dundas Ontario Canada: Health Utilities Incorporation.Google Scholar
- 60.Brazier J., Walters S. (2003). SF-6D UK Programme. SPSS-Syntax. 24 October 2003.Google Scholar
- 61.Gifi, A. (1990). Nonlinear multivariate analysis. New York: John Wiley.Google Scholar
- 62.Brachinger, H. W., & Ost, F. (1996). Modelle mit latenten Variablen: Faktorenanalyse, Latent-Structure-Analyse und LISREL-Analyse [Models with latent variables: Factor analysis, latent structure analysis and LISREL analysis]. In L. Fahrmeir, A. Hamerle, & G. Tutz (Eds.), Multivariate statistische Verfahren, 2. erweiterte Auflage [Multivariate statistical methods, 2nd extended edition] (pp. 639–766). Berlin: Walter de Gruyter.Google Scholar
- 63.Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. New York: Houghton Mifflin.Google Scholar
- 64.Munro, B. H. (1997). Regression. In B. H. Munro (Ed.), Statistical methods in health care (pp. 246–286). Philadelphia, New York: Lippincott.Google Scholar