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Predicting Quality of Well-being Scores from the SF-36

Author

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  • Dennis G. Fryback
  • William F. Lawrence
  • Patricia A. Martin
  • Ronald Klein
  • Barbara E.K. Klein

Abstract

Background. The SF-36 and the Quality of Well-being index (QWB) both quantify health status, yet have very different methodologic etiologies. The authors sought to develop an empirical equation allowing prediction of the QWB from the SF-36. Data. They used empirical observations of SF-36 profiles and QWB scores collected in in terviews of 1,430 persons during the Beaver Dam Health Outcomes Study, a com munity-based population study of health status, and 57 persons from a renal dialysis clinic. Method. The eight scales of the SF-36, their squares, and all pairwise cross- products, were used as candidate variables in stepwise and best-subsets regressions to predict QWB scores using 1,356 interviews reported in a previous paper. The re sulting equation was cross-validated on the remaining 74 cases and using the renal dialysis patients. Results. A six-variable regression equation drawing on five of the SF- 36 components predicted 56.9% of the observed QWB variance. The equation achieved an R 2 of 49.5% on cross-validation using Beaver Dam participants and an R 2 of 58.7% with the renal dialysis patients. An approximation for computing confidence intervals for predicted QWB mean scores is given. Conclusion. SF-36 data may be used to predict mean QWB scores for groups of patients, and thus may be useful to modelers who are secondary users of health status profile data. The equation may also be used to provide an overall health utility summary score to represent SF-36 profile data so long as the profiles are not severely limited by floor or ceiling effects of the SF-36 scales. The results of this study provide a quantitative link between two important measures of health status. Key words: health status; SF-36; Quality of Well- being index; quality of life; health-state utility; population study. (Med Decis Making 1997;17:1-9

Suggested Citation

  • Dennis G. Fryback & William F. Lawrence & Patricia A. Martin & Ronald Klein & Barbara E.K. Klein, 1997. "Predicting Quality of Well-being Scores from the SF-36," Medical Decision Making, , vol. 17(1), pages 1-9, February.
    • Handle: RePEc:sae:medema:v:17:y:1997:i:1:p:1-9
    DOI: 10.1177/0272989X9701700101
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    1. George W. Torrance & Michael H. Boyle & Sargent P. Horwood, 1982. "Application of Multi-Attribute Utility Theory to Measure Social Preferences for Health States," Operations Research, INFORMS, vol. 30(6), pages 1043-1069, December.
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    1. William F. Lawrence & John A. Fleishman, 2004. "Predicting EuroQoL EQ-5D Preference Scores from the SF-12 Health Survey in a Nationally Representative Sample," Medical Decision Making, , vol. 24(2), pages 160-169, March.
    2. 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.
    3. Maria Orlando Edelen & M. Audrey Burnam & Katherine E. Watkins & José J. Escarce & Haiden Huskamp & Howard H. Goldman & Gary Rachelefsky, 2008. "Obtaining Utility Estimates of the Health Value of Commonly Prescribed Treatments for Asthma and Depression," Medical Decision Making, , vol. 28(5), pages 732-750, September.
    4. W. Greiner & K. Lehmann & S. Earnshaw & C. Bug & R. Sabatowski, 2006. "Economic evaluation of Durogesic in moderate to severe, nonmalignant, chronic pain in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 7(4), pages 290-296, December.
    5. Jarmo Hahl & Helena Hämäläinen & Tuula Simell & Olli Simell, 2006. "The Effects of Type 1 Diabetes and its Long-Term Complications on Physical and Mental Health Status," PharmacoEconomics, Springer, vol. 24(6), pages 559-569, June.
    6. John Brazier & Yaling Yang & Aki Tsuchiya & Donna Rowen, 2010. "A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 215-225, April.
    7. Christine McDonough & Anna Tosteson, 2007. "Measuring Preferences for Cost-Utility Analysis," PharmacoEconomics, Springer, vol. 25(2), pages 93-106, February.
    8. Hirsch Ruchlin & Ralph Insinga, 2008. "A Review of Health-Utility Data for Osteoarthritis," PharmacoEconomics, Springer, vol. 26(11), pages 925-935, November.
    9. John A. Nyman, 2004. "Should the consumption of survivors be included as a cost in cost–utility analysis?," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 417-427, May.
    10. William Hollingworth & Richard A. Deyo & Sean D. Sullivan & Scott S. Emerson & Darryl T. Gray & Jeffrey G. Jarvik, 2002. "The practicality and validity of directly elicited and SF‐36 derived health state preferences in patients with low back pain," Health Economics, John Wiley & Sons, Ltd., vol. 11(1), pages 71-85, January.
    11. 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.
    12. 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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