Advertisement

PharmacoEconomics

, Volume 27, Issue 6, pp 491–505 | Cite as

Converting the SF-12 into the EQ-5D

An Empirical Comparison of Methodologies
  • Ling-Hsiang ChuangEmail author
  • Paul Kind
Original Research Article

Abstract

Background: For cost-utility analysis, analysts need a measure that summarizes health-status utilities in a single index of health-related quality of life (HR-QOL). It is common to find in clinical studies that only an HR-QOL profile measure such as the SF-36 is included, but not the summary HR-QOL index. Therefore, the economist’s usual practice is to reprocess the profile data into a single index format. Several ‘after-market’ tools are available to convert the SF-36 or SF-12 into a single form with or without utility-weighting metric property. However, there has been no consensus with regard to a regression method that should be recommended for such a mapping task.

Objective: To report on the performance of different regression methods that have previously been applied to the conversion of SF-12 data in the analysis of a single common dataset. The mapping between the SF-12 and EQ-5D is the focus.

Methods: The data were adopted from the Medical Expenditure Panel Survey 2003 where 19 678 adults completed both EQ-5D and SF-12 questionnaires. Four econometric techniques, namely ordinary least squares (OLS), censored least absolute deviation, multinomial logit model and two-part model regressions were investigated together with two main types of model specifications: item-based and summary score-based. The performance of each examined model was judged by various criteria, including its estimated mean, the size of mean absolute error and the number of errors.

Results: Among four compared econometric techniques, OLS regression was the most accurate model in estimating the group mean. Models with item-based model specification performed better than those with summary scorebased regardless of the chosen econometric technique. Nevertheless, the accuracy of OLS deteriorates in older and less healthy subgroups. The results also suggested that the two-part model, which addresses the heterogeneity issue, performs better in these vulnerable subgroups.

Conclusions: None of the mapping methods included in the current study are suitable for estimating at the individual level. The methodology exemplified here has wider applicability and might just as readily be applied to other members of the SF family or indeed to other profile measures of HR-QOL. However, it is recommended that a preference-based, single index measure of HR-QOL should be included in the clinical studies for the purpose of economic evaluation.

Keywords

Mean Square Error Ordinary Less Square Summary Score Ordinary Less Square Regression Medical Expenditure Panel Survey 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Paul Kind is Principal Investigator for Quality Outcomes (QUO), which specializes in the use of outcome measures including EQ-5D. L-H Chuang has no conflicts of interest that are directly relevant to the content of this study.

No sources of funding were used to assist in the preparation of this study.

References

  1. 1.
    Gold MR, Siegel JE, Russell LB, et al., editors. Cost-effectiveness in health and medicine. New York: Oxford University Press, 1996Google Scholar
  2. 2.
    Ware JE, Sonw KK, Kosinski M, et al. SF-36 health survey: manual and interpretation guide. Boston (MA): The Health Institute, New England Medical Center, 1993Google Scholar
  3. 3.
    Ware JE, Kosinski M, Keller SD. SF-36 physical and mental health summary scale: a user’s manual. Boston (MA): The Health Institute, New England Medical Center, 1994Google Scholar
  4. 4.
    Ware JE, Kosinski M, Turner-Bower D, et al. How to score version 2 of the SF-12 health survey. Lincoln (RI): QualityMetric, 2002Google Scholar
  5. 5.
    Mortimer D, Segal L. Comparing the incomparable? A systematic review of competing techniques for converting descriptive measures of health states into QALY-weights. Med Decis Making 2008; 28: 66–88PubMedCrossRefGoogle Scholar
  6. 6.
    Bosch JR, Hunink MGM. The relationship between descriptive and valuation quality-of-life measures in patients with intermittent claudication. Med Decis Making 1996; 16: 217–25PubMedCrossRefGoogle Scholar
  7. 7.
    Franks P, Lubetkin EI, Gold MR, et al. Mapping the SF-12 to preference-based instruments: convergent validity in a low-income, minority population. Med Care 2003; 41 (11): 1277–83PubMedCrossRefGoogle Scholar
  8. 8.
    Franks P, Lubetkin EI, Gold MR, et al. Mapping the SF-12 to the EuroQol EQ-5D index in a national US sample. Med Decis Making 2004; 24 (3): 247–54PubMedCrossRefGoogle Scholar
  9. 9.
    Fryback DG, Lawrence WF, Martin PA, et al. Predicting quality of well-being scores from the SF-36: results from the Beaver Dam Health Outcomes Study. Med Decis Making 1997; 17 (1): 1–9PubMedCrossRefGoogle Scholar
  10. 10.
    Gray A, Rivero-Arias O, Clarke P. Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Med Decis Making 2006; 26: 18–29PubMedCrossRefGoogle Scholar
  11. 11.
    Lawrence WF, Fleishman JA. Predicting EuroQoL EQ-5D preference scores from the SF-12 health survey in a nationally representative sample. Med Decis Making 2004; 24 (2): 160–9PubMedCrossRefGoogle Scholar
  12. 12.
    Lundberg L, Johannesson M, Isacson DG, et al. The relationship between health-state utilities and the SF-12 in a general population. Med Decis Making 1999; 19 (2): 128–40PubMedCrossRefGoogle Scholar
  13. 13.
    Nichol MB, Sengupta N, Globe DR. Evaluating quality-adjusted life years: estimation of the health utility index (HUI2) from the SF-36. Med Decis Making 2001; 21 (2): 105–12PubMedGoogle Scholar
  14. 14.
    Sengupta N, Nichol MB, Wu J, et al. Mapping the SF-12 to the HUI3 and VAS in a managed care population. Med Care 2004; 42 (9): 927–37PubMedCrossRefGoogle Scholar
  15. 15.
    Shmueli A. The SF36 profile and health-related quality of life: an interpretive analysis. Qual Life Res 1998; 7: 187–95PubMedCrossRefGoogle Scholar
  16. 16.
    Shmueli A. The relationship between the visual analogue scale and the SF-36 scales in the general population: an update. Med Decis Making 2004; 24 (1): 61–3PubMedCrossRefGoogle Scholar
  17. 17.
    Shmueli A. Subjective health status and health values in the general population. Med Decis Making 1999; 19 (2): 122–7PubMedCrossRefGoogle Scholar
  18. 18.
    Sullivan PW, Ghushchyan V. Mapping the EQ-5D index form the SF-12: US general population preferences in a nationally representative sample. Med Decis Making 2006; 26: 401–9PubMedCrossRefGoogle Scholar
  19. 19.
    Tesvat J, Solzan JG, Kuntz KM, et al. Health values of patients infected with human immunodeficiency virus: relationship to mental health and physical function. Med Care 1996; 34: 44–57CrossRefGoogle Scholar
  20. 20.
    Agency for Healthcare Research and Quality. Homepage [online]. Available from URL: http://www.ahrq.gov/ [Accessed 2006 Mar 23]
  21. 21.
    Agency for Healthcare Research and Quality (AHRQ). MEPS HC-079: 2003 full year consolidated data file [online]. Available from URL: http://www.meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-079 [Accessed 2006 Mar 23]
  22. 22.
    Agency for Healthcare Research and Quality. Medical Expenditure Panel survey [online]. Available from URL: http://www.meps.ahrq.gov/mepsweb/ [Accessed 2006 Mar 23]
  23. 23.
    Ware JE, Kosinski M, Keller SD. How to score the SF-12 physical & mental health summary scales. 3rd ed. Lincoln (RI): QualityMetric, 1998Google Scholar
  24. 24.
    Brook R. EuroQol: the current state of play. Health Policy 1996; 37: 53–72CrossRefGoogle Scholar
  25. 25.
    Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med 2001; 33: 337–43PubMedCrossRefGoogle Scholar
  26. 26.
    Szenda A, Oppe M, Devlin N, editors. EQ-5D value sets: inventory, comparative review and user guide. Dordrecht: Kluwer Academic Publishers, 2007Google Scholar
  27. 27.
    Kind K, Brooks R, Rabin R, editors. EQ-5D concepts and methods: a developmental history. Dordrecht: Kluwer Academic Publishers, 2005Google Scholar
  28. 28.
    Shaw JW, Johnson JA, Coons SJ. US valuation of the EQ-5D health states: development and testing of the D1 valuation model. Med Care 2005; 43 (3): 203–20PubMedCrossRefGoogle Scholar
  29. 29.
    Powell J. Least absolute deviations estimation for the censored regression model. J Econometr 1984; 25: 303–25CrossRefGoogle Scholar
  30. 30.
    Austin PC, Escobar M, Kopec JA. The use of the Tobit model for analyzing measures of health status. Qual Life Res 2000; 9: 901–10PubMedCrossRefGoogle Scholar
  31. 31.
    Austin PC. Bayesian extension of the Tobit model for analyzing measures of health status. Med Decis Making 2002; 22: 152–62PubMedGoogle Scholar
  32. 32.
    Austin PC. A comparison of methods for analyzing health-related quality-of-life measures. Value Health 2002; 5 (4): 329–37PubMedCrossRefGoogle Scholar
  33. 33.
    Cameron AC, Trivedi PK. Microeconometrics: methods and application. New York: Cambridge University Press, 2005CrossRefGoogle Scholar
  34. 34.
    StataCorp. Stata statistical software: release 9.2. College Station (TX): Stata Corporation, 2007Google Scholar
  35. 35.
    Bult JR, Bosch JL, Hunink MG. Heterogeneity in the relationship between the standard-gamble utility measure and health-status dimensions. Med Decis Making 1996; 16: 226–33PubMedCrossRefGoogle Scholar
  36. 36.
    Mortimer D, Segal L, Hawthorne G, et al. Item-based versus subscale-based mapping form the SF-36 to a preference-based quality of life measure. Value Health 2007; 10: 398–406PubMedCrossRefGoogle Scholar
  37. 37.
    Hollingworth W, Deyo RA, Sullivan SD, et al. The practicality and validity of directly elicited and SF-36 derived health state preferences in patients with low back pain. Health Econ 2002; 11 (1): 71–85PubMedCrossRefGoogle Scholar
  38. 38.
    Kaplan RM, Groessl EJ, Sengupta N, et al. Comparison of measured utility scores and imputed scores from the SF-36 in patients with rheumatoid arthritis. Med Care 2005; 43 (1): 79–87PubMedGoogle Scholar
  39. 39.
    Lee TA, Hollingworth W, Sullivan SD. Comparison of directly elicited preferences to preferences derived from the SF-36 in adults with asthma. Med Decis Making 2003; 23 (4): 323–34PubMedCrossRefGoogle Scholar
  40. 40.
    McDonough CM, Grove MR, Tosteson TD, et al. Comparison of EQ-5D, HUI, and SF-36-derived social health state values among Spine Patient Outcome Research Trial (SPORT) participants. Qual Life Res 2005; 14: 1321–32PubMedCrossRefGoogle Scholar
  41. 41.
    Lobo FS, Gross CR, Mattees BJ. Estimation and comparison of derived preference scores form the SF-36 in lung transplant patients. Qual Life Res 2004; 13: 377–88PubMedCrossRefGoogle Scholar
  42. 42.
    Sherbourne CD, Unutzer J, Schoenbaum M, et al. Can utility-weighted HRQoL estimate capture health effect of quality improvement for depression? Med Care 2001; 39: 1246–59CrossRefGoogle Scholar
  43. 43.
    Pickard AS, Wang Z, Walton SM, et al. Are decisions using cost-utility analyses robust to choice of SF-36/SF-12 preference-based algorithm? Health Qual Life Outcomes 2005; 3 (1): 1–11CrossRefGoogle Scholar
  44. 44.
    Brazier JE, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care 2004; 42 (9): 851–9PubMedCrossRefGoogle Scholar
  45. 45.
    Kind P. York expert workshop in the social economic evaluation of medicines, model 3: quality of life. Dordrecht: Kluwer Academic Publishers, 2006Google Scholar
  46. 46.
    Chuang L-H. Homepage [online]. Available from URL: http://www.york.ac.uk/inst/che/staff/chuang.htm [Accessed 2009 Feb 4]
  47. 47.
    Sugar CA, Srurm R, Lee TT, et al. Empirically defined health states for depression form the SF-12. Health Serv Res 1998; 33 (4): 911–28PubMedGoogle Scholar
  48. 48.
    Lenert LA, Sherbourne CD, Sugar CA, et al. Estimation of utilities for the effects of depression from the SF-12. Med Care 2000; 38 (7): 763–70PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2009

Authors and Affiliations

  1. 1.Outcomes Research Group, Centre for Health EconomicsUniversity of YorkYorkUK

Personalised recommendations