Quality of Life Research

, 17:955 | Cite as

Separating gains and losses in health when calculating the minimum important difference for mapped utility measures

  • Michael B. Nichol
  • Joshua D. Epstein



To estimate the minimum important difference (MID) for a variety of mapped utility measures and to determine whether patients perceiving gains and losses in health status should be treated equally when calculating the MID.


A longitudinal study within a California managed care population of 6,932 patients was retrospectively analyzed. Utilities were derived from the SF-36 short-form health survey using multiple validated mapping methods. Absolute utility changes for patients who considered their current health as ‘somewhat better’ or ‘somewhat worse’ in the prior year were compared to determine if gains and losses in utility values could be combined. The MIDs were calculated and compared using anchor- and distribution-based methods.


Two thousand one hundred patients reported ‘somewhat better’ or ‘somewhat worse’ health in the first year. When combining these patients, the average MID for all mapped utility measures was 0.03 (SD = 0.1), a magnitude similar to that identified by Walters. However, when separated, the mean MID utility change for those reporting ‘somewhat better’ and ‘somewhat worse’ health was 0.02 (SD = 0.1) and −0.06 (SD = 0.1), respectively (P < 0.0001).


Researchers should consider the effects of combining gains and losses when determining utility MID values.


Minimum important difference Preference-based measures SF-6D Utility Quality adjusted life year Utility mapping 



Chronic Disease Score


Cost-effectiveness analysis


Effect size


Functional Assessment of Cancer Therapy


Food and Drug Administration


Health-related quality of life


Health Utilities Index Mark 2


Minimum important difference


Patient-reported outcome


Quality-adjusted life year


Standard error of measurement


Standard deviation


Standard gamble


Visual analog scale


  1. 1.
    Food and Drug Administration. (FDA) (2006). Guidance for industry: patient-reported outcomes measures: use in medical product development to support labeling claims: draft guidance. FDA, Rockville, MD. Available online at:
  2. 2.
    Torrance, G. W., & Feeny, D. (1989). Utilities and quality-adjusted life years. International Journal of Technology Assessment in Health Care, 5, 559–575.PubMedGoogle Scholar
  3. 3.
    Gold, M. R., Siegel, J. E., Rusell, L. B., & Weinstein, M. C. (1996). Cost-effectiveness in health and medicine. Oxford: Oxford University Press.Google Scholar
  4. 4.
    Wyrwich, K. W., Bullinger, M., Aaronson, N., Hays, R. D., Patrick, D. L., & Symonds, T. (2005). Estimating clinically significant differences in quality of life outcomes. Quality of Life Research, 14, 285–295. doi: 10.1007/s11136-004-0705-2.PubMedCrossRefGoogle Scholar
  5. 5.
    Lydick, E., & Epstein, R. S. (1993). Interpretation of quality of life changes. Quality of Life Research, 2, 221–226.  10.1007/BF00435226.PubMedCrossRefGoogle Scholar
  6. 6.
    Revicki, D. A., Cella, D., Hays, R. D., Sloan, J. A., Lenderking, W. R., & Aaronson, N. K. (2006). Responsiveness and minimal important differences for patient reported outcomes. Health and Quality of Life Outcomes, 4, 70–74. doi: 10.1186/1477-7525-4-70.PubMedCrossRefGoogle Scholar
  7. 7.
    Kazis, L. E., Anderson, J. J., & Meenan, R. F. (1989). Effect sizes for interpreting changes in health status. Medical Care, 27(Suppl.), S178–S189. doi: 10.1097/00005650-198903001-00015 PubMedCrossRefGoogle Scholar
  8. 8.
    Wyrwich, K. W., Tierney, W., & Wolinsky, F. D. (1999). Further evidence supporting an SEM-based criterion for identifying meaningful intra-individual changes in health-related quality of life. Journal of Clinical Epidemiology, 52, 861–873. doi: 10.1016/S0895-4356(99)00071-2.PubMedCrossRefGoogle Scholar
  9. 9.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd edn.). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  10. 10.
    Norman, G. R., Sloan, J. A., & Wyrwich, K. W. (2003). Interpretation of changes in health-related quality of life: The remarkable universality of half a standard deviation. Medical Care, 14(5), 582–592. doi: 10.1097/00005650-200305000-00004.CrossRefGoogle Scholar
  11. 11.
    Ross, M. (1989). Relation of implicit theories to the construction of personal histories. Psychological Review, 96, 341–357. doi: 10.1037/0033-295X.96.2.341.CrossRefGoogle Scholar
  12. 12.
    Guyatt, G. H., Norman, G. R., Juniper, E. F., & Griffith, L. E. (2002). A critical look at transition ratings. Journal of Clinical Epidemiology, 55, 900–908. doi: 10.1016/S0895-4356(02)00435-3.PubMedCrossRefGoogle Scholar
  13. 13.
    Sprangers, M. A. G., & Schwartz, C. E. (1999). Integrating response shift into health-related quality of life research: A theoretical model. Social Science and Medicine, 48, 1507–1515. doi: 10.1016/S0277-9536(99)00045-3.PubMedCrossRefGoogle Scholar
  14. 14.
    Guyatt, G. H., Osoba, D., Wu, A. W., Wywrich, K. W., & Norman, G. R.; Clinical Significance Consensus Meeting Group. (2002). Methods to explain the clinical significance of health status measures. Mayo Clinic Proceedings, 77(4), 371–383.PubMedCrossRefGoogle Scholar
  15. 15.
    Walters, S. J., & Brazier, J. E. (2005). Comparison of the minimally important difference for two health state utility measures: EQ-5D and SF-6D. Quality of Life Research, 14, 1523–1532. doi: 10.1007/s11136-004-7713-0.PubMedCrossRefGoogle Scholar
  16. 16.
    Cella, D., Hahn, E. A., & Dineen, K. (2002). Meaningful change in cancer-specific quality of life scores: Differences between improvement and worsening. Quality of Life Research, 11, 207–221. doi: 10.1023/A:1015276414526.PubMedCrossRefGoogle Scholar
  17. 17.
    Lundberg, L., Johannesson, M., Isacson, D. G. L., & Borgquist, L. (1999). The relationship between health-state utilities and the SF-12 in a general population. Medical Decision Making, 19(2), 128–140. doi: 10.1177/0272989X9901900203.PubMedCrossRefGoogle Scholar
  18. 18.
    Shmueli, A. (2004). The relationship between the visual analog scale and the SF-36 scales in the general population: An update. Medical Decision Making, 24(1), 61–63. doi: 10.1177/0272989X03261562.PubMedCrossRefGoogle Scholar
  19. 19.
    Von Korff, M., Wagner, E. H., & Saunders, K. (1992). A chronic disease score from automated pharmacy data. Journal of Clinical Epidemiology, 45, 197–203. doi: 10.1016/0895-4356(92)90016-G.CrossRefGoogle Scholar
  20. 20.
    Walters, S. J., & Brazier, J. E. (2003). What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D. Health and Quality of Life Outcomes, 1, 4–11. doi: 10.1186/1477-7525-1-4.PubMedCrossRefGoogle Scholar
  21. 21.
    Ware, J. E., & Kosinski, M. (2001). The SF-36 physical and mental health summary scales. A manual for users of version 1, 2nd edn. Lincoln, RI: QualityMetric Incorporated.Google Scholar
  22. 22.
    Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271–292. doi: 10.1016/S0167-6296(01)00130-8.PubMedCrossRefGoogle Scholar
  23. 23.
    Nichol, M. B., Sengupta, N., & Globe, D. R. (2001). Evaluating quality-adjusted life years: Estimation of the health utility index (HUI2) from the SF-36. Medical Decision Making, 21, 105–112. doi: 10.1177/02729890122062352.PubMedCrossRefGoogle Scholar
  24. 24.
    Yost, K. J., Cella, D., Chawla, A., Holmgren, E., Eton, T., Ayanian, J. Z., West, D. W. (2005). Minimally important differences were estimated for the Functional Assessment of Cancer Therapy-Colorectal (FACT-C) instrument using a combination of distribution- and anchor-based approaches. Journal of Clinical Epidemiology, 58, 1241–1251. doi: 10.1016/j.jclinepi.2005.07.008.PubMedCrossRefGoogle Scholar
  25. 25.
    Pickard, A. S., Wang, Z., Walton, S. M., & Lee, T. A. (2005). Are decisions using cost-utility analyses robust to choice of SF-36/SF-12 preference-based algorithm? Health and Quality of Life Outcomes, 3, 11–19. doi: 10.1186/1477-7525-3-11.PubMedCrossRefGoogle Scholar
  26. 26.
    Brissette, I., Leventhal, H., & Leventhal, E. A. (2003). Observer ratings of health and sickness: Can other people tell us anything about our health that we don’t already know? Health Psychology, 22, 471–478. doi: 10.1037/0278-6133.22.5.471.PubMedCrossRefGoogle Scholar
  27. 27.
    Brissette, I., Scheier, M. F., & Carver, C. S. (2002). The role of optimism in social network development, coping, and psychological adjustment during a life transition. Journal of Personality and Social Psychology, 82, 102–111. doi: 10.1037/0022-3514.82.1.102.PubMedCrossRefGoogle Scholar
  28. 28.
    Wyrwich, K. W., Metz, S. M., Babu, A. N., Kroenke, K., Tierney, W. M., & Wolinsky, F. D. (2002). The reliability of retrospective change assessments. Quality of Life Research, 11(7), 636.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.University of Southern CaliforniaLos AngelesUSA

Personalised recommendations