Quality of Life Research

, Volume 23, Issue 1, pp 1–4 | Cite as

Don’t middle your MIDs: regression to the mean shrinks estimates of minimally important differences

  • Peter M. Fayers
  • Ron D. Hays


Minimal important differences (MIDs) for patient-reported outcomes (PROs) are often estimated by selecting a clinical variable to serve as an anchor. Then, differences in the clinical anchor regarded as clinically meaningful or important can be used to estimate the corresponding value of the PRO. Although these MID values are sometimes estimated by regression techniques, we show that this is a biased procedure and should not be used; alternative methods are proposed.


Minimally important difference Clinical significance Quality of life Patient-reported outcomes Regression to the mean 



Minimal clinically important difference


Minimal important difference


Correlation coefficient


Eye Institute Visual Function Questionnaire-25


Patient-reported outcome


Standard deviation



Ron D. Hays was supported in part by grants from the NIA (P30-AG021684) and the NIMHD (P20MD000182).


  1. 1.
    Jaeschke, R., Singer, J., & Guyatt, G. H. (1989). Measurement of health status: ascertaining the minimal clinically important difference. Controlled Clinical Trials, 10, 407–415.PubMedCrossRefGoogle Scholar
  2. 2.
    US Food and Drug Administration. (2009). Patient-reported outcome measures: Use in medical product development to support labeling claims. Guidance for industry. Accessed March 20, 2013.
  3. 3.
    McLeod, L. D., Coon, C. D., Martin, S., Fehnel, S. E., & Hays, R. D. (2011). Interpreting patient-reported outcome results: FDA guidance and emerging methods. Expert Review of Pharmacoeconomics and Outcomes Research, 11, 163–169.PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Kvam, A. K., Wisløff, F., & Fayers, P. M. (2010). Minimal important differences and response shift in health-related quality of life; A longitudinal study in patients with multiple myeloma. Health and Quality of Life Outcomes, 8, 79.PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Schwartz, N., & Sudman, S. (1994). Autobiographical memory and the validity of retrospective reports. New York: Springer.CrossRefGoogle Scholar
  6. 6.
    Norman, G. (2003). Hi! How are you? Response shift, implicit theories and differing epistemologies. Quality of Life Research, 12, 239–249.PubMedCrossRefGoogle Scholar
  7. 7.
    Hays, R. D., Farivar, S. S., & Liu, H. (2005). Approaches and recommendations for estimating minimally important differences for health-related quality of life measures. Journal of Chronic Obstructive Pulmonary Disease, 2, 63–67.PubMedCrossRefGoogle Scholar
  8. 8.
    Revicki, D., Hays, R. D., Cella, D., & Sloan, J. (2008). Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. Journal of Clinical Epidemiology, 61, 102–109.PubMedCrossRefGoogle Scholar
  9. 9.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  10. 10.
    Suňer, I. J., Kokame, G. T., Yu, E., Ward, J., Dolan, C., & Bressler, N. M. (2009). Responsiveness of NEI VFQ-25 to changes in visual acuity in neovascular AMD: Validation studies from two phase 3 clinical trials. Investigative Ophthalmology & Visual Science, 50, 3629–3635.CrossRefGoogle Scholar
  11. 11.
    Fayers, P. M., & Hays, R. D. (2013). Linking should replace regression when mapping from profile to preference-based measures. Value in Health (submitted).Google Scholar
  12. 12.
    Galton, F. (1889). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain, 15, 246–263.CrossRefGoogle Scholar
  13. 13.
    Dorans, N. J. (2007). Linking scores from multiple health outcome instruments. Quality of Life Research, 16(Suppl 1), 85–94.PubMedCrossRefGoogle Scholar
  14. 14.
    Norman, G. R., Sloan, J. A., & Wyrwich, K. W. (2003). Interpretation of changes in health-related quality of life: The remarkable universality of a half a standard deviation. Medical Care, 41, 582–592.PubMedGoogle Scholar
  15. 15.
    Farivar, S. S., Liu, H., & Hays, R. D. (2004). Half standard deviation estimate of the minimally important difference in HRQOL scores? Expert Review of Pharmacoeconomics and Outcomes Research, 4, 515–523.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Institute of Applied Health SciencesUniversity of AberdeenAberdeenUK
  2. 2.Department of Cancer Research and Molecular MedicineNorwegian University of Science and Technology (NTNU)TrondheimNorway
  3. 3.Department of MedicineUCLALos AngelesUSA

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