Skip to main content
Log in

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

  • Published:
Quality of Life Research Aims and scope Submit manuscript


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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others



Minimal clinically important difference


Minimal important difference


Correlation coefficient


Eye Institute Visual Function Questionnaire-25


Patient-reported outcome


Standard deviation


  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.

    Article  CAS  PubMed  Google Scholar 

  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. 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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Schwartz, N., & Sudman, S. (1994). Autobiographical memory and the validity of retrospective reports. New York: Springer.

    Book  Google Scholar 

  6. Norman, G. (2003). Hi! How are you? Response shift, implicit theories and differing epistemologies. Quality of Life Research, 12, 239–249.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  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.

    Article  Google Scholar 

  11. Fayers, P. M., & Hays, R. D. (2013). Linking should replace regression when mapping from profile to preference-based measures. Value in Health (submitted).

  12. Galton, F. (1889). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain, 15, 246–263.

    Article  Google Scholar 

  13. Dorans, N. J. (2007). Linking scores from multiple health outcome instruments. Quality of Life Research, 16(Suppl 1), 85–94.

    Article  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

Download references


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

Author information

Authors and Affiliations


Corresponding author

Correspondence to Peter M. Fayers.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fayers, P.M., Hays, R.D. Don’t middle your MIDs: regression to the mean shrinks estimates of minimally important differences. Qual Life Res 23, 1–4 (2014).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: