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

Peter M Fayers1, Ron D Hays

  • 1Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK, P.Fayers@abdn.ac.uk.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|June 1, 2013
PubMed

Insights

Estimating minimal important differences (MIDs) for patient-reported outcomes (PROs) using anchor-based clinical variables can be biased. Regression techniques for MID estimation are shown to be flawed, and alternative methods are recommended for accurate clinical interpretation.

Area of Science:

  • Clinical Epidemiology
  • Health Outcomes Research
  • Psychometrics

Background:

  • Patient-reported outcomes (PROs) are crucial for evaluating treatment effectiveness.
  • Minimal important differences (MIDs) quantify the smallest change in a PRO that patients perceive as beneficial.
  • Anchor-based methods are commonly used to estimate MIDs, linking PRO changes to clinical variables.

Purpose of the Study:

  • To critically evaluate the use of regression techniques for estimating MIDs based on clinical anchors.
  • To demonstrate the inherent bias in using regression for MID estimation.
  • To propose and advocate for alternative, less biased methods for calculating MIDs.

Main Methods:

  • The study theoretically analyzes the statistical properties of regression-based MID estimation.
  • It identifies sources of bias when using a clinical anchor to estimate PRO MIDs.
  • Alternative statistical approaches for anchor-based MID estimation are discussed.

Main Results:

  • Regression techniques applied to anchor-based MID estimation introduce significant bias.
  • This bias can lead to inaccurate estimations of clinically meaningful changes in PROs.
  • The findings highlight the limitations of current common practices.

Conclusions:

  • Regression-based methods for estimating MIDs from clinical anchors are inappropriate and should be avoided.
  • Accurate estimation of MIDs is essential for reliable interpretation of PRO data in clinical research and practice.
  • Further research should focus on validating and implementing alternative methods for robust MID determination.

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