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

Peter M. Fayers; Ron D. Hays

doi:10.1007/s11136-013-0443-4

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

Peter M. Fayers^* (Corresponding Author), Ron D. Hays

^*Corresponding author for this work

Applied Health Sciences

Research output: Contribution to journal › Article › peer-review

61 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1-4
Number of pages	4
Journal	Quality of Life Research
Volume	23
Issue number	1
Early online date	31 May 2013
DOIs	https://doi.org/10.1007/s11136-013-0443-4
Publication status	Published - Feb 2014

Bibliographical note

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

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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