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

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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.

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Abbreviations

MCID:

Minimal clinically important difference

MID:

Minimal important difference

r:

Correlation coefficient

NEI VFQ-25:

Eye Institute Visual Function Questionnaire-25

PRO:

Patient-reported outcome

SD:

Standard deviation

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Acknowledgments

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

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Authors and Affiliations

  1. Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK

    Peter M. Fayers

  2. Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Peter M. Fayers

  3. Department of Medicine, UCLA, 911 Broxton Avenue, Los Angeles, CA, 90024, USA

    Ron D. Hays

Authors
  1. Peter M. Fayers
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  2. Ron D. Hays
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Correspondence to Peter M. Fayers.

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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). https://doi.org/10.1007/s11136-013-0443-4

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  • DOI: https://doi.org/10.1007/s11136-013-0443-4

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