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Quality of Life Research

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15-06-2021 | Commentary

Between-group minimally important change versus individual treatment responders

Auteurs: Ron D. Hays, John Devin Peipert

Gepubliceerd in: Quality of Life Research | Uitgave 10/2021

Abstract

Purpose

Estimates of the minimally important change (MIC) can be used to evaluate whether group-level differences are large enough to be important. But responders to treatment have been based upon group-level MIC thresholds, resulting in inaccurate classification of change over time. This article reviews options and provides suggestions about individual-level statistics to assess whether individuals have improved, stayed the same, or declined.

Methods

Review of MIC estimation and an example of misapplication of MIC group-level estimates to assess individual change. Secondary data analysis to show how perceptions about meaningful change can be used along with significance of individual change.

Results

MIC thresholds yield over-optimistic conclusions about responders to treatment because they classify those who have not changed as responders.

Conclusions

Future studies need to evaluate the significance of individual change using appropriate individual-level statistics such as the reliable change index or the equivalent coefficient of repeatability. Supplementing individual statistical significance with retrospective assessments of change is desirable.

vorige artikel Triangulation of multiple meaningful change thresholds for patient-reported outcome scores

volgende artikel Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score!

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Titel: Between-group minimally important change versus individual treatment responders
Auteurs: Ron D. Hays
John Devin Peipert
Publicatiedatum: 15-06-2021
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 10/2021
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-021-02897-z

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Abstract

Purpose

Methods

Results

Conclusions

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