Selected article for: "change significance and statistical significance"

Author: Hays, Ron D.; Peipert, John Devin
Title: Between-group minimally important change versus individual treatment responders
  • Cord-id: xj4km18k
  • Document date: 2021_6_15
  • ID: xj4km18k
    Snippet: 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
    Document: 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.

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