Department of Medicine, UCLA Division of General Internal Medicine and Health Services Research, 1100 Glendon Avenue, Suite 850, Los Angeles, CA, 90024, USA.
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
Qual Life Res. 2021 Oct;30(10):2765-2772. doi: 10.1007/s11136-021-02897-z. Epub 2021 Jun 15.
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.
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.
MIC thresholds yield over-optimistic conclusions about responders to treatment because they classify those who have not changed as responders.
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.
最小有意义变化（MIC）的估计可用于评估组水平的差异是否足够大到具有重要意义。但治疗的反应者是基于组水平的 MIC 阈值，从而导致随时间变化的分类不准确。本文回顾了各种选择，并提供了关于个体水平统计数据的建议，以评估个体是否有所改善、保持不变或下降。
MIC 估计的回顾以及 MIC 组水平估计错误应用于评估个体变化的例子。二次数据分析，以展示如何使用对有意义变化的看法，并结合个体变化的显著性。
MIC 阈值产生了对治疗反应者过于乐观的结论，因为它们将没有变化的人归类为反应者。
未来的研究需要使用适当的个体水平统计数据，如可靠变化指数或等效可重复性系数，来评估个体变化的显著性。用变化的回顾性评估来补充个体统计显著性是可取的。
