Kazis L E, Anderson J J, Meenan R F
Multipurpose Arthritis Center, Boston University School of Medicine, MA 02118.
Med Care. 1989 Mar;27(3 Suppl):S178-89. doi: 10.1097/00005650-198903001-00015.
Health status measures are being used with increasing frequency in clinical research. Up to now the emphasis has been on the reliability and validity of these measures. Less attention has been given to the sensitivity of these measures for detecting clinical change. As health status measures are applied more frequently in the clinical setting, we need a useful way to estimate and communicate whether particular changes in health status are clinically relevant. This report considers effect sizes as a useful way to interpret changes in health status. Effect sizes are defined as the mean change found in a variable divided by the standard deviation of that variable. Effect sizes are used to translate "the before and after changes" in a "one group" situation into a standard unit of measurement that will provide a clearer understanding of health status results. The utility of effect sizes is demonstrated from four different perspectives using three health status data sets derived from arthritis populations administered the Arthritis Impact Measurement Scales (AIMS). The first perspective shows how general and instrument-specific benchmarks can be developed and how they can be used to translate the meaning of clinical change. The second perspective shows how effect sizes can be used to compare traditional clinical measures with health status measures in a standard clinical drug trial. The third application demonstrates the use of effect sizes when comparing two drugs tested in separate drug trials and shows how they can facilitate this type of comparison. Finally, our health status results show how effect sizes can supplement standard statistical testing to give a more complete and clinically relevant picture of health status change. We conclude that effect sizes are an important tool that will facilitate the use and interpretation of health status measures in clinical research in arthritis and other chronic diseases.
健康状况测量方法在临床研究中的使用频率日益增加。到目前为止，重点一直放在这些测量方法的可靠性和有效性上。而对于这些测量方法检测临床变化的敏感性关注较少。随着健康状况测量方法在临床环境中应用得越来越频繁，我们需要一种有用的方法来评估并传达健康状况的特定变化是否具有临床相关性。本报告认为效应量是解释健康状况变化的一种有用方法。效应量被定义为变量中发现的平均变化除以该变量的标准差。效应量用于将“一组”情况下的“前后变化”转化为一个标准测量单位，以便更清楚地理解健康状况结果。使用来自接受关节炎影响测量量表（AIMS）的关节炎人群的三个健康状况数据集，从四个不同角度展示了效应量的实用性。第一个角度展示了如何制定一般的和特定工具的基准，以及如何使用它们来解释临床变化的意义。第二个角度展示了在标准临床药物试验中，效应量如何用于比较传统临床测量方法与健康状况测量方法。第三个应用展示了在比较分别在不同药物试验中测试的两种药物时效应量的使用，并说明了它们如何促进这种类型的比较。最后，我们的健康状况结果表明效应量如何能够补充标准统计测试，以更完整且具有临床相关性地呈现健康状况变化。我们得出结论，效应量是一种重要工具，将有助于在关节炎和其他慢性病的临床研究中使用和解释健康状况测量方法。
