Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Stat Methods Med Res. 2021 Feb;30(2):376-395. doi: 10.1177/0962280220948550. Epub 2020 Sep 21.
Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.
治疗效果异质性在荟萃分析中通常用于确定治疗效果是否在研究之间存在差异。当进行汇总水平数据荟萃分析时，通常使用 I 平方统计量来描述任何治疗效果异质性的大小，这是一个直观且易于理解的概念。在聚类随机试验中，治疗效果也可能在聚类之间存在差异，或者在多中心随机试验中，在中心之间存在差异，在分析阶段探索这一点可能很有趣。在交叉试验和其他随机设计中，聚类或中心同时暴露于治疗和对照条件下，这种治疗效果异质性可以被识别。在这里，我们推导出并评估了一个可比较的 I 平方度量来描述随机试验中聚类或中心之间治疗效果异质性的大小。我们进一步展示了如何在个体患者数据荟萃分析中使用该方法来估计治疗效果异质性。
