Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis

Karla Hemming, James P Hughes, Joanne E McKenzie, Andrew B Forbes

Research output: Contribution to journalArticlepeer-review

82 Downloads (Pure)

Abstract

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.

Original languageEnglish
JournalStatistical Methods in Medical Research
Early online date21 Sep 2020
DOIs
Publication statusE-pub ahead of print - 21 Sep 2020

Keywords

  • Cluster-randomized trials
  • multi-centre randomized trials
  • individual patient data meta-analysis
  • treatment effect heterogeneity
  • I-squared

Fingerprint

Dive into the research topics of 'Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis'. Together they form a unique fingerprint.

Cite this