The I-squared index was proposed in 2002 as a measure to help understand the consistency of study results in a meta-analysis. It was developed to overcome some of the limitations of existing measures, principally the chi-squared test for heterogeneity and the between-study variance as estimated in a random-effects meta-analysis.
I-squared measures approximately the proportion of total variability in results that is due to true heterogeneity rather than random error; it is also conveniently interpreted as a measure of inconsistency in the results of the studies. The index has become extremely widely used, although it is often misinterpreted as an absolute measure of the amount of heterogeneity, which it is not.
In this webinar, the presenter will reflect on the I-squared index and the different ways it can be defined, computed, and interpreted. He will explain why he believes many systematic reviews use the index inappropriately and will provide recommendations on how it should be used.
The session is open to everyone and will be of particular interest to review authors, statisticians and editors.
Presenter bio
Julian Higgins is Professor of Evidence Synthesis at the University of Bristol, UK, where he co-directs two of the NIHR Evidence Synthesis Groups. A biostatistician by training, he has worked for over 30 years on the methods and application of meta-analysis and systematic review. He has been a senior editor of the Cochrane Handbook for Systematic Reviews of Interventions since 2003.
