ReviewResearch synthesis methods2026
Reflections on the I-squared index for measuring inconsistency in meta-analysis.
Review in Research synthesis methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of Digital Health Interventions in Chronic Musculoskeletal Pain: Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2026Pooled it
- Comment on "Efficacy and safety of antihypertensive drugs deprescribing in older adults: A systematic review and meta-analysis of randomized controlled trials".International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Interpreting Heterogeneity Beyond IJournal of evaluation in clinical practice · 2026Article
- Practical Guidance on Fisher's z Transformation for Meta-Analysis of Pearson's Correlation Coefficients.Journal of evidence-based medicine · 2026Article
- From Five-Number Summary to Absolute Heterogeneity: Recent Methodological Advances in Meta-Analysis With Continuous Outcomes.Journal of evidence-based medicine · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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. Here, we discuss the I-squared index and the different ways it can be defined, computed, and interpreted. We introduce a new interpretation of I-squared as a weighted sum of squares, which we propose may be helpful when setting up simulation studies. We discuss some of the extensions and repurposes that have been proposed for I-squared and offer some recommendations on the appropriate use of the index in practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.