Basics of meta-analysis: I2 is not an absolute measure of heterogeneity

Michael Borenstein, Julian Higgins, Larry Hedges, Hannah Rothstein

Research output: Contribution to journalArticle (Academic Journal)peer-review

454 Citations (Scopus)
1295 Downloads (Pure)

Abstract

When we speak about heterogeneity in a meta-analysis, our intent is usually to understand the substantive implications of the heterogeneity. If an intervention yields a mean effect size of 50 points, we want to know if the effect size in different populations varies from 40 to 60, or from 10 to 90, because this speaks to the potential utility of the intervention. While there is a common belief that the I2 statistic provides this information, it actually does not. In this example, if we’re told that I2 is 50%, we have no way of knowing if the effects range from 40 to 60, or from 10 to 90, or across some other range. Rather, if we want to communicate the predicted range of effects, then we should simply report this range. This gives readers the information they think is being captured by I2, and does so in a way that is concise and unambiguous.
Original languageEnglish
Pages (from-to)5-18
Number of pages14
JournalResearch Synthesis Methods
Volume8
Issue number1
Early online date6 Jan 2017
DOIs
Publication statusPublished - Mar 2017

Keywords

  • l-squared
  • l2
  • Heterogeneity
  • Meta-analysis
  • Prediction intervals
  • Inconsistency

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