Basics of meta-analysis: I2 is not an absolute measure of heterogeneity - PubMed (original) (raw)
doi: 10.1002/jrsm.1230. Epub 2017 Jan 6.
Affiliations
- PMID: 28058794
- DOI: 10.1002/jrsm.1230
Basics of meta-analysis: I2 is not an absolute measure of heterogeneity
Michael Borenstein et al. Res Synth Methods. 2017 Mar.
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 are 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. Copyright © 2017 John Wiley & Sons, Ltd.
Keywords: I-squared; I2; heterogeneity; inconsistency; meta-analysis; prediction intervals.
Copyright © 2017 John Wiley & Sons, Ltd.
Comment in
- Practical challenges of I2 as a measure of heterogeneity.
Hoaglin DC. Hoaglin DC. Res Synth Methods. 2017 Sep;8(3):254. doi: 10.1002/jrsm.1251. Epub 2017 Jun 20. Res Synth Methods. 2017. PMID: 28631294 No abstract available.
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