BMJ 2003;327:557-560 (6 September), doi:10.1136/bmj.327.7414.557
Education and debate
Measuring inconsistency in meta-analyses
Julian P T Higgins, statistician1,
Simon G Thompson, director1,
Jonathan J Deeks, senior medical statistician2,
Douglas G Altman, professor of statistics in medicine2
1 MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR,
2 Cancer Research UK/NHS Centre for Statistics in Medicine, Institute of Health Sciences, Oxford OX3 7LF
Correspondence to: J P T Higgins julian.higgins@mrc-bsu.cam.ac.uk
Cochrane Reviews have recently started including the quantity I2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice?
| The first 150 words of the full text of this article appear below. |
Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I2, which we believe gives a better measure of the consistency between trials in a meta-analysis.
Need for consistency
Assessment of the consistency of effects across studies is an essential part of . . . [Full text of this article]
Testing for heterogeneity
Quantifying heterogeneity: a better approach
Examples of values of
I2
Further applications of
I2
Methodological subgroupsHeterogeneity related to choice of effect measureClinically important subgroupsHow much is too much heterogeneity?

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Rapid Responses:
Read all Rapid Responses
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