Quantifying heterogeneity in a meta‐analysis
Abstract
The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.
Cite this
Julian P. T. Higgins, Simon G. Thompson (2002). Quantifying heterogeneity in a meta‐analysis. Statistics in Medicine. https://doi.org/10.1002/sim.1186
@article{t2002quantifying,
title = {Quantifying heterogeneity in a meta‐analysis},
author = {Higgins, Julian P. T. and Thompson, Simon G.},
year = {2002},
journal = {Statistics in Medicine},
doi = {10.1002/sim.1186}
}Citations in this library
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