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The heterogeneity statistic I2 can be biased in small meta-analyses

Paul T. von Hippel
Organization: University of Texas, Austin
Department: Center for Health and Social Policy, LBJ School of Public Affairs,
Journal / Anthology

BMC Medical Research Methodology
Year: 2015
Volume: 15

Background: Estimated effects vary across studies, partly because of random sampling error and partly because of heterogeneity. In meta-analysis, the fraction of variance that is due to heterogeneity is estimated by the statistic I2. We calculate the bias of I2, focusing on the situation where the number of studies in the meta-analysis is small. Small meta-analyses are common; in the Cochrane Library, the median number of studies per meta-analysis is 7 or fewer. Methods: We use Mathematica software to calculate the expectation and bias of I2. Results: I2 has a substantial bias when the number of studies is small. The bias is positive when the true fraction of heterogeneity is small, but the bias is typically negative when the true fraction of heterogeneity is large. For example, with 7 studies and no true heterogeneity, I2 will overestimate heterogeneity by an average of 12 percentage points, but with 7 studies and 80 percent true heterogeneity, I2 can underestimate heterogeneity by an average of 28 percentage points. Biases of 1228 percentage points are not trivial when one considers that, in the Cochrane Library, the median I2 estimate is 21 percent. Conclusions: The point estimate I2 should be interpreted cautiously when a meta-analysis has few studies. In small meta-analyses, confidence intervals should supplement or replace the biased point estimate I2.

*Science > Biochemistry