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Evolution of Heterogeneity (I2) Estimates and Their 95% Confidence Intervals in Large Meta-Analyses
Published: Wednesday, July 25, 2012
Author: Kristian Thorlund et al.

by Kristian Thorlund, Georgina Imberger, Bradley C. Johnston, Michael Walsh, Tahany Awad, Lehana Thabane, Christian Gluud, P. J. Devereaux, Jørn Wetterslev

Background

Assessment of heterogeneity is essential in systematic reviews and meta-analyses of clinical trials. The most commonly used heterogeneity measure, I2, provides an estimate of the proportion of variability in a meta-analysis that is explained by differences between the included trials rather than by sampling error. Recent studies have raised concerns about the reliability of I2 estimates, due to their dependence on the precision of included trials and time-dependent biases. Authors have also advocated use of 95% confidence intervals (CIs) to express the uncertainty associated with I2 estimates. However, no previous studies have explored how many trials and events are required to ensure stable and reliable I2 estimates, or how 95% CIs perform as evidence accumulates.

Methodology/Principal Findings

To assess the stability and reliability of I2 estimates and their 95% CIs, in relation to the cumulative number of trials and events in meta-analysis, we looked at 16 large Cochrane meta-analyses - each including a sufficient number of trials and events to reliably estimate I2 - and monitored the I2 estimates and their 95% CIs for each year of publication. In 10 of the 16 meta-analyses, the I2 estimates fluctuated more than 40% over time. The median number of events and trials required before the cumulative I2 estimates stayed within +/-20% of the final I2 estimate was 467 and 11. No major fluctuations were observed after 500 events and 14 trials. The 95% confidence intervals provided good coverage over time.

Conclusions/Significance

I2 estimates need to be interpreted with caution when the meta-analysis only includes a limited number of events or trials. Confidence intervals for I2 estimates provide good coverage as evidence accumulates, and are thus valuable for reflecting the uncertainty associated with estimating I2.

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