Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
In quantitative systematic reviews, the results of individual studies are combined into an overall pooled estimate by calculating a weighted average.1 It is natural to expect some differences among the results of the studies included; indeed, it would be astonishing if the results were identical. However, such variation raises the key question of whether combining the studies is tenable or highly suspect. If this variation (or heterogeneity) is consistent with the “play of chance,” then obtaining a pooled estimate is appropriate. If it is not, the cause of this variation should be investigated.
Ways to identify heterogeneity
Heterogeneity can often be identified through a simple visual inspection of a forest plot of all individual study results.1 If the results of individual studies broadly appear to “line up” with each other, then there is probably little heterogeneity. However, it is not always easy to see whether the results of different studies are consistent with one another. In such cases, formal statistical approaches can be used to quantify the amount of heterogeneity, and whether it is consistent with the play of chance.
One common approach is to calculate the Q statistic, which tests the null hypothesis of no true heterogeneity.2 A related approach is to calculate the I2 statistic, …