The biggest benefit of a meta-analysis is that is allows multiple studies' findings to be pooled into a single effect estimate, raising the statistical power of the test and potentially raising our certainty in the effect estimate in turn. However, a single estimate may be misleading if there is significant heterogeneity (or inconsistency in GRADE terminology) among the individual studies. One study, for instance, may point to a potential harm of an intervention while the others in the same meta-analysis suggest a benefit; this study may vary from the others in important ways regarding its population, the performance of the intervention, or even the study design itself. A brief primer on heterogeneity newly published by Cordero and Dans details how it can be identified and managed to improve the way implications of a meta-analysis are presented and applied.
Of eyeballs and i: detecting heterogeneity
Identifying the presence of heterogeneity among a group of pooled studies may be as simple as visually inspecting a forest plot for confidence intervals that show poor overlap or discordance in their estimate of effects (i.e., some showing a likely benefit while others showing a likely harm).
However, some statistical analyses can also provide more nuanced and objective measures of potentially worrisome heterogeneity:
- the Q statistic, which tests the null hypothesis that no heterogeneity is present and provides a p-value for this likelihood (however, large p-values should not necessarily be interpreted as the absence of heterogeneity).
- i is a measure based on the Q statistic and can be interpreted generally as the amount of total variability within the sample that is due to differences between studies. The larger the i , the greater the likelihood of "real" heterogeneity. A 95% confidence interval surrounding the estimate should be presented when using i to detect heterogeneity.