Editors: Jonathan J Deeks, Julian PT Higgins and Douglas G Altman on behalf of the Cochrane Statistical Methods Group.
Meta-analysis is the statistical combination of results from two or more separate studies.
Potential advantages of meta-analyses include an increase in power, an improvement in precision, the ability to answer questions not posed by individual studies, and the opportunity to settle controversies arising from conflicting claims. However, they also have the potential to mislead seriously, particularly if specific study designs, within-study biases, variation across studies, and reporting biases are not carefully considered.
It is important to be familiar with the type of data (e.g. dichotomous, continuous) that result from measurement of an outcome in an individual study, and to choose suitable effect measures for comparing intervention groups.
Most meta-analysis methods are variations on a weighted average of the effect estimates from the different studies.
Variation across studies (heterogeneity) must be considered, although most Cochrane reviews do not have enough studies to allow the reliable investigation of the reasons for it. Random-effects meta-analyses allow for heterogeneity by assuming that underlying effects follow a normal distribution.
Many judgements are required in the process of preparing a Cochrane review or meta-analysis. Sensitivity analyses should be used to examine whether overall findings are robust to potentially influential decisions.
9.2 Types of data and effect measures
9.3 Study designs and identifying the unit of analysis
9.4 Summarizing effects across studies
9.6 Investigating heterogeneity
9.7 Sensitivity analyses
9.8 Chapter information
Box 9.8.a: The Cochrane Statistical Methods Group