Understanding and evaluating meta-analysis

Meta-analysis refers to statistical methodology used to combine data from many studies to obtain an overall assessment of disease risk or treatment outcomes. In this article, the authors review basic methods, interpretation, and limitations of meta-analysis.

Investigators use meta-analysis approaches to combine data from available studies to obtain an answer to a specific question. An investigator uses a fixed model if there is homogeneity among the combined studies and a random-effects model if there is heterogeneity. The random-effects model results in wider confidence limits and more conservative estimates of overall results. A meta-analysis can be biased because studies with negative results (no differences in treatment outcomes) are less likely to be published (publication bias).

A meta-analysis should include a well-specified and reproducible set of procedures, including description of data abstraction procedures, attempts to include unpublished studies, and appropriate statistical analysis that includes thorough consideration of heterogeneity and potential bias.

Meta-analysis cannot correct shortcomings of existing studies or data. However, if potential pitfalls are recognized, meta-analysis can be a useful tool for summarizing existing studies, providing a means to address conflicting reports. Meta-analysis can lead to increased precision, providing greater power to detect existing relationships or treatment effects. Furthermore, meta-analysis may make it possible to address questions that cannot be answered by means of individual studies.

Meta-analysis provides an objective, quantitative synthesis of available studies but needs to be understood and assessed critically by those who use it to assess risk or make treatment decisions.