Understanding the Basics of Meta-Analysis and How to Read a Forest Plot: As Simple as It Gets

The results of research on a specific question differ across studies, some to a small extent and some to a large extent. Meta-analysis is a way to statistically combine and summarize the results of different studies so as to obtain a pooled or summary estimate that may better represent what is true in the population. Meta-analysis can be conducted for a variety of statistics, including means, mean differences, standardized mean differences, proportions, differences in proportions, relative risks, odds ratios, and others. The results of meta-analysis are presented in forest plots. This article explains why meta-analysis may be necessary, how a systematic review is conducted to identify studies for meta-analysis, and how to interpret the various elements in a forest plot. Brief discussions are provided about important concepts relevant to meta-analysis, including heterogeneity, subgroup analyses, sensitivity analyses, fixed effect and random effects meta-analyses, and the detection of publication bias. Other procedures briefly explained include meta-regression analysis, pooled analysis, individual participant data meta-analysis, and network meta-analysis. The limitations of meta-analysis are also discussed.