Meta-analysis: A brief introduction

Abstract

Meta-analysis is the process of combining data from multiple sources and analyzing it together to increase power and provide a clearer picture of the effect of intervention or exposure on an outcome. The process is not complicated, but requires a great deal of attention to detail. A specific set of inclusion criteria for studies must be defined. Published or available study results may be affected by publication bias of several different types, so the researcher should be sure to conduct a thorough search of available databases in order to include unpublished findings. Following selection, the group of studies should be examined using funnel plots or statistical tests. Meta-analysis models themselves must be selected to properly reflect the combined studies. Both fixed- and random-effects modeling are discussed. Two case studies are presented, illustrating a well-conducted meta-analysis and a meta-analysis that was more controversial.

Figure 1

(A) A funnel plot with pooled estimate (solid line) and approximate point‐wise 95% confidence limits showing no apparent publication bias. (B) A funnel plot suggesting publication bias in favor of larger studies and positive results.

Figure 2

A forest plot from a weighted meta‐analysis of odds ratios.