Stata has some of the best statistical tools available for doing meta-analysis. The unusual thing about these tools is that none of them are part of official Stata, so you will not find them in the Stata documentation. They are all contributed and documented by researchers in the field who also happen to be proficient Stata developers.

Meta-analysis allows researchers to combine results of several studies into a unified analysis that provides an overall estimate of the effect of interest and to quantify the uncertainty of that estimate. This collection of articles from the Stata Journal makes the work of 21 authors available in one collection. Previously, you had to dig through many Stata Journal articles (and older Stata Technical Bulletin inserts) to find all the programs. No more! All the articles are now in one volume, and the associated commands can be installed at one time.

This is not merely a retrospective collection. Editor Jonathan Sterne convinced over half the authors to update their software and articles for the collection, resulting in a much more cohesive volume. The programs have a more unified syntax than in their original forms and, among the commands that draw graphs, almost all now produce modern Stata graphs—they can even be edited in the Graph Editor.

In his opening comments and the introductions to each section, Sterne relates how the articles tie together and how they fit in the overall literature of meta-analysis. He organizes the collection into four areas: classic meta-analysis; meta-regression; graphical and analytic tools for detecting bias; and recent advances such as meta-analysis for dose–response curves, diagnostic accuracy, multivariate analyses, and studies containing missing values. The collection addresses both common and complex methods for conducting a meta-analysis, including implementations of contemporary advances that will help keep the reader up to date.

The collection includes 16 articles and 15 new Stata commands for meta-analysis. The articles cover topics ranging from standard and cumulative meta-analysis and forest plots to contour-enhanced funnel plots and nonparametric analysis of publication bias. In their articles, the authors present conceptual overviews of the techniques, thorough explanations, and detailed descriptions and syntax of new commands. They also provide examples using real-world data. In short, this collection is a complete introduction and reference for performing meta-analyses in Stata.

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