Meta-analysis allows researchers to combine the results of several studies into a unified analysis that provides an overall estimate of the effect of interest. This collection of articles from the Stata Journal and Stata Technical Bulletin will be indispensable to researchers who wish to conduct meta-analyses using Stata and learn about the full range of user-written Stata meta-analysis commands. With these articles and the associated Stata software, you gain access to the statistical methods behind the rapid increase in the number of meta-analyses reported in the social and medical literature.
Collectively, the articles provide a detailed description of a range of meta-analytic methods. They show how to conduct and interpret meta-analyses; how to produce highly flexible graphical displays; how to use meta-regression; how to examine bias; how to conduct individual participant data meta-analysis; and how to conduct multivariate meta-analysis. This edition also contains three articles on network metaanalysis, a major recent development in meta-analysis methodology.
Table of Contents
Meta-analysis in Stata: metan, metaan, metacum, and metap. Meta-regression: metareg. Investigating bias in meta-analysis: metafunnel, confunnel, metabias, metatrim, and extfunnel. Multivariate meta-analysis: metandi, mvmeta. Network meta-analysis: indirect, network package, network graphs package.
Tom M. Palmer is a lecturer in statistics in the Department of Mathematics and Statistics at Lancaster University, UK. He is the author of the confunnel command for contour-enhanced funnel plots. His research focuses on statistical methodology for epidemiological studies, including Mendelian randomization studies. He is also the author of several other Stata commands, including bpbounds, the reffadjust package, and the winbugsfromstata package.
Jonathan A. C. Sterne is professor of medical statistics and epidemiology and of social and community medicine, University of Bristol, UK. His research interests include methods for systematic reviews and meta-analyses, the clinical epidemiology of HIV and AIDS in the era of effective therapy, statistical methods for epidemiology, and the epidemiology of asthma and allergic diseases.