Author Description

<p>Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, <i>dmetar</i>, is introduced at the beginning of the guide. It contains data sets and several helper functions for the <i>meta</i> and <i>metafor</i> package used in the guide. </p><p>The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible.</p><p><b>Features</b></p><ul> <li>Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises</li> <li>Describes statistical concepts clearly and concisely before applying them in R</li> <li>Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book </li> </ul>