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,
dmetar
, is introduced at the beginning of the guide. It contains data sets and several helper functions for the
meta
and
metafor
package used in the guide.
The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible.
Features
Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises
Describes statistical concepts clearly and concisely before applying them in R
Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book