Meta-analysis, a statistical procedure that compares, combines, and synthesizes research findings from multiple studies in a principled manner, has become popular in a variety of fields. Meta-analyses using study-level (or equivalently aggregate) data are of particular interest due to data availability and modeling flexibility. In this paper, we describe an R package metapack that introduces a unified formula interface for both meta-analysis and network meta-analysis. The user interface—and therefore the package—allows flexible variance-covariance modeling for multivariate meta-analysis models and univariate network meta-analysis models. Complicated computing for these models has prevented their widespread adoption. The package also provides functions to generate relevant plots and perform statistical inferences like model assessments. Use cases are demonstrated using two real data sets contained in metapack.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-047.zip
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For attribution, please cite this work as
Lim, et al., "metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface", The R Journal, 2022
BibTeX citation
@article{RJ-2022-047,
author = {Lim, Daeyoung and Chen, Ming-Hui and Ibrahim, Joseph G. and Kim, Sungduk and Shah, Arvind K. and Lin, Jianxin},
title = {metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-047},
doi = {10.32614/RJ-2022-047},
volume = {14},
issue = {3},
issn = {2073-4859},
pages = {142-161}
}