The metaplus package is described with examples of its use for fitting meta-analysis and meta-regression. For either meta-analysis or meta-regression it is possible to fit one of three models: standard normal random effect, t-distribution random effect or mixture of normal random effects. The latter two models allow for robustness by allowing for a random effect distribution with heavier tails than the normal distribution, and for both robust models the presence of outliers may be tested using the parametric bootstrap. For the mixture of normal random effects model the outlier studies may be identified through their posterior probability of membership in the outlier component of the mixture. Plots allow the results of the different models to be compared. The package is demonstrated on three examples: a meta-analysis with no outliers, a meta-analysis with an outlier and a meta-regression with an outlier.
metaplus, metafor, bbmle, forestplot, extrafont
MetaAnalysis, ClinicalTrials, Phylogenetics
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For attribution, please cite this work as
Beath, "The R Journal: metaplus: An R Package for the Analysis of Robust Meta-Analysis and Meta-Regression", The R Journal, 2016
BibTeX citation
@article{RJ-2016-001,
author = {Beath, Ken J.},
title = {The R Journal: metaplus: An R Package for the Analysis of Robust Meta-Analysis and Meta-Regression},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-001},
doi = {10.32614/RJ-2016-001},
volume = {8},
issue = {1},
issn = {2073-4859},
pages = {5-16}
}