Conduct penalized meta-analysis (“pema”) In meta-analysis, there are often between-study differences. These can be coded as moderator variables, and controlled for using meta-regression. However, if the number of moderators is large relative to the number of studies, such an analysis may be overfitted. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero.
For most users, the recommended starting point is to read the paper published in Research Synthesis Methods, which introduces the method, validates it, and provides a tutorial example.
Use CRAN to
install the latest release of pema:
install.packages("pema")Alternatively, use R-universe to install the
development version of pema by running the following
code:
options(repos = c(
cjvanlissa = 'https://cjvanlissa.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
install.packages('pema')You can cite pema using the following citation (please
use the same citation for either the package, or the paper):
Van Lissa, C. J., van Erp, S., & Clapper, E. B. (2023). Selecting relevant moderators with Bayesian regularized meta-regression. Research Synthesis Methods. https://doi.org/10.31234/osf.io/6phs5
This repository contains the source code for the R-package called
pema.
We are always eager to receive user feedback and contributions to help us improve both the workflow and the software. Major contributions warrant coauthorship to the package. Please contact the lead author at c.j.vanlissa@uu.nl, or:
By participating in this project, you agree to abide by the Contributor Code of Conduct
v2.0. Contributions to the package must adhere to the tidyverse style guide. When
contributing code, please add tests for that contribution to the
tests/testthat folder, and ensure that these tests pass in
the GitHub
Actions panel.