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metagroup provides a suite of tools to uncover hidden
structures in meta-analytic data. It uses a two-step process to perform
meaningful subgroup analysis:
mgbin(), mgcont()) to partition studies into
statistically homogeneous clusters based on their effect size data.meaning() function
and its associated plot() method to analyze these new
subgroups and understand their composition based on study-level
characteristics (e.g., country, setting).This approach helps to provide a deeper, more data-driven interpretation of heterogeneity in a meta-analysis.
You can install the development version of metagroup
from GitHub with:
# install.packages("remotes")
remotes::install_github("asmpro7/metagroup")Here is a basic example of the core workflow: first grouping the studies, then finding the meaning behind the groups.
# 1. Load the package
library(metagroup)
# 2. Step 1: Group the studies by homogeneity
# The result contains the original data with a new 'subgroup' column
grouped_results <- mgbin(
data = study_data,
event.e = event.e,
n.e = n.e,
event.c = event.c,
n.c = n.c,
studlab = studlab,
sm = "OR"
)
# 3. Step 2: Analyze the composition of the new subgroups
meaning_results <- meaning(
data = grouped_results,
variables = c("country", "setting")
)
# Print the summary table to see the dominant characteristics of each group
print(meaning_results)
# Plot the results to visualize the composition of all groups
plot(meaning_results)This package is licensed under the GPL-3 License.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.