Meaningful Grouping of Studies in Meta-Analysis

Performs meaningful subgrouping in a meta-analysis. This is a two-step process; first, use the iterative grouping functions (e.g., mgbin(), mgcont() ) to partition studies into statistically homogeneous clusters based on their effect size data. Second, use the meaning() function to analyze these new subgroups and understand their composition based on study-level characteristics (e.g., country, setting). This approach helps to uncover hidden structures in meta-analytic data and provide a deeper interpretation of heterogeneity.

metagroup

Meaningful Grouping of Studies in Meta-Analysis

Overview

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:

1. Group: Use iterative grouping functions (e.g., mgbin(), mgcont()) to partition studies into statistically homogeneous clusters based on their effect size data.
2. Interpret: Use the 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.

Installation

You can install the development version of metagroup from GitHub with:

# install.packages("remotes")
remotes::install_github("asmpro7/metagroup")

Example Usage

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)

License

This package is licensed under the GPL-3 License.