Meaningful Grouping of Studies in Meta-Analysis
Group Studies with Binary Outcome Data by Homogeneity
Group Studies with Single Proportions by Homogeneity
Group Studies with Single Incidence Rates by Homogeneity
Pipe operator
metagroup: Meaningful Grouping of Studies in Meta-Analysis
Print and Plot Methods for 'grouped' Objects
Explore Composition of Homogeneous Study Subgroups
Group Studies with Continuous Outcome Data by Homogeneity
Group Studies with Correlation Data by Homogeneity
Group Studies Using the Generic Inverse Variance Method
Group Studies with Incidence Rate Data by Homogeneity
Group Studies with Single Means by Homogeneity
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.