mgbin: Group Studies with Binary Outcome Data by Homogeneity

In metagroup: Meaningful Grouping of Studies in Meta-Analysis

Group Studies with Binary Outcome Data by Homogeneity

Description

This function iteratively assigns studies to subgroups based on a homogeneity test. The goal is to create statistically homogeneous groups of studies before performing a final meta-analysis with binary outcome data.

Usage

mgbin(data, event.e, n.e, event.c, n.c, studlab, ...)

Arguments

data	A data frame containing the meta-analysis data.
event.e	A vector of event counts in the experimental group.
n.e	A vector of sample sizes in the experimental group.
event.c	A vector of event counts in the control group.
n.c	A vector of sample sizes in the control group.
studlab	A vector of study labels.
...	Additional arguments passed on to 'meta::metabin'.

Details

The algorithm starts with a single study in "group 1". It then processes each subsequent study, attempting to place it in an existing group. A study is added to a group only if its inclusion does not result in significant within-group heterogeneity. If no suitable group is found, a new one is created.

Value

A list containing the final data with subgroup assignments ('data'), the final 'metabin' model ('model'), and the number of attempts ('attempts').

Author(s)

Ahmed Abdelmageed [e-mail]

See Also

meaning

metagroup documentation built on Sept. 10, 2025, 10:26 a.m.