meaning: Explore Composition of Homogeneous Study Subgroups

In metagroup: Meaningful Grouping of Studies in Meta-Analysis

Explore Composition of Homogeneous Study Subgroups

Description

After grouping studies using a meta-grouping function (e.g., 'mgbin'), this function analyzes the composition of each subgroup based on specified categorical variables. It helps to give "meaning" to the statistically derived groups.

Usage

meaning(data, subgroup = "subgroup", variables, min = 3)

Arguments

data	A data frame or the list object returned by a meta-grouping function. If a list, the function will automatically use the 'data' element.
subgroup	A string specifying the name of the subgroup column. Defaults to '"subgroup"'.
variables	A character vector of column names (categorical variables) to analyze.
min	An integer specifying the minimum number of studies a group must have to be included in the final summarized output. Defaults to 3.

Details

For each subgroup and each specified variable, the function calculates the percentage distribution of its categories. It identifies the most frequent (dominant) category and performs a chi-squared test to assess if the distribution of categories within that group is significantly different from random.

Value

An S3 object of class 'grouped', which is a list containing three data frames:

• 'detailed': Contains the percentage distribution for all categories of all variables within every group.

• 'up.hand': A subset of 'detailed', showing only the dominant category for each variable in each group.

• 'final': A summarized version of 'up.hand', showing the single most dominant characteristic for each group that meets the 'min' size threshold. This is the default print output.

Author(s)

Ahmed Abdelmageed [e-mail]

See Also

mgbin, mgcont, mgcor

Examples

# Create a sample dataset of studies
study_data <- data.frame(
  author = letters[1:10],
  country = sample(c("USA", "China", "Egypt"), 10, replace = TRUE),
  setting = sample(c("Hospital", "Community"), 10, replace = TRUE),
  subgroup = c("group 1", "group 2", "group 1", "group 1", "group 2",
               "group 2", "group 1", "group 3", "group 2", "group 2")
)

# Analyze the composition of the subgroups
meaning_result <- meaning(study_data, variables = c("country", "setting"))

# The default print shows the 'final' summary table
print(meaning_result)

# Access detailed results
# meaning_result$detailed

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