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Meta-Analysis Methods in R

Automates meta-analyses in R using the metafor package.

6|1|Updated Dec 10, 2025
Search Tags:#statistics#meta-analysis#R#heterogeneity#systematic-review#metafor
One-click install
npx skills add https://github.com/choxos/TidyRModelling --skill meta-analysis-methods-in-r
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Meta-Analysis Methods in R
Source: https://github.com/choxos/TidyRModelling/tree/main/plugins/r-tidy-modelling/skills/meta-analysis
Command: npx skills add https://github.com/choxos/TidyRModelling --skill meta-analysis-methods-in-r

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Meta-analyses synthesize results from multiple studies to estimate overall effects, reducing uncertainty and enabling evidence-based conclusions.

Core Features & Use Cases

  • Fixed and Random Effects: Pool effect sizes using standard models across diverse studies.
  • Heterogeneity & Bias: Assess between-study variability and publication bias with established diagnostics.
  • Subgroup Analysis & Meta-Regression: Explore moderators and study-level factors to explain heterogeneity.
  • Use Case: Combine results from several clinical trials to produce a single, pooled effect estimate and generate forest plots for reporting.

Quick Start

Load your study data into R as a data frame named studies, compute effect sizes with escalc from metafor, fit a meta-analysis with rma, and generate forest plots to summarize results.

Frequently Asked Questions about Meta-Analysis Methods in R

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform a meta-analysis in R using the metafor package?▼

To perform a meta-analysis in R, load your study data as a data frame, compute effect sizes with the escalc function, fit models using rma, and generate forest plots to summarize pooled effect estimates.

Can I assess heterogeneity and publication bias in a systematic review with R?▼

Yes, you can assess heterogeneity and publication bias in a systematic review using R packages like metafor to compute between-study variability diagnostics and run established bias checks across multiple studies.

What is the difference between fixed and random effects pooling for clinical trials?▼

Fixed effects pooling assumes studies share a single true effect, while random effects accounts for between-study variability. The rma function in metafor fits both models to synthesize clinical trial outcomes.

Do I need the meta or dmetar packages to run subgroup analysis and meta-regression?▼

You do not strictly need meta or dmetar to run subgroup analysis and meta-regression, as the metafor package handles these directly. However, dmetar and robumeta provide complementary tools for specialized analyses.

How do I calculate effect sizes for continuous and binary outcomes across multiple studies?▼

You can calculate effect sizes for continuous, binary, and other outcomes across multiple studies using the escalc function in R, which prepares the required metrics for fitting fixed or random effects models.

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