Meta-Analysis Methods in R

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.