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ma-meta-analysis

Compute effect sizes, heterogeneity, and publication-bias diagnostics in R.

115|45|Updated Feb 5, 2026
Search Tags:#r#meta-analysis#reproducibility#heterogeneity#effect-size#renv#publication-bias
One-click install
npx skills add https://github.com/htlin222/meta-pipe --skill ma-meta-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ma-meta-analysis
Source: https://github.com/htlin222/meta-pipe/tree/main/ma-meta-analysis
Command: npx skills add https://github.com/htlin222/meta-pipe --skill ma-meta-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables researchers to perform and reproduce statistical meta-analyses in R, from data extraction to manuscript-ready outputs, using a standardized, renv-powered workflow.

Core Features & Use Cases

  • Automated meta-analysis workflow: compute effect sizes, fit REML + Hartung-Knapp models, evaluate heterogeneity, and assess publication bias.
  • Publication-ready outputs: generate forest plots, funnel plots, subgroup analyses, and tables formatted for manuscripts.
  • End-to-end project support: manage data and templates across the pipeline (01_protocol to 09_qa) and export outputs for reporting.

Quick Start

Create a new ma-meta-analysis project and run the 9-stage pipeline to generate all outputs.

Frequently Asked Questions about ma-meta-analysis

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

FAQPage Schema
How do I compute effect sizes and heterogeneity for a meta-analysis in R?▼

You can compute meta-analysis effect sizes and heterogeneity in R by feeding extracted study data into the Skill's 9-stage pipeline, which fits REML and Hartung-Knapp models to evaluate continuous, binary, and time-to-event outcomes.

How do I assess publication bias and generate forest plots in R?▼

Assess publication bias and generate forest plots by running the standardized R workflow, which produces funnel plots and manuscript-ready diagnostic tables directly from your extracted study data.

Do I need a renv-enabled project to reproduce meta-analysis results?▼

Yes, you need a preconfigured renv-enabled project and the ma-meta-analysis templates, including the 06_analysis scripts and assets, to execute the pipeline and reproduce meta-analysis results.

Can I use this meta-analysis workflow for binary and time-to-event outcomes?▼

Yes, the meta-analysis workflow supports continuous, binary, and time-to-event measures, allowing you to calculate standardized effect sizes across diverse research datasets.

What is the best way to manage an end-to-end meta-analysis pipeline from data extraction to reporting?▼

Manage an end-to-end meta-analysis pipeline by using a structured 9-stage workflow spanning from 01_protocol to 09_qa, which handles data management and exports manuscript-ready outputs.

Why does my meta-analysis workflow lack reproducibility across different environments?▼

Meta-analysis reproducibility issues often arise without an renv-powered project; this workflow requires a preconfigured renv environment to lock dependencies and ensure consistent results across systems.

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