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meta-analysis

Pool effect sizes across studies with fixed/random-effects models and heterogeneity metrics.

33|6|Updated Mar 17, 2026
Search Tags:#statistical modeling#meta-analysis#heterogeneity#publication bias#forest plot#effect sizes
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill meta-analysis-xjtulyc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meta-analysis
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/00-universal/meta-analysis
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill meta-analysis-xjtulyc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Meta-analysis consolidates multiple study findings into a single pooled effect while quantifying heterogeneity and assessing potential publication bias.

Core Features & Use Cases

  • Fixed- and random-effects pooling: compute precision-weighted fixed effects or heterogeneity-aware random effects (DerSimonian–Laird / REML via pymare).
  • Heterogeneity diagnostics: report Cochran Q, I², and τ² to determine how consistent effects are across studies.
  • Publication bias assessment and correction: generate forest and funnel plots and run Egger’s test, plus PET-PEESE and trim-and-fill workflows.
  • Subgroup/moderator analysis: estimate pooled effects within subgroups to explore why effects differ.

Quick Start

Use the meta-analysis skill to pool your study-level effect sizes and standard errors into a random-effects estimate with an accompanying forest plot and Egger test.

Frequently Asked Questions about meta-analysis

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

FAQPage Schema
How do I pool effect sizes from multiple studies into a single estimate?▼

To pool effect sizes, compute precision-weighted fixed effects or heterogeneity-aware random effects models using DerSimonian–Laird or REML estimators to produce a unified estimate with quantified uncertainty.

What is the best way to assess heterogeneity in a meta-analysis?▼

Assess heterogeneity in a meta-analysis by computing Cochran Q, I², and τ² metrics to determine how consistent effects are across studies and decide between fixed or random effects pooling.

Does this approach support subgroup and moderator analysis for systematic reviews?▼

Yes, this approach supports subgroup and moderator analysis by estimating pooled effects within subgroups to explore why effects differ across studies in a systematic review.

Can I use Python libraries like numpy and scipy for meta-analysis computations?▼

You can use Python libraries like numpy, scipy, pandas, and matplotlib for manual computations, or leverage pymare to fit fixed and random-effects models for your study-level effect sizes.

What effect size types can I pool in a systematic review?▼

You can pool effect size types including Cohen’s d, Hedges’ g, log-OR, log-RR, and Fisher’s z for correlations across two or more quantitative studies on the same outcome.

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