| name | alterlab-meta-analysis |
| description | Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's Q), draws forest and funnel plots, and tests publication bias (Egger's regression, trim-and-fill) — using statsmodels.stats.meta_analysis in Python or the field-standard R metafor via Rscript. It enforces PRISMA reporting and the random- vs fixed-effect decision. Use when pooling effect sizes across studies, running a systematic review's quantitative synthesis, or assessing heterogeneity and publication bias. For finding and screening the literature prefer alterlab-deep-research; for a single study's statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite. |
| license | MIT |
| allowed-tools | Read Bash(python:*) |
| compatibility | Requires (declare in-session, no runtime install on Anthropic API): Python statsmodels>=0.14 (statsmodels.stats.meta_analysis) — OR the field-standard R metafor>=5.0 via Rscript (escalc/rma/forest/funnel/regtest/trimfill). Note: the PyPI PythonMeta package is stale/unmaintained — prefer statsmodels or metafor. Runs locally via `uv run python` / `Rscript`; no API key. |
| metadata | {"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-deep-research (literature search/screening), alterlab-statistical-analysis; audited by alterlab-ssci-inference-gate"} |
Meta-Analysis — Pool Honestly, Then Interrogate Heterogeneity and Bias
Skill type: ANALYSIS MODULE. Synthesizes effect sizes across studies. The discipline is not the
pooled point estimate — it is the model choice (fixed vs random effects), the heterogeneity
you must characterize, and the publication-bias diagnostics that decide whether the pooled
estimate is trustworthy at all.
Core Mission
THE POOLED EFFECT IS ONLY AS GOOD AS ITS HETEROGENEITY STORY AND ITS PUBLICATION-BIAS CHECK.
When to Use This Skill
- "Pool these effect sizes / run a meta-analysis across N studies."
- "Compute I² / τ² — how heterogeneous are my studies?"
- "Is there publication bias? Draw a funnel plot / run Egger's test."
- "Convert these means and SDs (or 2×2 tables) into effect sizes and combine them."
Does NOT Trigger
| The request is really about… | Route to | Why not this skill |
|---|
| Finding / screening the literature (search, PRISMA flow) | alterlab-deep-research | Discovery & screening, upstream of pooling. |
| A single study's descriptive/inferential statistics | alterlab-statistical-analysis | One dataset, not cross-study synthesis. |
| Fitting one regression / GLM | alterlab-statsmodels | Not effect-size pooling. |
| Whether the review question/design is sound | alterlab-ssci-design-gate | Design routing. |
Fixed-effect vs random-effects (the first decision)
- Fixed-effect — assumes one true effect; studies differ only by sampling error. Rarely
defensible across heterogeneous social-science studies.
- Random-effects — assumes a distribution of true effects (between-study variance τ²); the
default when studies vary in population, measure, or design. Report the model and why.
Verified calls (pinned)
Python — statsmodels:
from statsmodels.stats.meta_analysis import effectsize_smd, combine_effects
eff, var = effectsize_smd(m1, sd1, n1, m2, sd2, n2)
res = combine_effects(eff, var, method_re=)
res.summary_frame()
res.tau2, res.i2, res.q
res.plot_forest()
