meta-analysis
meta-analysis synthesizes findings across studies by computing pooled effect sizes, testing for heterogeneity, and detecting publication bias through forest and funnel plots. It supports multiple effect size types including odds ratios, risk ratios, standardized mean differences, and hazard ratios, with random-effects modeling and bias diagnostics built in.
meta-analysis combines results from multiple studies to calculate pooled effect sizes, heterogeneity, and publication bias.
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Install
beita6969/ScienceClaw/meta-analysis · repository language: TypeScript
git clone https://github.com/beita6969/ScienceClaw
cp -r ScienceClaw/skills/meta-analysis ~/.claude/skills/meta-analysisFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does meta-analysis combine study results into a pooled effect size?
meta-analysis synthesizes findings across studies by computing pooled effect sizes that aggregate data from multiple trials into a single summary estimate. The skill supports various effect size types including odds ratios, risk ratios, standardized mean differences (Cohen's d, Hedges' g), and hazard ratios, allowing you to combine heterogeneous study designs into unified estimates with confidence intervals.
What forest plot and funnel plot visualizations does meta-analysis generate?
meta-analysis generates forest plots to display individual study effects and the pooled estimate, and funnel plots to assess publication bias visually. These plots help you identify outlier studies, evaluate the symmetry of effect distributions, and detect potential reporting bias across your meta-analysis dataset.
How does meta-analysis assess heterogeneity and publication bias?
meta-analysis evaluates heterogeneity using I-squared statistics and tests for publication bias through methods like Egger's test and Begg rank correlation. It also supports trim-and-fill adjustments to estimate the impact of missing studies, helping you understand whether results are consistent across trials or driven by selective reporting.
Can meta-analysis calculate odds ratio and other effect sizes from trial data?
meta-analysis calculates effect sizes directly from trial data, including odds ratios, risk ratios, standardized mean differences, and hazard ratios. You can input raw counts or summary statistics, and the skill computes individual study estimates before pooling them using random-effects or fixed-effects models.
What random-effects modeling and subgroup analysis features does meta-analysis offer?
meta-analysis performs random-effects meta-analysis using DerSimonian-Laird methods with confidence intervals, and supports subgroup analysis to explore heterogeneity across study covariates. Metaregression capabilities allow you to model how study-level characteristics influence pooled estimates and effect variation.
Is meta-analysis suitable for systematic reviews and PRISMA reporting?
meta-analysis is designed to support systematic review workflows and PRISMA-compliant reporting. Its comprehensive bias diagnostics, effect size calculations, and visualization tools help you document study selection, heterogeneity assessment, and publication bias evaluation—all key components of transparent meta-analysis reporting.
SKILL.md
rendered from the published skill — quoted content, verbatim
Meta-Analysis
Quantitative synthesis of results from multiple studies. Calculates pooled effect sizes, assesses heterogeneity, detects publication bias, and generates forest and funnel plots.
When to Use
- "Combine these study results into a meta-analysis"
- "Calculate the pooled odds ratio from these trials"
- "Create a forest plot of these effect sizes"
- "Test for publication bias with a funnel plot"
- "What's the heterogeneity (I²) across these studies?"
- "Run a random-effects meta-analysis"
When NOT to Use
- Designing a systematic review protocol (use systematic-review)
- Searching for studies (use literature-search)
- Single-study statistical analysis (use statsmodels-stats)
- Narrative literature
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