meta-analysis
Conduct quantitative synthesis through meta-analysis. Use when: (1) Combining effect sizes across studies, (2) Systematic review synthesis, (3) Calculating summary effects, (4) Assessing heterogeneity.
When & Why to Use This Skill
This Claude skill facilitates advanced quantitative synthesis by performing rigorous meta-analyses on research data. It streamlines the process of combining effect sizes from multiple studies, calculating summary effects using fixed or random-effects models, and evaluating statistical heterogeneity (I² and Q tests) to derive robust, evidence-based conclusions for systematic reviews.
Use Cases
- Clinical Research: Combining results from multiple randomized controlled trials (RCTs) to determine the overall efficacy and safety of medical treatments or interventions.
- Evidence-Based Policy: Synthesizing social science studies to evaluate the impact of educational or public policy changes across different demographics and regions.
- Academic Literature Reviews: Automating the calculation of summary effects, 95% confidence intervals, and forest plot data for high-impact systematic reviews.
- Bias & Quality Assessment: Identifying publication bias through funnel plots and Egger’s tests, and conducting sensitivity analyses to ensure the integrity of research findings.
| name | meta-analysis |
|---|---|
| description | "Conduct quantitative synthesis through meta-analysis. Use when: (1) Combining effect sizes across studies, (2) Systematic review synthesis, (3) Calculating summary effects, (4) Assessing heterogeneity." |
| allowed-tools | Read, Write, Bash |
| version | 1.0.0 |
Meta-Analysis Skill
Purpose
Quantitatively synthesize results across multiple studies.
Meta-Analysis Steps
1. Extract Effect Sizes
- Convert to common metric (d, OR, RR)
- Calculate standard errors
2. Choose Model
- Fixed-effect: Assumes single true effect
- Random-effects: Allows heterogeneity
3. Pool Results
- Weight studies (inverse variance)
- Calculate summary effect
- 95% confidence interval
4. Assess Heterogeneity
- I² statistic (0-100%)
- 0-40%: Low heterogeneity
- 40-75%: Moderate
- 75-100%: High
- Q test (statistical significance)
5. Investigate Heterogeneity
- Subgroup analysis
- Meta-regression
- Sensitivity analysis
6. Publication Bias
- Funnel plot
- Egger's test
- Trim-and-fill
Reporting
Example: "Meta-analysis of 15 RCTs (N=1,234) showed a moderate effect, g=0.52, 95% CI[0.38, 0.66], p<.001. Heterogeneity was moderate, I²=58%, suggesting variability in effects."
Version: 1.0.0