- name
- meta-analysis-forest-plotter
- description
- Use when creating forest plots for meta-analyses, visualizing effect sizes across studies, or generating publication-ready meta-analysis figures. Produces high-quality forest plots with confidence intervals, heterogeneity metrics, and subgroup analyses.
- allowed-tools
- Read Write Bash Edit
- license
- MIT
- metadata
- skill-author
- AIPOCH
- version
- 1.0
Meta-Analysis Forest Plot Generator
Create publication-ready forest plots for systematic reviews and meta-analyses with customizable styling and statistical annotations.
Quick Start
from scripts.forest_plotter import ForestPlotter
plotter = ForestPlotter()
# Generate forest plot
plot = plotter.create_plot(
studies=["Study A", "Study B", "Study C"],
effect_sizes=[1.2, 0.8, 1.5],
ci_lower=[0.9, 0.5, 1.1],
ci_upper=[1.5, 1.1, 1.9],
overall_effect=1.15
)Core Capabilities
1. Basic Forest Plot
fig = plotter.plot(
data=studies_df,
effect_col="HR",
ci_lower_col="CI_lower",
ci_upper_col="CI_upper",
study_col="study_name"
)Required Data Columns:
- Study name/identifier
- Effect size (OR, HR, RR, MD, etc.)
- Confidence interval lower bound
- Confidence interval upper bound
- Weight (optional, for precision)
2. Statistical Annotations
fig = plotter.plot_with_stats(
data,
heterogeneity_stats={
"I2": 45.2,
"p_value": 0.03,
"Q_statistic": 18.4
},
overall_effect={
"estimate": 1.15,
"ci": [0.98, 1.35],
"p_value": 0.08
}
)Heterogeneity Metrics:
| Metric | Interpretation |
|---|---|
| I² < 25% | Low heterogeneity |
| I² 25-50% | Moderate heterogeneity |
| I² > 50% | High heterogeneity |
| Q p-value < 0.05 | Significant heterogeneity |
3. Subgroup Analysis
fig = plotter.subgroup_plot(
data,
subgroup_col="treatment_type",
subgroups=["Surgery", "Radiation", "Combined"]
)4. Custom Styling
fig = plotter.plot(
data,
style="publication",
journal="lancet", # or "nejm", "jama", "nature"
color_scheme="monochrome",
show_weights=True
)CLI Usage
# From CSV data
python scripts/forest_plotter.py \
--input meta_analysis_data.csv \
--effect-col OR \
--output forest_plot.pdf
# With custom styling
python scripts/forest_plotter.py \
--input data.csv \
--style lancet \
--width 8 --height 10Output Formats
- PDF: Publication quality, vector graphics
- PNG: Web/presentation, 300 DPI
- SVG: Editable in Illustrator/Inkscape
- TIFF: Journal submission format
References
references/forest-plot-styles.md- Journal-specific formattingexamples/sample-plots/- Example outputs
Skill ID: 207 | Version: 1.0 | License: MIT