Skills meta-analysis-forest-plotter

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.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/meta-analysis-forest-plotter" ~/.claude/skills/clawdbot-skills-meta-analysis-forest-plotter && rm -rf "$T"

manifest: skills/aipoch-ai/meta-analysis-forest-plotter/SKILL.md

source content

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 10

Output 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 formatting
```
examples/sample-plots/
```
- Example outputs

Skill ID: 207 | Version: 1.0 | License: MIT