Meta-Analysis Forest Plot Generator

---

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:**

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

References

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