Forest Plot Styler

ID: 157

Beautifies Meta-analysis or subgroup analysis forest plots, customizes Odds Ratio point sizes and confidence interval line styles.

Features

• Reads Meta-analysis data (CSV/Excel format)
• Draws high-quality forest plots
• Customizes Odds Ratio point sizes, colors, and shapes
• Customizes confidence interval line styles (color, thickness, endpoint style)
• Supports subgroup analysis display
• Automatically calculates and displays pooled effect values
• Outputs to PNG, PDF, or SVG format

Usage

python scripts/main.py --input <data.csv> [options]

Parameters

Input Data Format

CSV/Excel files must contain the following columns:

Sample Data

study,or,ci_lower,ci_upper,weight,subgroup
Study A,0.85,0.65,1.12,15.2,Drug A
Study B,0.72,0.55,0.94,18.5,Drug A
Study C,1.15,0.88,1.50,12.3,Drug B
Study D,0.95,0.75,1.20,14.8,Drug B

Examples

Basic Usage

python scripts/main.py -i meta_data.csv

Custom Style

python scripts/main.py -i meta_data.csv \
    --point-color="#E63946" \
    --ci-color="#457B9D" \
    --point-size=10 \
    --ci-linewidth=3 \
    -t "Meta-Analysis of Treatment Effects"

Subgroup Analysis

python scripts/main.py -i meta_data.csv \
    --subgroup subgroup_column \
    --summary-color="#F4A261" \
    -o subgroup_forest.png

Output PDF Vector Graphic

python scripts/main.py -i meta_data.csv \
    -f pdf \
    -o forest_plot.pdf

Preset Styles

default

• Blue color scheme
• Standard font size
• White background

minimal

• Clean lines
• Grayscale color scheme
• No grid lines

dark

• Dark background
• Bright data points
• Suitable for dark theme presentations

Dependencies

• Python >= 3.8
• matplotlib >= 3.5.0
• pandas >= 1.3.0
• numpy >= 1.20.0
• openpyxl >= 3.0.0 (for reading Excel)

Output Example

Generated forest plot contains:

• Left side: Study name list
• Middle: OR values and confidence intervals
• Right side: Weight percentage (if available)
• Bottom: Pooled effect value (diamond marker)
• Reference line (OR=1)

Notes

1. Ensure input file encoding is UTF-8
2. OR values are automatically converted when log scale is suggested
3. Studies with confidence intervals crossing 1 are not statistically significant
4. Weight values are used to adjust point size, reflecting study contribution

Risk Assessment

Security Checklist

• [ ] No hardcoded credentials or API keys
• [ ] No unauthorized file system access (../)
• [ ] Output does not expose sensitive information
• [ ] Prompt injection protections in place
• [ ] Input file paths validated (no ../ traversal)
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no stack traces exposed)
• [ ] Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
• Performance optimization
• Additional feature support