Forest Plot Styler

ID: 157

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

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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

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Usage

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

Parameters

Parameter	Type	Default	Required	Description
--input, -i	string	-	Yes	Input data file (CSV or Excel)
--output, -o	string	forest_plot.png	No	Output file path
--format, -f	string	png	No	Output format (png/pdf/svg)
--point-size	int	8	No	OR point size
--point-color	string	#2E86AB	No	OR point color
--ci-color	string	#2E86AB	No	Confidence interval line color
--ci-linewidth	int	2	No	Confidence interval line thickness
--ci-capwidth	int	5	No	Confidence interval endpoint width
--summary-color	string	#A23B72	No	Pooled effect point color
--summary-shape	string	diamond	No	Pooled effect point shape
--subgroup	string	-	No	Subgroup analysis column name
--title, -t	string	Forest Plot	No	Chart title
--xlabel, -x	string	Odds Ratio (95% CI)	No	X-axis label
--reference-line	float	1.0	No	Reference line position
--width, -W	int	12	No	Image width (inches)
--height, -H	int	auto	No	Image height (inches)
--dpi	int	300	No	Image resolution
--font-size	int	10	No	Font size
--style, -s	string	default	No	Preset style (default/minimal/dark)

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Input Data Format

CSV/Excel files must contain the following columns:

Column Name	Description	Type
study	Study name	Text
or	Odds Ratio value	Numeric
ci_lower	Confidence interval lower bound	Numeric
ci_upper	Confidence interval upper bound	Numeric
weight	Weight (optional, for point size)	Numeric
subgroup	Subgroup label (optional)	Text

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

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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

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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

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Dependencies

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

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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)

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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

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

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