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

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Parameters

Parameter	Type	Default	Required	Description
INLINECODE0, INLINECODE1	string	-	Yes	Input data file (CSV or Excel)
INLINECODE2, INLINECODE3

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
INLINECODE27	Study name	Text
INLINECODE28

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

CODEBLOCK1

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Examples

Basic Usage

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Custom Style

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Subgroup Analysis

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Output PDF Vector Graphic

CODEBLOCK5

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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. 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

CODEBLOCK6

Evaluation Criteria

Success Metrics

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

Test Cases

1. 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