Forest Plot Styler

Source: https://github.com/aipoch/medical-research-skills

Forest Plot Styler

ID: 157

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

When to Use

• Use this skill when the task needs Beautify meta-analysis forest plots with customizable odds ratio points.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Analyze data with forest-plot-styler using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

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

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/forest-plot-styler"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan." --format json

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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 -m py_compile scripts/main.py

# Example invocation: 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

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