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Meta-Analysis Pipeline

GitHub stars Last commit License Python

AI-assisted, end-to-end meta-analysis with reproducible tooling.

From research question to manuscript-ready output -- powered by Claude Code.


Quick Start

# 1. Setup (one-time)
cp .env.example .env # Add your API keys
cd tooling/python && uv init
# 2. Create a new project
uv run tooling/python/init_project.py --name my-meta-analysis
# 3. Edit your research question
# Open projects/my-meta-analysis/TOPIC.txt and paste your topic
# 4. Launch Claude Code and say:

"Start project my-meta-analysis" or "See projects/my-meta-analysis/TOPIC.txt and start"

That's it. Claude will handle the rest.

Don't have a topic yet? Say:

"Help me brainstorm a topic"

Claude will guide you through an interactive session to develop your research question and create the project for you.


What Claude Does

  1. Creates your project in projects/<your-project-name>/
  2. Reads your topic from projects/<your-project-name>/TOPIC.txt
  3. Asks clarifying questions (databases, outcomes, dates)
  4. Runs the 9-stage pipeline automatically
  5. Generates manuscript-ready outputs

Pipeline Overview

All stages are created inside projects/<your-project-name>/:

Stage Output
01_protocol pico.yaml, eligibility.md
02_search dedupe.bib
03_screening decisions.csv
04_fulltext manifest.csv
05_extraction extraction.csv
06_analysis figures/, tables/
07_manuscript manuscript.pdf
08_reviews grade_summary.md
09_qa final_qa_report.md

Example: Completed Project

See a real meta-analysisprojects/ici-breast-cancer/

This is a 99% complete meta-analysis on immune checkpoint inhibitors in triple-negative breast cancer:

  • 5 RCTs, N=2,402 patients
  • Primary outcome: RR 1.26 (95% CI 1.16-1.37), p=0.0015, ⊕⊕⊕⊕ HIGH quality
  • Manuscript: 4,921 words (Lancet Oncology compliant)
  • Time invested: ~14 hours (vs 100+ hours manual)

Quick tour:

  1. projects/ici-breast-cancer/README.md - Project overview
  2. projects/ici-breast-cancer/00_overview/FINAL_PROJECT_SUMMARY.md - Key findings
  3. projects/ici-breast-cancer/07_manuscript/ - Complete manuscript (5 sections)

Use as template for your own meta-analysis workflow.

Project Structure

meta-pipe/
├── ma-*/ # Framework code modules
├── docs/archive/ # Archived documentation
├── tooling/ # Shared tools and scripts
└── projects/ # All your meta-analysis projects
 ├── ici-breast-cancer/ # Example: complete meta-analysis
 ├── legacy/ # Historical data (pre-2026年02月08日)
 └── your-project/ # Your new projects
 ├── 01_protocol/
 ├── 02_search/
 ├── ...
 ├── 09_qa/
 └── TOPIC.txt

Note: Projects are isolated and NOT tracked by Git (see .gitignore). Only the example project ici-breast-cancer/ is tracked for reference.


Documentation

Doc Purpose
GETTING_STARTED.md Manual step-by-step guide
API Setup Database API keys
CLAUDE.md Agent behavior (auto-loaded)
tooling/scripts/ Utility scripts

Citation

If you use meta-pipe in your research, please cite it:

AMA Format:

Lin HT, Yeh JT. meta-pipe: AI-assisted, end-to-end meta-analysis pipeline with reproducible tooling. GitHub; 2025. Accessed 2026. https://github.com/htlin222/meta-pipe

BibTeX:

@software{lin_metapipe_2025,
 author = {Lin, Hsieh-Ting and Yeh, Jiunn-Tyng},
 title = {meta-pipe: AI-assisted, end-to-end meta-analysis pipeline with reproducible tooling},
 year = {2025},
 url = {https://github.com/htlin222/meta-pipe},
 note = {Accessed: 2026}
}

Requirements

  • uv (Python)
  • R ≥ 4.2 + renv
  • cmake (required for building R packages like fs on macOS ARM)
  • Quarto
  • API keys in .env

Citation

If you use meta-pipe in your research, please cite it.

AMA format:

Lin HT, Yeh JT. meta-pipe: AI-assisted, end-to-end meta-analysis pipeline with reproducible tooling. GitHub; 2026. Accessed March 22, 2026. https://github.com/htlin222/meta-pipe

BibTeX:

@software{lin2026metapipe,
 author = {Lin, Hsieh-Ting and Yeh, Jiunn-Tyng},
 title = {meta-pipe: AI-Assisted, End-to-End Meta-Analysis Pipeline with Reproducible Tooling},
 year = {2026},
 url = {https://github.com/htlin222/meta-pipe},
 note = {Software}
}

About

Claude Code-powered end-to-end meta-analysis automation: AI-assisted literature review, screening, extraction, analysis, and manuscript generation for systematic reviews and clinical evidence synthesis

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