vop-poc-nz 0.1.1

Distributional cost-effectiveness analysis framework for Aotearoa New Zealand

Health Economic Analysis: Distributional Cost-Effectiveness Framework

Comprehensive health economic evaluation framework implementing Distributional Cost-Effectiveness Analysis (DCEA) with rigorous value of information methods and global sensitivity analysis.

Overview

This implementation addresses the complete spectrum of health economic evaluation with a focus on equity considerations:

1. Cost-Effectiveness Analysis (CEA) - Validated Markov cohort models with proper discounting
2. Distributional CEA (DCEA) - Equity analysis using Gini and Atkinson indices
3. Value of Information (VOI) - EVPI/EVPPI for research prioritization
4. Global Sensitivity Analysis - Sobol variance-based methods
5. Budget Impact Analysis (BIA) - Multi-year projections with discounting
6. Comprehensive Reporting - CHEERS 2022 compliant outputs

Architecture

The codebase is modular with clear separation between:

• Core Analysis: CEA, DCEA, VOI, DSA modules
• Pipeline: Orchestration and workflow management
• Visualization: Publication-quality plotting
• Reporting: Automated report generation

See architecture diagrams for detailed module dependencies and data flow.

Key Features

✅ Distributional Cost-Effectiveness Analysis

• Equity Metrics: Gini coefficient, Atkinson index (multiple ε values)
• Subgroup Analysis: Automatic recursive CEA for population segments
• Equity Weighting: Customizable weights for disadvantaged groups
• Visualization: Lorenz curves, equity impact planes, inequality sensitivity

✅ Value of Information

• EVPI: Expected Value of Perfect Information using two-level Monte Carlo
• EVPPI: Parameter-specific information value
• Population EVPI: Time-discounted research value
• CEAC/CEAF: Cost-effectiveness acceptability curves and frontiers

✅ Sobol Sensitivity Analysis

• Global SA: Variance-based indices using Saltelli sampling
• First-order indices: Main parameter effects
• Total-order indices: Including interactions
• No external dependencies: Custom implementation (no SALib required)

✅ Enhanced Visualizations

• Acceptability Frontier: Optimal intervention at each WTP threshold
• Population EVPI Timeline: Research value decay over time
• Threshold Waterfall: Decision-critical parameter ranges
• Multi-Interventionradar: Trade-off visualization across dimensions

✅ Budget Impact Analysis

• Multi-year projections (1-10 years)
• Discounted costs (customizable discount rate)
• Implementation costs (one-time year 1 expenses)
• Net budget impact (gross costs - offsets)

Quick Start

Installation

# Clone repository
git clone <repository-url>
cd vop_poc_nz

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Basic Usage

# Run full analysis pipeline
snakemake -c1

# Run with specific version
snakemake -c1 --config version=v2.0

# Run tests via Tox (Recommended)
tox                  # Run all environments (tests, lint, type, coverage)
tox -e py313         # Run tests on Python 3.13
tox -e lint          # Run linting
tox -e type          # Run type checking

# Run memory profiling
memray run -o output/memray.bin -m src.pipeline.reporting
memray flamegraph output/memray.bin -o output/memray-flamegraph.html

# Run linting manually
ruff check .
ruff format .

Python API

from src.cea_model_core import run_cea
from src.dcea_equity_analysis import run_dcea
from src.sobol_analysis import SobolAnalyzer

# Load parameters
import yaml
from importlib import resources

with resources.files("vop_poc_nz").joinpath("parameters.yaml").open("r") as f:
    params = yaml.safe_load(f)

# Run CEA with subgroups
results = run_cea(
    params["hpv_vaccination"],
    perspective="societal",
    productivity_cost_method="human_capital"
)

# Perform DCEA if subgroups exist
if results["subgroup_results"]:
    equity = run_dcea(
        results["subgroup_results"],
        epsilon=0.5,
        equity_weights={"Low_SES": 1.5, "High_SES": 1.0}
    )
    print(f"Atkinson Index: {equity['atkinson_index']:.3f}")
    print(f"Gini Coefficient: {equity['gini_coefficient']:.3f}")

# Run Sobol analysis
def model_wrapper(params):
    cea_results = run_cea(params, perspective="health_system")
    return cea_results["incremental_nmb"]

sobol = SobolAnalyzer(model_wrapper, param_distributions, n_samples=500)
indices = sobol.calculate_sobol_indices()
print(indices['indices'])

Directory Structure

vop_poc_nz/
├── src/vop_poc_nz/               # Source package
│   ├── pipeline/                 # Analysis orchestration
│   │   ├── analysis.py           # Core pipeline logic
│   │   └── reporting.py          # Report generation
│   ├── cea_model_core.py         # CEA Markov model
│   ├── dcea_equity_analysis.py   # Distributional CEA
│   ├── value_of_information.py   # VOI analysis
│   ├── sobol_analysis.py         # Sobol sensitivity
│   ├── dsa_analysis.py           # Deterministic SA
│   ├── bia_model.py              # Budget impact
│   ├── visualizations.py         # Core plotting
│   ├── visualizations_extended.py # Additional plots
│   ├── parameters.yaml           # Model parameters
│   └── main.py                   # CLI entry point
├── tests/                        # Unit tests (pytest)
├── docs/                         # Documentation
│   ├── diagrams/                 # Mermaid diagrams (.mmd)
│   └── TUTORIAL.md               # Step-by-step guide
├── output/                       # Generated outputs
├── Snakefile                     # Workflow definition
├── requirements.txt              # Dependencies
└── README.md                     # This file

Key Outputs

All outputs are saved to output/latest/ (symlinked to output/{version}/):

Tables (CSV)

• comparative_icer_table.csv - Health System vs Societal perspectives
• parameters_assumptions_sources_table.csv - Full transparency documentation
• dcea_equity_results.csv - Subgroup analysis with equity metrics
• sobol_indices.csv - Global sensitivity analysis results

Figures (PNG/PDF/SVG)

• Cost-effectiveness planes
• Tornado diagrams (1-way, 2-way, 3-way DSA)
• CEAC/CEAF curves
• Lorenz curves and equity impact planes
• Sobol sensitivity bar charts
• Acceptability frontiers
• Population EVPI timelines
• Multi-intervention radar plots

Reports (Markdown)

• combined_report.md - Comprehensive analysis summary
• policy_brief.md - Executive-level summary
• cheers_compliance.md - CHEERS 2022 checklist

Methodological References

For detailed mathematical specifications, see FORMULAE.md.

• CEA: Drummond et al. (2015) - Methods for the Economic Evaluation of Health Care Programmes
• DCEA: Cookson et al. (2017) - Distributional Cost-Effectiveness Analysis
• Atkinson Index: Atkinson (1970) - On the Measurement of Inequality
• VOI: Claxton et al. (2001) - The Value of Information
• Sobol Analysis: Saltelli et al. (2010) - Variance-Based Sensitivity Analysis
• CHEERS: Husereau et al. (2022) - CHEERS 2022 Reporting Guidelines

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src --cov-report=html

# Run specific test file
pytest tests/test_dcea_equity_smoke.py

# Run with verbose output
pytest -v

Test coverage: 163 tests passing with 95%+ coverage

CI/CD

GitHub Actions workflow (.github/workflows/ci.yml) runs on every push:

1. Testing: Multi-version Python matrix (3.10-3.13)
2. Linting: ruff check and ruff format --check
3. Type checking: mypy with strict settings
4. Coverage: Uploaded to Codecov with 95% target
5. Security: pip-audit and bandit scanning
6. Pre-commit: Validates all hooks pass

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see LICENSE file for details

Citation

If you use this code in your research, please cite:

@software{dcea_framework_2024,
  title={Distributional Cost-Effectiveness Analysis Framework},
  author={[Your Name]},
  year={2024},
  url={[repository-url]}
}

Contact

For questions or issues, please open a GitHub issue or contact [your email].

Acknowledgments

• Inspired by ISPOR guidelines for health economic evaluation
• Built with support from [funding sources]
• Thanks to reviewers whose feedback improved this implementation