Obesity Cost-Effectiveness Model (HEOR)

A deterministic Health Economics and Outcomes Research (HEOR) model for evaluating the cost-effectiveness of obesity treatments. This repository contains the core simulation engine, an interactive Streamlit dashboard for real-time scenario analysis, and a comprehensive validation suite.

Project repository reorganized for audit-readiness and modularity.

Key Features

• Interactive Dashboard: Streamlit-based UI for dynamic parameter adjustment and instant visualization of ICER, QALYs, and costs.
• Deterministic Engine: Modular core engine for calculating treatment outcomes, gastrointestinal (GI) adverse events, and Probabilistic Sensitivity Analysis (PSA).
• Scientific Validation: Built-in validation suite (validator/) to ensure the accuracy of calculations against expected thresholds.

Repository Structure

• /calculator: Core simulation engine and Streamlit dashboard.
  • deterministic_model_v1.py: Main entry point.
  • sidebar_ui.py: UI configuration.
  • deterministic_core/: Engine logic, PSA, and GI modules.
  • ENVIRONMENT_SETUP.md & USER_MANUAL.md: Setup and user documentation.
  • requirements.txt: Python dependencies.
  • validator/: Scientific validation suite and unit tests.

Quick Start

Launching the Dashboard

Install the required dependencies and start the application:

pip install -r calculator/requirements.txt
streamlit run calculator/deterministic_model_v1.py

Scientific Validation

To run the full validation suite (pytest):

pytest calculator/validator/test_heor_engine.py

Authors & Citation

Developed for HEOR analysis. If you use this model in your research, please ensure appropriate citation according to the included methodology documentation.

Cite this repository:

Polignano, Maurizio (2026). Obesity Cost-Effectiveness Model (HEOR). figshare. Software. https://doi.org/10.6084/m9.figshare.33138014

For questions or contributions, please open an issue or submit a pull request.