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https://github.com/mirsadra/pharmacoeconomics-decision-tree
A Python-based tool for building and analyzing decision trees in pharmacoeconomics.
https://github.com/mirsadra/pharmacoeconomics-decision-tree
cost-effectiveness cost-effectiveness-analysis decision-tree decision-trees pharmaceutical-science pharmacoeconomics
Last synced: 9 days ago
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A Python-based tool for building and analyzing decision trees in pharmacoeconomics.
- Host: GitHub
- URL: https://github.com/mirsadra/pharmacoeconomics-decision-tree
- Owner: mirsadra
- License: mit
- Created: 2024-09-14T18:42:31.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-09-14T21:05:49.000Z (5 months ago)
- Last Synced: 2024-11-26T04:21:54.709Z (2 months ago)
- Topics: cost-effectiveness, cost-effectiveness-analysis, decision-tree, decision-trees, pharmaceutical-science, pharmacoeconomics
- Language: Python
- Homepage: https://github.com/mirsadra/Pharmacoeconomics-Decision-Tree
- Size: 53.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pharmacoeconomics Decision Tree Model
[](https://www.linkedin.com/in/mirsadra/)
A Python-based tool for building and analyzing decision trees in pharmacoeconomics.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Requirements](#requirements)
- [Installation](#installation)
- [Usage](#usage)
- [Quick Start](#quick-start)
- [Examples](#examples)
- [Project Structure](#project-structure)
- [License](#license)
- [Contact](#contact)
## Introduction
**Pharmacoeconomics Decision Tree Model** is a tool designed to help healthcare professionals, researchers, and students perform cost-effectiveness analysis using decision tree models. It allows users to build customizable decision trees, calculate expected costs and utilities, and visualize the results.
Decision trees are a fundamental tool in pharmacoeconomics for evaluating the economic outcomes of different healthcare interventions. This project aims to make decision tree modeling accessible, even for those with minimal programming experience.
## Features
- **Easy-to-use Classes**: Simplified classes for decision and chance nodes.
- **Cost-Effectiveness Analysis**: Calculate expected costs, utilities, and Incremental Cost-Effectiveness Ratios (ICERs).
- **Visualization**: Visualize decision trees using simple plotting functions.
- **Extensibility**: Modular code that can be extended for more complex analyses.
## Requirements
- **Python 3.6 or higher**
### Python Packages
- `numpy`
- `pandas`
- `matplotlib`
- `networkx`
- `scipy`
You can install these packages using `pip`. Instructions are provided in the [Installation](#installation) section.
## Installation
### 1. Clone the Repository
First, you need to download the project files from GitHub.
```bash
git clone https://github.com/yourusername/Pharmacoeconomics-Decision-Tree.git
```
Alternatively, you can download the ZIP file from the GitHub repository and extract it to your desired location.
### 2. Navigate to the Project Directory
```bash
cd Pharmacoeconomics-Decision-Tree
```
### 3. Install the Required Packages
It's recommended to use a virtual environment to avoid conflicts with other Python projects.
Using `virtualenv` (Optional but Recommended)
```bash
# Install virtualenv if you haven't already
pip install virtualenv
# Create a virtual environment named 'venv'
virtualenv venv
# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate
```
#### Install Dependencies
Install the required Python packages using the requirements.txt file.
```bash
pip install -r requirements.txt
```
If you encounter any issues, you can install the packages individually:
```bash
pip install numpy matplotlib networkx
```
## Usage
### Quick Start
Follow these steps to run a basic example of the decision tree model.
#### 1. Open the Example Notebook
An example Jupyter Notebook is provided to demonstrate how to use the tool.
```bash
jupyter notebook examples/example_usage.ipynb
```
If you don't have Jupyter Notebook installed, you can install it using:
```bash
pip install notebook
```
#### 2. Run the Notebook Cells
- Open example_usage.ipynb in Jupyter Notebook.
- Run each cell one by one by clicking on the cell and pressing Shift + Enter.
- The notebook includes explanations and outputs to help you understand each step.
## Examples
Below is a basic example of how to use the tool in a Python script.
#### Example: Evaluating Treatment Options
```python
# Import the necessary classes
from src.decision_tree import DecisionNode, ChanceNode
from src.analysis import calculate_icer
from src.visualization import plot_decision_tree
# Create the decision node
treatment_decision = DecisionNode('Treatment Decision')
# Option A: Medication
medication = ChanceNode('Medication', probability=1.0, cost=1500, utility=0)
# Outcomes for Medication
success_med = ChanceNode('Success', probability=0.6, cost=0, utility=0.9)
failure_med = ChanceNode('Failure', probability=0.4, cost=3000, utility=0.4)
medication.add_next_node(success_med)
medication.add_next_node(failure_med)
# Option B: Surgery
surgery = ChanceNode('Surgery', probability=1.0, cost=5000, utility=0)
# Outcomes for Surgery
success_surg = ChanceNode('Success', probability=0.8, cost=0, utility=0.95)
failure_surg = ChanceNode('Failure', probability=0.2, cost=8000, utility=0.3)
surgery.add_next_node(success_surg)
surgery.add_next_node(failure_surg)
# Add options to the decision node
treatment_decision.add_branch(medication)
treatment_decision.add_branch(surgery)
# Calculate expected values
cost_med, utility_med = medication.expected_values()
cost_surg, utility_surg = surgery.expected_values()
print(f"Medication - Expected Cost: ${cost_med:.2f}, Expected Utility: {utility_med:.2f}")
print(f"Surgery - Expected Cost: ${cost_surg:.2f}, Expected Utility: {utility_surg:.2f}")
# Calculate ICER
icer_value = calculate_icer(cost_med, utility_med, cost_surg, utility_surg)
print(f"ICER of Surgery over Medication: ${icer_value:.2f} per additional utility")
# Visualize the decision tree
plot_decision_tree(treatment_decision)
```
#### Explanation
- Decision Node: Represents a point where a decision is made (e.g., choosing between treatments).
- Chance Node: Represents a point where an outcome is determined by chance (e.g., success or failure of a treatment).
- Expected Values: The average cost and utility considering the probabilities of different outcomes.
- ICER: Incremental Cost-Effectiveness Ratio, used to compare the cost-effectiveness of two interventions.
## Project Structure
Here's an overview of the project's directory structure:
```bash
Pharmacoeconomics-Decision-Tree/
├── src/
│ ├── decision_tree.py # Core classes for building the decision tree
│ ├── analysis.py # Functions for economic analysis
│ └── visualization.py # Functions for visualizing the decision tree
├── examples/
│ └── example_usage.ipynb # Jupyter Notebook with example usage
├── tests/
│ └── test_decision_tree.py # Unit tests for the decision tree module
├── docs/
│ └── documentation.md # Detailed documentation (optional)
├── README.md # This file
├── requirements.txt # List of required Python packages
├── LICENSE # License information
└── .gitignore # Files and folders to be ignored by Git
```
## License
This project is licensed under the MIT License.
## Contact
If you have any questions or need further assistance, feel free to reach out:
- Email: [email protected]
- GitHub Issues: [Create an issue](https://github.com/mirsadra/Pharmacoeconomics-Decision-Tree/issues)
**Additional Tips for Beginners**
- **Python Basics**: If you're new to Python, consider completing a basic tutorial or course to familiarize yourself with the language.
- **Understanding Decision Trees**: Research the fundamentals of decision tree analysis in pharmacoeconomics to get the most out of this tool.
- **Ask for Help**: Don't hesitate to reach out via email or GitHub issues if you run into problems.
---
**Frequently Asked Questions**
1. **I get an ImportError when running the code. What should I do?**
Make sure all the required packages are installed and that you're running the script from the project directory. If you're using a virtual environment, ensure it's activated.
2. **How do I install Python and pip?**
- **Windows**: Download the installer from [python.org](https://www.python.org/downloads/windows/) and follow the instructions. Make sure to check the box that says "Add Python to PATH."
- **macOS**: Python 2.7 comes pre-installed. For Python 3, use Homebrew (`brew install python3`) or download from [python.org](https://www.python.org/downloads/mac-osx/).
- **Linux**: Use your package manager (`sudo apt-get install python3` for Debian-based systems).
3. **Can I use this tool for other types of decision trees?**
Yes, the tool is designed to be flexible and can be adapted for various decision tree analyses beyond pharmacoeconomics.
4. **I found a bug. How can I report it?**
Please open an issue on the GitHub repository with details about the bug and steps to reproduce it.
---
Thank you for choosing the **Pharmacoeconomics Decision Tree Model** for your analysis needs.
We hope this tool assists you in making informed decisions in healthcare economics.
[](https://www.buymeacoffee.com/miirsadra)