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BART-Survival: A Bayesian machine learning approach to survival analyses in Python
Jacob Tiegs1,2, Julia Raykin1, Ilia Rochlin1
1Inform and Disseminate Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Journal of Open Source Software
|February 25, 2025
Summary
BART-Survival is a new Python package for discrete-time survival analysis. It uses the Bayesian Additive Regression Trees (BART) algorithm, offering a flexible, non-parametric alternative for time-to-event modeling.
Area of Science:
- Computational statistics
- Machine learning in biostatistics
Background:
- Traditional survival analyses often rely on parametric or semi-parametric models.
- There is a growing need for flexible, non-parametric approaches in time-to-event data analysis.
- Bayesian Additive Regression Trees (BART) is a powerful non-parametric machine learning algorithm.
Purpose of the Study:
- Introduce BART-Survival, a Python package designed for discrete-time survival analysis.
- To provide an accessible yet powerful tool for researchers and analysts.
- To leverage the capabilities of BART for time-to-event modeling.
Main Methods:
- Developed BART-Survival as a Python package.
- Implemented the Bayesian Additive Regression Trees (BART) algorithm for survival analysis.
- Designed a user-friendly Application Programming Interface (API) for ease of use and flexibility.
Main Results:
- BART-Survival enables time-to-event analyses in discrete time using BART.
- The package integrates BART's performance with necessary data and model formatting for survival analysis.
- Offers a simple API for basic use, with options for advanced customization.
Conclusions:
- BART-Survival provides a valuable non-parametric alternative to conventional survival analysis methods.
- The package facilitates the application of BART to discrete-time survival data.
- It empowers analysts to explore advanced, flexible modeling techniques for time-to-event data.
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