https://github.com/CamDavidsonPilon/lifelines

Survival analysis in Python
https://github.com/CamDavidsonPilon/lifelines

cox-regression data-science maximum-likelihood python reliability-analysis statistics survival-analysis

Last synced: 2 months ago
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Survival analysis in Python

• Host: GitHub
• URL: https://github.com/CamDavidsonPilon/lifelines
• Owner: CamDavidsonPilon
• License: mit
• Created: 2013-08-28T00:16:42.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2024-10-29T10:31:03.000Z (2 months ago)
• Last Synced: 2024-10-29T11:02:11.371Z (2 months ago)
• Topics: cox-regression, data-science, maximum-likelihood, python, reliability-analysis, statistics, survival-analysis
• Language: Python
• Homepage: lifelines.readthedocs.org
• Size: 43.1 MB
• Stars: 2,362
• Watchers: 70
• Forks: 559
• Open Issues: 268
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: .github/CONTRIBUTING.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

Awesome Lists containing this project

• awesome-time-series - lifelines
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• awesome-marketing-machine-learning - lifelines
• awesome-python-machine-learning-resources - GitHub - 25% open · ⏱️ 17.07.2022): (医疗领域)

README

        
![](http://i.imgur.com/EOowdSD.png)

[![PyPI version](https://badge.fury.io/py/lifelines.svg)](https://badge.fury.io/py/lifelines)

[![Anaconda-Server Badge](https://anaconda.org/conda-forge/lifelines/badges/version.svg

)](https://conda.anaconda.org/conda-forge)

[![DOI](https://zenodo.org/badge/12420595.svg)](https://zenodo.org/badge/latestdoi/12420595)

[What is survival analysis and why should I learn it?](http://lifelines.readthedocs.org/en/latest/Survival%20Analysis%20intro.html)

 Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer *why do events occur now versus later* under uncertainty (where *events* might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like *what factors might influence deaths?*

But outside of medicine and actuarial science, there are many other interesting and exciting applications of survival analysis. For example:

- SaaS providers are interested in measuring subscriber lifetimes, or time to some first action

- inventory stock out is a censoring event for true "demand" of a good.

- sociologists are interested in measuring political parties' lifetimes, or relationships, or marriages

- A/B tests to determine how long it takes different groups to perform an action.

*lifelines* is a pure Python implementation of the best parts of survival analysis.

## Documentation and intro to survival analysis

If you are new to survival analysis, wondering why it is useful, or are interested in *lifelines* examples, API, and syntax, please read the [Documentation and Tutorials page](http://lifelines.readthedocs.org/en/latest/index.html)

## Contact

 - Start a conversation in our [Discussions room](https://github.com/CamDavidsonPilon/lifelines/discussions).

 - Some users have posted common questions at [stats.stackexchange.com](https://stats.stackexchange.com/search?tab=votes&q=%22lifelines%22%20is%3aquestion).

 - Creating an issue in the [Github repository](https://github.com/camdavidsonpilon/lifelines).

## Development

See our [Contributing](https://github.com/CamDavidsonPilon/lifelines/blob/master/.github/CONTRIBUTING.md) guidelines.