Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression
What is survival analysis and why should I learn it? 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 this 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. We'd love to hear if you are using lifelines, please leave an Issue and let us know your thoughts on the library.
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
- There is a Gitter channel available.
- Some users have posted common questions at stats.stackexchange.com
- creating an issue in the Github repository.
You can find the roadmap for lifelines here.
See our Contributing guidelines.
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