Repository files navigation

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 answerwhy do events occur now versus later under uncertainty (whereevents might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions likewhat 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.

If you are new to survival analysis, wondering why it is useful, or are interested inlifelines examples, API, and syntax, please read theDocumentation and Tutorials page

• Start a conversation in ourDiscussions room.
• Some users have posted common questions atstats.stackexchange.com.
• Creating an issue in theGithub repository.

See ourContributing guidelines.