survival analysis / time-to-event analysis in numpyro
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Description
in my research domain (healthcare) many outcomes are right-censored (aka survival analysis / time-to-event); the data for an individual patient are basically a tuple of (observed_time, event / censoring indicator).
What would be a good way to incorporate this into numpyro? There are several distributions already implemented in the library that are often used for (bayesian) survival analysis (e.g. exponential, weibull), but calculating the likelihood correctly for censored data would require some extra work.
are others interested in adding support for survival analysis to numpyro?
an example implemented in problow is here: https://probflow.readthedocs.io/en/stable/examples/time_to_event.html
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Research direction
Start by reviewing NumPyro's existing exponential and Weibull distributions and the linked ProbFlow time-to-event example. Determine how right-censored observations represented as (observed_time, event / censoring indicator) could be supported, and define the expected likelihood behavior and tests before implementation.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100