Examples

In this section, we present the main functionalities of PyDTS through usage examples.
We demonstrate how to:

1. Use EventTimesSampler to simulate discrete-time survival data with competing events, including random and hazard-based censoring.
2. Perform estimation and prediction with TwoStagesFitter and DataExpansionFitter.
3. Apply evaluation metrics.
4. Add regularization.
5. Handle small sample sizes using TwoStagesFitterExact.
6. Conduct screening with SISTwoStagesFitter.
7. Carry out model selection procedures.
8. Propose data-preprocessing strategies to mitigate estimation errors that arise when the number of observed events is too small at specific times.
9. Work with a simulated hospitalization length-of-stay use case.

Note - The figures included in the module pydts.example_utils are provided solely for visualization in the documentation and are not generalizable to arbitrary datasets.