The User Guide covers the most important aspects of doing to survival analysis with scikit-survival.
It is assumed that users have a basic understanding of survival analysis. If you are brand-new to survival
analysis, consider studying the basics first, e.g. by reading an introductory book, such as
David G. Kleinbaum and Mitchel Klein (2012), Survival Analysis: A Self-Learning Text, Springer.
John P. Klein and Melvin L. Moeschberger (2003), Survival Analysis: Techniques for Censored and Truncated Data, Springer.
Users new to scikit-survival should read Understanding Predictions in Survival Analysis to get familiar with the basic concepts.
The interactive guide Introduction to Survival Analysis with scikit-survival gives a brief overview of how to use scikit-survival for survival analysis.
Once you are familiar with the basics, it is highly recommended reading the guide Evaluating Survival Models,
which discusses common pitfalls when evaluating the predictive performance of survival models.
Finally, there are several model-specific guides that discuss details about particular models, with many examples throughout.