Here are some books and courses for survival analysis that are useful for learning it. People of various skill levels want to learn about survival analysis. Some want to read enough to apply it using existing packages. Others want more mathematical detail, including going through derivations of the estimators and some of their basic properties, but are not looking for fully rigorous proofs of all the major theorems. Finally, a third group has experience with measure-theoretic probability and wants a rigorous martingale-based treatment of the subject. I’ll divide these categories into applied, methodological, and theory, and mention a few courses and books that I think look good.
People using survival analysis should generally use R. It has by far the best libraries for survival. The only exception might be if you need to build something highly customized that requires some libraries from Python (for instance some neural network libraries).
This is a short course on survival from Princeton http://data.princeton.edu/pop509. It has good notes on the major topics as well as stata and R examples. As a short course, it doesn’t go into a ton of detail on any topic. I don’t seem to see any exercises.
This is a course from Stanford https://web.stanford.edu/~lutian/stat331.HTML. The notes are more mathematical than the Princeton one, but are still easier to read than the Fleming and Harrington book below. The homework problems don’t look super difficult, while the exercises in the notes look potentially more challenging.
Applied Survival Analysis Using R
This actually gives code examples, which few other books do. Most of the exercises involve analyzing data.
Survival Analysis: A Self-Learning Text, Third Edition
This book has two nice features: tons of examples, and bullet point ‘cliff notes’ for most of the concepts it explains.
Survival and Event History Analysis: A Process Point of View
This book is great. It’s more mathematical than the above two: the material requires some level of comfort with undergrad level mathematical statistics and mathematical probability (at the level of Rice and Ross). Most of the exercises involve derivations and proofs, but many of the exercises aren’t super difficult, although they do get a bit more difficult as the chapters go on. It does not presume knowledge of measure theoretic probability. I’d recommend reading at least the first four chapters before the more mathematical treatments to get the broad ideas in a technical but not fully rigorous form.
Counting Processes and Survival Analysis 2nd Edition
I’m starting to read this. It seems doable for someone with background in measure-theoretic probability but who hasn’t studied stochastic calculus. They introduce stochastic calculus concepts as needed for survival analysis.