References for survival analysis I am looking for a good book/tutorial to learn about survival analysis. I am also interested in references on doing survival analysis in R.
 A: For a very clear, succinct and applied approach, I highly recommend Event History Modeling by Box-Steffenmeier and Jones
A: "Survival analysis using SAS: a practical guide" by Paul D. Allison provides a good guide to the connection between the math and SAS code - how to think about your information, how to code, how to interpret results. Even if you are using R, there will be parallels that could prove useful.
A: David Collett. Modelling Survival Data in Medical Research, Second Edition. Chapman & Hall/CRC. 2003. ISBN 978-1584883258
Software section focuses on SAS not R though.
A: Take a look at the course page for Sociology 761: Statistical Applications in Social Research. Professor John Fox at McMaster University has course notes on survival analysis as well as an example R script and several data files.
For another perspective, see  Models for Quantifying Risk, 3/e, the standard textbook for actuarial exam 3/MLC. The bulk of the book, chapters 3-10, covers survival-contingent payment models.
A: I learned from Hosmer, Lemeshow & May "Applied Survival Analysis: Regression Modeling of Time-to-Event Data" (2nd ed., 2008), which covers the basics. It also helped that I found a really cheap copy...
A: Survival Analysis: A Self-Learning Text 
by Kleinbaum and Klein
is pretty good.  It depends on what you want.  This is more of a non-technical introduction.  It's focused on practical applications and minimizes the mathematics.  Pedegocially, it's also intended for learning outside of the classroom.
A: I like:


*

*Survival Analysis: Techniques for Censored and Truncated Data (Klein & Moeschberger)

*Modeling Survival Data: Extending the Cox Model (Therneau)


The first does a good job of straddling theory and model building issues.  It's mostly focused on semi-parametric techniques, but there is reasonable coverage of parametric methods.  It doesn't really provide any R or other code examples, if that's what you're after.
The second is heavy with modeling on the Cox PH side (as the title might indicate).  It's by the author of the survival package in R and there are plenty of R examples and mini-case studies.  I think both books complement each other, but I'd recommend the first for getting started.  
A quick way to get started in R is David Diez's guide.
A: I found "Analysis of survival data" by Cox and Oakes (Chapman and Hall Monographs on Statistics and Applied Probability - vol. 21) to be very readable and informative.  No material on survival analysis in R though.
A: Dirk F. Moore
Applied Survival Analysis
Using R
A: Sage pubs book, Introducing Survival and Event History Analysis by Melinda Mills, has been build for an R users' adience.
A: I'm surprised no one has mentioned it, but there is a book that exactly meets your specifications:  
Tableman & Kim. Survival Analysis using S. Chapman & Hall/CRC.
A: For survival analysis with R see Event History Analysis with R by Broström. With alot of R examples of survival analysis on historical demographic data.
A: The book we used as a text book is called 
Applied Survival Analysis by David W Hosmer
This book is from a biostat perspective and I found it was covered almost everything I used in my work. Also they have R/state/SAS code on their website according to their examples in the book. 
A: The book "Survival Analysis, Techniques for Censored and Truncated Data" written by Klein & Moeschberger (2003) is always the 1st reference I would recommend for the people who are interested in learning, practicing and studying survival analysis. This book not only provides comprehensive discussions to the problems we will face when analyzing the time-to-event data, with lots of examples of variety, and useful techniques we can apply to correct the "bias" induced from the above problems, but also prepares tons of practical notes and theoretical notes to lead us to the front door of the beautiful applications and methodologies in survival analysis.
The 2nd book I would recommend is "The Statistical Analysis of Failure Time Data" by Kalbfleisch & Prentice (2002). Both professors are masters in this challenging field, and in this book they lecture not-so-trivial concepts in a very clear way and derive lots of state-of-the-art techniques at that time, with their guidance we are well prepared to step into the abundant world of survival analysis.
If we really spend quality time to study these two books, we can acquire lots of fundamental and deep knowledge to analyze censored and/or truncated data, which will cause seriously biased conclusions if we just ignore these problems inherently almost everywhere in real-world applications. Enjoy reading.
A: Tutz & Schmid "Modeling Discrete Time-to-Event Data" (2016). It is fairly terse, dry and technical but has only 200+ pages and contains some references to R packages and functions. The authors suggest in the preface that while most of the textbooks focus on continuous time, this one focuses on discrete time. An advantage of discrete time modelling is amenability to smoothing and regularization methods that have proliferated in recent decades.
