Survival Analysis: A Self-Learning Text, Third Edition

by David G. Kleinbaum, Mitchel Klein

This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis.

The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded this third edition to include the software package R.

Genres: Textbooks

715 pages, Hardcover

First published August 16, 2005

Reviews

[Zow Ormazabal · Rating 5 out of 5]

Excellent intro book! I actually re-read several chapters multiple times, since it's great for testing if you actually understand and can extrapolate the formulations to other settings, not described in the book. It's also highly recommended if you are looking for a mostly jargon-free text that you can reference for explaining survival analysis concepts to interdisciplinary groups.

[Hojjat Sayyadi · Rating 5 out of 5]

کاربردی ترین کتاب تحلیل بقا از نظر انتقال مفاهیم و نرم افزار

[Kara · Rating 4 out of 5]

If you are looking for an easy to use and understand book on survival analysis basics, I recommend this. The "walk you through it with examples and highlighted key terms" approach is unique among textbooks and make it a go to book for me (I'm an epidemiologist). I appreciate the book's candid discussions on the mathematical assumptions of the models, as well as the many examples of SAS and Stata code. If you have a unique data problem or question (or are a statistician), you may find this doesn't go in depth enough. However, understanding the concepts reviewed in this book will give you a huge leg up professionally--and let you understand just how many people use survival modeling but really know little about it. ;)

[Walter U. · Rating 5 out of 5]

More books should be written like this, for the self-learning student in mind. Pedagogically, extremely effective, well-organized, clear exposition, numerous examples, and zero pedantry.
Highly recommended!

[Olatomiwa Bifarin · Rating 4 out of 5]

Standard regression methods will not work for events that are censored (observation partially known), hence survival analysis.

I asked around for where to start on the subject, and I was invariably led to this text. Drs Kleinbaum and Klein delivered on this. Starting from the general introduction to the subject, Kaplan-Meier estimates, Log-rank tests, Nelson-Aalen estimates, RMST, Cox models, etcetera.

I worked through several sections on the text, aided by Python's lifelines package, and it was 👌🏿.

[Rie · Rating 4 out of 5]

I was hoping this can solve the mystery of survival analysis but it didn't. Overall, still a nice approachable book.

[Pilar Devoto · Rating 5 out of 5]

gracias🙏🏻🙏🏻 muy facil de leer