Survival Analysis: A Self-Learning Text

by David G. Kleinbaum

This text on survival analysis provides a straightforward and easy-to-follow introduction to the main concepts and techniques of the subject. It is based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Throughout, there is an emphasis on presenting each new topic motivated with real examples of a survival analysis investigation, and then presenting thorough analyses of real data sets. Each chapter concludes with practice exercises to help readers reinforce their understanding of the concepts covered in the chapter. Readers can then extend their knowledge with a more thoroughgoing test. Answers to both are included. Beginning with the basic concepts of survival analysis-time to an event as a variable, censored data, and the hazard function-the author then introduces the Kaplan-Meier survival curves, the log-rank test, the Peto test, and the most widely used technique in survival analysis, the Cox proportional hazards model. Later chapters cover techniques for evaluating the proportional hazards assumptions, the stratified Cox procedure, and extending the Cox model to time-dependent variables. Readers will enjoy David Kleinbaum's style of presentation with numerous figures and diagrams illustrating each idea. As a result, this text makes an excellent introduction for all those coming to the subject for the first time.

Genres: Textbooks

324 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. ;)

[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 👌🏿.

[Rui · 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