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Survival Analysis: A Self-Learning Text (Statistics for Biology and Health)

By David G. KleinbaumMitchel Klein ( Springer )

Release Date: 2005-08-16
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List Price: $94.95
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Product Description

This greatly expanded second 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 second edition continues to use the unique "lecture-book" format of the first (1996) edition with the addition of three new chapters on advanced topics:

Chapter 7: Parametric Models

Chapter 8: Recurrent events

Chapter 9: Competing Risks.

Also, the Computer Appendix has been revised to provide step-by-step instructions for using the computer packages STATA (Version 7.0), SAS (Version 8.2), and SPSS (version 11.5) to carry out the procedures presented in the main text.

The original six chapters have been modified slightly

to expand and clarify aspects of survival analysis in response to suggestions by students, colleagues and reviewers, and

to add theoretical background, particularly regarding the formulation of the (partial) likelihood functions for proportional hazards, stratified, and extended Cox regression models

David Kleinbaum is Professor of Epidemiology at the Rollins School of Public Health at Emory University, Atlanta, Georgia. Dr. Kleinbaum is internationally known for innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has provided extensive worldwide short-course training in over 150 short courses on statistical and epidemiological methods. He is also the author of ActivEpi (2002), an interactive computer-based instructional text on fundamentals of epidemiology, which has been used in a variety of educational environments including distance learning.

Mitchel Klein is Research Assistant Professor with a joint appointment in the Department of Environmental and Occupational Health (EOH) and the Department of Epidemiology, also at the Rollins School of Public Health at Emory University. Dr. Klein is also co-author with Dr. Kleinbaum of the second edition of Logistic Regression- A Self-Learning Text (2002). He has regularly taught epidemiologic methods courses at Emory to graduate students in public health and in clinical medicine. He is responsible for the epidemiologic methods training of physicians enrolled in Emory’s Master of Science in Clinical Research Program, and has collaborated with Dr. Kleinbaum both nationally and internationally in teaching several short courses on various topics in epidemiologic methods.

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Logistic Regression (2nd Edition)	Survival Analysis Using SAS: A Practical Guide	Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics)	Modern Epidemiology	The Little SAS Book: A Primer, Third Edition

Product Reviews:

Excellent, excellent resource!
Like other reviewers, I've scoured the bookstores looking for a good resource to provide a practical approach to conducting a survival analysis, and this book--hands down--is the best resource I have found yet. It is written in a very accessible style, and the formulas are written out in plain English so that you can intelligently communicate them to others. The chapters are well written, and each ends with practice questions that are extremely helpful.

My only criticism is that the authors do not address the issue of discrete-time survival analysis, which is often necessary when the underlying hazard rates in your unit of time (e.g., day, week, month) exceeds .20 or so at any point. If you find yourself in that situation, then I would recommend the text by Singer and Willett (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. However, the Willett and Singer book is much more dense and difficult. If you don't have to worry with discrete-time analysis, then "Survival Analysis: A Self-Learning Text" is as close to 'one-stop-shopping' you can get.

Great introduction book ( tch28 )
I am statistics major and I use this book to self-taught. This book clears the basic concepts. It is very easy to read and follow. If you are like me, I guess this the book that you need.

Poorly Written, Poorly Conceived ( dcscientist )
This book struck almost every single one of my biggest peeves about texts. Numbers written out as words: this is, essentially, a mathematics book. We don't like letters, we like numbers, and writing out "thirteen point five" is only outdone by the obnoxiousness of writing out whole equations in the text. Readers who need to be told that "X-3/ln(X+5)" means "x plus three divided by the natural log of x plus five" should probably not be using this text.

Second, the details of examples are sparsely filled, and examples don't go all the way through. Many of them are great for theoretical concepts, like how a statistic works, but give no hint as to how one might actually employ it. Page after page of SAS, STATA, and SPIDA output are useless without the accompanying code to create them.

The index is astoundingly cursory. It's really hard to find anything.

Frankly a worthless text, I'm glad I bought it used.

Clarity at last! ( mkwoodward )
I'm ABD Economics and down to the dissertation. I have 10 titles on my shelf that deal directly with survival/event-history analysis. I've plowed though them all. Finally (!) I have one that is useful; and, this is the one. If you are not already familiar with this method and/or you are only going to get one book - this is the one to acquire. Far and away it beats everything else I've purchased. Don't be put off the by epidemiological examples - they're easy enough to read through. The authors' personal preference seems to be for STATA, but SAS and SPSS code are available in the appendix.

useful book
this is a very useful book to introduce you to the concepts of survival analysis. It is better for those who already have basic knoweledge abour regression models but it can be used by beginners as well. Basic knowledge of statistics is strongly required.

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