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Survival Analysis: A Self-Learning Text, Third Edition (Statistics for Biology and Health), by David G. Kleinbaum, Mitchel Klein
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An excellent introduction for all those coming to the subject for the first time.
New material has been added to the second edition and the original six chapters have been modified.
The previous edition sold 9500 copies world wide since its release in 1996.
Based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind.
Provides a "user-friendly" layout and includes numerous illustrations and exercises.
Written in such a way so as to enable readers learn directly without the assistance of a classroom instructor.
Throughout, there is an emphasis on presenting each new topic backed by real examples of a survival analysis investigation, followed up with thorough analyses of real data sets.
- Sales Rank: #208455 in Books
- Brand: Brand: Springer
- Published on: 2011-08-31
- Original language: English
- Number of items: 1
- Dimensions: 10.20" h x 1.80" w x 7.40" l, 3.15 pounds
- Binding: Hardcover
- 700 pages
- Used Book in Good Condition
Review
From the book reviews:
“The authors present fundamental and basic ideas and methods of analysis of survival/event-history data from both applications and methodological points of view. … This book is clearly written and well structured for a graduate course as well as for practitioners and consulting statisticians. … There are many good examples in this edition, and more importantly, this new edition offers additional exercises, making it a good candidate for adoption as a textbook.” (Technometrics, August, 2012)
"This text is … an elementary introduction to survival analysis. It is primarily intended for self-study, but it has also proven useful as a basic text in a standard classroom course … . Each chapter starts with an Introduction, an Abbreviated outline, and Objectives, and ends with self tests, exercises and a detailed outline. Solutions to tests and exercises are also provided." (G�ran Brostr�m, Zentralblatt MATH, Vol. 1093 (19), 2006)
"The most meaningful accolade that I can give to this text is that it admirably lives up to its title." Journal of the American Statistical Association, September 2006
"Imagine---a statistics textbook that actually explains things in English instead of explaining a topic by bombarding the reader with page-width equations requiring an advanced degree in Math just to read the book. If it weren't for this book, I would be really stuck." (David Britz)
From the Back Cover
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�to�include the software package R.
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.
About the Author
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.
Most helpful customer reviews
13 of 14 people found the following review helpful.
Great for running stat packages, not for understanding what those packages are doing.
By Stephen
My relatively poor review compared to the others has to do with my expectations. My goal was to learn about survival analysis. I have some knowledge of things like multivariate regression, correlation coefficients, and chi squared analysis. I was hoping to learn about more sophisticated techniques.
Instead, the book teaches how to use 3 or 4 computer programs that do these analyses. There is a difference. For example, Chapter 3 talks about the Cox proportional hazards model. It describes, in great detail, the input data and then shows the output given by one of the computer programs that the book uses. In pointing to one of the numbers from the output file, the authors say that it is "approximately a standard normal or Z variable. This Z statistic is known as a Wald Statistic, which is one of two test statistics typically used with ML estimates. The other test statistic, called the likelihood ratio makes use of the log likelihood statistic. The log likelihood statistic is obtained by multiplying the log likelihood in the computer output by -2. " My problem is that the book hasn't defined what a Z statistic is, or how maximum likelihood estimates are determined, and doesn't describe the significance of a log likelihood statistic. All we get is the formula to multiply one of the output values by -2 to get another value. We get no clue what this factor of -2 means.
For me the bottom line is this. The book is very carefully written so that the reader will be able to run several statistical packages and get output files whose numbers can be understood. If that's what you want, this book is perfection itself. However, if you want to understand what those programs are actually doing, you'll need to go elsewhere.
0 of 0 people found the following review helpful.
Very good but not great
By I Teach Typing
This is a very good gentle introduction to survival analysis ... which could be better. Its organization, with one column of text and a column of math/tables/figures on each page, makes it a pleasant read for people who want to learn the material but who do not learn well from math formulas. The column for math includes both straight forward algebra (for the folks who want to see worked problems) as well as fairly advanced formulas (for the others who can read calculus notation). The writing is exceptionally clear and the examples are perfect. The material covered includes the classic methods like Kaplan-Meier and Cox regression as well as more modern techniques like extended Cox with time dependent predictors and Fine and Gray competing risk methods.
The book does have a major flaw. Its coverage of computer software is not good. There is an appendix that covers code but the code is incomplete and was out of date when it was published. There are many graphics and analyses in the body of the book that are not covered in the appendix. I teach with SAS so I can see many places where the authors failed to take advantage of methods that were in place when the book came out and their is no attempt to update the website to point out where the book is now badly out of date. For example the book clearly states that Fine and Gray analysis can't be done with off the shelf software (and SAS has been doing it since around 2013).
So, this is a very good book that could be great if the authors would update it (or the website) to include the code to do all the graphics/analysis using modern software.
4 of 4 people found the following review helpful.
Survival Analysis: A Self-Learning Text
By Deepak Parakkal
I used this book along with an online course on the same topic by Statistics.com. The book is extremely user friendly, my background being that of a physician with knowledge of basic stats and regression analysis, not a background of mathematics or advanced statistics. Plus having worked out examples in the text using codes covering most of the commonly used stats program made it appropriate for a hands-on learning format that I prefer. Thus, it makes one confident to apply the techniques in future projects involving survival analysis.
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