Download PDF Survival Analysis: A Self-Learning Text, Third Edition (Statistics for Biology and Health)
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Survival Analysis: A Self-Learning Text, Third Edition (Statistics for Biology and Health)
Download PDF Survival Analysis: A Self-Learning Text, Third Edition (Statistics for Biology and Health)
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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.
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Product details
Series: Statistics for Biology and Health
Hardcover: 700 pages
Publisher: Springer; 3rd ed. 2012 edition (August 31, 2011)
Language: English
ISBN-10: 1441966455
ISBN-13: 978-1441966452
Product Dimensions:
7.4 x 1.8 x 10.2 inches
Shipping Weight: 3.4 pounds (View shipping rates and policies)
Average Customer Review:
4.2 out of 5 stars
29 customer reviews
Amazon Best Sellers Rank:
#570,903 in Books (See Top 100 in Books)
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.
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.
This is a very lucidly written text. It justifies every word of the "Self Learning Text" concept. I have been following this as a textbook for my graduate course in survival analysis. This text lacks a bit in numerical derivations, but I think the author aims to skip difficult derivations in order to keep the essence of simpleness. In this text everything has been written in plain simple English and will serve as an excellent text for someone who is learning Survival for the first time and also for those relatively scared of hardcore mathematical statistics. I would highly recommend this book for learning the core concepts of survival data modelling.
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.
Very good for self learning. Missing a little on proofs but you can find them on math.stackexchange.
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.
This book is easy to read, yet will teach you a lot about survival analysis. The format with formulae off to the side and coding (SAS, Stata, R, etc) in an appendix provides all information needed without cluttering the main text. I definitely recommend this as a self-learning text or as a valuable way of reinforcing information for a course you're taking.
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