Customer Reviews

Survival Analysis

5 star:		(4)
4 star:		(2)
3 star:		(1)
2 star:		(1)
1 star:		(1)

 

 

Klein and Moeschberger's Survival Analysis: Techniques for Censored and Truncated Data is a valuable resource for those who use survival analysis in their research or job. Survival analysis is techniques to analyze time to event problems. For example, how long does it take for a released felon to go back to jail. The main point to understand about the book is it's a graduate level text. The authors rely heavily on mathematics and use it to derive the procedures used in survival analysis. One needs a strong background in math, including calculus and linear algebra, and first year graduate level courses in statistics and econometrics to understand the book. If you do not feel comfortable with math or do not have a graduate level background in statistics then this book would be a waste of your time and money. Klein and Moeschberger make extensive use of practical examples to illustrate how the techniques are used. This helped greatly because one might get lost with the math. The other major benefit of the book is it covers a wide range of topics including: censored data, estimation of survival and hazard functions, hypothesis testing, Cox proportional hazards model, additive models, regression diagnostics, parametric models, and multivariate survival analysis. I would recommend you buy a more nontechnical book first, for example Kleinbaum's Survival Analysis or Hosmer and Lemshow's Applied Survival Analysis. These books would provide a more intuitive understanding of survival analysis. Then if you desire a more technical understanding or need to understand topics not covered in the other books then buy Klein and Moeschberger's book.

The authors present an intermediate level text on survival analysis introducing the concepts and techniques and providing many real examples. Covers all the standard methods including the Cox proportional hazard model (with stratification) and some methods not commonly covered including regression diagnostics and multivariate survival methods (including fraility models).

I used this book for a class in survival analysis (a graduate level biostats course) and I found it very useful. Much of the first several chapters are fairly quick relative to many graduate statistics texts and focuses on application with less emphasis on theory. Overall, I have no major qualms with the book. The author goes on a bit longer than necessary but I'd rather end up skimming text than be stuck deciphering terse material. This extra explanation also opens the book up to a wider audience.

A solid understanding of basic statistics is necessary to get started in this book. To get more, 4+ semester-long statistics courses, at least one based in regression, would be ideal. A basic knowledge in mathematical analysis as it pertains to statistics (mainly dealing with convergence in law) will be beneficial to understanding some of the intricacies of the topics and answer many of the 'whys'.

In conjunction with the course and the book, I worked problems in R with the 'survival' package, which I found very useful. (R is a free statistical program. A basic understanding of R would be necessary before trying to use the survival package -- I would recommend Dalgaard's book for an intro to R if this is of interest.) I have a good understanding of R and found the survival package documentation supplemented by rseek . org searches (when I got stuck) sufficient to figure out how to implement the survival functions in R.

On the example setup and problems...
at the end of each chapter, this book is a bit hit-or-miss. Some problems are good. Many are not. There is a lot of confusion created by some of the problems, which leads into the part of the book I take the most issue with. The authors refer to scattered examples in problems (take for example, referring to example 8.3 in problem 9.5). The thing is, Example 8.3 starts on page 251 and then it continues randomly throughout the remainder of the chapter until page 274 (I had to page through the chapter to find those page numbers). The examples in mid-to-late chapters can be very scatter-brained and some of the problems start to become this way as well. The authors seem to forget that keeping track of the 15-20 studies they use in this text is no small task and that they've spent a lot more time looking at them than others. Self-contained examples where I don't need to flip back to chapter 1 or some other example to read about the study would be really nice. The examples and problems could have been much more user-friendly to accelerate the learning process.

In this new edition, most of the errata are corrected and the texts are explained in a more detailed way.

The formulae are correct and the examples are explained in a more direct and expressive way than that in the 1st edition.

The most valuable one is its Theoretical Notes and Practical Notes. They show a lot of different points of views.

A good-buy and must-read for those want to have an intense level in Survival Analysis. Suitable for elementary and intermediate candidates to read and study.

Ian Lauder

This book describes formulas and list of applications, but it don't give more accurate statistical reasons. I get how to use the formulas, but i think i am more interested in how to get the formulas

In this new edition, many errata are corrected and each of the theories has a reason why this is true. Although they may be more practical in view, it is very good to use them to learn Survival Analysis, which is more realistic in sense.

In its Theoretical Notes and Practical Notes, there are a lot of different views and sights to show that which is the best to use. The examples are more or less good one and explained in a more detailed way than that in the 1st edition. A good-buy and must-read for those want to have a thorough view in this aspects. Read them carefully! Better than Cox's in this new edition. Buy and read this new edition!

I am a computer scientist and using this book for my research to address a problem. This book is well written but of course target audience are people with solid background in probability theory and parameteric estimation (pattern recognition). Therefore please do not expect that author will teach you basic probability theory. Contents are more applied in nature therefore natural audience are staticians and researchers.

Murphy's Law of College Textbooks: When an author prepares a textbook on a topic he doesn't understand thoroughly, his work will only be understood by those readers who understand more about the topic than the writer . . . thus defeating the prime purpose of writing, which is to inform those who understand less about the topic than the writer.

The above statement applies to this textbook more than to any other that I have ever read in my life. Each chapter reduces to nothing more than an inscrutable collection of formulas. The authors do not provide even the slightest beginning of an insight to a student attempting to learn the material from this text.

I don't want to say hard words, but this is really a completely USELESS book. It says nothing but only presenting tons of lengthy, non-understandable formulas, without a single explanation. Why would people need such a JUNK book?

There are people that need it. It is a prescribed textbook for Society of Actuary course 4 exam. Other than those guys taking this exam, DON'T TOUCH THIS BOOK.

P.S.: Many of those formulas in the books are wrong.