Customer Reviews

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

5 star:		(8)
4 star:		(3)
3 star:		(0)
2 star:		(3)
1 star:		(0)

 

 

Kleinbaum's Survival Analysis: A Self-Learning Text is an excellent nontechnical introduction to survival analysis. Survival analysis are statistical techniques that addresses the problem of how much time it takes for an event to occur. The techniques is widely used in medical research, and my interest in it comes from wanting to explore how long it will take for a person to refinance a loan. Kleinbaum explores the topic in a straightforward, and easy-to-follow manner. The topics are illustrated through numerous figures, diagrams, and analysis of real data sets. Kleinbaum uses a minimial amount of mathematics and carefully leads the reader through any math that is used. The book concentrates on the Cox Proportional Hazard model which is the most widely used technique in survival analysis. Given the introductory nature of the book one will not find materials covering other models. Someone with some mathematical knowledge, one semester of calculus, and a semester of statistics and a semester of undergraduate econometrics would get the most out of this book. If you are looking for an introduction to survival analysis this is a great place to start. I feel I have a strong foundation to start using survival analysis at my job and continue with a more technical exploration.

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.

I'm a graduate student in public health at Emory University and have had the opportunity to actually take the course in Epidemiologic Modeling with Dr. Kleinbaum. This book, as well as his self-learning text for logistic regression, are fabulous. Both books provide a good background to the methods needed to use each analytical technique. Survival Analysis: A Self-Learning Text, in particular, flows very well with good examples, diagrams, and explanations for the student who wishes to learn this technique. It also serves a great reference for those who use this analytical method.

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.

A very well written, step by step book on survival analysis, recommended for all, absolute beginners as well as experienced

biostatisticians.

The authors truly deserve praise.

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.

Survival analysis is a key topic in medical research and practice. This book allowed me to understand better its purpose, methodology and techniques and improved my critical view of published reports. It also allowed me to tackle more specific and statistically-minded books. Highly recommended for beginners.

Unlike other reviewers, I did not find this book very helpful, especially considering the price I paid. The book is essentially a PowerPoint course presentation published with the notes pages as text. Unfortunately, the book is laid out so that the reader must make the connection between the text and the slide itself (they're stacked side-by-side with no separation). Often, the text discusses the material as though the instructor had a pointer in hand to make the connection -- without those visual clues the argument is hard to follow. On the other hand, if you know nothing about survival analysis and only want to run computer programs (specifically SPIDA) and read the output, I guess this book isn't bad. I'll keep looking for a good textbook.

An outstanding book from the same authors that wrote the Logistic Regression self-learning text. Provides excellent descriptions of the methods with examples and details the theory in an understandable way. You really could learn basic survival analysis (Kaplan-Meier, Log-Rank, Cox Regression, etc) with this book alone. It also included instructions for using SAS and Stata which is very helpful. I prefer this text over Hosmer and Lemeshow for 'usability'.

This book has been useful as an epidemiology tool to maintain the methodology used in epidemiological studies.