Survival Analysis A Self-Learning Text
ISBN-10: 0387945431
ISBN-13: 9780387945439
Edition: 1997
List price: $89.95
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Description:
This text on survival analysis provides a straightforward and easy-to-follow introduction to the main concepts and techniques of the subject. It is based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Throughout, there is an emphasis on presenting each new topic motivated with real examples of a survival analysis investigation, and then presenting thorough analyses of real data sets. Each chapter concludes with practice exercises to help readers reinforce their understanding of the concepts covered in the chapter. Readers can then extend their knowledge with a more thoroughgoing test. Answers to both are included.Beginning with the basic concepts of survival analysis-time to an event as a variable, censored data, and the hazard function-the author then introduces the Kaplan-Meier survival curves, the log-rank test, the Peto test, and the most widely used technique in survival analysis, the Cox proportional hazards model. Later chapters cover techniques for evaluating the proportional hazards assumptions, the stratified Cox procedure, and extending the Cox model to time-dependent variables.Readers will enjoy David Kleinbaum's style of presentation with numerous figures and diagrams illustrating each idea. As a result, this text makes an excellent introduction for all those coming to the subject for the first time.
Book details
List price: $89.95
Copyright year: 1997
Publisher: Springer
Publication date: 2/20/1997
Binding: Hardcover
Pages: 324
Size: 8.50" wide x 9.75" long x 0.75" tall
Weight: 1.936
Language: English
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| Introduction to Survival Analysis | |
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| Kaplan-Meier Survival Curves and the Log-Rank Test | |
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| The Cox Proportional Hazards Model and Its Characteristics | |
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| Evaluating the Proportional Hazards Assumption | |
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| The Stratified Cox Procedure | |
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| Extension of the Cox Proportional Hazards Model for Time-Dependent Variables | |
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