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Survival Analysis: Models and Applications

Handbook / Manual

By: Xian Liu(Author)

446 pages, b/w illustrations, tables

John Wiley & Sons

Hardback | Jul 2012 | #199777 | ISBN-13: 9780470977156
Availability: Usually dispatched within 4 days Details
NHBS Price: £59.99 $76/€67 approx

About this book

Survival analysis concerns sequential occurrences of events governed by probabilistic laws. Recent decades have witnessed many applications of survival analysis in various disciplines. Survival Analysis: Models and Applications introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS.

Survival Analysis: Models and Applications
- Presents basic techniques before leading onto some of the most advanced topics in survival analysis.
- Assumes only a minimal knowledge of SAS whilst enabling more experienced users to learn new techniques of data input and manipulation.
- Provides numerous examples of SAS code to illustrate each of the methods, along with step-by-step instructions to perform each technique.
- Highlights the strengths and limitations of each technique covered.

Covering a wide scope of survival techniques and methods, from the introductory to the advanced, Survival Analysis: Models and Applications can be used as a useful reference book for planners, researchers, and professors who are working in settings involving various lifetime events. Scientists interested in survival analysis should find it a useful guidebook for the incorporation of survival data and methods into their projects.


Preface xi

1 Introduction 1
2 Descriptive approaches of survival analysis 20
3 Some popular survival distribution functions 63
4 Parametric regression models of survival analysis 93
5 The Cox proportional hazard regression model and advances 144
6 Counting processes and diagnostics of the Cox model 201
7 Competing risks models and repeated events 255
8 Structural hazard rate regression models 310
9 Special topics 347

Appendix A The delta method 405
Appendix B Approximation of the variance–covariance matrix for the predicted probabilities from results of the multinomial logit model 407
Appendix C Simulated patient data on treatment of PTSD (n = 255) 410
Appendix D SAS code for derivation of ? estimates in reduced-form equations 417
Appendix E The analytic result of ?*(x) 422

References 424
Index 438

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