Survival Analysis: A Self-Learning Text

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Springer Science & Business Media, Jan 1, 2005 - Science - 590 pages
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This greatly expanded second edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. The second edition continues to use the unique "lecture-book" format of the first (1996) edition with the addition of three new chapters on advanced topics: Chapter 7: Parametric Models Chapter 8: Recurrent events Chapter 9: Competing Risks. Also, the Computer Appendix has been revised to provide step-by-step instructions for using the computer packages STATA (Version 7.0), SAS (Version 8.2), and SPSS (version 11.5) to carry out the procedures presented in the main text. The original six chapters have been modified slightly to expand and clarify aspects of survival analysis in response to suggestions by students, colleagues and reviewers, and to add theoretical background, particularly regarding the formulation of the (partial) likelihood functions for proportional hazards, stratified, and extended Cox regression models David Kleinbaum is Professor of Epidemiology at the Rollins School of Public Health at Emory University, Atlanta, Georgia. Dr. Kleinbaum is internationally known for innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has provided extensive worldwide short-course training in over 150 short courses on statistical and epidemiological methods. He is also the author of ActivEpi (2002), an interactive computer-based instructional text on fundamentals of epidemiology, which has been used in a variety of educational environments including distance learning. Mitchel Klein is Research Assistant Professor with a joint appointment in the Department of Environmental and Occupational Health (EOH) and the Department of Epidemiology, also at the Rollins School of Public Health at Emory University. Dr. Klein is also co-author with Dr. Kleinbaum of the second edition of Logistic Regression- A Self-Learning Text (2002). He has regularly taught epidemiologic methods courses at Emory to graduate students in public health and in clinical medicine. He is responsible for the epidemiologic methods training of physicians enrolled in Emorya??s Master of Science in Clinical Research Program, and has collaborated with Dr. Kleinbaum both nationally and internationally in teaching several short courses on various topics in epidemiologic methods.
  

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Contents

Introduction to Survival Analysis
1
Introduction
2
Objectives
3
Presentation
4
Detailed Outline
34
Practice Exercises
38
Test
40
Answers to Practice Exercises
42
Extension of the Cox Proportional Hazards Model for TimeDependent Variables
211
Introduction
212
Objectives
213
Presentation
214
Detailed Outline
246
Practice Exercises
249
Test
253
Answers to Practice Exercises
255

KaplanMeier Survival Curves and the LogRank Test
45
Introduction
46
Objectives
47
Presentation
48
Detailed Outline
70
Practice Exercises
73
Test
77
Answers to Practice Exercises
79
Matrix Formula for the LogRank Statistic for Several Groups
82
The Cox Proportional Hazards Model and Its Characteristics
83
Introduction
84
Objectives
85
Presentation
86
Detailed Outline
117
Practice Exercises
119
Test
123
Answers to Practice Exercises
127
Evaluating the Proportional Hazards Assumption
131
Introduction
132
Objectives
133
Presentation
134
Detailed Outline
158
Practice Exercises
161
Test
164
Answers to Practice Exercises
167
The Stratified Cox Procedure
173
Introduction
174
Objectives
175
Presentation
176
Detailed Outline
198
Practice Exercises
201
Test
204
Answers to Practice Exercises
207
Parametric Survival Models
257
Introduction
258
Objectives
259
Presentation
260
Detailed Outline
313
Practice Exercises
319
Test
324
Answers to Practice Exercises
327
Recurrent Event Survival Analysis
331
Introduction
332
Objectives
333
Presentation
334
Detailed Outline
371
Practice Exercises
377
Test
381
Answers to Practice Exercises
389
Competing Risks Survival Analysis
391
Introduction
392
Abbreviated Outline
394
Objectives
395
Presentation
396
Detailed Outline
440
Practice Exercises
447
Test
452
Answers to Practice Exercises
458
Survival Analysis on the Computer
463
A STATA
465
B SAS
508
C SPSS
542
Test Answers
557
References
581
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Page 582 - Bell J. Methadone dosage and retention of patients in maintenance treatment. Med J Aust 1991; 154:195.
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About the author (2005)

David Kleinbaum is Professor of Epidemiology at the Rollins School of Public Health at Emory University, Atlanta, Georgia. Dr. Kleinbaum is internationally known for innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has provided extensive worldwide short-course training in over 150 short courses on statistical and epidemiological methods. He is also the author of ActivEpi (2002), an interactive computer-based instructional text on fundamentals of epidemiology, which has been used in a variety of educational environments including distance learning.

Mitchel Klein is Research Assistant Professor with a joint appointment in the Department of Environmental and Occupational Health (EOH) and the Department of Epidemiology, also at the Rollins School of Public Health at Emory University. Dr. Klein is also co-author with Dr. Kleinbaum of the second edition of Logistic Regression- A Self-Learning Text (2002). He has regularly taught epidemiologic methods courses at Emory to graduate students in public health and in clinical medicine. He is responsible for the epidemiologic methods training of physicians enrolled in Emory's Master of Science in Clinical Research Program, and has collaborated with Dr. Kleinbaum both nationally and internationally in teaching several short courses on various topics in epidemiologic methods.

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