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E-raamat: Handbook of Survival Analysis

Edited by (Medical College of Wisconsin, Milwaukee, USA), Edited by (University of Copenhagen, Denmark), Edited by (Leiden University, The Netherlands), Edited by (University of North Carolina, Chapel Hill, USA)
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"Preface This volume examines modern techniques and research problems in the analysis of life time data analysis. This area of statistics deals with time to event data which is complicated not only by the dynamic nature of events occurring in time but bycensoring where some events are not observed directly but rather they are known to fall in some interval or range. Historically survival analysis is one of the oldest areas of statistics dating its origin to classic life table construction begun in the 1600's. Much of the early work in this area involved constructing better life tables and long tedious extensions of non-censored nonparametric estimators. Modern survival analysis began in the late 1980's with pioneering work by Odd Aalen on adapting classical Martingale theory to these more applied problems. Theory based on these counting process martingales made the development of techniques for censored and truncated data in most cases easier and opened the door to both Bayesian and classical statisticsfor a wide range of problems and applications. In this volume we present a series of papers which provide an introduction to the advances in survival analysis techniques in the past thirty years. These papers can serve four complimentary purposes. First,they provide an introduction to various areas in survival analysis for graduates students and other new researchers to this eld. Second, they provide a reference to more established investigators in this area of modern investigations into survival analysis. Third, with a bit of supplementation on counting process theory this volume is useful as a text for a second or advanced course in survival analysis. We have found that the instructor of such a course can pick and chose papers in areas he/she deem most useful to the"--

"This handbook focuses on the analysis of lifetime data arising from the biological and medical sciences. It deals with semiparametric and nonparametric methods. For investigators new to this field, the book provides an overview of the topic along with examples of the methods discussed. It presents both classical methods and modern Bayesian approaches to the analysis of data"--



Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time.

With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides:

  • An introduction to various areas in survival analysis for graduate students and novices
  • A reference to modern investigations into survival analysis for more established researchers
  • A text or supplement for a second or advanced course in survival analysis
  • A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

Arvustused

"The great strength of the book lies in its comprehensive treatment of both classical and novel methods, covering almost all aspects of survival analysis that biostatisticians are confronted with in everyday practice. the text is very well organized, and both writing style and notation are remarkably homogeneous. readers will appreciate the inclusion of real data applications in every chapter of the book. highly recommended to both practitioners and researchers in the biostatistics field." Biometrical Journal, 57, 2015

"Anyone already familiar with analysis of survival data should own a copy of this text, as it serves as a wonderful reference for the most recent advances in the field. Advanced PhD students are particularly encouraged to purchase it, especially if they are at the stage of trying to pick a dissertation topic. The authors of the text are to be commended for completing an extremely difficult task at such a high level. the reader will undoubtedly find tremendous value in this text for many years." Daniel J. Frobish, Journal of the American Statistical Association, September 2014, Vol. 109

"This book is a great reference tool for both researchers applying the current survival analysis methods and for statisticians developing new methodologies. This book is an excellent collection on current survival analysis methods and can lead the audience to learn about them and discover appropriate literature. Practitioners can find easy access to many advanced survival methods through this book. There are many excellent survival analysis books published. This is by far the one with the broadest coverage for current survival analysis techniques that I have seen." Zhangsheng Yu, Journal of Biopharmaceutical Statistics, 2014

"This handbook presents methodology of modern survival analysis developed within the past thirty years including both frequentist and Bayesian techniques. The aims of the book are to provide introductory as well as more advanced material for graduate students and new researchers, to give a reference of modern survival analysis as well as to help practitioners with their survival data experiments." Claudia Kirch, in Zentralblatt MATH 1282 "The great strength of the book lies in its comprehensive treatment of both classical and novel methods, covering almost all aspects of survival analysis that biostatisticians are confronted with in everyday practice. the text is very well organized, and both writing style and notation are remarkably homogeneous. readers will appreciate the inclusion of real data applications in every chapter of the book. highly recommended to both practitioners and researchers in the biostatistics field." Biometrical Journal, 57, 2015

"Anyone already familiar with analysis of survival data should own a copy of this text, as it serves as a wonderful reference for the most recent advances in the field. Advanced PhD students are particularly encouraged to purchase it, especially if they are at the stage of trying to pick a dissertation topic. The authors of the text are to be commended for completing an extremely difficult task at such a high level. the reader will undoubtedly find tremendous value in this text for many years." Daniel J. Frobish, Journal of the American Statistical Association, September 2014, Vol. 109

"This book is a great reference tool for both researchers applying the current survival analysis methods and for statisticians developing new methodologies. This book is an excellent collection on current survival analysis methods and can lead the audience to learn about them and discover appropriate literature. Practitioners can find easy access to many advanced survival methods through this book. There are many excellent survival analysis books published. This is by far the one with the broadest coverage for current survival analysis techniques that I have seen." Zhangsheng Yu, Journal of Biopharmaceutical Statistics, 2014

"This handbook presents methodology of modern survival analysis developed within the past thirty years including both frequentist and Bayesian techniques. The aims of the book are to provide introductory as well as more advanced material for graduate students and new researchers, to give a reference of modern survival analysis as well as to help practitioners with their survival data experiments." Claudia Kirch, in Zentralblatt MATH 1282

"This book is an excellent reference guide on applications and methods for graduate students and researchers. References to relevant theories are extensively covered in every chapter with worked examples and results that are discussed with their corresponding software in R." - Morteza Aalabaf-Sabaghi, Journal of the Royal Statistical Society Series A, September 2022

Preface ix
About the Editors xi
List of Contributors
xiii
I Regression Models for Right Censoring
1(152)
1 Cox Regression Model
5(22)
Hans C. van Houwelingen
Theo Stijnen
2 Bayesian Analysis of the Cox Model
27(22)
Joseph G. Ibrahim
Ming-Hui Chen
Danjie Zhang
Debajyoti Sinha
3 Alternatives to the Cox Model
49(28)
Torben Martinussen
Limin Peng
4 Transformation Models
77(16)
Danyu Lin
5 High-Dimensional Regression Models
93(20)
Jennifer A. Sinnott
Tianxi Cai
6 Cure Models
113(22)
Yingwei Peng
Jeremy M. G. Taylor
7 Causal Models
135(18)
Theis Lange
Naja H. Rod
II Competing Risks
153(110)
8 Classical Regression Models for Competing Risks
157(22)
Jan Beyersmann
Thomas H. Scheike
9 Bayesian Regression Models for Competing Risks
179(20)
Ming-Hui Chen
Mario de Castro
Miaomiao Ge
Yuanye Zhang
10 Pseudo-Value Regression Models
199(22)
Brent R. Logan
Tao Wang
11 Binomial Regression Models
221(22)
Randi Grn
Thomas A. Gerds
12 Regression Models in Bone Marrow Transplantation - A Case Study
243(20)
Mei-Jie Zhang
Marcelo C. Pasquini
Kwang Woo Ahn
III Model Selection and Validation
263(78)
13 Classical Model Selection
265(20)
Florence H. Yong
Tianxi Cai
L.J. Wei
Lu Tian
14 Bayesian Model Selection
285(16)
Purushottam W. Laud
15 Model Selection for High-Dimensional Models
301(22)
Rosa J. Meijer
Jelle J. Goeman
16 Robustness of Proportional Hazards Regression
323(18)
John O'Quigley
Ronghui Xu
IV Other Censoring Schemes
341(72)
17 Nested Case-Control and Case-Cohort Studies
343(26)
Ornulf Borgan
Sven Ove Samuelsen
18 Interval Censoring
369(22)
Jianguo Sun
Junlong Li
19 Current Status Data: An Illustration with Data on Avalanche Victims
391(22)
Nicholas P. Jewell
Ruth Emerson
V Multivariate/Multistate Models
413(156)
20 Multistate Models
417(24)
Per Kragh Andersen
Maja Pohar Perme
21 Landmarking
441(16)
Hein Putter
22 Frailty Models
457(18)
Philip Hougaard
23 Bayesian Analysis of Frailty Models
475(14)
Paul Gustafson
24 Copula Models
489(22)
Joanna H. Shih
25 Clustered Competing Risks
511(12)
Guoqing Diao
Donglin Zeng
26 Joint Models of Longitudinal and Survival Data
523(26)
Wen Ye
Menggang Yu
27 Familial Studies
549(20)
Karen Bandeen-Roche
VI Clinical Trials
569(64)
28 Sample Size Calculations for Clinical Trials
571(24)
Kristin Ohneberg
Martin Schumacher
29 Group Sequential Designs for Survival Data
595(20)
Chris Jennison
Bruce Turnbull
30 Inference for Paired Survival Data
615(18)
Jennifer Le-Rademacher
Ruta Brazauskas
Index 633
John P. Klein is a professor and director of the Division of Biostatistics at the Medical College of Wisconsin. An elected member of the International Statistical Institute (ISI) and a fellow of the American Statistical Association (ASA), Dr. Klein is the author of 230 research papers, a co-author of Survival Analysis: Techniques for Censored and Truncated Data, an associate editor of Biometrics, Life Time Data Analysis, Dysphagia, and the Iranian Journal of Statistics. He received a Ph.D. from the University of Missouri.

Hans C. van Houwelingen retired from Leiden University Medical Center in 2009 and was appointed Knight in the Order of the Dutch Lion. Dr. van Houwelingen is an elected member of the ISI, a fellow of the ASA, and an honorary member of the International Society for Clinical Biostatistics, Dutch Statistical Society, and the Dutch Region of the International Biometric Society. He is also the co-author of Dynamic Prediction in Clinical Survival Analysis. He received a Ph.D. in mathematical statistics from the University of Utrecht.

Joseph G. Ibrahim is an alumni distinguished professor of biostatistics at the University of North Carolina, Chapel Hill, where he directs the Center for Innovative Clinical Trials. An elected member of the ISI and an elected fellow of the ASA and the Institute of Mathematical Statistics, Dr. Ibrahim has published over 230 research papers and two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He received a Ph.D. in statistics from the University of Minnesota.

Thomas H. Scheike is a professor in the Department of Biostatistics at the University of Copenhagen. Dr. Scheike is the co-author of Dynamic Regression Models for Survival Data and has been involved in several R packages for the biostatistical community. He received a Ph.D. in mathematical statistics from the University of California, Berkley, and a Dr. Scient from the University of Copenhagen.