Handbook of Survival Analysis
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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
Regression Models for Right Censoring
Cox Regression Model Hans C. van Houwelingen and Theo Stijnen
Bayesian Analysis of the Cox Model Joseph G. Ibrahim, Ming-Hui Chen, Danjie Zhang, and Debajyoti Sinha
Alternatives to the Cox Model Torben Martinussen and Limin Peng
Transformation Models D.Y. Lin
High-Dimensional Regression Models Jennifer A. Sinnott and Tianxi Cai
Cure Models Yingwei Peng and Jeremy M.G. Taylor
Causal Models Theis Lange and Naja H. Rod
Classical Regression Models for Competing Risks Jan Beyersmann and Thomas Scheike
Bayesian Regression Models for Competing Risks Ming-Hui Chen, Mario de Castro, Miaomiao Ge, and Yuanye Zhang
Pseudo-Value Regression Models Brent R. Logan and Tao Wang
Binomial Regression Models Randi Grøn and Thomas A. Gerds
Regression Models in Bone Marrow Transplantation—A Case Study Mei-Jie Zhang, Marcelo C. Pasquini, and Kwang Woo Ahn
Model Selection and Validation
Classical Model Selection Florence H. Yong, Tianxi Cai, LJ Wei, and Lu Tian
Bayesian Model Selection Purushottam W. Laud
Model Selection for High-Dimensional Models Rosa J. Meijer and Jelle J. Goeman
Robustness of Proportional Hazards Regression John O’Quigley and Ronghui Xu
Other Censoring Schemes
Nested Case-Control and Case-Cohort Studies Ørnulf Borgan and Sven Ove Samuelsen
Interval Censoring Jianguo Sun and Junlong Li
Current Status Data: An Illustration with Data on Avalanche Victims Nicholas P. Jewell and Ruth Emerson
Multistate Models Per Kragh Andersen and Maja Pohar Perme
Landmarking Hein Putter
Frailty Models Philip Hougaard
Bayesian Analysis of Frailty Models Paul Gustafson
Copula Models Joanna H. Shih
Clustered Competing Risks Guoqing Diao and Donglin Zeng
Joint Models of Longitudinal and Survival Data Wen Ye and Menggang Yu
Familial Studies Karen Bandeen-Roche
Sample Size Calculations for Clinical Trials Kristin Ohneberg and Martin Schumacher
Group Sequential Designs for Survival Data Chris Jennison and Bruce Turnbull
Inference for Paired Survival Data Jennifer Le-Rademacher and Ruta Brazauskas
"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
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