656 Pages 89 B/W Illustrations
    by Chapman & Hall

    656 Pages 89 B/W Illustrations
    by Chapman & Hall

    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

    Competing Risks
    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

    Multivariate/Multistate Models
    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

    Clinical Trials
    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



    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.

    "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