Handbook of Survival Analysis

Handbook of Survival Analysis

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Features

  • Helps statisticians pick the best statistical method to analyze their survival data experiment
  • Provides statistical methods for right-censored and left-truncated survival data
  • Describes numerous techniques for regression modeling of competing risks data
  • Examines techniques for model selection and validation as well as the robustness of the Cox regression model
  • Discusses the estimation of models with more complex censoring and sampling schemes than simple right censoring
  • Presents multistate models for a patient’s complete disease/recovery process and multivariate models that have some dependency between a set of event times
  • Covers topics useful in the design and analysis of clinical trials where the outcome is the time to some event

Summary

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

Table of Contents

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

Index

Editor Bio(s)

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.

Editorial Reviews

"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

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