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    978-1-46-655566-2
    July 21st 2013

Description

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

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

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

Author Bio

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.

Name: Handbook of Survival Analysis (Hardback)Chapman and Hall/CRC 
Description: Edited by John P. Klein, Hans C. van Houwelingen, Joseph G. Ibrahim, Thomas H. Scheike. 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...
Categories: Statistics for the Biological Sciences, Epidemiology, Statistical Theory & Methods