Modelling Survival Data in Medical Research

"Now in its third edition, Colletts book provides a comprehensive overview of survival analyses and extensions. The book has been expanded considerably; it has increased to 532 pages from the 395 pages in the second edition. Most notably, Collett has expanded his chapter on the Cox regression model and added more information on extensions to standard survival analyses such as frailty models, competing risks, multiple events, and event history modelingA strength of this book is the authors emphasis on diagnostics and model-checking and the integration of thorough references to other works so the reader can seek more background or additional information as necessary. As Collett describes it, he is aiming his writing at an intermediate level. This book is quite well written and straightforward to follow, but does require some statistics background of the readerthis book is definitely worth a look for anyone who teaches or conducts survival analyses. It is an excellent resource for applied statisticians and biostatisticians, and has strong potential as a textbook for upper-year statistics undergraduate or graduate-level courses in survival analysis." Bethany J. G. White, The University of Western Ontario, in The American Statistician, March 2016 "The third edition of Dave Colletts book enhances its position as a superb introduction to the area of survival analysis for medical statisticians, academic statisticians, their students, and statistically aware medical practitioners. It is easy to read and has clear explanations and enough mathematical material to satisfy the statistician but not too much that it would deter others. The extra material added since the second edition keeps this book at the forefront." Dr. Trevor Cox, Director of Statistics, Cancer Research UK Liverpool Cancer Trials Unit "This is an excellent book, which should appeal to anyone involved in quantitative medical research or research training. Earlier editions have already established this remarkable book as a standard reference to one of the most important topics in medical research: survival analysis. In the latest edition, the author has widened the scope of his coverage of the subject far beyond the standard Cox analysis that still dominates the medical literature. He does this with the same lucid style and systematic harmonization of theory and practice as before. The key equations are provided, but not allowed to distract the practitioner. Examples are used both to explain all the important concepts and methodologies and to motivate the theory." Mark Woodward, Professor of Statistics and Epidemiology, Nuffield Department of Population Health, University of Oxford "Dr. Collett has provided an invaluable resource for all students of biostatistics and epidemiology, whether new learners or long-time professionals in the field. He covers the fundamentals of survival analysis by providing thorough treatments of the theory and underpinnings of the concepts while making the material accessible to the reader by providing numerous real-world examples that nicely illustrate the concepts. In addition, he covers many recent additions to the field ensuring that the text is up to date and relevant to todays practicing biostatistician. Dr. Colletts text will be an indispensable resource to all who are charged with drawing proper inference from survival data." Jon J. Snyder, PhD, Director of Transplant Epidemiology, Minneapolis Medical Research Foundation "As a masters student in biostatistics, with a medical background, I missed having a good reference book for survival analysis that is interlarded with clinical examples. Albeit too late for my studies, I was glad to see the appearance of the first edition of this book. It has been a good friend since that time and the second editionagain full of examples of medical datafulfilled all expectations. this newest edition remains f

David Collett, PhD, associate director of statistics and clinical studies, NHS Blood and Transplant, Bristol and visiting professor of statistics, Southampton Statistical Sciences Research Institute, University of Southampton, UK

Survival Analysis. Some Non-Parametric Procedures. The Cox Regression Model. Model Checking in the Cox Regression Model. Parametric Proportional Hazards Models. Accelerated Failure Time and Other Parametric Models. Model Checking In Parametric Models. Time-Dependent Variables. Interval-Censored Survival Data. Frailty Models. Non-Proportional Hazards and Institutional Comparisons. Competing Risks. Multiple Events and Event History Modelling. Dependent Censoring. Sample Size Requirements for a Survival Study. Appendix A: Maximum Likelihood Estimation. Appendix B: Additional Data Sets. Bibliography. Index of Examples. Index.