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This textbook is for a second course in survival analysis, gathering together advanced survival models, including frailty, cure, competing risks, and joint models. It includes lots of real data examples to illustrate the methods, with implementation using R software, and can be used for an advanced course on survival analysis.
List of contents
1. Introduction
2. Classical Survival Analysis
3. Frailty Models
4. Cure Models
5. Competing Risks
6. Joint Modeling
About the author
Catherine Legrand is Professor in Statistics and Biostatistics at the Institute of Statistics, Biostatistics, and Actuarial Sciences (ISBA-LIDAM) of the Université Catholique de Louvain (UCLouvain, Belgium). She obtained a Master Degree in Mathematics from the Université Libre de Bruxelles (ULB, Belgium) in 1998. She worked for 7 years at the European Organization for Research and Treatment of Cancer (EORTC, Brussels) and became the primary statistician of the EORTC Lung Cancer Group. She was also a member of the EORTC Treatment Outcome Research Group, the Elderly Task Force, and coordinator of the EORTC Independent Data Monitoring Committee. In parallel, she completed a PhD in 2005 at the Center for Statistics, Hasselt University, in the field of survival analysis (frailty models). Early 2006, she started working as biometrician at Merck Sharp & Dohme (MSD) where she was involved in the design and analysis of clinical trials in respiratory diseases. In September 2007, she joined the Université Catholique de Louvain (UCLouvain). Her area of research includes survival data analysis, design and analysis of clinical trials and analysis of medical data. Along with these professional experiences, she co-authored more than 80 papers in peer-reviewed clinical and statistical journals.
Summary
This textbook is for a second course in survival analysis, gathering together advanced survival models, including frailty, cure, competing risks, and joint models. It includes lots of real data examples to illustrate the methods, with implementation using R software, and can be used for an advanced course on survival analysis.
Additional text
"The book provides, in a single reference, today’s status of survival data modeling. Emphasis is on implementation and interpretation of the output of such models. The narrative and the technical style are nicely in balance, making the reading light and pleasant. The book includes a number of data sets, mainly from oncology, that are used to demonstrate the methodology via case studies, including details on the R and SAS programming. All chapters include a ‘further reading’ section, with important references for deeper digging into the subject. I highly recommended the text, not only for the applied statisticians working with time-to-event data but also for statisticians looking for a comprehensive single reference that provides an excellent overview of advanced survival modeling."- Paul Janssen, Hasselt University
