Survival data analysis is a very broad field of statistics, encompassing
a large variety of methods used in a wide range of applications, and in
particular in medical research. During the last twenty years, several
extensions of "classical" survival models have been developed to address
particular situations often encountered in practice. This book aims to
gather in a single reference the most commonly used extensions, such as
frailty models (in case of unobserved heterogeneity or clustered data),
cure models (when a fraction of the population will not experience the
event of interest), competing risk models (in case of different types of
event), and joint survival models for a time-to-event endpoint and a
longitudinal outcome.
Features
- Presents state-of-the art approaches for different advanced survival
models including frailty models, cure models, competing risk models
and joint models for a longitudinal and a survival outcome
- Uses consistent notation throughout the book for the different
techniques presented
- Explains in which situation each of these models should be used, and
how they are linked to specific research questions
- Focuses on the understanding of the models, their implementation, and
their interpretation, with an appropriate level of methodological
development for masters students and applied statisticians
- Provides references to existing R packages and SAS procedure or
macros, and illustrates the use of the main ones on real datasets
This book is primarily aimed at applied statisticians and graduate
students of statistics and biostatistics. It can also serve as an
introductory reference for methodological researchers interested in the
main extensions of classical survival analysis.
