Applied Survival Analysis Using R covers the main principles of survival analysis gives
examples of how it is applied and teaches how to put those principles to use to analyze data
using R as a vehicle. Survival data where the primary outcome is time to a specific event
arise in many areas of biomedical research including clinical trials epidemiological studies
and studies of animals. Many survival methods are extensions of techniques used in linear
regression and categorical data while other aspects of this field are unique to survival data.
This text employs numerous actual examples to illustrate survival curve estimation comparison
of survivals of different groups proper accounting for censoring and truncation model
variable selection and residual analysis. Because explaining survival analysis requires more
advanced mathematics than many other statistical topics this book is organized with basic
concepts and most frequently used procedures covered in earlier chapters with more advanced
topics near the end and in the appendices. A background in basic linear regression and
categorical data analysis as well as a basic knowledge of calculus and the R system will help
the reader to fully appreciate the information presented. Examples are simple and
straightforward while still illustrating key points shedding light on the application of
survival analysis in a way that is useful for graduate students researchers and practitioners
in biostatistics.
