Modelling Recurrent Event Data with Application to Cancer Research

The aim of this book is to show how to analyzesurvival data with the presence of recurrent eventsapplied to cancer settings. Throughout the emphasisis on presenting analysis of real data. Many of themodels discussed are those widely used in this area.In addition a new model specially designed foranalyzing cancer data is presented. Modern techniquessuch as penalized likelihood approach nonparametricsmoothig and bootstrapping are developed and usedwhen appropriate. The author jointly with other colleagues haswritten three R packages freely available at CRAN(http:://www.r-project.org) designed to analyzerecurrent event data: gcmrec survrec andfrailtypack. These packages also contain the realdata sets analyzed in this book. Each chapter of thisbook ends with an illustration of how to use thesepackages to fit models. These analyses should helpbiostatisticians clinicians or medical doctors toanalyze their own data arising form studies where themain aim is to describe those clinical factors thatare associated with the time until a new event occurstaking into account the repeated nature of the data.