Modelling Recurrent Event Data with Application to Cancer Research
Data Analysis with R
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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) design...
The aim of this book is to show how to analyze
survival data with the presence of recurrent events
applied to cancer settings. Throughout, the emphasis
is on presenting analysis of real data. Many of the
models discussed are those widely used in this area.
In addition, a new model specially designed for
analyzing cancer data is presented. Modern techniques
such as penalized likelihood approach, nonparametric
smoothig and bootstrapping are developed and used
when appropriate.
The author, jointly with other colleagues, has
written three R packages, freely available at CRAN
(http:://www.r-project.org) designed to analyze
recurrent event data: gcmrec, survrec and
frailtypack. These packages also contain the real
data sets analyzed in this book. Each chapter of this
book ends with an illustration of how to use these
packages to fit models. These analyses should help
biostatisticians, clinicians or medical doctors to
analyze their own data arising form studies where the
main aim is to describe those clinical factors that
are associated with the time until a new event occurs
taking into account the repeated nature of the data.
survival data with the presence of recurrent events
applied to cancer settings. Throughout, the emphasis
is on presenting analysis of real data. Many of the
models discussed are those widely used in this area.
In addition, a new model specially designed for
analyzing cancer data is presented. Modern techniques
such as penalized likelihood approach, nonparametric
smoothig and bootstrapping are developed and used
when appropriate.
The author, jointly with other colleagues, has
written three R packages, freely available at CRAN
(http:://www.r-project.org) designed to analyze
recurrent event data: gcmrec, survrec and
frailtypack. These packages also contain the real
data sets analyzed in this book. Each chapter of this
book ends with an illustration of how to use these
packages to fit models. These analyses should help
biostatisticians, clinicians or medical doctors to
analyze their own data arising form studies where the
main aim is to describe those clinical factors that
are associated with the time until a new event occurs
taking into account the repeated nature of the data.
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