Survival And Event History Analysis: A Process Point Of View (statistics For Biology And Health)
by Ornulf Borgan /
2008 / English / PDF
3.6 MB Download
The aim of this book is to bridge the gap between standard
textbook models and a range of models where the dynamic structure
of the data manifests itself fully. The common denominator of
such models is stochastic processes. The authors show how
counting processes, martingales, and stochastic integrals fit
very nicely with censored data. Beginning with standard analyses
such as Kaplan-Meier plots and Cox regression, the presentation
progresses to the additive hazard model and recurrent event data.
Stochastic processes are also used as natural models for
individual frailty; they allow sensible interpretations of a
number of surprising artifacts seen in population data.
The aim of this book is to bridge the gap between standard
textbook models and a range of models where the dynamic structure
of the data manifests itself fully. The common denominator of
such models is stochastic processes. The authors show how
counting processes, martingales, and stochastic integrals fit
very nicely with censored data. Beginning with standard analyses
such as Kaplan-Meier plots and Cox regression, the presentation
progresses to the additive hazard model and recurrent event data.
Stochastic processes are also used as natural models for
individual frailty; they allow sensible interpretations of a
number of surprising artifacts seen in population data.
The stochastic process framework is naturally connected to
causality. The authors show how dynamic path analyses can
incorporate many modern causality ideas in a framework that takes
the time aspect seriously.
The stochastic process framework is naturally connected to
causality. The authors show how dynamic path analyses can
incorporate many modern causality ideas in a framework that takes
the time aspect seriously.
To make the material accessible to the reader, a large number of
practical examples, mainly from medicine, are developed in
detail. Stochastic processes are introduced in an intuitive and
non-technical manner. The book is aimed at investigators who use
event history methods and want a better understanding of the
statistical concepts. It is suitable as a textbook for graduate
courses in statistics and biostatistics.
To make the material accessible to the reader, a large number of
practical examples, mainly from medicine, are developed in
detail. Stochastic processes are introduced in an intuitive and
non-technical manner. The book is aimed at investigators who use
event history methods and want a better understanding of the
statistical concepts. It is suitable as a textbook for graduate
courses in statistics and biostatistics.