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.
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.
General
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