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.
