Survival and Event History Analysis

This text bridges the gap between standard models, and those where the dynamic structure of the data manifests itself fully. The common thread is stochastic processes. The authors show how martingales and stochastic integrals fit with censored data.

This book is practical and directed to people who want to apply the methods with reference to computer programs.

The book presents the necessary theoretical background without being overly technical All topics are covered by thoroughly prepared examples using real data, with ample graphical illustrations Up-to-date presentation with many new topics, including chapters on causality in time-to-event data and analyses of multivariate survival data Includes supplementary material: sn.pub/extras

Inhalt
An introduction to survival and event history analysis.- Stochastic processes in event history analysis.- Nonparametric analysis of survival and event history data.- Regression models.- Parametric counting process models.- Unobserved heterogeneity: The odd effects of frailty.- Multivariate frailty models.- Marginal and dynamic models for recurrent events and clustered survival data.- Causality.- First passage time models: Understanding the shape of the hazard rate.- Diffusion and L#x00E9;vy process models for dynamic frailty.