Titlebook: Survival and Event History Analysis; A Process Point of V Odd O. Aalen,Ørnulf Borgan,Håkon K. Gjessing Book 2008 Springer-Verlag New York 2

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Multivariate frailty models,y, including Kaplan-Meier plots and Cox analysis, is geared toward handling this situation. However, multivariate survival data also arise naturally in many contexts. Such data pose a problem for ordinary multivariate methods, which will have difficulty handling censored data..There are two typical

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Marginal and dynamic models for recurrent events and clustered survival data,f recurrent events we focus on the concept of dynamic models, which represent an attempt to understand in explicit terms how the past influences the present and the future. We may think of this as causal influences, but statistical dependence on the past may also be a reflection of heterogeneity. In

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Causality,hes to defining and studying causality in statistical terms. Causality is based on a notion of the past influencing the present and the future. This has very natural links to the type of stochastic processes considered in this book, and it is therefore appropriate to incorporate material on causalit

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First passage time models: Understanding the shape of the hazard rate,he hazard in a multiplicative fashion, with the individual hazard . described as .(.) = .α(.). Here, α is a “basic” rate and . is a nonnegative random variable taking distinct values for each individual. This may be an appropriate way to account for missing (time-independent) covariates such as thos

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,Diffusion and Lévy process models for dynamic frailty,on level. As soon as the risk process of an individual hits a barrier, that individual has had an event, leaving behind only those with a risk process not yet at the level of the barrier. The barrier hitting models can be seen as a way of relaxing the assumption of time-constant frailty found in the

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,Diffusion and Lévy process models for dynamic frailty, usual frailty models (Chapter 6). Another way of relaxing the constancy assumption is to allow dynamic covariates to capture a part of the variation experienced by an individual, as demonstrated in Chapter 8.

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