This dissertation presents methodological advances in the field of frailty models. Time to event data is very common in biomedical applications. Multivariate data, such as recurrent event histories or clustered failures, typically require special modeling techniques.
Frailty models extend the proportional hazards models by employing random effects. In this book, the first chapter represents an introduction to the field of frailty models. The second chapter studies the small sample properties of such models in more depth. In particular, it addresses the questions of identifiability of frailty models. The third chapter describes a score test that may be used to test whether a recurrent events process is associated with a terminal event. The fourth chapter studies the problem of event-dependent selection in the context of recurrent events data. This problem is relevant when the data are collected from registries. The fifth chapter...
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This dissertation presents methodological advances in the field of frailty models. Time to event data is very common in biomedical applications. Multivariate data, such as recurrent event histories or clustered failures, typically require special modeling techniques.
Frailty models extend the proportional hazards models by employing random effects. In this book, the first chapter represents an introduction to the field of frailty models. The second chapter studies the small sample properties of such models in more depth. In particular, it addresses the questions of identifiability of frailty models. The third chapter describes a score test that may be used to test whether a recurrent events process is associated with a terminal event. The fourth chapter studies the problem of event-dependent selection in the context of recurrent events data. This problem is relevant when the data are collected from registries. The fifth chapter introduces a novel R package and presents an overview of available software for estimating frailty models.
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