Clustered survival data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health, and demography. Frailty models provide a powerful tool to analyze clustered survival data. In contrast to the large number of research publications on frailty models, relatively few statistical software packages contain frailty models. It is difficult for statistical practitioners and graduate students to understand frailty models from the existing literature. This book provides an in-depth discussion and explanation of the basics of frailty model methodology for such readers. accelerated failure time models. Common techniques to fit frailty models include the EM-algorithm, penalized likelihood techniques, Laplacian integration and Bayesian techniques. More advanced frailty models for hierarchical data are also included.Real-life examples are used to demonstrate how particular frailty models can be fitted and how the results should be interpreted. the Springer website with most of the programs developed in the freeware packages R and Winbugs. The book starts with a brief overview of some basic concepts in classical survival analysis, collecting what is needed for the reading on the more complex frailty models.

Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.