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.
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