  Dependent survival data arise in many contexts. One context is clustered
survival data, where survival data are collected on clusters such as families
or medical centers. Dependent survival data also arise when multiple survival
times are recorded for each individual. Frailty models is one common approach
to handle such data. In frailty models, the dependence is expressed in terms of
a random effect, called the frailty. Frailty models have been used with both
Cox proportional hazards model and the accelerated failure time model. This
paper reviews recent developments in the area of frailty models in a variety of
settings. In each setting we provide a detailed model description, assumptions,
available estimation methods, and R packages.
