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 …

The hazard function plays a central role in survival analysis. In a homogeneous population,
the distribution of the time to event, described by the hazard, is the same for each individual …

We provide a simple estimation procedure for a general frailty model for the analysis of
prospective correlated failure times. The large-sample properties of the proposed estimators …

Frailty models are the survival data analog to regression models, which account for
heterogeneity and random effects. A frailty is a latent multiplicative effect on the hazard …

Frailty models are used in survival analysis to model unobserved heterogeneity. They
accommodate such heterogeneity by the inclusion of a random term, the frailty, which is …

Clustered survival data are encountered in many scientific disciplines including human and
veterinary medicine, biology, epidemiology, public health and demography. Frailty models …

When analyzing correlated time to event data, shared frailty (random effect) models are
particularly attractive. However, the estimation of such models has proved challenging. In …

Standard survival models assume independence between survival times and frailty models
provide a useful extension of the standard survival models by introducing a random effect …

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and
associations into models for survival data. In its simplest form, frailty is an unobserved …

A frailty model is a random effects model for time variables, where the random effect (the
frailty) has a multiplicative effect on the hazard. It can be used for univariate (independent) …