Spatially correlated survival data are frequently observed in ecological and epidemiological
studies. An assumption in the clustered survival models is inter-cluster independence, which …

The use of survival models involving a random effect or 'frailty'term is becoming more
common. Usually the random effects are assumed to represent different clusters, and …

Consider a fixed number of clustered areas identified by their geographical coordinates that
are monitored for the occurrences of an event such as a pandemic, epidemic, or migration …

Many authors in recent years have proposed extensions of familiar survival analysis
methodologies to apply in dependent data settings, for example, when data are clustered or …

Frailty models are useful for measuring unobserved heterogeneity in risk of failures across
clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared …

In many survival studies, covariates effects are time-varying and there is presence of spatial
effects. Dynamic models can be used to cope with the variations of the effects and spatial …

A marginal likelihood approach is proposed for estimating the parameters in a frailty model
using clustered survival data. To overcome the analytic intractability of the marginal …

Clustered failure time data are commonly encountered in biomedical research where the
study subjects from the same cluster (eg, family) share the common genetic and/or …

In this study, we describe a frailty model for spatially correlated survival data based on the
class of Archimedean copulas in the spatial regression, assuming that counties are allowed …

We propose a new class of semiparametric frailty models for spatially correlated survival
data. Specifically, we extend the ordinary frailty models by allowing random effects …