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 …

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 …

One of the most widely used models for fitting SURVIVAL DATA is COX PROPORTIONAL
HAZARDS MODEL that is based on homogeneity, independence and equi-distributed of …

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 …

The analysis of recurrent event data is of particular importance in medical statistics where
patients suffering from chronic diseases often present with multiple recurring relapses or …

Flexible incorporation of both geographical patterning and risk effects in cancer survival
models is becoming increasingly important, due in part to the recent availability of large …

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 …

Flexible incorporation of both geographical patterning and risk effects in cancer survival
models is becoming increasingly important, due in part to the recent availability of large …

Recent developments in GIS have encouraged health science databases to incorporate
geographical information about the subjects under study. Such databases have in turn …

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