Most of the few published models used to obtain small‐area estimates of relative survival
are based on a generalized linear model with piecewise constant hazards under a Bayesian …

Spatial survival analysis has received a great deal of attention over the last 20 years due to
the important role that geographical information can play in predicting survival. This paper …

Mortality counts are usually aggregated over age groups assuming similar effects of both
time and region, yet the spatio‐temporal evolution of cancer mortality rates may depend on …

Epidemiologists and biostatisticians investigating spatial variation in diseases are often
interested in estimating spatial effects in survival data, where patients are monitored until …

The Cox proportional hazard model is one of the most popular tools in analyzing time‐to‐
event data in public health studies. When outcomes observed in clinical data from different …

During the past three decades, prostate cancer incidence has changed substantially in the
United States. A fully Bayesian hierarchical spatio‐temporal interaction model is proposed to …

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

With the proliferation of spatially oriented time-to-event data, spatial modeling in the survival
context has received increased recent attention. A traditional way to capture a spatial pattern …

Survival methods are used for the statistical modelling of time-to-event data. Survival data
are characterized by a set of complete records, in which the time of the event is known; and …

In clustered survival settings where the clusters correspond to geographic regions,
biostatisticians are increasingly turning to models with spatially distributed random effects …