The emergence of geographical information systems and related softwares nowadays
enables medical databases to incorporate the geographical information on patients …

The last decade has witnessed major developments in Geographical Information Systems
(GIS) technology resulting in the need for statisticians to develop models that account for …

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

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 …

Incorporating spatial variation could potentially enhance information coming from survival
data. In addition, simultaneous (joint) modeling of time‐to‐event data from different …

This paper starts with a short overview of basic concepts in disease mapping such as
relative risk and age-standardization. Then two recent methods for advanced statistical …

We propose a hierarchical Bayesian methodology to model spatially or spatio-temporal
clustered survival data with possibility of cure. A flexible continuous transformation class of …

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 …

Bayesian methods for adjusting mortality and morbidity rates to account for variations
caused by small numbers are presented. Although such methods produce statistically …

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 …