Survival models which incorporate frailties are common in time-to-event data collected over
distinct spatial regions. While incomplete data are unavoidable and a common complication …

The inclusion of geographically-based information in many epidemiological studies has led
to the development of statistical and estimation methods that account for spatially dependent …

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 …

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

Relative survival represents the preferred framework for the analysis of population cancer
survival data. The aim is to model the survival probability associated with cancer in the …

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 …

Spatial confounding is how is called the confounding between fixed and spatial random
effects. It has been widely studied and it gained attention in the past years in the spatial …

The last decade has seen a resurgence of statistical methods for the analysis of spatial data
arising from various scientific fields. This chapter reviews these methodologies, mainly …

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 …

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 …