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

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

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 …

In this paper, we extend the spatially explicit survival model for small area cancer data by
allowing dependency between space and time and using accelerated failure time models …

In this paper we propose a novel Bayesian statistical methodology for spatial survival data.
Our methodology broadens the definition of the survival, density and hazard functions by …

Incorporating temporal and spatial variation could potentially enhance information gathered
from survival data. This paper proposes a Bayesian semi-parametric model for capturing …

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 …

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 …

Survival models have a long history in the biomedical and biostatistical literature, and are
enormously popular in the analysis of time-to-event data. Very often these data will be …

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