This article develops a Bayesian semiparametric approach to the extended hazard model,
with generalization to high‐dimensional spatially grouped data. County‐level spatial …

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 …

Prostate cancer (PrCA) is the most common cancer diagnosed in American men and the
second leading cause of death from malignancies. There are large geographical variation …

This thesis will be dealing with the problem of Bayesian estimation in additive survival data
models accounting for spatial dependencies. We consider the Aalen's additive hazards …

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

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 …

Prostate cancer (PrCA) is the most common malignancy in men and a leading cause of
cancer mortality among males in the United States. Large geographical variation and racial …

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 …

Summary Markov chain Monte Carlo methods are used to estimate mortality rates under a
Bayesian hierarchical model. Spatial correlations are introduced to examine spatial e ects …