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 …

In the United States, prostate cancer is the third most common cause of death from cancer in
males of all ages, and the most common cause of death from cancer in males over age 75. It …

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 …

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 …

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

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

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 …

We extend the baseline‐category logits model for categorical response data to
accommodate two distinct kinds of clustering. Our extension introduces random effects that …

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 …

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