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

Spatial survival refers to the analysis of geographically referenced time to event data, in
which the survival curve is allowed to vary spatially. Spatial survival typically involves …

The accelerated failure time (AFT) model is a commonly used tool in analyzing survival data.
In public health studies, data is often collected from medical service providers in different …

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

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 …

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 …

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 …