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 …

The emergence of geographical information systems and related softwares nowadays
enables medical databases to incorporate the geographical information on patients …

The latest advances in survival analysis have been centered on multivariate systems.
Multivariate survival analysis has two major categories of models: one is multi-state …

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 …

In many survival studies, covariates effects are time-varying and there is presence of spatial
effects. Dynamic models can be used to cope with the variations of the effects and spatial …

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 …

With the proliferation of spatially oriented time-to-event data, spatial modeling in the survival
context has received increased recent attention. A traditional way to capture a spatial pattern …

Loblolly pine, a native pine species of the southeastern United States, is the most-planted
species for commercial timber. Predicting survival of loblolly pine following planting is of …

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 …

Graphics processing units (GPUs) are widely used in many high-performance computing
(HPC) applications such as imaging/video processing and training deep-learning models in …