WESTERN MICHIGAN UNIVERSITY GAUSSIAN SOFTWARE SOFTWARE
In section 4, we discuss various software for spatial survival analysis. In section 3, we introduce current Bayesian models that describe the spatial variation in survival, such as using random effects (subsection 3.1), cure-rates (subsection 3.2) and direct spatial models (subsection 3.3). In section 2, we describe some fundamental survival analysis concepts. In this paper we provide an overview of the fundamental and more advanced Bayesian spatial survival methodologies that can be applied to cancer research. Also, Bayesian methods enable the development of more complex models, inferences and analyses. Advantages of Bayesian models in comparison to other methods include the ease of drawing strength from neighboring regions, usually via spatially correlated or uncorrelated random effects. Bayesian approaches are increasingly commonly used for modelling small area spatial survival data. The geographical location can be used as a surrogate for environmental or lifestyle factors that may influence population cancer survival. Investigating spatial variations in survival patterns is important since it provides evidence to identify areas with poorer cancer outcomes requiring attention, thus assisting public health professionals in their decision making.
The predictors, such as racial composition ( 4) and socio-economic status ( 5) can also have a geographical influence on survival ( 6). Health initiatives, such as Healthy People 2020 in the United States ( 2) and the World Health Organization Health Equity Monitor ( 3) aim to eliminate cancer health disparities, such as those due to geographical location. The use of spatial survival methods in cancer research has become more widespread due to the increased recognition of the association between the spatial location and health outcomes, increased availability of spatial data and improvements in computing power. Survival analysis is an old subfield of statistics, dating back to the development of life tables ( 1). Received: 01 October 2019 Accepted: Published: 30 June 2020. Statistical modeling and analysis of geographically referenced datasets, Bayesian statistics (theory and methods) and hierarchical modelling, statistical computing and related software development.Īll statistics graduate students are expected to attend.Keywords: Cancer survival spatial Markov chain Monte Carlo Bayesian He is currently professor and Chair of Biostatistics at the in Statistics from the University of Connecticut in 2000.
The focus will be on a variety of modeling and computational strategies to implement massively scalable Gaussian process models and conduct Bayesian inference in settings involving massive amounts of spatial data. Massively scalable Gaussian process models, such as the Nearest-Neighbor Gaussian Process (NNGP), that can be estimated using algorithms requiring floating point operations (flops) and storage linear in the number of spatiotemporal points.
However, fitting hierarchical spatiotemporal models is computationally onerous with complexity increasing in cubic order for the number of spatial locations and temporal points. Spatiotemporal process models have been, and continue to be, widely deployed by researchers to better understand the complex nature of spatial and temporal variability. Important areas of application include environmental exposure assessment and construction of risk maps based upon massive amounts of spatiotemporal data. With the growing capabilities of Geographic Information Systems (GIS) and related software, statisticians today routinely encounter spatial data containing observations from a massive number of locations and time points.
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