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- 3Geographic epidemiology
- 2Conditional autoregressive
- 2Generalized linear mixed model
- 1Bayesian statistic
- 1Cancer cases
- 1Childhood cancer
This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates. This class of models adopts spatially correlated random effects and random temporal components. Spatio-temporal models that use conditional autoregressive smoothing...
To analyze childhood cancer diagnoses in the province of Alberta, Canada during 1983-2004, we construct a generalized linear mixed model for the analysis of geographic and temporal variability of cancer rates. In this model, spatially correlated random e®ects and temporal components are adopted....
An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, CanadaDownload
Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. In this paper, we study...
In this paper, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of cancer ratios. In this class of models, spatially correlated random effects and temporal components are adopted. Spatio-temporal models that use intrinsic conditionally...