Multivariate parametric spatiotemporal models for county level breast cancer survival data

DOI10.1007/S10985-004-5637-1zbMath1076.62109OpenAlexW1981043831WikidataQ30984156 ScholiaQ30984156MaRDI QIDQ2576813

Xiaoping Jin, Bradley P. Carlin

Publication date: 14 December 2005

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-004-5637-1

zbMATH Keywords

proportional hazardsMarkov chain Monte Carlo methodslattice datarandom effects modelcancer survival dataGeographic Information System (GIS)

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (4)

Inducing high spatial correlation with randomly edge-weighted neighborhood graphs ⋮ Bayesian spatial models with a mixture neighborhood structure ⋮ Inferring upon heterogeneous associations in dairy cattle performance using a bivariate hierarchical model ⋮ Hierarchical failure time regression using mixtures for classification of the immune response of atlantic salmon

Cites Work

• Multi-dimensional multivariate Gaussian Markov random fields with application to image processing
• Estimating the dimension of a model
• A close look at the spatial structure implied by the CAR and SAR models.
• Bayesian computation and stochastic systems. With comments and reply.
• A Parametric Dynamic Survival Model Applied to Breast Cancer Survival Times
• Sampling-Based Approaches to Calculating Marginal Densities
• Model choice: a minimum posterior predictive loss approach
• Hierarchical Spatio-Temporal Mapping of Disease Rates
• Hierarchical Proportional Hazards Regression Models for Highly Stratified Data
• Bayesian Measures of Model Complexity and Fit
• Efficient parametrisations for normal linear mixed models
• Proper multivariate conditional autoregressive models for spatial data analysis
• Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota

This page was built for publication: Multivariate parametric spatiotemporal models for county level breast cancer survival data