Dynamic survival models with spatial frailty.
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 components are introduced to handle spatial variation. This paper proposes a methodology to simultaneously introduce these components into the model. A number of specifications for the spatial components are considered. Estimation is performed via a Bayesian approach through Markov chain Monte Carlo methods. Models are compared to assess relevance of their components. Analysis of a real data set is performed, showing the relevance of both time-varying covariate effects and spatial components. Extensions to the methodology are proposed along with concluding remarks.
Bastos
Leonardo Soares
LS
Departamento de Estatística, Universidade Federal do Paraná, Curitiba, PR, Brazil. lbastos@est.ufpr.br
Gamerman
Dani
D
D001499:Bayes Theorem; D001699:Biometry; D001938:Brazil; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D012456:Salaries and Fringe Benefits / Q000706:statistics & numerical data; D016019:Survival Analysis