Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/30997 
Erscheinungsjahr: 
2003
Schriftenreihe/Nr.: 
Discussion Paper No. 333
Verlag: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Zusammenfassung: 
We propose geoadditive survival models for analyzing effects jointly with possibly nonlinear effects of other covariates. Within a unified Bayesian frame work, our approach extends the classical Cox model to a more general multiplicative hazard rate model, augmenting the common linear predictor with a spatial component and nonparametric terms for nonlinear effects of time and metrical covariates. Markov random fields and penalized regression splines are used as basic building blocks. Inference is fully Bayesian and uses computationally efficient MCMC sampling schemes. Smoothing parameters are an integral part of the model and are estimated automatically. Perfomance is investigated through simulation studies. We apply our approach to data from a case study in London and Essex that aims to estimate the effect of area of residence and further covariates on waiting times to coronary artery bypass graft (CABG).
Schlagwörter: 
Bayesian hazard rate models
Markov random fields
penalized splines
MCMC
semiparametric models
spatial survival data
CABG
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