Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/30997
Authors: 
Hennerfeind, Andrea
Brezger, Andreas
Fahrmeir, Ludwig
Year of Publication: 
2003
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 333
Abstract: 
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).
Subjects: 
Bayesian hazard rate models
Markov random fields
penalized splines
MCMC
semiparametric models
spatial survival data
CABG
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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