Statistical Modelling 8 (2008), 117139
A Bayesian analysis of relative cancer survival with geoadditive models
Andrea Hennerfeind
Department of Statistics,
Luwigs-Maximilian University
Germany
Leonhard Held
Biostatistics Unit, Institute of Social and Preventive Medicine
University of Zurich
Switzerland
eMail:
leonhard.held@ifspm.uzh.ch
Erik A Sauleau
Registre des Cancers du Haut-Rhin
France
Abstract:
In this paper, we develop a so-called relative survival analysis that is
used to model the excess risk of a certain sub-population relative to
the natural mortality risk which is present in the whole population.
Such models are typically used in population-based studies that aim at
identifying prognostic factors for disease-specific mortality, with data
on specific causes of death not being available. This paper combines
relative survival with Bayesian geoadditive regression allowing for a
flexible semiparametric analysis. Our work has been motivated by
continuous-time spatially referenced survival data on breast cancer
where causes of death are not known. A detailed analysis of these data
is given. The usefulness of the approach is further illustrated by
means of a simulated data set.
Keywords:
Bayesian penalized splines; breast cancer; Gaussian Markov random fields;
MCMC; relative survival; structured hazard regression
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