A Bayesian geoadditive relative survival analysis of registry data on breast cancer mortality

In this paper we develop a so called relative survival analysis,
that is used to model the excess risk of a certain subpopulation
relative to the natural mortality risk, i.e. the base risk that is
present in the whole population. Such models are typically used in
the area of clinical studies, that aim at identifying prognostic
factors for disease specific mortality with data on specific causes
of death being not available. Our work has been motivated by
continuous-time spatially referenced survival data on breast cancer
where causes of death are not known. This paper forms an extension
of the analyses presented in Sauleau et al. (2007), where those
data are analysed via a geoadditive, semiparametric approach,
however without allowance to incorporate natural mortality. The
usefulness of this relative survival approach is supported by means
of a simulated data set.