  Relative survival represents the preferred framework for the analysis of
population cancer survival data. The aim is to model the survival probability
associated to cancer in the absence of information about the cause of death.
Recent data linkage developments have allowed for incorporating the place of
residence into the population cancer data bases; however, modeling this spatial
information has received little attention in the relative survival setting. We
propose a flexible parametric class of spatial excess hazard models (along with
inference tools), named "Relative Survival Spatial General Hazard" (RS-SGH),
that allows for the inclusion of fixed and spatial effects in both time-level
and hazard-level components. We illustrate the performance of the proposed
model using an extensive simulation study, and provide guidelines about the
interplay of sample size, censoring, and model misspecification. We present a
case study using real data from colon cancer patients in England. This case
study illustrates how a spatial model can be used to identify geographical
areas with low cancer survival, as well as how to summarize such a model
through marginal survival quantities and spatial effects.
