Bayesian Spatial Relative Survival Model to Estimate the Loss in Life Expectancy and Crude Probability of Death for Cancer Patients

To date, there have not been any population-based cancer studies quantifying geographical patterns of the loss in life expectancy (LLE) and crude probability of death due to cancer ( $C{r}_C$ ). These absolute measures of survival are complementary to the more typically used relative measures of excess mortality and relative survival, and, together, they provide a fuller understanding of geographical disparities in survival outcomes for cancer patients. We propose using a spatially flexible parametric relative survival model in the Bayesian framework, which allows for the inclusion of spatial effects in hazard-level model components. The relative survival framework is the preferred approach to analyze cancer survival data because it does not require information on the cause of death, and the Bayesian spatial modeling approach allows complex and robust small-area estimation. The calculation of spatial estimates for LLE and $C{r}_C$ are demonstrated using publicly available simulated datasets. The associated computer program scripts are available to support the understanding and implementation of our methodology in other spatial cancer modelling applications.