Flexible incorporation of both geographical patterning and risk effects in cancer survival
models is becoming increasingly important, due in part to the recent availability of large …

Most of the few published models used to obtain small‐area estimates of relative survival
are based on a generalized linear model with piecewise constant hazards under a Bayesian …

In this paper, we extend the spatially explicit survival model for small area cancer data by
allowing dependency between space and time and using accelerated failure time models …

Relative survival represents the preferred framework for the analysis of population cancer
survival data. The aim is to model the survival probability associated with cancer in the …

Epidemiologists and biostatisticians investigating spatial variation in diseases are often
interested in estimating spatial effects in survival data, where patients are monitored until …

The introduction of spatial and temporal frailty parameters in survival models furnishes a
way to represent unmeasured confounding in the outcome of interest. Using a Bayesian …

Recent developments in GIS have encouraged health science databases to incorporate
geographical information about the subjects under study. Such databases have in turn …

In clustered survival settings where the clusters correspond to geographic regions,
biostatisticians are increasingly turning to models with spatially distributed random effects …

With the proliferation of spatially oriented time-to-event data, spatial modeling in the survival
context has received increased recent attention. A traditional way to capture a spatial pattern …

The inclusion of geographically-based information in many epidemiological studies has led
to the development of statistical and estimation methods that account for spatially dependent …