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Incorporating spatial variation could potentially enhance information coming from survival data. In addition, simultaneous (joint) modeling of time-to-event data from different diseases, such as cancers, from the same patient could provide useful insights as to how these diseases behave together. This paper proposes Bayesian hierarchical survival models for capturing spatial correlations within the proportional hazards (PH) and proportional odds (PO) frameworks. Parametric (Weibull for the PH and log-logistic for the PO) models were used for the baseline distribution while spatial correlation is introduced in the form of county-cancer-level frailties. We illustrate with data from the Surveillance Epidemiology and End Results database of the National Cancer Institute on patients in Iowa diagnosed with multiple gastrointestinal cancers. Model checking and comparison among competing models were performed and some implementation issues were presented. We recommend the use of the spatial PH model for this data set., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

The last decade has witnessed major developments in Geographical Information Systems (GIS) technology resulting in the need for statisticians to develop models that account for spatial clustering and variation. In public health settings, epidemiologists and health-care professionals are interested in discerning spatial patterns in survival data that might exist among the counties. This paper develops a Bayesian hierarchical model for capturing spatial heterogeneity within the framework of proportional odds. This is deemed more appropriate when a substantial percentage of subjects enjoy prolonged survival. We discuss the implementation issues of our models, perform comparisons among competing models and illustrate with data from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute, paying particular attention to the underlying spatial story.