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Modelling spatially correlated survival data for individuals with multiple cancers

Ulysses Diva, Global Biometric Sciences, Bristol–Myers Squibb Company, US

Sudipto Banerjee

Sudipto Banerjee, Division of Biostatistics, School of Public Health, University of Minnesota, US E-Mail: sudiptob{at}biostat.umm.edu

Dipak K Dey

Dipak K Dey, Department of Statistics, University of Connecticut, US

Epidemiologists and biostatisticians investigating spatial variation in diseases are often interested in estimating spatial effects in survival data, where patients are monitored until their time to failure (for example, death, relapse). Spatial variation in survival patterns often reveals underlying lurking factors that could assist public health professionals in their decision–making process to identify regions requiring attention. The Surveillance Epidemiology and End Results (SEER) database of the National Cancer Institute provides a fairly sophisticated platform for exploring novel approaches in modelling cancer survival, particularly with models accounting for spatial clustering and variation. Modelling survival data for patients with multiple cancers poses unique challenges in itself and in capturing the spatial associations of the different cancers. This paper develops the Bayesian hierarchical survival models for capturing spatial patterns within the framework of proportional hazard. Spatial variation is introduced in the form of county–cancer level frailties. The baseline hazard function is modelled semiparametrically using mixtures of beta distributions. We illustrate with data from the SEER database, perform model checking and comparison among competing models, and discuss implementation issues.

Key Words: Bayesian hierarchical models • frailty models • Markov Chain Monte Carlo (MCMC) • mixture of beta functions • spatial association • survival modelling

Statistical Modelling, Vol. 7, No. 2, 191-213 (2007)
DOI: 10.1177/1471082X0700700205


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