Statistical Modelling 7 (2007), 191–213
Modelling spatially correlated survival data for individuals with
multiple cancers
Ulysses Diva
Global Biometric Sciences,
Bristol-Myers Squibb Company
USA
Sudipto Banerjee
Division of Biostatistics,
School of Public Health,
University of Minnesota,
A460 Mayo Building, MMC 303
420 Delaware Street SE, MN 55455
USA
eMail:
sudiptob@biostat.umm.edu
Dipak K Dey
Department of Statistics,
University of Connecticut
USA
Abstract:
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.
Keywords:
Bayesian hierarchical models; frailty models;
Markov Chain Monte Carlo (MCMC); mixture of beta functions;
spatial association; survival modelling
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