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Stat Methods Med Res. 2017 Oct;26(5):2244-2256. doi: 10.1177/0962280215596186. Epub 2015 Jul 28.

Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model.

Author information

1
1 Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA.
2
2 Department of Biostatistics and Epidemiology, Arnold School of Public Health, University of South Carolina, Columbia, USA.

Abstract

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. Spatial dependency is modeled directly in the definition of the survival, density, and hazard functions. The models are developed in the context of county level aggregated data. Two cases are considered: the first assumes that the spatial and temporal distributions are independent; the second allows for dependency between the spatial and temporal components. We apply the models to prostate cancer data from the Louisiana SEER cancer registry.

KEYWORDS:

Accelerated failure time; Bayesian hierarchical models; Markov chain Monte Carlo; prostate cancer; spatial analysis

PMID:
26220537
PMCID:
PMC4972700
DOI:
10.1177/0962280215596186
[Indexed for MEDLINE]
Free PMC Article

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