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Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model
Journal article   Open access   Peer reviewed

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

Georgiana Onicescu, Andrew Lawson, Jiajia Zhang, Mulugeta Gebregziabher, Kristin Wallace and Jan M Eberth
Statistical methods in medical research, v 26(5), pp 2244-2256
01 Oct 2017
DOI: https://doi.org/10.1177/0962280215596186
PMID: 26220537
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://europepmc.org/articles/pmc4972700?pdf=renderView
Accepted (AM)Open Access (License Unspecified),  Open

Abstract

Bayesian hierarchical models spatial analysis Markov chain Monte Carlo Accelerated failure time Prostate Cancer
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.

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11 citations in Web of Science
13 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Health Care Sciences & Services
Mathematical & Computational Biology
Medical Informatics
Statistics & Probability
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