Journal article
Spatially explicit survival modeling for small area cancer data
Journal of applied statistics, v 45(3), pp 568-585
17 Feb 2018
PMID: 30906096
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results registry prostate cancer data.
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Details
- Title
- Spatially explicit survival modeling for small area cancer data
- Creators
- Georgiana Onicescu - Western Michigan UniversityAndrew B. Lawson - Medical University of South CarolinaJiajia Zhang - University of South CarolinaMulugeta Gebregziabher - Medical University of South CarolinaKristin Wallace - Medical University of South CarolinaJan M. Eberth - University of South Carolina
- Publication Details
- Journal of applied statistics, v 45(3), pp 568-585
- Publisher
- Taylor & Francis
- Number of pages
- 18
- Grant note
- CA176702-01A1 / National Cancer Institute [R03 Grant]
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Health Management and Policy
- Web of Science ID
- WOS:000419961100012
- Scopus ID
- 2-s2.0-85012052974
- Other Identifier
- 991021855275704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability