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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Spatially-explicit survival modeling with discrete grouping of cancer predictors
Spatial and spatio-temporal epidemiology, v 29
01 Jun 2019
PMID: 31128623
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper, the spatially explicit survival model is extended by allowing the relation with the explanatory covariates to be spatially adaptive using a threshold conditional autoregressive (CAR) model, further extended to allow the inclusion of multiple threshold levels. The model is applied to prostate cancer survival based on Louisiana SEER registry, which holds individual records linked to vital outcomes and is geocoded at the parish level. (C) 2018 Elsevier Ltd. All rights reserved.
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Details
- Title
- Spatially-explicit survival modeling with discrete grouping of cancer predictors
- 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
- Spatial and spatio-temporal epidemiology, v 29
- Publisher
- Elsevier
- Number of pages
- 10
- Grant note
- CA176702-01A1 / National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Health Management and Policy
- Web of Science ID
- WOS:000468628600013
- Scopus ID
- 2-s2.0-85049330674
- Other Identifier
- 991021855178604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health