Journal article
Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model
Statistical methods in medical research, v 26(5), pp 2244-2256
01 Oct 2017
PMID: 26220537
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model
- Creators
- Georgiana Onicescu - Western Michigan UniversityAndrew 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 - Drexel University, Health Management and Policy
- Publication Details
- Statistical methods in medical research, v 26(5), pp 2244-2256
- Publisher
- SAGE Publications
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Health Management and Policy
- Web of Science ID
- WOS:000412931500017
- Scopus ID
- 2-s2.0-85031687121
- Other Identifier
- 991021855514004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Health Care Sciences & Services
- Mathematical & Computational Biology
- Medical Informatics
- Statistics & Probability