A Bayesian two-stage spatially dependent variable selection model for space-time health data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Jungsoon | - |
dc.contributor.author | Lawson, Andrew B. | - |
dc.date.accessioned | 2022-07-09T07:34:10Z | - |
dc.date.available | 2022-07-09T07:34:10Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2019-09 | - |
dc.identifier.issn | 0962-2802 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147191 | - |
dc.description.abstract | In space-time epidemiological modeling, most studies have considered the overall variations in relative risk to better estimate the effects of risk factors on health outcomes. However, the associations between risk factors and health outcomes may vary across space and time. Especially, the temporal patterns of the covariate effects may depend on space. Thus, we propose a Bayesian two-stage spatially dependent variable selection approach for space-time health data to determine the spatially varying subsets of regression coefficients with common temporal dependence. The two-stage structure allows reduction of the spatial confounding bias in the estimates of the regression coefficients. A simulation study is conducted to examine the performance of the proposed two-stage model. We apply the proposed model to the number of inpatients with lung cancer in 159 counties of Georgia, USA. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SAGE PUBLICATIONS LTD | - |
dc.title | A Bayesian two-stage spatially dependent variable selection model for space-time health data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Jungsoon | - |
dc.identifier.doi | 10.1177/0962280218767980 | - |
dc.identifier.scopusid | 2-s2.0-85045283217 | - |
dc.identifier.wosid | 000484532300002 | - |
dc.identifier.bibliographicCitation | STATISTICAL METHODS IN MEDICAL RESEARCH, v.28, no.9, pp.2570 - 2582 | - |
dc.relation.isPartOf | STATISTICAL METHODS IN MEDICAL RESEARCH | - |
dc.citation.title | STATISTICAL METHODS IN MEDICAL RESEARCH | - |
dc.citation.volume | 28 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 2570 | - |
dc.citation.endPage | 2582 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Health Care Sciences & Services | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | DISEASE | - |
dc.subject.keywordPlus | RISK | - |
dc.subject.keywordAuthor | Spatial confounding problem | - |
dc.subject.keywordAuthor | Bayesian spatial variable selection | - |
dc.subject.keywordAuthor | spatial random component | - |
dc.identifier.url | https://journals.sagepub.com/doi/10.1177/0962280218767980 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.