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Article: Binary latent variable modelling and its application in the study of air pollution in Hong Kong

TitleBinary latent variable modelling and its application in the study of air pollution in Hong Kong
Authors
Issue Date2004
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2004, v. 23 n. 4, p. 667-684 How to Cite?
AbstractA binary latent variable is constructed to account for the correlation between multiple binary outcomes and is treated as a dependent variable in modelling for covariate effects. This modelling method is similar to the structural equation modelling. Three models are considered: (1) all covariates affecting the binary latent variable directly; (2) some covariates affecting the binary latent variable while other affecting the manifestation of the binary outcomes; and (3) no covariates are included. Gibbs sampling, a special case of the Markov Chain Monte Carlo method, is used to estimate the parameters in the models. Simulation studies show that this method is valid and reliable in estimating covariate effects. But Model (1) fitted the data best with lowest value in the deviance information criteria. The method is illustrated by applying it to the data analysis of an environmental air pollution study. The results show that air pollution (i.e. the most versus the least polluted district) (odds ratio 1.20; 95% confidence interval 0.97-1.49; p=0.102), smoking (relative to not smoking) (2.75; 2.21-3.41; p<0.001) and mosquito coil use (relative to non-use) (1.27; 0.99-1.62; p=0.058) had an impact on the respiratory health of male adults in Hong Kong. Copyright © 2004 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/86842
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHu, ZGen_HK
dc.contributor.authorWong, CMen_HK
dc.contributor.authorThach, TQen_HK
dc.contributor.authorLam, THen_HK
dc.contributor.authorHedley, AJen_HK
dc.date.accessioned2010-09-06T09:22:00Z-
dc.date.available2010-09-06T09:22:00Z-
dc.date.issued2004en_HK
dc.identifier.citationStatistics In Medicine, 2004, v. 23 n. 4, p. 667-684en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/86842-
dc.description.abstractA binary latent variable is constructed to account for the correlation between multiple binary outcomes and is treated as a dependent variable in modelling for covariate effects. This modelling method is similar to the structural equation modelling. Three models are considered: (1) all covariates affecting the binary latent variable directly; (2) some covariates affecting the binary latent variable while other affecting the manifestation of the binary outcomes; and (3) no covariates are included. Gibbs sampling, a special case of the Markov Chain Monte Carlo method, is used to estimate the parameters in the models. Simulation studies show that this method is valid and reliable in estimating covariate effects. But Model (1) fitted the data best with lowest value in the deviance information criteria. The method is illustrated by applying it to the data analysis of an environmental air pollution study. The results show that air pollution (i.e. the most versus the least polluted district) (odds ratio 1.20; 95% confidence interval 0.97-1.49; p=0.102), smoking (relative to not smoking) (2.75; 2.21-3.41; p<0.001) and mosquito coil use (relative to non-use) (1.27; 0.99-1.62; p=0.058) had an impact on the respiratory health of male adults in Hong Kong. Copyright © 2004 John Wiley & Sons, Ltd.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_HK
dc.relation.ispartofStatistics in Medicineen_HK
dc.rightsStatistics in Medicine. Copyright © John Wiley & Sons Ltd.en_HK
dc.subject.meshAir Pollution - analysisen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshEnvironmental Exposureen_HK
dc.subject.meshHong Kong - epidemiologyen_HK
dc.subject.meshHumansen_HK
dc.subject.meshMaleen_HK
dc.subject.meshMarkov Chainsen_HK
dc.subject.meshModels, Statisticalen_HK
dc.subject.meshMonte Carlo Methoden_HK
dc.subject.meshQuestionnairesen_HK
dc.subject.meshRespiratory Tract Diseases - epidemiologyen_HK
dc.titleBinary latent variable modelling and its application in the study of air pollution in Hong Kongen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-6715&volume=23&spage=667&epage=684&date=2004&atitle=Binary+latent+variable+modelling+and+its+application+in+the+study+of+air+pollution+in+Hong+Kongen_HK
dc.identifier.emailWong, CM:hrmrwcm@hkucc.hku.hken_HK
dc.identifier.emailThach, TQ:thach@hku.hken_HK
dc.identifier.emailLam, TH:hrmrlth@hkucc.hku.hken_HK
dc.identifier.emailHedley, AJ:hrmrajh@hkucc.hku.hken_HK
dc.identifier.authorityWong, CM=rp00338en_HK
dc.identifier.authorityThach, TQ=rp00450en_HK
dc.identifier.authorityLam, TH=rp00326en_HK
dc.identifier.authorityHedley, AJ=rp00357en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1002/sim.1661en_HK
dc.identifier.pmid14755396-
dc.identifier.scopuseid_2-s2.0-1142275381en_HK
dc.identifier.hkuros85605en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1142275381&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_HK
dc.identifier.issue4en_HK
dc.identifier.spage667en_HK
dc.identifier.epage684en_HK
dc.identifier.isiWOS:000188737900010-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridHu, ZG=7404211146en_HK
dc.identifier.scopusauthoridWong, CM=7404954904en_HK
dc.identifier.scopusauthoridThach, TQ=6602850066en_HK
dc.identifier.scopusauthoridLam, TH=7202522876en_HK
dc.identifier.scopusauthoridHedley, AJ=7102584095en_HK

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