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Article: A likelihood approach to analysing longitudinal bivariate binary data

TitleA likelihood approach to analysing longitudinal bivariate binary data
Authors
KeywordsConcordance
Correlated bivariate binary data
EM algorithm
Log odds ratio
Mixture model with latent groups
Issue Date1997
PublisherWiley-VCH Verlag GmbH & Co. KGaA. The Journal's web site is located at http://www.interscience.wiley.com/biometricaljournal
Citation
Biometrical Journal, 1997, v. 39 n. 4, p. 409-421 How to Cite?
AbstractTo study the effect of methadone treatment in reducing multiple drug use, say heroin and benzodiazepines while controlling for their possible interaction, we analyse the results of urine drug screens from patients in treatment at a Sydney clinic in 1986. Weekly tests are either positive or negative for each type of drug and a bivariate binary model was developed to analyse such repeated bivariate binary outcomes. It models simultaneously the logit of each type of drug use and their log odds ratio linearly in some covariates. The serial correlation within subject is accounted for by including the previous outcome of both drugs and their interaction as covariates. Our main conclusion is that drug use is reduced over time and the interaction between dose and time effects is not significant. It also suggests that while methadone maintenance is effective in reducing heroin use (CHAN et al., 1995), it does not suppress non-opioid drug use. Concerning the association between the two drugs, it is found that the present strength of their association depends on the previous outcomes only through a measure of concordance. The proposed model has a tractable likelihood function and so a full likelihood analysis is possible. It can be easily extended to incorporate mixture effects. The EM algorithm is used for the estimation of parameters in the mixture model and model selection can be based on the Akaike Information Criterion.
Persistent Identifierhttp://hdl.handle.net/10722/224682
ISSN
2015 Impact Factor: 0.683
2015 SCImago Journal Rankings: 0.784

 

DC FieldValueLanguage
dc.contributor.authorChan, JSK-
dc.contributor.authorKuk, AYC-
dc.contributor.authorBell, J-
dc.date.accessioned2016-04-12T02:48:53Z-
dc.date.available2016-04-12T02:48:53Z-
dc.date.issued1997-
dc.identifier.citationBiometrical Journal, 1997, v. 39 n. 4, p. 409-421-
dc.identifier.issn0323-3847-
dc.identifier.urihttp://hdl.handle.net/10722/224682-
dc.description.abstractTo study the effect of methadone treatment in reducing multiple drug use, say heroin and benzodiazepines while controlling for their possible interaction, we analyse the results of urine drug screens from patients in treatment at a Sydney clinic in 1986. Weekly tests are either positive or negative for each type of drug and a bivariate binary model was developed to analyse such repeated bivariate binary outcomes. It models simultaneously the logit of each type of drug use and their log odds ratio linearly in some covariates. The serial correlation within subject is accounted for by including the previous outcome of both drugs and their interaction as covariates. Our main conclusion is that drug use is reduced over time and the interaction between dose and time effects is not significant. It also suggests that while methadone maintenance is effective in reducing heroin use (CHAN et al., 1995), it does not suppress non-opioid drug use. Concerning the association between the two drugs, it is found that the present strength of their association depends on the previous outcomes only through a measure of concordance. The proposed model has a tractable likelihood function and so a full likelihood analysis is possible. It can be easily extended to incorporate mixture effects. The EM algorithm is used for the estimation of parameters in the mixture model and model selection can be based on the Akaike Information Criterion.-
dc.languageeng-
dc.publisherWiley-VCH Verlag GmbH & Co. KGaA. The Journal's web site is located at http://www.interscience.wiley.com/biometricaljournal-
dc.relation.ispartofBiometrical Journal-
dc.rightspostprint: This is the accepted version of the following article: FULL CITE, which has been published in final form at [Link to final article]. Preprint This is the pre-peer reviewed version of the following article: FULL CITE, which has been published in final form at [Link to final article].-
dc.subjectConcordance-
dc.subjectCorrelated bivariate binary data-
dc.subjectEM algorithm-
dc.subjectLog odds ratio-
dc.subjectMixture model with latent groups-
dc.titleA likelihood approach to analysing longitudinal bivariate binary data-
dc.typeArticle-
dc.identifier.emailChan, JSK: jchan@hkustasc.hku.hk-
dc.identifier.doi10.1002/bimj.4710390403-
dc.identifier.hkuros31268-
dc.identifier.volume39-
dc.identifier.issue4-
dc.identifier.spage409-
dc.identifier.epage421-
dc.publisher.placeGermany-

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