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Article: Bayesian quantile regression for longitudinal studies with nonignorable missing data

TitleBayesian quantile regression for longitudinal studies with nonignorable missing data
Authors
KeywordsBayesian inference
Informative missing data
Nonignorable dropout
Penalized function
Random effects
Repeated measures
Shared-parameter model
Issue Date2010
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM
Citation
Biometrics, 2010, v. 66 n. 1, p. 105-114 How to Cite?
AbstractWe study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a ℓ 2 penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial. © 2009, The International Biometric Society.
Persistent Identifierhttp://hdl.handle.net/10722/139724
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYuan, Yen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2011-09-23T05:54:48Z-
dc.date.available2011-09-23T05:54:48Z-
dc.date.issued2010en_HK
dc.identifier.citationBiometrics, 2010, v. 66 n. 1, p. 105-114en_HK
dc.identifier.issn0006-341Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/139724-
dc.description.abstractWe study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a ℓ 2 penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial. © 2009, The International Biometric Society.en_HK
dc.languageengen_US
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOMen_HK
dc.relation.ispartofBiometricsen_HK
dc.rightsThe definitive version is available at www.blackwell-synergy.com-
dc.subjectBayesian inferenceen_HK
dc.subjectInformative missing dataen_HK
dc.subjectNonignorable dropouten_HK
dc.subjectPenalized functionen_HK
dc.subjectRandom effectsen_HK
dc.subjectRepeated measuresen_HK
dc.subjectShared-parameter modelen_HK
dc.subject.meshAlgorithms-
dc.subject.meshBayes Theorem-
dc.subject.meshBiometry - methods-
dc.subject.meshData Interpretation, Statistical-
dc.subject.meshInformation Storage and Retrieval - methods-
dc.titleBayesian quantile regression for longitudinal studies with nonignorable missing dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-341X&volume=66&issue=1&spage=105&epage=114&date=2010&atitle=Bayesian+quantile+regression+for+longitudinal+studies+with+nonignorable+missing+data-
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1541-0420.2009.01269.xen_HK
dc.identifier.pmid19459836-
dc.identifier.scopuseid_2-s2.0-77949724484en_HK
dc.identifier.hkuros195658en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77949724484&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume66en_HK
dc.identifier.issue1en_HK
dc.identifier.spage105en_HK
dc.identifier.epage114en_HK
dc.identifier.isiWOS:000275727200013-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYuan, Y=7402709174en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.citeulike4914054-

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