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Article: Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output

TitleNonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output
Authors
KeywordsActive area
Auto-regressive
Binary observation
Covariates
First order
Issue Date2009
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2009, v. 53 n. 12, p. 4530-4545 How to Cite?
AbstractThe analysis of longitudinal data with nonignorable dropout remains an active area in biostatistics research. Nonignorable dropout (ND) refers to the type of dropout when the probability of dropout depends on the missing observations at or after the time of dropout. Failure to account for such dependence may result in biased inference. Motivated by a methadone clinic data of longitudinal binary observations with dropouts, we propose a conditional first order autoregressive (AR1) logit model for the outcome measurements. The model is further extended to incorporate random effects in order to account for the population heterogeneity and intra-cluster correlation. The purposed models account for the dropout mechanism by a separate logit model in some covariates and missing outcomes for the binary dropout indicators. For model implementation, we proposed a likelihood approach through Monte Carlo approximation to the Gibbs output that evaluates the complicated likelihood function for the random effect ND model without tear. Finally simulation studies are performed to evaluate the biases on the parameter estimates of the outcome model for different dropout mechanisms. © 2009 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/88248
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, JSKen_HK
dc.contributor.authorLeung, DYPen_HK
dc.contributor.authorBoris Choy, STen_HK
dc.contributor.authorWan, WYen_HK
dc.date.accessioned2010-09-06T09:40:49Z-
dc.date.available2010-09-06T09:40:49Z-
dc.date.issued2009en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2009, v. 53 n. 12, p. 4530-4545en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/88248-
dc.description.abstractThe analysis of longitudinal data with nonignorable dropout remains an active area in biostatistics research. Nonignorable dropout (ND) refers to the type of dropout when the probability of dropout depends on the missing observations at or after the time of dropout. Failure to account for such dependence may result in biased inference. Motivated by a methadone clinic data of longitudinal binary observations with dropouts, we propose a conditional first order autoregressive (AR1) logit model for the outcome measurements. The model is further extended to incorporate random effects in order to account for the population heterogeneity and intra-cluster correlation. The purposed models account for the dropout mechanism by a separate logit model in some covariates and missing outcomes for the binary dropout indicators. For model implementation, we proposed a likelihood approach through Monte Carlo approximation to the Gibbs output that evaluates the complicated likelihood function for the random effect ND model without tear. Finally simulation studies are performed to evaluate the biases on the parameter estimates of the outcome model for different dropout mechanisms. © 2009 Elsevier B.V.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.subjectActive area-
dc.subjectAuto-regressive-
dc.subjectBinary observation-
dc.subjectCovariates-
dc.subjectFirst order-
dc.titleNonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs outputen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9473&volume=53&issue=12&spage=4530&epage=4545&date=2009&atitle=Nonignorable+dropout+models+for+longitudinal+binary+data+with+random+effects:+an+application+of+Monte+Carlo+approximation+through+the+Gibbs+outputen_HK
dc.identifier.emailLeung, DYP: dorisl@hkucc.hku.hken_HK
dc.identifier.authorityLeung, DYP=rp00465en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2009.07.020en_HK
dc.identifier.scopuseid_2-s2.0-69449085181en_HK
dc.identifier.hkuros165764en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-69449085181&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume53en_HK
dc.identifier.issue12en_HK
dc.identifier.spage4530en_HK
dc.identifier.epage4545en_HK
dc.identifier.isiWOS:000270624600054-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridChan, JSK=24467617500en_HK
dc.identifier.scopusauthoridLeung, DYP=16304486500en_HK
dc.identifier.scopusauthoridBoris Choy, ST=8599766000en_HK
dc.identifier.scopusauthoridWan, WY=25825839700en_HK
dc.identifier.citeulike5322187-

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