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Article: Explicit Bayesian solution for incomplete pre-post test problems using inverse Bayes formulae

TitleExplicit Bayesian solution for incomplete pre-post test problems using inverse Bayes formulae
Authors
KeywordsEm Algorithm
Gibbs Sampler
Inverse Bayes Formulae
Sir Method
Issue Date2001
PublisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610926.asp
Citation
Communications In Statistics - Theory And Methods, 2001, v. 30 n. 6, p. 1111-1129 How to Cite?
AbstractPre-Post test design is one of the commonly used designs in clinical trials where patients serve as their own control and the differentail effect of a treatment is assessed. The same scenario also arises in comparing pre-post test changes in either physiological variables or molecular or genetic targets. When missing data occurs due to either patients drop-out or some other unknown reasons, an important issue is how to best use the incomplete data to make inference. We develop an explicit Bayesian solution using noninformative priors on the parameters of interest assuming the data is missing at random. The explicit joint posterior is obtained via a noniterative sampling approach based on the inverse Bayes formulae (IBF) as opposed to the iterative sampling using Markov Chain Monte Carlo (MCMC) methods. Therefore, convergence diagnostics are no longer needed and analysis is greatly facilitated. We consider both the cases of a known and an unknown covariance matrix. The method is illustrated with an ongoing study of childhood sickle cell disease. Copyright © 2001 by Marcel Dekker, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/172386
ISSN
2015 Impact Factor: 0.3
2015 SCImago Journal Rankings: 0.518
References

 

DC FieldValueLanguage
dc.contributor.authorTan, Men_US
dc.contributor.authorTian, GLen_US
dc.contributor.authorXiong, Xen_US
dc.date.accessioned2012-10-30T06:22:17Z-
dc.date.available2012-10-30T06:22:17Z-
dc.date.issued2001en_US
dc.identifier.citationCommunications In Statistics - Theory And Methods, 2001, v. 30 n. 6, p. 1111-1129en_US
dc.identifier.issn0361-0926en_US
dc.identifier.urihttp://hdl.handle.net/10722/172386-
dc.description.abstractPre-Post test design is one of the commonly used designs in clinical trials where patients serve as their own control and the differentail effect of a treatment is assessed. The same scenario also arises in comparing pre-post test changes in either physiological variables or molecular or genetic targets. When missing data occurs due to either patients drop-out or some other unknown reasons, an important issue is how to best use the incomplete data to make inference. We develop an explicit Bayesian solution using noninformative priors on the parameters of interest assuming the data is missing at random. The explicit joint posterior is obtained via a noniterative sampling approach based on the inverse Bayes formulae (IBF) as opposed to the iterative sampling using Markov Chain Monte Carlo (MCMC) methods. Therefore, convergence diagnostics are no longer needed and analysis is greatly facilitated. We consider both the cases of a known and an unknown covariance matrix. The method is illustrated with an ongoing study of childhood sickle cell disease. Copyright © 2001 by Marcel Dekker, Inc.en_US
dc.languageengen_US
dc.publisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610926.aspen_US
dc.relation.ispartofCommunications in Statistics - Theory and Methodsen_US
dc.subjectEm Algorithmen_US
dc.subjectGibbs Sampleren_US
dc.subjectInverse Bayes Formulaeen_US
dc.subjectSir Methoden_US
dc.titleExplicit Bayesian solution for incomplete pre-post test problems using inverse Bayes formulaeen_US
dc.typeArticleen_US
dc.identifier.emailTian, GL: gltian@hku.hken_US
dc.identifier.authorityTian, GL=rp00789en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0034870178en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034870178&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume30en_US
dc.identifier.issue6en_US
dc.identifier.spage1111en_US
dc.identifier.epage1129en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridTan, M=7401464906en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US
dc.identifier.scopusauthoridXiong, X=7201634310en_US

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