Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
28
pubmed:dateCreated
2009-11-17
pubmed:abstractText
Dental research gives rise to data with potentially complex correlation structure. Assessments of dental caries yield a binary outcome indicating the presence or absence of caries experience for each surface of each tooth in a subject's mouth. In addition to this nesting, caries outcome exhibit spatial structure among neighboring teeth. We develop a Bayesian multivariate model for spatial binary data using random effects autologistic regression that controls for the correlation within tooth surfaces and spatial correlation among neighboring teeth. Using a sample from a clinical study conducted at the Medical University of South Carolina, we compare this autologistic model with covariates to alternative models to demonstrate the improvement in predictions and also to assess the effects of covariates on caries experience.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1097-0258
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2009 John Wiley & Sons, Ltd.
pubmed:issnType
Electronic
pubmed:day
10
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3492-508
pubmed:dateRevised
2011-5-5
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Bayesian modeling of multivariate spatial binary data with applications to dental caries.
pubmed:affiliation
Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. bandyopd@musc.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, N.I.H., Extramural