Linked Life Data

8213748

Source:http://linkedlifedata.com/resource/pubmed/id/8213748

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
1993-11-18
pubmed:abstractText
Risk factors used in epidemiology are often measured with error which can seriously affect the assessment of the relation between risk factors and disease outcome. In this paper, a Bayesian perspective on measurement error problems in epidemiology is taken and it is shown how the information available in this setting can be structured in terms of conditional independence models. The modeling of common designs used in the presence of measurement error (validation group, repeated measures, ancillary data) is described. The authors indicate how Bayesian estimation can be carried out in these settings using Gibbs sampling, a sampling technique which is being increasingly referred to in statistical and biomedical applications. The method is illustrated by analyzing a design with two measuring instruments and no validation group.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0002-9262
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
138
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
430-42
pubmed:dateRevised
2000-12-18
pubmed:meshHeading
pubmed:year
1993
pubmed:articleTitle
A Bayesian approach to measurement error problems in epidemiology using conditional independence models.
pubmed:affiliation
Unité 170, Institut National de la Santé et de la Recherche Médicale, Villejuif, France.
pubmed:publicationType
Journal Article