Linked Life Data

8711279

Source:http://linkedlifedata.com/resource/pubmed/id/8711279

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
21-22
pubmed:dateCreated
1996-9-10
pubmed:abstractText
The analysis of variation of risk for a given disease in space and time is a key issue in descriptive epidemiology. When the data are scarce, maximum likelihood estimates of the area-specific risk and of its linear time-trend can be seriously affected by random variation. In this paper, we propose a Bayesian model in which both area-specific intercept and trend are modelled as random effects and correlation between them is allowed for. This model is an extension of that originally proposed for disease mapping. It is illustrated by the analysis of the cumulative prevalence of insulin dependent diabetes mellitus as observed at the military examination of 18-year-old conscripts born in Sardinia during the period 1936-1971. Data concerning the genetic differentiation of the Sardinian population are used to interpret the results.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2433-43
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:articleTitle
Bayesian analysis of space-time variation in disease risk.
pubmed:affiliation
Istituto Scienze Sanitarie Applicate-Universita di Pavia, Italy.
pubmed:publicationType
Journal Article