Linked Life Data

12898546

Source:http://linkedlifedata.com/resource/pubmed/id/12898546

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
16
pubmed:dateCreated
2003-8-4
pubmed:abstractText
The focus of this paper is regression analysis of clustered data. Although the presence of intracluster correlation (the tendency for items within a cluster to respond alike) is typically viewed as an obstacle to good inference, the complex structure of clustered data offers significant analytic advantages over independent data. One key advantage is the ability to separate effects at the individual (or item-specific) level and the group (or cluster-specific) level. We review different approaches for the separation of individual-level and cluster-level effects on response, their appropriate interpretation and give recommendations for model fitting based on the intent of the data analyst. Unlike many earlier papers on this topic, we place particular emphasis on the interpretation of the cluster-level covariate effect. The main ideas of the paper are highlighted in an analysis of the relationship between birth weight and IQ using sibling data from a large birth cohort study.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright 2003 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2591-602
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Separation of individual-level and cluster-level covariate effects in regression analysis of correlated data.
pubmed:affiliation
Department of Biostatistics, Mailman School of Public Health of Columbia University, 722 West 168th Street (R626B), New York, NY 10032, U.S.A. mdb3@columbia.edu
pubmed:publicationType
Journal Article