Linked Life Data

16650625

Source:http://linkedlifedata.com/resource/pubmed/id/16650625

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-11-13
pubmed:abstractText
Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0306-4603
pubmed:author
pubmed:issnType
Print
pubmed:volume
32
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
187-93
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
The use of GEE for analyzing longitudinal binomial data: a primer using data from a tobacco intervention.
pubmed:affiliation
H. Lee Moffitt Cancer Center and Research Institute, The University of South Florida, Tampa, Florida 33612, USA. leej@moffitt.usf.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural