Linked Life Data

17342700

Source:http://linkedlifedata.com/resource/pubmed/id/17342700

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
22
pubmed:dateCreated
2007-8-23
pubmed:abstractText
Correlation analysis is widely used in biomedical and psychosocial research for assessing rater reliability, precision of diagnosis and accuracy of proxy outcomes. The popularity of longitudinal study designs has propelled the proliferation in recent years of new methods for longitudinal and other multi-level clustered data designs, such as the mixed-effect models and generalized estimating equations. Despite these advances, research and methodological development on addressing missing data for correlation analysis is woefully lacking. In this paper, we consider non-parametric inference for the product-moment correlation within a longitudinal data setting and address missing data under both the missing completely at random and missing at random assumptions. We illustrate the approach with real study data in mental health and HIV prevention research.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4116-38
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Correlation analysis for longitudinal data: applications to HIV and psychosocial research.
pubmed:affiliation
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA. xin_tu@urmc.rochester.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural