Linked Life Data

15344194

Source:http://linkedlifedata.com/resource/pubmed/id/15344194

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
18
pubmed:dateCreated
2004-9-2
pubmed:abstractText
In this tutorial, we describe regression-based methods for analysing multiple source data arising from complex sample survey designs. We use the term 'multiple-source' data to encompass all cases where data are simultaneously obtained from multiple informants, or raters (e.g. self-reports, family members, health care providers, administrators) or via different/parallel instruments, indicators or methods (e.g. symptom rating scales, standardized diagnostic interviews, or clinical diagnoses). We review regression models for analysing multiple source risk factors or multiple source outcomes and show that they can be considered special cases of generalized linear models, albeit with correlated outcomes. We show how these methods can be extended to handle the common survey features of stratification, clustering, and sampling weights. We describe how to fit regression models with multiple source reports derived from complex sample surveys using general purpose statistical software. Finally, the methods are illustrated using data from two studies: the Stirling County Study and the Eastern Connecticut Child Survey.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright 2004 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2911-33
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Regression analysis of multiple source and multiple informant data from complex survey samples.
pubmed:affiliation
Department of Mathematics, Smith College, College Lane, Northampton, MA 01063, USA. nhorton@smith.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Review