Linked Life Data

16755531

Source:http://linkedlifedata.com/resource/pubmed/id/16755531

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2007-2-5
pubmed:abstractText
Multiple informant data refers to information obtained from different individuals or sources used to measure the same construct; for example, researchers might collect information regarding child psychopathology from the child's teacher and the child's parent. Frequently, studies with multiple informants have incomplete observations; in some cases the missingness of informants is substantial. We introduce a Maximum Likelihood (ML) technique to fit models with multiple informants as predictors that permits missingness in the predictors as well as the response. We provide closed form solutions when possible and analytically compare the ML technique to the existing Generalized Estimating Equations (GEE) approach. We demonstrate that the ML approach can be used to compare the effect of the informants on response without standardizing the data. Simulations incorporating missingness show that ML is more efficient than the existing GEE method. In the presence of MCAR missing data, we find through a simulation study that the ML approach is robust to a relatively extreme departure from the normality assumption. We implement both methods in a study investigating the association between physical activity and obesity with activity measured using multiple informants (children and their mothers).
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2006 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
28
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1055-68
pubmed:dateRevised
2011-9-26
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Incorporating missingness for estimation of marginal regression models with multiple source predictors.
pubmed:affiliation
New England Research Institutes, 9 Galen St, Watertown, MA 02472, USA. hlitman@neriscience.com
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural