12369080

Source:http://linkedlifedata.com/resource/pubmed/id/12369080

Download in:

Switch to

Custom View

Named Graph Language Inference

Statements in which the resource exists as a subject.
Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0205127, umls-concept:C0439605, umls-concept:C0552449, umls-concept:C0597198, umls-concept:C1552664, umls-concept:C1705492
pubmed:issue	20
pubmed:dateCreated	2002-10-7
pubmed:abstractText	The generalized estimating equations (GEE) approach is commonly used to model incomplete longitudinal binary data. When drop-outs are missing at random through dependence on observed responses (MAR), GEE may give biased parameter estimates in the model for the marginal means. A weighted estimating equations approach gives consistent estimation under MAR when the drop-out mechanism is correctly specified. In this approach, observations or person-visits are weighted inversely proportional to their probability of being observed. Using a simulation study, we compare the performance of unweighted and weighted GEE in models for time-specific means of a repeated binary response with MAR drop-outs. Weighted GEE resulted in smaller finite sample bias than GEE. However, when the drop-out model was misspecified, weighted GEE sometimes performed worse than GEE. Weighted GEE with observation-level weights gave more efficient estimates than a weighted GEE procedure with cluster-level weights.
pubmed:grant	http://linkedlifedata.com/resource/pubmed/grant/AG14131
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/8215016
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Oct
pubmed:issn	0277-6715
pubmed:author	pubmed-author:LohmanKurt KKK, pubmed-author:PreisserJohn SJS, pubmed-author:RathouzPaul JPJ
pubmed:copyrightInfo	Copyright 2002 John Wiley & Sons, Ltd.
pubmed:issnType	Print
pubmed:day	30
pubmed:volume	21
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	3035-54
pubmed:dateRevised	2007-11-14
pubmed:meshHeading	pubmed-meshheading:12369080-Computer Simulation, pubmed-meshheading:12369080-Female, pubmed-meshheading:12369080-Humans, pubmed-meshheading:12369080-Longitudinal Studies, pubmed-meshheading:12369080-Male, pubmed-meshheading:12369080-Models, Statistical, pubmed-meshheading:12369080-Patient Dropouts
pubmed:year	2002
pubmed:articleTitle	Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random.
pubmed:affiliation	Department of Biostatistics, CB #7420, School of Public Health, University of North Carolina, Chapel Hill 27599, USA. jpreisse@bios.unc.edu
pubmed:publicationType	Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't