Linked Life Data

11318205

Source:http://linkedlifedata.com/resource/pubmed/id/11318205

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2001-4-24
pubmed:abstractText
We propose methods for regression analysis of repeatedly measured ordinal categorical data when there is nonmonotone missingness in these responses and when a key covariate is missing depending on observables. The methods use ordinal regression models in conjunction with generalized estimating equations (GEEs). We extend the GEE methodology to accommodate arbitrary patterns of missingness in the responses when this missingness is independent of the unobserved responses. We further extend the methodology to provide correction for possible bias when missingness in knowledge of a key covariate may depend on observables. The approach is illustrated with the analysis of data from a study in diagnostic oncology in which multiple correlated receiver operating characteristic curves are estimated and corrected for possible verification bias when the true disease status is missing depending on observables.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
55
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
488-96
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Generalized estimating equations for ordinal categorical data: arbitrary patterns of missing responses and missingness in a key covariate.
pubmed:affiliation
Department of Anesthesia and Critical Care, The University of Chicago Medical Center, Illinois 60637, USA. toledano@dacc-41.bsd.uchicago.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.