Linked Life Data

9423260

Source:http://linkedlifedata.com/resource/pubmed/id/9423260

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
1998-2-4
pubmed:abstractText
This paper presents an approach to handling missing covariates in the generalized estimating equation (GEE) model for binary outcomes when the probability of missingness depends on the observed outcomes and covariates. The proposed method is to replace the missing quantities in the estimating function with consistent estimates. In special cases, the proposed model reduces to a weighted GEE model for the completely observed units, where the weight is the inverse of the probability of missingness. Our method can be viewed as an extension of the mean score method by Reilly and Pepe (1995, Biometrika 82, 299-314) to the GEE context. Under certain regularity conditions, the estimates of the regression coefficients obtained by the proposed method are consistent and asymptotically normally distributed. The finite sample properties of the estimates are illustrated via computer simulations. An application to the study of dementia among stroke patients is presented.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
53
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1458-66
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Generalized estimating equation model for binary outcomes with missing covariates.
pubmed:affiliation
Department of Clinical Statistics and Data Management, Wyeth-Lederle Vaccines and Pediatrics, Pearl River, New York 10965, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.