Linked Life Data

1341660

Source:http://linkedlifedata.com/resource/pubmed/id/1341660

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1994-1-27
pubmed:abstractText
Dependent binary response data arise frequently in practice due to repeated measurements in longitudinal studies or to subsampling primary sampling units as in fields such as teratology and ophthalmology. Several classes of approaches have recently been proposed to analyse such repeated binary outcome data. The different classes of approaches measure different effects of covariates on binary responses and address different statistical questions. This article compares the different classes of approaches in terms of parameter interpretation and magnitude, standard errors of model parameters and Wald tests for covariate effects. The results help to clarify the substantive questions which data analysts can address with each approach, as well as why the covariate effects measured by different approaches may be different. Finally, I will provide guidelines to the advantages and disadvantages of alternative approaches for analysing dependent binary responses. Simulations and example data illustrate these findings.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0962-2802
pubmed:author
pubmed:issnType
Print
pubmed:volume
1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
249-73
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1992
pubmed:articleTitle
Statistical methods for longitudinal and clustered designs with binary responses.
pubmed:affiliation
Department of Epidemiology and Biostatistics, University of California, San Francisco 94143-0560.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Review