Linked Life Data

9385109

Source:http://linkedlifedata.com/resource/pubmed/id/9385109

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
1997-11-25
pubmed:abstractText
Duration time models often should include correlated failure times, due to clustered data. These random effects hierarchical models sometimes are called "frailty models" when used for survival analyses. The data analyzed here involve such correlations because patient level outcomes (the times until graft failure following kidney transplantation) are observed, but patients are clustered in different transplant centers. We describe fitting such models by combining two kinds of software, one for parametric survival regression models, and the other for doing Poisson regression in a hierarchical setting. The latter is implemented by using PRIMM (Poisson Regression and Interactive Multilevel Modeling) methods and software (Christiansen & Morris, 1994a). An illustrative example for profiling data is included with k = 11 kidney transplant centers and N = 412 patients.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1380-7870
pubmed:author
pubmed:issnType
Print
pubmed:volume
1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
347-59
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1995
pubmed:articleTitle
Fitting Weibull duration models with random effects.
pubmed:affiliation
Department of Statistics, Harvard University, Cambridge, MA 02138, USA. morris@stat.harvard.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't