Linked Life Data

16196081

Source:http://linkedlifedata.com/resource/pubmed/id/16196081

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2006-5-17
pubmed:abstractText
In many biomedical studies, it is of interest to assess dependence between bivariate failure time data. We focus here on a special type of such data, referred to as semi-competing risks data. In this article, we develop methods for making inferences regarding dependence of semi-competing risks data across strata of a discrete covariate Z. A class of rank statistics for testing constancy of association across strata are proposed; its asymptotic properties are also derived. We develop a novel re-sampling-based technique for calculating the variances of the proposed test statistics. In addition, we develop methods for combining test statistics for assessing marginal effects of Z on the dependent censoring variable as well as its effects on association. The finite-sample properties of the proposed methodology are assessed using simulation studies, and they are applied to data from a leukaemia transplantation study.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2005 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2059-70
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Semi-parametric inferences for association with semi-competing risks data.
pubmed:affiliation
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, USA. ghoshd@umich.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural