Linked Life Data

21259303

Source:http://linkedlifedata.com/resource/pubmed/id/21259303

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2011-5-11
pubmed:abstractText
Methods of estimation and inference about survival distributions based on length-biased samples are well-established. Comparatively little attention has been given to the assessment of covariate effects in the context of length-biased samples, but prevalent cohort studies often have this objective. We show that, like the survival distribution, the covariate distribution from a prevalent cohort study is length-biased, and that this distribution may contain parametric information about covariate effects on the survival time. As a result, a likelihood based on the joint distribution of the survival time and the covariates yields estimates of covariate effects which are at least as efficient as estimates arising from a traditional likelihood which conditions on covariate values in the length-biased sample. We also investigate the empirical bias of estimators arising from a joint likelihood when the population covariate distribution is misspecified. The asymptotic relative efficiencies and empirical biases under model misspecification are assessed for both proportional hazards and accelerated failure time models. The various methods considered are applied in an illustrative analysis of risk factors for death following onset of dementia using data collected in the Canadian Study of Health and Aging.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1097-0258
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 John Wiley & Sons, Ltd.
pubmed:issnType
Electronic
pubmed:day
30
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1397-409
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Information in the sample covariate distribution in prevalent cohorts.
pubmed:affiliation
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ont., Canada N2L 3G1. rjcook@uwaterloo.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't