Linked Life Data

16345018

Source:http://linkedlifedata.com/resource/pubmed/id/16345018

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
16
pubmed:dateCreated
2006-8-23
pubmed:abstractText
In this paper we consider longitudinal studies in which the outcome to be measured over time is binary, and the covariates of interest are categorical. In longitudinal studies it is common for the outcomes and any time-varying covariates to be missing due to missed study visits, resulting in non-monotone patterns of missingness. Moreover, the reasons for missed visits may be related to the specific values of the response and/or covariates that should have been obtained, i.e. missingness is non-ignorable. With non-monotone non-ignorable missing response and covariate data, a full likelihood approach is quite complicated, and maximum likelihood estimation can be computationally prohibitive when there are many occasions of follow-up. Furthermore, the full likelihood must be correctly specified to obtain consistent parameter estimates. We propose a pseudo-likelihood method for jointly estimating the covariate effects on the marginal probabilities of the outcomes and the parameters of the missing data mechanism. The pseudo-likelihood requires specification of the marginal distributions of the missingness indicator, outcome, and possibly missing covariates at each occasions, but avoids making assumptions about the joint distribution of the data at two or more occasions. Thus, the proposed method can be considered semi-parametric. The proposed method is an extension of the pseudo-likelihood approach in Troxel et al. to handle binary responses and possibly missing time-varying covariates. The method is illustrated using data from the Six Cities study, a longitudinal study of the health effects of air pollution.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
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
2784-96
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates.
pubmed:affiliation
Department of Decision and Information Analysis, Goizueta Business School, USA. michael_parzen@bus.emory.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, N.I.H., Extramural