Linked Life Data

10877323

Source:http://linkedlifedata.com/resource/pubmed/id/10877323

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2000-10-10
pubmed:abstractText
Binary longitudinal data are often collected in clinical trials when interest is on assessing the effect of a treatment over time. Our application is a recent study of opiate addiction that examined the effect of a new treatment on repeated urine tests to assess opiate use over an extended follow-up. Drug addiction is episodic, and a new treatment may affect various features of the opiate-use process such as the proportion of positive urine tests over follow-up and the time to the first occurrence of a positive test. Complications in this trial were the large amounts of dropout and intermittent missing data and the large number of observations on each subject. We develop a transitional model for longitudinal binary data subject to nonignorable missing data and propose an EM algorithm for parameter estimation. We use the transitional model to derive summary measures of the opiate-use process that can be compared across treatment groups to assess treatment effect. Through analyses and simulations, we show the importance of properly accounting for the missing data mechanism when assessing the treatment effect in our example.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
56
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
602-8
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
A transitional model for longitudinal binary data subject to nonignorable missing data.
pubmed:affiliation
Biometric Research Branch, National Cancer Institute, Bethesda, Maryland 20892-7434, USA. albertp@ctep.nci.nih.gov
pubmed:publicationType
Journal Article, Comparative Study