Linked Life Data

17688490

Source:http://linkedlifedata.com/resource/pubmed/id/17688490

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2007-8-10
pubmed:abstractText
An individual's health condition can affect the frequency and intensity of episodes that can occur repeatedly and that may be related to an event time of interest. For example, bleeding episodes during pregnancy may indicate problems predictive of preterm delivery. Motivated by this application, we propose a joint model for a multiple episode process and an event time. The frequency of occurrence and severity of the episodes are characterized by a latent variable model, which allows an individual's episode intensity to change dynamically over time. This latent episode intensity is then incorporated as a predictor in a discrete time model for the terminating event. Time-varying coefficients are used to distinguish among effects earlier versus later in gestation. Formulating the model within a Bayesian framework, prior distributions are chosen so that conditional posterior distributions are conjugate after data augmentation. Posterior computation proceeds via an efficient Gibbs sampling algorithm. The methods are illustrated using bleeding episode and gestational length data from a pregnancy study.
pubmed:grant
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
63
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
381-8
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Bayesian modeling of multiple episode occurrence and severity with a terminating event.
pubmed:affiliation
Department of Biostatistics, The University of North Carolina at Chapel Hill, Carolina Population Center, Campus Box 7420, Chapel Hill, North Carolina 27599, USA. aherring@bios.unc.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural