Linked Life Data

17136621

Source:http://linkedlifedata.com/resource/pubmed/id/17136621

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-7-3
pubmed:abstractText
In this paper, we propose a new class of semi-parametric cure rate models. Specifically, we construct dynamic models for piecewise hazard functions over a finite partition of the time axis. Allowing the size of partition and the levels of baseline hazard to be random, our proposed models provide a great flexibility in controlling the degree of parametricity in the right tail of the survival distribution and the amount of correlations among the log-baseline hazard levels. Several properties of the proposed models are derived, and propriety of the implied posteriors with improper noninformative priors for regression coefficients based on the proposed models is established for the fixed partition of the time axis. In addition, an efficient reversible jump computational algorithm is developed for carrying out posterior computation. A real data set from a melanoma clinical trial is analyzed in detail to further demonstrate the proposed methodology.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1380-7870
pubmed:author
pubmed:issnType
Print
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
17-35
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Bayesian dynamic models for survival data with a cure fraction.
pubmed:affiliation
Department of Statistics, University of Connecticut, Storrs, CT 06269, USA. sdkim@stat.uconn.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural