Linked Life Data

17888040

Source:http://linkedlifedata.com/resource/pubmed/id/17888040

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2008-5-16
pubmed:abstractText
In clinical studies, longitudinal biomarkers are often used to monitor disease progression and failure time. Joint modeling of longitudinal and survival data has certain advantages and has emerged as an effective way to mutually enhance information. Typically, a parametric longitudinal model is assumed to facilitate the likelihood approach. However, the choice of a proper parametric model turns out to be more elusive than models for standard longitudinal studies in which no survival endpoint occurs. In this article, we propose a nonparametric multiplicative random effects model for the longitudinal process, which has many applications and leads to a flexible yet parsimonious nonparametric random effects model. A proportional hazards model is then used to link the biomarkers and event time. We use B-splines to represent the nonparametric longitudinal process, and select the number of knots and degrees based on a version of the Akaike information criterion (AIC). Unknown model parameters are estimated through maximizing the observed joint likelihood, which is iteratively maximized by the Monte Carlo Expectation Maximization (MCEM) algorithm. Due to the simplicity of the model structure, the proposed approach has good numerical stability and compares well with the competing parametric longitudinal approaches. The new approach is illustrated with primary biliary cirrhosis (PBC) data, aiming to capture nonlinear patterns of serum bilirubin time courses and their relationship with survival time of PBC patients.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-11252607, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-12495128, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-12933568, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-14601769, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-15339300, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-15737079, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-16845903, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-17156277, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-8020881, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-8086616, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-9147598, http://linkedlifedata.com/resource/pubmed/commentcorrection/17888040-9789914
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1541-0420
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
64
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
546-56
pubmed:dateRevised
2011-9-26
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data.
pubmed:affiliation
Mathematics Department, Washington University at St. Louis, Missouri 63130, USA. jmding@math.wustl.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.