Linked Life Data

20146240

Source:http://linkedlifedata.com/resource/pubmed/id/20146240

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2010-3-1
pubmed:abstractText
In many chronic disease processes subjects are at risk of two or more types of events. We describe a bivariate mixed Poisson model in which a copula function is used to model the association between two gamma distributed random effects. The resulting model is a bivariate negative binomial process in which each type of event arises from a negative binomial process. Methods for parameter estimation are described for parametric and semiparametric models based on an EM algorithm. We also consider the issue of event-dependent censoring based on one type of event, which arises when one event is sufficiently serious that its occurence may influence the decision of whether to withdraw a patient from a study. The asymptotic biases of estimators of rate and mean functions from naive marginal analyses are discussed, as well as associated treatment effects. Because the joint model is fit based on a likelihood, consistent estimates are obtained. Simulation studies are carried out to evaluate the empirical performance of the proposed estimators with independent and event-dependent censoring and applications to a trial of breast cancer patients with skeletal metastases and a study of patients with chronic obstructive pulmonary disease illustrate the approach.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1097-0258
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2010 John Wiley & Sons, Ltd.
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
694-707
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
A copula-based mixed Poisson model for bivariate recurrent events under event-dependent censoring.
pubmed:affiliation
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1. rjcook@uwaterloo.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't