Linked Life Data

21344475

Source:http://linkedlifedata.com/resource/pubmed/id/21344475

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2011-5-3
pubmed:abstractText
Kaplan-Meier survival curve estimation is a commonly used non-parametric method to evaluate survival distributions for groups of patients in the clinical trial setting. However, this method does not permit covariate adjustment which may reduce bias and increase precision. The Cox proportional hazards model is a commonly used semi-parametric method for conducting adjusted inferences and may be used to estimate covariate-adjusted survival curves. However, this model relies on the proportional hazards assumption that is often difficult to validate. Research work has been carried out to introduce a non-parametric covariate-adjusted method to estimate survival rates for certain given time intervals. We extend the non-parametric covariate-adjusted method to develop a new model to estimate the survival rates for treatment groups at any time point when an event occurs. Simulation studies are conducted to investigate the model's performance. This model is illustrated with an oncology clinical trial example.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1097-0258
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 John Wiley & Sons, Ltd.
pubmed:issnType
Electronic
pubmed:day
20
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1243-53
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Covariate-adjusted non-parametric survival curve estimation.
pubmed:affiliation
Eli Lilly and Company, US Commercial Information Sciences, IN 46285, USA. jianghh@lilly.com
pubmed:publicationType
Journal Article