Linked Life Data

10986536

Source:http://linkedlifedata.com/resource/pubmed/id/10986536

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
19
pubmed:dateCreated
2001-2-2
pubmed:abstractText
We introduce the subpopulation treatment effect pattern plot (STEPP) method, designed to facilitate the interpretation of estimates of treatment effect derived from different but potentially overlapping subsets of clinical trial data. In particular, we consider sequences of subpopulations defined with respect to a covariate, and obtain confidence bands for the collection of treatment effects (here obtained from the Cox proportional hazards model) associated with the sequences. The method is aimed at determining whether the magnitude of the treatment effect changes as a function of the values of the covariate. We apply STEPP to a breast cancer clinical trial data set to evaluate the treatment effect as a function of the oestrogen receptor content of the primary tumour.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2595-609
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data.
pubmed:affiliation
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, MA 02115, USA. bonetti@jimmy.harvard.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't