Linked Life Data

21044964

Source:http://linkedlifedata.com/resource/pubmed/id/21044964

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
23
pubmed:dateCreated
2010-12-1
pubmed:abstractText
We define personalized medicine as the administration of treatment to only persons thought most likely to benefit, typically those at high risk for mortality or another detrimental outcome. To evaluate personalized medicine, we propose a new design for a randomized trial that makes efficient use of high-throughput data (such as gene expression microarrays) and clinical data (such as tumor stage) collected at baseline from all participants. Under this design for a randomized trial involving experimental and control arms with a survival outcome, investigators first estimate the risk of mortality in the control arm based on the high-throughput and clinical data. Then investigators use data from both randomization arms to estimate both the effect of treatment among all participants and among participants in the highest prespecified category of risk. This design requires only an 18.1% increase in sample size compared with a standard randomized trial. A trial based on this design that has a 90% power to detect a realistic increase in survival from 70% to 80% among all participants, would also have a 90% power to detect an increase in survival from 50% to 73% in the highest quintile of risk.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1460-2105
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
102
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1756-9
pubmed:dateRevised
2011-4-8
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Designing a randomized clinical trial to evaluate personalized medicine: a new approach based on risk prediction.
pubmed:affiliation
National Cancer Institute, Bethesda, MD 20892-7354, USA. sb16i@nih.gov
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural