Linked Life Data

19170247

Source:http://linkedlifedata.com/resource/pubmed/id/19170247

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2009-3-19
pubmed:abstractText
Complex diseases often aggregate within families and using the history of family members' disease can potentially increase the accuracy of the risk assessment and allow clinicians to better target on high risk individuals. However, available family risk scores do not reflect the age of disease onset, gender and family structures simultaneously. In this paper, we propose an alternative approach for a family risk score, the stratified log-rank family score (SLFS), which incorporates the age of disease onset of family members, gender differences and the relationship among family members. Via simulation, we demonstrate that the new SLFS is more closely associated with the true family risk for the disease and more robust to family sizes than two existing methods. We apply our proposed method and the two existing methods to a study of stroke and heart disease. The results show that assessing family history can improve the prediction of disease risks and the SLFS has strongest positive associations with both myocardial infarction and stroke.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1269-83
pubmed:dateRevised
2011-10-17
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A new estimate of family disease history providing improved prediction of disease risks.
pubmed:affiliation
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. rfeng@mssoph.uab.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural