Linked Life Data

11157414

Source:http://linkedlifedata.com/resource/pubmed/id/11157414

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2001-2-22
pubmed:abstractText
The interest in studying gene-environment interaction is increasing for complex diseases. However, most methods of detecting gene-environment interactions may not be appropriate for the study of interactions involving rare genes (G:) or uncommon environmental exposures (E:), because of poor statistical power. To increase this power, the authors propose the counter-matching design. This design increases the number of subjects with the rare factor without increasing the number of measurements that must be performed. In this paper, the efficiency and feasibility (required sample sizes) of counter-matching designs are evaluated and discussed. Counter-matching on both G: and E: appears to be the most efficient design for detecting gene-environment interaction. The sensitivity and specificity of the surrogate measures, the frequencies of G: and E:, and, to a lesser extent, the value of the interaction effect are the most important parameters for determining efficiency. Feasibility is also more dependent on the exposure frequencies and the interaction effect than on the main effects of G: and E: Although the efficiency of counter-matching is greatest when the risk factors are very rare, the study of such rare factors is not realistic unless one is interested in very strong interaction effects. Nevertheless, counter-matching appears to be more appropriate than most traditional epidemiologic methods for the study of interactions involving rare factors.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0002-9262
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
153
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
265-74
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Counter-matching in studies of gene-environment interaction: efficiency and feasibility.
pubmed:affiliation
Unité de Recherche en Epidémiologie des Cancers, Institut de la Santé et de la Recherche Médicale (INSERM) U521, Institut Gustave-Roussy, 94805 Villejuif, France. nandrieu@igr.fr
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't