Linked Life Data

16125929

Source:http://linkedlifedata.com/resource/pubmed/id/16125929

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
14
pubmed:dateCreated
2005-9-26
pubmed:abstractText
A systematic meta-analysis of observational studies of melanoma and family history, actinic damage and phenotypic factors was conducted as part of a comprehensive meta-analysis of all major risk factors for melanoma. Following a systematic literature search, relative risks were extracted from 60 studies published before September 2002. Fixed and random effects models were used to obtain pooled estimates for family history (RR = 1.74, 1.41-2.14), skin type (I vs. IV: RR = 2.09, 1.67-2.58), high density of freckles (RR = 2.10, 1.80-2.45), skin colour (Fair vs. Dark: RR = 2.06, 1.68-2.52), eye colour (Blue vs. Dark: RR = 1.47, 1.28-1.69) and hair colour (Red vs. Dark: RR = 3.64, 2.56-5.37), pre-malignant and skin cancer lesions (RR = 4.28, 2.80-6.55) and actinic damage indicators (RR = 2.02, 1.24-3.29). Sub-group analysis and meta-regression were carried out to explore sources of between-study variation and bias. Sensitivity analyses investigated reliability of results and publication bias. Latitude and adjustment for phenotype were two study characteristics that significantly influenced the estimates.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0959-8049
pubmed:author
pubmed:issnType
Print
pubmed:volume
41
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2040-59
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Meta-analysis of risk factors for cutaneous melanoma: III. Family history, actinic damage and phenotypic factors.
pubmed:affiliation
Department of Epidemiology and Biostatistics, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy. sara.gandini@ieo.it
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't, Meta-Analysis