Linked Life Data

15026468

Source:http://linkedlifedata.com/resource/pubmed/id/15026468

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2004-3-17
pubmed:abstractText
Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below.05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the observed P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or "noteworthy." We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and readers of research articles to protect themselves from overinterpreting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for deciding whether to call a finding noteworthy formalizes the process already used informally by investigators--that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1460-2105
pubmed:author
pubmed:issnType
Electronic
pubmed:day
17
pubmed:volume
96
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
434-42
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Assessing the probability that a positive report is false: an approach for molecular epidemiology studies.
pubmed:affiliation
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-7244, USA. wacholder@nih.gov
pubmed:publicationType
Journal Article