https://scholars.lib.ntu.edu.tw/handle/123456789/521780
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Lo M.-T. | en_US |
dc.contributor.author | WEN-CHUNG LEE | en_US |
dc.creator | Lo M.-T.;Wen-Chung Lee | - |
dc.date.accessioned | 2020-11-19T08:19:22Z | - |
dc.date.available | 2020-11-19T08:19:22Z | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901719997&doi=10.1038%2fsrep05081&partnerID=40&md5=e02cbfb7a4eb6043da4b2f5abfa9f3cc | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/521780 | - |
dc.description.abstract | Many risk factors/interventions in epidemiologic/biomedical studies are of minuscule effects. To detect such weak associations, one needs a study with a very large sample size (the number of subjects, n). The n of a study can be increased but unfortunately only to an extent. Here, we propose a novel method which hinges on increasing sample size in a different direction-the total number of variables (p). We construct a p-based 'multiple perturbation test', and conduct power calculations and computer simulations to show that it can achieve a very high power to detect weak associations when p can be made very large. As a demonstration, we apply the method to analyze a genome-wide association study on age-related macular degeneration and identify two novel genetic variants that are significantly associated with the disease. The p-based method may set a stage for a new paradigm of statistical tests. | - |
dc.language.iso | English | - |
dc.publisher | Nature Publishing Groups | - |
dc.relation.ispartof | Scientific Reports | - |
dc.subject.classification | [SDGs]SDG3 | - |
dc.subject.other | biological model; data mining; genetic association; genetics; human; macular degeneration; pathology; single nucleotide polymorphism; Data Mining; Genome-Wide Association Study; Humans; Macular Degeneration; Models, Genetic; Polymorphism, Single Nucleotide | - |
dc.title | Detecting a weak association by testing its multiple perturbations: A data mining approach | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1038/srep05081 | - |
dc.identifier.pmid | 24866319 | - |
dc.identifier.scopus | 2-s2.0-84901719997 | - |
dc.relation.journalvolume | 4 | - |
item.openairetype | journal article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.languageiso639-1 | English | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Institute of Health Data Analytics and Statistics | - |
crisitem.author.dept | Public Health | - |
crisitem.author.orcid | 0000-0003-3171-7672 | - |
crisitem.author.parentorg | College of Public Health | - |
crisitem.author.parentorg | College of Public Health | - |
顯示於: | 流行病學與預防醫學研究所 |
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