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  Distinguishing true from false positives in genomic studies: p values

Broer, L., Lill, C., Schuur, M., Amin, N., Roehr, C., Bertram, L., et al. (2013). Distinguishing true from false positives in genomic studies: p values. European Journal of Epidemiology, 28(2), 131-138. doi:DOI 10.1007/s10654-012-9755-x.

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Broer.pdf (Verlagsversion), 243KB
 
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2013-02-01
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Springer Science+Business Media Dordrecht 2013
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 Urheber:
Broer, L., Autor
Lill, C.1, Autor           
Schuur, M., Autor
Amin, N., Autor
Roehr, C.2, Autor           
Bertram, L.1, Autor           
Ioannidis, J. P. A., Autor
van Duijn, C. M., Autor
Affiliations:
1Neuropsychiatric Genetics (Lars Bertram), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479655              
2Cancer Genomics (Michal-Ruth Schweiger), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479649              

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Schlagwörter: venice criteria significance thresholds "-omics" alzheimer's disease genetic association alzheimer-disease metaanalyses probability trials
 Zusammenfassung: Distinguishing true from false positive findings is a major challenge in human genetic epidemiology. Several strategies have been devised to facilitate this, including the positive predictive value (PPV) and a set of epidemiological criteria, known as the "Venice" criteria. The PPV measures the probability of a true association, given a statistically significant finding, while the Venice criteria grade the credibility based on the amount of evidence, consistency of replication and protection from bias. A vast majority of journals use significance thresholds to identify the true positive findings. We studied the effect of p value thresholds on the PPV and used the PPV and Venice criteria to define usable thresholds of statistical significance. Theoretical and empirical analyses of data published on AlzGene show that at a nominal p value threshold of 0.05 most "positive" findings will turn out to be false if the prior probability of association is below 0.10 even if the statistical power of the study is higher than 0.80. However, in underpowered studies (0.25) with a low prior probability of 1 x 10(-3), a p value of 1 x 10(-5) yields a high PPV (> 96 %). Here we have shown that the p value threshold of 1 x 10(-5) gives a very strong evidence of association in almost all studies. However, in the case of a very high prior probability of association (0.50) a p value threshold of 0.05 may be sufficient, while for studies with very low prior probability of association (1 x 10(-4); genome-wide association studies for instance) 1 x 10(-7) may serve as a useful threshold to declare significance.

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Sprache(n): eng - English
 Datum: 2012-05-012012-12-112013-02-012013
 Publikationsstatus: Erschienen
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 Identifikatoren: Anderer: WOS:000316638900003
DOI: DOI 10.1007/s10654-012-9755-x
ISSN: 0393-2990
URI: ://WOS:000316638900003http://download.springer.com/static/pdf/847/art%253A10.1007%252Fs10654-012-9755-x.pdf?auth66=1396692323_ccd10c7080a3959d3922a603e4b65594&ext=.pdf
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Titel: European Journal of Epidemiology
  Kurztitel : Eur J Epidemiol
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Dordrecht : Springer
Seiten: - Band / Heft: 28 (2) Artikelnummer: - Start- / Endseite: 131 - 138 Identifikator: -