Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation
When many (up to millions) of statistical tests are conducted in discovery set analyses such as genome-wide association studies (GWAS), approaches controlling family-wise error rate (FWER) or false discovery rate (FDR) are required to reduce the number of false positive decisions. Some methods were...
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| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2 March 2018
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| In: |
BMC bioinformatics
Year: 2018, Volume: 19 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-018-2081-x |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1186/s12859-018-2081-x Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12859-018-2081-x 
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| Author Notes: | Regina Brinster, Anna Köttgen, Bamidele O. Tayo, Martin Schumacher, Peggy Sekula |
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| 520 | |a When many (up to millions) of statistical tests are conducted in discovery set analyses such as genome-wide association studies (GWAS), approaches controlling family-wise error rate (FWER) or false discovery rate (FDR) are required to reduce the number of false positive decisions. Some methods were specifically developed in the context of high-dimensional settings and partially rely on the estimation of the proportion of true null hypotheses. However, these approaches are also applied in low-dimensional settings such as replication set analyses that might be restricted to a small number of specific hypotheses. The aim of this study was to compare different approaches in low-dimensional settings using (a) real data from the CKDGen Consortium and (b) a simulation study. | ||
| 650 | 4 | |a False discovery rate | |
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| 650 | 4 | |a Simulation study | |
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