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False discovery rate control in two-stage designs

Overview of attention for article published in BMC Bioinformatics, May 2012
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Title
False discovery rate control in two-stage designs
Published in
BMC Bioinformatics, May 2012
DOI 10.1186/1471-2105-13-81
Pubmed ID
Authors

Sonja Zehetmayer, Martin Posch

Abstract

For gene expression or gene association studies with a large number of hypotheses the number of measurements per marker in a conventional single-stage design is often low due to limited resources. Two-stage designs have been proposed where in a first stage promising hypotheses are identified and further investigated in the second stage with larger sample sizes. For two types of two-stage designs proposed in the literature we derive multiple testing procedures controlling the False Discovery Rate (FDR) demonstrating FDR control by simulations: designs where a fixed number of top-ranked hypotheses are selected and designs where the selection in the interim analysis is based on an FDR threshold. In contrast to earlier approaches which use only the second-stage data in the hypothesis tests (pilot approach), the proposed testing procedures are based on the pooled data from both stages (integrated approach).

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 9%
Spain 1 5%
Russia 1 5%
Unknown 18 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 36%
Student > Ph. D. Student 5 23%
Professor > Associate Professor 4 18%
Student > Master 2 9%
Professor 2 9%
Other 0 0%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 32%
Mathematics 4 18%
Biochemistry, Genetics and Molecular Biology 3 14%
Computer Science 2 9%
Psychology 2 9%
Other 1 5%
Unknown 3 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 May 2012.
All research outputs
#18,305,773
of 22,664,644 outputs
Outputs from BMC Bioinformatics
#6,284
of 7,247 outputs
Outputs of similar age
#126,291
of 163,548 outputs
Outputs of similar age from BMC Bioinformatics
#84
of 102 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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