Two-stage false discovery rate in microarray studies

In genome-wide association studies, we normally discover associations between genetic variants and diseases/traits in primary studies, and validate the findings in replication studies. We consider the associations identified in both primary and replication studies as true findings. An important question under this two-stage setting is how to determine significance levels in both studies. In traditional methods, significance levels of the primary and replication studies are determined separately. We argue that the separate determination strategy reduces the power in the overall two-stage study. Therefore, we propose a novel method to determine significance levels jointly. Our method is a reanalysis method that needs summary statistics from both studies. We find the most powerful significance levels when controlling the false discovery rate in the two-stage study. To enjoy the power improvement from the joint determination method, we need to select single nucleotide polymorphisms for replication at a less stringent significance level. This is a common practice in studies designed for discovery purpose. We suggest this practice is also suitable in studies with validation purpose in order to identify more true findings. Simulation experiments show that our method can provide more power than traditional methods and that the false discovery rate is well-controlled. Empirical experiments on datasets of five diseases/traits demonstrate that our method can help identify more associations. The R-package is available at: http://bioinformatics.ust.hk/RFdr.html .

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Sample size reassessment for a two-stage design controlling the false discovery rate

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2014-0025 ◽

2015 ◽

Vol 14 (5) ◽

Cited By ~ 1

Author(s):

Sonja Zehetmayer ◽

Alexandra C. Graf ◽

Martin Posch

Keyword(s):

Sample Size ◽

False Discovery Rate ◽

Effect Sizes ◽

Rna Seq ◽

Two Stage ◽

Stage Design ◽

False Discovery ◽

Sample Size Calculations ◽

Two Stage Design ◽

High Uncertainty

AbstractSample size calculations for gene expression microarray and NGS-RNA-Seq experiments are challenging because the overall power depends on unknown quantities as the proportion of true null hypotheses and the distribution of the effect sizes under the alternative. We propose a two-stage design with an adaptive interim analysis where these quantities are estimated from the interim data. The second stage sample size is chosen based on these estimates to achieve a specific overall power. The proposed procedure controls the power in all considered scenarios except for very low first stage sample sizes. The false discovery rate (FDR) is controlled despite of the data dependent choice of sample size. The two-stage design can be a useful tool to determine the sample size of high-dimensional studies if in the planning phase there is high uncertainty regarding the expected effect sizes and variability.

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On estimating the proportion of true null hypotheses for false discovery rate controlling procedures in exploratory DNA microarray studies

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2004.01.006 ◽

2004 ◽

Vol 47 (3) ◽

pp. 611-637 ◽

Cited By ~ 15

Author(s):

Danh V. Nguyen

Keyword(s):

False Discovery Rate ◽

Dna Microarray ◽

False Discovery ◽

True Null Hypotheses ◽

Microarray Studies

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A generalized false discovery rate in microarray studies

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2010.06.017 ◽

2011 ◽

Vol 55 (1) ◽

pp. 731-737 ◽

Cited By ~ 1

Author(s):

Moonsu Kang ◽

Heuiju Chun

Keyword(s):

False Discovery Rate ◽

False Discovery ◽

Microarray Studies

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False Discovery in A/B Testing

Management Science ◽

10.1287/mnsc.2021.4207 ◽

2021 ◽

Author(s):

Ron Berman ◽

Christophe Van den Bulte

Keyword(s):

Low Power ◽

False Discovery Rate ◽

Decision Makers ◽

Two Stage ◽

Testing Platform ◽

False Discovery ◽

High Fraction ◽

B Test

We investigate what fraction of all significant results in website A/B testing is actually null effects (i.e., the false discovery rate (FDR)). Our data consist of 4,964 effects from 2,766 experiments conducted on a commercial A/B testing platform. Using three different methods, we find that the FDR ranges between 28% and 37% for tests conducted at 10% significance and between 18% and 25% for tests at 5% significance (two sided). These high FDRs stem mostly from the high fraction of true null effects, about 70%, rather than from low power. Using our estimates, we also assess the potential of various A/B test designs to reduce the FDR. The two main implications are that decision makers should expect one in five interventions achieving significance at 5% confidence to be ineffective when deployed in the field and that analysts should consider using two-stage designs with multiple variations rather than basic A/B tests. This paper was accepted by Eric Anderson, marketing.

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