Implementing false discovery rate control: increasing your power

By K. Verhoeven, K. Simonsen, Lauren M. McIntyre. 2005

972 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c2fa495ef43dc0fc032b2c45ed997e3538594c86

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Co-limitation of diatoms by iron and silicic acid in the equatorial Pacific (2011, methodology): “As an alternative, the mean effects in each experimental treatment were compared independent of location using False Discovery Rate procedures (q-FDR; e.g. Benjamini and Hochberg, 1995; Verhoeven et al., 2005).”
Personality predicts social dominance in female zebra finches, Taeniopygia guttata, in a feeding context. (2011, methodology): “Hence, as an alternative to Bonferroni correction (see Moran 2003; Nakagawa 2004), we relied on the procedure introduced by Benjamini & Hochberg (1995), which is similar to Bonferroni’s, but also reduces type II errors by controlling for the false discovery rate (Verhoeven et al. 2005).”
Planting Seedlings in Tree Islands Versus Plantations as a Large‐Scale Tropical Forest Restoration Strategy (2011, methodology): “Given the large number of correlations, we corrected the level of significance for multiple comparison following Benjamini and Hochberg (1995), which reduces type I errors but has higher power than Bonferroni adjustments (Verhoeven et al. 2005).”
Chronic nitrogen additions fundamentally restructure the soil fungal community in a temperate forest (2016, methodology): “Type I error was controlled using the Benjamini and Hochberg FDR procedure (Verhoeven et al., 2005).”
False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. (2014, background): “Simulation analyses [1,12,29] have indicated that false discovery rate control has uniformly better power than other competitor methods (including FWER control), and the fraction of false positives is about what would be expected, even in small to moderate numbers of simultaneous tests.”
Ecological factors drive differentiation in wolves from British Columbia (2009, methodology): “We controlled for type I error in multiple comparisons using the method of Benjamini & Hochberg (1995) (also see Verhoeven et al., 2005).”
Meta-analysis of diffusion tensor imaging studies in schizophrenia. (2009, methodology): “We used the False Discovery Rate for this step, a method which controls the proportion of type 1 errors (false positives) among significant results (Verhoeven et al., 2005).”
Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0) (2010, methodology): “Because p-values are computed for a number of test statistics, we used the method of Benjamini and Hochberg [43] to control the false discovery rate (see [44] for a comparative study of several methods dealing with false discovery rate control and [23] for an application in the context of an ABC stu”
Ectomycorrhizal fungi: diversity and community srtucture in Estonia, Seychelles and Australia (2007, methodology): “For example, the P-value distribution-based sharpened Benjamini-Hochberg procedure both reduces familywise error rate and controls false discovery rate (Verhoeven et al. 2005), providing 2.”