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Related Concept Videos

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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P-value is one of the most crucial concepts in statistics.
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Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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False discovery rate control for multiple testing based on discrete p-values.

Xiongzhi Chen1

  • 1Department of Mathematics and Statistics, Washington State University, Pullman, WA, USA.

Biometrical Journal. Biometrische Zeitschrift
|January 21, 2020
PubMed
Summary

We introduce BH+, a conservative false discovery rate (FDR) procedure for discrete p-values. BH+ enhances discovery power, especially with mid-p-values, outperforming the Benjamini-Hochberg procedure in real-world studies.

Keywords:
discrete p-valuesfalse discovery ratemid-p-values

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Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Clinical Trials

Background:

  • Multiple testing with discrete p-values presents challenges for traditional false discovery rate (FDR) control.
  • Existing methods like the Benjamini-Hochberg (BH) procedure may lack power or applicability to certain p-value distributions.

Purpose of the Study:

  • To propose and validate a novel, conservative FDR procedure (BH+) for discrete p-values.
  • To demonstrate the enhanced power of BH+ compared to the BH procedure, particularly with mid-p-values.
  • To introduce an adaptive version of BH+ and evaluate its performance.

Main Methods:

  • Development of the BH+ FDR procedure for discrete p-values.
  • Theoretical analysis of BH+ conservativeness and power relative to the BH procedure.
  • Application of BH+ to real-world datasets from methylation, HIV, and clinical safety studies.
  • Simulation studies to assess the performance of an adaptive BH+ procedure.

Main Results:

  • BH+ is proven to be conservative for discrete p-values.
  • BH+ demonstrates at least equal power to the BH procedure for superuniform p-values.
  • BH+ shows increased power when applied to mid-p-values compared to conventional p-values.
  • BH+ yielded considerably more discoveries than the BH procedure in methylation, HIV, and clinical safety studies.
  • An adaptive BH+ procedure shows excellent performance in simulations.

Conclusions:

  • BH+ is a powerful and conservative FDR procedure applicable to discrete p-values, including mid-p-values and general distributions.
  • BH+ offers improved discovery rates in practical applications compared to the standard BH procedure.
  • The adaptive BH+ procedure represents a promising advancement for FDR control in complex biological and clinical research.