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Related Concept Videos

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Bonferroni Test01:10

Bonferroni Test

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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The null hypothesis of the...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...

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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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False discovery rate control in two-stage designs.

Sonja Zehetmayer1, Martin Posch

  • 1Center for Medical Statistics, Informatics, and Complex Systems, Medical University of Vienna, Austria. sonja.zehetmayer@meduniwien.ac.at

BMC Bioinformatics
|May 8, 2012
PubMed
Summary

This study introduces novel multiple testing procedures for two-stage gene association studies, controlling the False Discovery Rate (FDR). Integrated approaches using pooled data offer improved power over pilot methods, especially with small effect sizes.

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Area of Science:

  • Biostatistics
  • Genomics
  • Statistical Genetics

Background:

  • Limited resources in large-scale genetic studies necessitate efficient hypothesis testing designs.
  • Two-stage designs enhance statistical power by investigating promising hypotheses in a second stage with larger sample sizes.
  • Conventional single-stage designs often suffer from low measurements per marker.

Purpose of the Study:

  • To develop and evaluate multiple testing procedures for two-stage designs that control the False Discovery Rate (FDR).
  • To compare integrated approaches (using pooled data) with pilot approaches (using only second-stage data).
  • To assess the performance of these procedures under various correlation structures and effect sizes.

Main Methods:

  • Derivation of multiple testing procedures for two types of two-stage designs: fixed number selection and FDR threshold selection.
  • Simulation studies to demonstrate FDR control and compare power between integrated and pilot approaches.
  • Application of procedures to pooled data from both stages versus only second-stage data.

Main Results:

  • The proposed multiple testing procedures effectively control the FDR across different selection rules and correlation structures.
  • Integrated approaches, utilizing pooled data, demonstrated a considerable power improvement compared to pilot approaches, particularly in scenarios with small effect sizes.
  • FDR control was maintained for both independent and correlated observations across hypotheses.

Conclusions:

  • The developed hypothesis tests offer a robust tool for FDR control in two-stage genetic association studies.
  • Integrated approaches provide a significant advantage in statistical power over pilot approaches, making them more efficient for detecting true associations.
  • These methods enhance the reliability and power of genetic association studies with limited initial resources.