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How to analyze many contingency tables simultaneously in genetic association studies
Thorsten Dickhaus1, Klaus Straßburger, Daniel Schunk
1Humboldt-University, Berlin.
Statistical Applications in Genetics and Molecular Biology
|August 2, 2012
Summary
This study introduces realized randomized p-values for exact tests in contingency tables, improving accuracy in multiple testing scenarios like genetic association studies. The method enhances true null hypothesis estimation and addresses correlated p-values effectively.
Area of Science:
- Biostatistics
- Statistical Genetics
- Computational Biology
Background:
- Exact tests for contingency tables (2x2, 2x3) are crucial but often non-randomized, leading to underused significance levels.
- This underuse poses issues in multiple testing, particularly in genetic association studies with numerous simultaneous tests.
Purpose of the Study:
- To propose realized randomized p-values as a solution for exact tests in contingency tables.
- To improve the estimation of true null hypotheses in data-adaptive procedures.
- To address multiplicity and correlated p-values in association studies.
Main Methods:
- Development of realized randomized p-values for exact chi-squared and Fisher-type tests.
- Techniques for reducing multiplicity by estimating the 'effective number of tests' from correlation structures.
- Algorithm implementation and simulation studies.
Main Results:
- Realized randomized p-values offer more accurate estimation of true null hypotheses compared to non-randomized methods.
- The proposed methods effectively handle positively correlated p-values in association analyses.
- An integrated algorithm and efficient implementations are provided.
Conclusions:
- Realized randomized p-values provide a robust solution for exact testing in complex scenarios like genetic association studies.
- The approach enhances statistical power and accuracy in multiple testing frameworks.
- Efficient computational tools are available for practical application.
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