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Power of testing for exposure effects under incomplete mediation
Ruixuan R Zhou1, David M Zucker2, Sihai D Zhao3
1Waymo LLC, Mountain View, CA, USA.
The International Journal of Biostatistics
|April 21, 2023
Summary
This study introduces a more powerful statistical test for mediation analysis, even when direct effects exist. The method enhances power for detecting exposure effects, particularly useful in genomics and epigenetics research.
Area of Science:
- Statistics
- Genomics
- Epigenetics
Background:
- Mediation analysis examines direct and indirect effects of exposures on outcomes via mediators.
- Standard methods may lack power for small effect sizes, common in genomics.
Purpose of the Study:
- To develop a more powerful statistical test for mediation analysis under incomplete mediation.
- To enhance detection of exposure effects when direct pathways are present.
Main Methods:
- Investigated linear mediation models with both direct and indirect effects.
- Developed and applied novel procedures for testing the null hypothesis of no direct or indirect effect.
- Evaluated methods for low- and high-dimensional mediators.
Main Results:
- Demonstrated that power gain is achievable in incomplete mediation under specific conditions.
- Procedures were effective in simulations and real-world data analysis.
- Identified effects of cigarette smoking on gene expression via DNA methylation mediators.
Conclusions:
- The proposed methods offer increased statistical power for mediation analysis.
- This approach is valuable for complex biological systems, such as gene expression regulation.
- The study provides a robust framework for analyzing mediation in genomics and epigenetics.
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