MELODY: Mediation Analysis in Logistic Regression for High-Dimensional Mediators and a Binary Outcome

Area of Science:

• Biostatistics
• Genomics
• Epidemiology

Background:

• Mediation analysis is crucial for understanding indirect effects of exposures on disease via omics data.
• Traditional methods struggle with high-dimensional mediators, non-rare diseases, and opposing mediator effects.

Purpose of the Study:

• To introduce MELODY (MEdiation analysis in LOgistic regression for high-Dimensional mediators and binarY outcome).
• To develop a robust framework for mediation analysis with high-dimensional omics data and binary outcomes.

Main Methods:

• MELODY utilizes a second-moment-based measure (analogous to R-squared) to quantify total mediation effects.
• A variable selection procedure is incorporated to mitigate bias from non-mediators in high-dimensional settings.

Main Results:

• Simulations confirm MELODY's superior performance with non-rare diseases and high-dimensional mediators.
• MELODY effectively analyzes mediation pathways in large cohort studies.

Conclusions:

• MELODY offers a powerful new approach for mediation analysis in complex biological and epidemiological studies.
• The framework advances the analysis of omics data in relation to disease etiology.