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High dimensional mediation analysis with latent variables.

Andriy Derkach1, Ruth M Pfeiffer1, Ting-Huei Chen2

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland.

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|March 13, 2019
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Summary
This summary is machine-generated.

This study introduces a novel high-dimensional mediation analysis model using latent variables. The method enhances the detection of mediating biomarkers in complex epidemiological studies, like breast cancer research.

Keywords:
direct effectfactor analysismediation analysisoracle propertypenalized likelihood

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Genomics

Background:

  • High-dimensional data presents challenges in mediation analysis.
  • Identifying specific biomarkers mediating exposure-outcome relationships is crucial in epidemiology.
  • Latent variables can explain complex biological pathways.

Purpose of the Study:

  • To develop a novel high-dimensional mediation analysis model incorporating latent variables.
  • To identify mediating biomarkers in epidemiological studies with numerous potential mediators.
  • To improve the power of detecting mediating biomarkers compared to existing methods.

Main Methods:

  • Proposed a statistical model for high-dimensional mediation analysis with latent variables.
  • Derived the model likelihood and developed an expectation-maximization algorithm.
  • Utilized L1-penalized likelihood to select relevant latent factors and biomarkers.

Main Results:

  • The proposed model provides consistent parameter estimates.
  • Estimates of non-zero parameters exhibit asymptotically normal distribution.
  • Simulations demonstrated significantly higher power in detecting mediating biomarkers.

Conclusions:

  • The novel model effectively handles high-dimensional mediation analysis.
  • The approach offers a powerful tool for identifying biomarkers in complex epidemiological research.
  • Applied to breast cancer study, it links body mass index, metabolic measurements, and cancer risk via latent factors.