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Nonparametric Bayes modeling for case control studies with many predictors.

Jing Zhou1, Amy H Herring1,2, Anirban Bhattacharya3

  • 1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.

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Summary

This study introduces a novel Bayesian tensor factorization model for identifying disease-associated predictors in high-dimensional biomedical data. The method effectively detects direct and interactive effects, improving predictor screening in case-control studies.

Keywords:
Bayesian nonparametricsBig dataEpidemiologyRetrospective likelihoodSparse parallel factor analysis modelTensor factorization

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Epidemiology

Background:

  • Case-control studies often involve high-dimensional predictors for disease association detection.
  • Traditional methods like independent screening or logistic regression have limitations in capturing complex predictor relationships and interactions.

Purpose of the Study:

  • To propose a novel nonparametric Bayesian low rank tensor factorization model for analyzing retrospective likelihood in high-dimensional case-control studies.
  • To develop a flexible and omnibus approach for screening significant predictors, accounting for direct effects and interactions.

Main Methods:

  • A nonparametric Bayesian low rank tensor factorization model was developed for the retrospective likelihood.
  • The model allows flexible characterization of multivariate variable distributions without linear assumptions.
  • An efficient Gibbs sampler was employed for computation.

Main Results:

  • The proposed method demonstrated high power in simulation studies for identifying important predictors.
  • Low false discovery rates were observed, indicating effective screening.
  • The approach was applied to an epidemiology study of birth defects.

Conclusions:

  • The Bayesian tensor factorization model offers a powerful and flexible alternative to conventional methods for predictor screening in high-dimensional biomedical research.
  • This omnibus approach effectively identifies predictors with direct or interactive associations with disease risk.
  • The method shows promise for applications in epidemiological studies and other fields dealing with complex, high-dimensional data.