Nonparametric Bayes modeling for case control studies with many predictors.

Published

Journal Article

It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.

Full Text

Duke Authors

Cited Authors

  • Zhou, J; Herring, AH; Bhattacharya, A; Olshan, AF; Dunson, DB; National Birth Defects Prevention Study,

Published Date

  • March 2016

Published In

Volume / Issue

  • 72 / 1

Start / End Page

  • 184 - 192

PubMed ID

  • 26394204

Pubmed Central ID

  • 26394204

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/biom.12411

Language

  • eng