Nonparametric Bayes modeling for case control studies with many predictors.

ABSTRACT: 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 int

SUBMITTER: Zhou J 

PROVIDER: S-EPMC4803642 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature