Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#322541] of David B. Dunson


Papers Published

  1. Zhou, J; Herring, AH; Bhattacharya, A; Olshan, AF; Dunson, DB; National Birth Defects Prevention Study,, Nonparametric Bayes modeling for case control studies with many predictors., Biometrics, vol. 72 no. 1 (March, 2016), pp. 184-192 [doi]
    (last updated on 2019/06/18)

    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.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320