Nonparametric Bayes Modeling for Case Control Studies with Many Predictors

Details

• Personal Author:
  Bhattacharya, Amit ; Dunson DB ; Herring AH ; Olshan AF ; Zhou J
• Corporate Authors:
  National Birth Defects Prevention Network
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Epidemiology Occupational Health Safety
• Keywords:
  Analytical Processes Author Keywords: Bayesian Nonparametrics Big Data Case Control Studies Retrospective Likelihood Sparse Parallel Factor Analysis Model Statistical Analysis Statistical Quality Control Tensor Factorization
• ISSN:
  0006-341X
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  North Carolina ; OSHA Region 4 ; OSHA Region 6 ; Texas
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  184-192
• Volume:
  72
• Issue:
  1
• NIOSHTIC Number:
  nn:20069276
• Citation:
  Biometrics 2016 Mar; 72(1):184-192
• Contact Point Address:
  Jing Zhou, Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
• Email:
  amanistat@gmail.com
• Federal Fiscal Year:
  2016
• Performing Organization:
  Battelle
• Peer Reviewed:
  True
• Start Date:
  20011001
• Source Full Name:
  Biometrics
• End Date:
  20060630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:903d4d128105de483ba06aa222731a2e68c52a41dd9c08fee5963b87322b05ae35fc906f3e7608533566758446ab9c721095fa77c5068c8296c5138e9546e282
• Download URL:
  https://stacks.cdc.gov/view/cdc/207780/cdc_207780_DS1.pdf
• File Type:
  [PDF - 282.61 KB ]