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Nonparametric Bayes modeling for case control studies with many predictors

Author

Listed:
  • Jing Zhou
  • Amy H. Herring
  • Anirban Bhattacharya
  • Andrew F. Olshan
  • David B. Dunson

Abstract

type="main" xml:lang="en"> 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.

Suggested Citation

  • Jing Zhou & Amy H. Herring & Anirban Bhattacharya & Andrew F. Olshan & David B. Dunson, 2016. "Nonparametric Bayes modeling for case control studies with many predictors," Biometrics, The International Biometric Society, vol. 72(1), pages 184-192, March.
  • Handle: RePEc:bla:biomet:v:72:y:2016:i:1:p:184-192
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    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    2. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Ryo Kato & Takahiro Hoshino, 2018. "Semiparametric Bayes Multiple Imputation for Regression Models with Missing Mixed Continuous-Discrete Covariates," Discussion Paper Series DP2018-15, Research Institute for Economics & Business Administration, Kobe University.

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