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Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies

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  • Jaya M. Satagopan
  • Ananda Sen
  • Qin Zhou
  • Qing Lan
  • Nathaniel Rothman
  • Hilde Langseth
  • Lawrence S. Engel

Abstract

type="main" xml:lang="en"> Matched case-control studies are popular designs used in epidemiology for assessing the effects of exposures on binary traits. Modern studies increasingly enjoy the ability to examine a large number of exposures in a comprehensive manner. However, several risk factors often tend to be related in a nontrivial way, undermining efforts to identify the risk factors using standard analytic methods due to inflated type-I errors and possible masking of effects. Epidemiologists often use data reduction techniques by grouping the prognostic factors using a thematic approach, with themes deriving from biological considerations. We propose shrinkage-type estimators based on Bayesian penalization methods to estimate the effects of the risk factors using these themes. The properties of the estimators are examined using extensive simulations. The methodology is illustrated using data from a matched case-control study of polychlorinated biphenyls in relation to the etiology of non-Hodgkin's lymphoma.

Suggested Citation

  • Jaya M. Satagopan & Ananda Sen & Qin Zhou & Qing Lan & Nathaniel Rothman & Hilde Langseth & Lawrence S. Engel, 2016. "Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies," Biometrics, The International Biometric Society, vol. 72(2), pages 584-595, June.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:2:p:584-595
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