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Bayesian Variable Selection Methods for Matched Case-Control Studies

Author

Listed:
  • Asafu-Adjei Josephine

    (Departmetn of Biostatistics, University of North Carolina at Chapel Hill, 3104-E McGavran-Greenberg Hall, Chapel Hill, NC 27515, USA; Department of Nursing, University of North Carolina at Chapel Hill, 2005 Carrington Hall, Chapel Hill, NC 27515, USA)

  • Tadesse Mahlet G.

    (Department of Mathematics & Statistics, Georgetown University, Washington, DC, USA)

  • Coull Brent

    (Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA)

  • Balasubramanian Raji

    (University of Massachusetts, Amherst, MA, USA)

  • Lev Michael

    (Department of Radiology, Massachusetts General Hospital, Boston, MA, USA)

  • Schwamm Lee

    (Department of Neurology, Massachusetts General Hospital, Boston, MA, USA)

  • Betensky Rebecca

    (Harvard University, Cambridge, MA 02138, USA)

Abstract

Matched case-control designs are currently used in many biomedical applications. To ensure high efficiency and statistical power in identifying features that best discriminate cases from controls, it is important to account for the use of matched designs. However, in the setting of high dimensional data, few variable selection methods account for matching. Bayesian approaches to variable selection have several advantages, including the fact that such approaches visit a wider range of model subsets. In this paper, we propose a variable selection method to account for case-control matching in a Bayesian context and apply it using simulation studies, a matched brain imaging study conducted at Massachusetts General Hospital, and a matched cardiovascular biomarker study conducted by the High Risk Plaque Initiative.

Suggested Citation

  • Asafu-Adjei Josephine & Tadesse Mahlet G. & Coull Brent & Balasubramanian Raji & Lev Michael & Schwamm Lee & Betensky Rebecca, 2017. "Bayesian Variable Selection Methods for Matched Case-Control Studies," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-23, May.
    • Handle: RePEc:bpj:ijbist:v:13:y:2017:i:1:p:22:n:4
    DOI: 10.1515/ijb-2016-0043
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    References listed on IDEAS

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    1. Ishwaran, Hemant & Sunil Rao, J., 2008. "Clustering gene expression profile data by selective shrinkage," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1490-1497, September.
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