Bayesian Variable Selection Methods for Matched Case-Control Studies

Josephine Asafu-Adjei, G. Tadesse Mahlet, Brent Coull, Raji Balasubramanian, Michael Lev, Lee Schwamm, Rebecca Betensky

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number20160043
JournalInternational Journal of Biostatistics
Volume13
Issue number1
DOIs
StatePublished - 2017

Keywords

  • Bayesian analysis
  • Conditional logistic regression
  • Matched case-control studies
  • Variable selection methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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