Bayesian Variable Selection Methods for Matched Case-Control Studies

Asafu-Adjei Josephine (), Tadesse Mahlet G., Coull Brent, Balasubramanian Raji, Lev Michael, Schwamm Lee and Betensky Rebecca
Additional contact information
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

The International Journal of Biostatistics, 2017, vol. 13, issue 1, 23

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.

Keywords: Bayesian analysis; conditional logistic regression; matched case-control studies; variable selection methods (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2016-0043 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:13:y:2017:i:1:p:22:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2016-0043

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().