Abstract
In many clinical studies, researchers are interested in parsimonious models that simultaneously achieve consistent variable selection and optimal prediction. The resulting parsimonious models will facilitate meaningful biological interpretation and scientific findings. Variable selection via Bayesian inference has been receiving significant advancement in recent years. Despite its increasing popularity, there is limited practical guidance for implementing these Bayesian approaches and evaluating their comparative performance in clinical datasets. In this paper, we review several commonly used Bayesian approaches to variable selection, with emphasis on application and implementation through R software. These approaches can be roughly categorized into four classes: namely the Bayesian model selection, spike-and-slab priors, shrinkage priors, and the hybrid of both. To evaluate their variable selection performance under various scenarios, we compare these four classes of approaches using real and simulated datasets. These results provide practical guidance to researchers who are interested in applying Bayesian approaches for the purpose of variable selection.
Acknowledgments
This research was supported by funding from a Canadian Institutes of Health Research Doctoral Award (Frederick Banting and Charles Best Canada Graduate Scholarship) to the first author while he was studying at the University of Toronto. The authors would like to thank the reviewers for their comments and suggestions, which have significantly improved the quality of the manuscript.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: Canadian Institutes of Health Research Doctoral Award.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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