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Bethany Jacobs Wolf PhD

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  • Professor
  • College of Medicine
  • Public Health Sciences
Academic Focus
  • Statistical methods for discovering gene-gene and gene-environment interactions
  • Clinical prediction modeling
  • Hypothesis testing framework for dichotomization
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Dr. Bethany Wolf received her BS in Chemistry and Anthropology from Rice University in 1995, her MS in Environmental Analytic Chemistry from University of North Carolina at Wilmington in 2000, and her PhD in Biostatistics from MUSC in 2010. Her dissertation focused on extending logic regression, a decision tree method for modeling Boolean interactions among binary predictors to predict disease outcomes, to an ensemble of logic regression trees model which led to considerable improvement in prediction and provided a quantifiable measure of importance of interactions identified by the logic ensemble.  After graduating, she received funding for a post-doctoral fellowship through an American Cancer Society Institutional Research grant to extend her logic forest method to identify subpopulations among breast cancer patients.  

She was appointed as tenure-track faculty at MUSC in December of 2010 in the Division of Biostatistics within the Department of Public Health Sciences.  Since becoming faculty, she has served as a co-investigator of the Methodology Cores for an MCRC of Rheumatic Diseases in African Americans and the Biostatistics Core of the South Carolina Clinical and Translational Research Institute (SCTR). She is currently the Associate Director of the Methodology Core for a program project grant with the Division of Rheumatology entitled “Improving Minority Health in Rheumatic Disease (I aM HeaRD) and serves as the primary statistician for the Department of Anesthesia and Perioperative Medicine.  Her primary research is in the field of machine learning for developing statistical approaches to identify gene x gene and gene x environment interactions associated with patient outcomes without an a priori hypothesis and development of a new statistical hypothesis testing framework to evaluate whether it is appropriate to dichotomize a continuous predictor for disease discrimination. As a member of the CU and through her involvement in the Methodology Cores for the CTSA and the MCRC, she also has the opportunity to interact with scientist and clinical faculty across a multitude of disciplines at MUSC to design research studies and develop analysis plans for grant proposals, analyze a variety of data, and collaborate in writing manuscripts.