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keywords
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Machine Learning
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overview
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I see myself as a collaborative biostatistician that develops statistical methodology for problems encountered when working with collaborative teams. I am a faculty biostatistician a part of the Biostatistics, Epidemiology, and Research Design (BERD) group in the Wake Forest Clinical and Translational Science Institute. From my involvement in BERD and general collaboration with other researchers across WF and beyond, I work across a wide range of medical and public health disciplines, including: cancer, neurology, intensive care, pediatrics, telehealth, and more. My areas of applied statistical experience and expertise include, but are not limited to: design and analysis of clinical trials (particularly dose-finding trials), longitudinal/clustered data analysis, mixed models, analysis of EHR data, analysis of continuously monitored patient data, Bayesian methods, missing data, and prediction modeling using traditional and machine learning methods (e.g. Random Forests).
My statistical methodology research interests stem from my collaborative work and include: design of dose-finding cancer trials, the development of composite toxicity burden scores in cancer patients, methods for handling missing data (particularly from daily patient diaries), and prediction modelling.
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