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Rebecca Hubbard's research focuses on the development and application of methods to improve analyses using real-world data sources, including electronic health records (EHR) and medical claims data. In the era of data science, there is a demand for novel analytic methods to transform the wealth of data generated by digital interactions into valid, generalizable knowledge. Her work emphasizes statistical methods designed to meet challenges related to the messiness and complexity of real-world data, including informative observation schemes, phenotyping error, and missingness confounders. These methods support the advancement of a broad range of research areas that utilize EHR and claims data, thereby contributing to health services research, cancer epidemiology, aging, dementia, and pharmacoepidemiology.
Department: Department of Economics