Rebecca A. Hubbard, PhD
Dr. 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 claims data. The data science era demands novel analytic methods to transform the wealth of data created as a byproduct of our digital interactions into valid and generalizable knowledge. Dr. Hubbard’s research emphasizes statistical methods designed to meet this challenge by addressing the messiness and complexity of real world data including informative observation schemes, phenotyping error, and error and missingness in confounders. Her methods have been applied to support the advancement of a broad range of research areas through use of EHR and claims data including health services research, cancer epidemiology, aging and dementia, and pharmacoepidemiology.
Read more about the work of Dr. Hubbard's research group.
Cancer epidemiology, pharmacoepidemiology, health services research, aging and dementia
Bayesian biostatistics, outcome misclassification, measurement error, missing data, multi-state models