Data Science ARES: Rebecca Hubbard

Join us at the Data Science Applied Research and Education Seminar (ARES) with:

Dr. Rebecca Hubbard
Professor of Biostatistics
Department of Biostatistics, Epidemiology & Informatics
University of Pennsylvania Perelman School of Medicine

Free Event | Registration Required

Talk Title: Using electronic health records to accelerate research without sacrificing scientific rigor

Abstract: Opportunities to use “real world data,” including electronic health records and medical claims, have exploded over the past decade. Such data sources facilitate research in a naturalistic setting that can potentially proceed much more quickly than research relying on primary data collection. However, using data that were not collected for research purposes comes at a cost, and naïve use of such data without considering their complexity and imperfect quality can lead to biased inference. Real-world data frequently violate the assumptions of standard statistical methods, and it is not practicable to develop new methods to address every possible complication arising in their analysis. The scientist is faced with a quandary: how to effectively utilize real-world data to advance research without compromising best practices for principled data analysis. Data science, bridging scientific domain expertise with technical facility in working with complex data, offers a solution to this quandary. In this talk I will use examples from my research on methods for the analysis of electronic health records (EHR) derived-data to illustrate approaches to leveraging a scientific understanding of the data generating mechanism to improve the analysis of real-world data. Drawing on this understanding, I will discuss approaches to identify, use, and develop principled methods for EHR data analysis. The overarching goal of this presentation is to raise awareness of challenges associated with the analysis of EHR data and demonstrate how a principled approach can be grounded in an understanding of the scientific context and data generating process.

Speaker Profile: Dr. Hubbard is a Professor of Biostatistics in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania and a Senior Fellow in the Institute for Biomedical Informatics. Her research focuses on development and application of statistical methodology for studies using data from electronic health records (EHR) and medical claims. This work encompasses evaluation of screening and diagnostic tests, methods for comparative-effectiveness studies, clinical risk prediction, and health services research. Dr. Hubbard’s methodological research emphasizes development of statistical tools to support valid inference for EHR-based analyses, accounting for complex data availability and data quality issues, and has been applied across a broad range of areas of application including oncology, neurology, and pharmacoepidemiology. She is an elected Fellow of the American Statistical Association, a statistical editor for the New England Journal of Medicine, and has published over 150 peer-reviewed papers in the statistical and medical literature.