Conducting Prospective Sequential Surveillance in Real‐World Dynamic Distributed Databases

Project Title Conducting Prospective Sequential Surveillance in Real‐World Dynamic Distributed Databases
Date
Monday, May 25, 2020
Location
Description

The U.S. Food and Drug Administration leverages real-world electronic healthcare data (e.g., electronic health records, insurance claims) to support regulatory decision-making. Potential uses of real-world data (RWD) include quantifying the risk of outcomes too rare to fully assess in preapproval clinical trials or among excluded or underrepresented subpopulations, and continuous monitoring of important clinical outcomes. Prospective sequential surveillance involves multiple statistical evaluations on RWD that accumulate over time (i.e., adding new data for the same patient or adding new patients).

Corresponding Author

Judith C. Maro, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States

Email: judy_maro@harvardpilgrim.org

Authors

Judith C. Maro, Efe Eworuke, Laura Hou, Emily C. Welch, Margie R. Goulding, Rima Izem, Joo‐Yeon Lee, Sengwee Toh, Bruce Fireman, Michael D. Nguyen

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Publication Type
Publication
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