Conducting Prospective Sequential Surveillance in Real‐World Dynamic Distributed Databases

Basic Details
Date
Monday, May 25, 2020
Type
Publication
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).

Author(s)

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

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