1. Course Aim This course will introduce participants to the principles of surveillance in pharmacovigilance with a focus on quantitative aspects and recent advances in the field. The course will cover classical pharmacovigilance signal detection using spontaneous adverse event reports and also describe quantitative signal detection methods for electronic medical record and claims data. Real-world examples will be used across the lecture sessions to illustrate and connect different concepts. Participants will engage in interactive discussions about contemporary issues in and challenges facing pharmacovigilance.
2. Requisites Statement Entry level. We will be making some pre-course material available for download for the interactive workshop.
3. Course Objectives a. Explain the need for pharmacovigilance and its component activities with a focus on safety surveillance b. Describe the potential data sources (e.g., spontaneous reports, claims, electronic medical records, social media data) for safety surveillance in pharmacovigilance c. Understand the array of core analytic methods for pharmacovigilance in spontaneous adverse events reports and signal detection and active monitoring in electronic healthcare databases 4. Recognize the limitations of each data source and approach and how they complement each other 5. Appreciate some of the recent advances and controversies in quantitative signal detection
4. Syllabus Outline The course will be live followed by a panel Q&A session 1. Signal detection and quantitative analysis of spontaneous reports and beyond (Gregory Powell): This session will cover basic concepts of safety surveillance in pharmacovigilance, describe how spontaneous adverse event reports are generated and collected, introduce quantitative tools for separating potential signals from noise among large collections of adverse event reports, and describe how the concepts and tools of signal detection can be applied to observational databases. It will also touch on some preliminary work using social media for signal detection.
2. Statistical methods for signal detection in spontaneous reports (Niklas Norén): Many large organizations rely on statistical pattern discovery as one component in their pharmacovigilance processes to direct and inform clinical review of individual case reports. This session will describe some of the core analytic methods, with an emphasis on their strengths and limitations. It will highlight some recent advances including methods to detect possible risk factors and to improve statistical signal detection with multivariate analyses.
3. Novel quantitative methods for identifying drug safety signals in electronic healthcare data (Judy Maro): With the expanding availability of electronic healthcare data and the increasing role that they are playing in clinical and regulatory decision-making, new quantitative methods have been developed and evaluated to improve the ability to the performance safety signal detection. This session will focus on technical aspects of these new methods and include a “laboratory” portion designed for a live exercise on quantitative signal detection. Attendees should have downloaded prepared materials and software for the exercise.
4. Pharmacovigilance in practice: Getting from signal to insight (Monica Muñoz): This session will provide an overview of how identified signals are managed from a regulatory and clinical point of view. Attendees will gain an understanding of signal management principles with an emphasis on case series development and information synthesis.
In addition to questions raised by participants, the live panel discussion will cover topics related to contemporary issues in and challenges facing pharmacovigilance and signal detection