This webinar explores strategies for implementing risk-based quality control (QC), emphasizing the use of peer-review QC to optimize robust validation. By tailoring the QC level to project-specific risk factors such as analysis purpose and data complexity, the delivery process for high-quality pharmacometric analysis datasets can be streamlined. The session will discuss differentiating between QC levels, such as self-QC, peer review, and double programming. Participants will discover practical methods for fostering collaboration, establishing clear data derivation rules, and utilizing comprehensive QC checklists, particularly for peer-review processes. Additionally, the webinar will highlight tools to support peer-review QC, enhancing QC efficiency and effectiveness.
Join us to build a stronger QC framework and adopt best practices in pharmacometric data validation.