Azelix monitors are highly trained and experienced in field monitoring with our Senior CRAs averaging between 5-10 years in the industry. All of our monitors receive ongoing training to ensure they are current with industry requirements, guidance, legislation, and technologies. Our field-based monitors are involved in performing site initiation visits, interim monitoring visits, and closeout visits and will complete visit reports at the conclusion of each visit.
Based on a client’s requirements, we will develop a comprehensive monitoring plan imploring 100% source data verification (SDV) or risk-based monitoring (RBM) strategies.
In the case of RBM, we will work with the client’s project team to develop a targeted clinical monitoring strategy that focuses on key endpoints and safety measures of a study. A typical RBM strategy would include a combination of centralized monitoring activities and targeted SDV during on-site monitoring visits.
Centralized monitoring involves the remote (i.e. offsite) monitoring and review of clinical data. Our centralized monitoring team would perform remote SDV of variables such as laboratory values, ECGs and other centrally collected data, IXRS reconciliation, query follow up, and remote correspondence. Our centralized monitoring team works jointly with our data management and biometrics teams to identify study-level trends, issues, and data inconsistencies throughout the life cycle of the study in order to focus the efforts of our field-based monitors.
Once on site, our field-based monitors perform targeted SDV focused primary endpoints and safety assessments. Pre-defined key risk indicators are detailed in the clinical monitoring plan to allow an adaptive monitoring approach such that the frequency or intensity of monitoring is increased or decreased based on meeting the thresholds of key risk indicators.
Imploring an RBM strategy not alone reduces the burden for investigator sites to accommodate onsite monitoring visits, but it also reduces the overall monitoring budget without sacrificing data quality.