The industry phrase risk-based monitoring (RBM) initially may appear to be a poor fit with rare and uncommon diseases
However, applying an adaptive and intelligent approach to RBM can lead to a more focused review of critical data elements that are essential to end point assessments. Conventional wisdom in the rare diseases space dictates the use of 100 percent source document verification (SDV) and frequent on-site monitoring. This traditional approach provides a sense of quality and robustness to the monitoring process, but does not address the issue of errors in the data that may occur between reviews or procedures at sites that are not detected until the on-site monitoring event occurs. Adaptive and intelligent monitoring offers solutions to improve data flow, and has more frequent and ongoing assessment of data with trend analysis to detect and prevent issues.
Adaptive and Intelligent Solutions Applied to Risk-Based Monitoring
Over the last decade, RBM has been an evolving platform in the biopharmaceutical industry. In its early stages, RBM was simply a reduction in review of the total number of patients or the case report form (CRF) fields. These early approaches included basic sampling guidelines. For example, monitoring every fourth patient at a site, which does not work well in rare disease studies, where very few patients are enrolled at each site. With the expansion of electronic data capture (EDC), simple SDV sampling assignments could be made in some EDC systems to support a field level sampling approach. However, these had to be determined at the start of the study and could not be easily modified based on site performance or the emergence of new execution risks as the study progressed.