Machine Learning To Drive Clinical Trial Efficiency Via Adverse Event Data And Predictions

As clinical studies evolve in complexity, clinical trial data management becomes increasingly challenging. For those managing clinical research and clinical development, one common pain point is quickly accessing and analyzing all the study data to assess how a trial is progressing and aligning with patient safety goals. When all critical data is readily available, clinical managers are empowered to make timely adjustments to the trial to ensure the drug candidate is safe, efficacious, and continuing to progress toward regulatory approval.

This white paper examines how clinical trial data can be accessed in near real-time and subsequently analyzed for proactive, information-driven study management by clinicians. Additionally, it investigates the utilization of adverse event (AE) data for overseeing the safety framework and performance of trials. A clinical analytics platform that fosters collaboration across functions and a workflow for monitoring emerging trial data are discussed. Furthermore, this paper showcases a new Machine-Learning (ML) algorithm that employs bootstrapping to analyze site and patient AE data, enabling the prediction of sites that may be overperforming or underperforming in their AE reporting. By harnessing AE data, sponsors can promptly identify at-risk clinical sites that require heightened oversight to ensure optimal performance. The synergistic deployment of these tools can identify potential improvements in trial design through amendment or clinical strategy decision-making.