Advanced Analytics For The Monitoring Of Clinical Trials

By Jennifer Dennis-Wall, Ph.D., Mujataba Sharief, Ph.D., Hemalatha Raju, Ph.D.

Datasets are one of the most valuable commodities of the 21st century. Retailers, manufacturers, and even government agencies are increasingly turning to datacentric approaches that rely on large datasets to make better and more informed decisions, run leaner operations, and drive bottom-line revenue.

Clinical research generates a massive amount of data. Every step in the clinical trial process–including the planning, execution, analysis, and closing phases of studies–creates enough information to completely fill multiple hard drives. Unfortunately, because of the way most clinical data programs currently work, much of these data are not used to their fullest extent; they are simply filed away.

But, what if there was a way to harness this data and turn it into a strategic advantage? Plenty of other traditional industries have found ways to leverage their customer and operations data into more efficient business processes, and clinical trial operations can do the same. For starters, clinical trial data scientists can:

1. Create more efficient, real-time data collection employing centralized monitoring and CDISC standard datasets
2. Provide superior analytics beyond those used for standard risk-based monitoring (RBM)
3. Implement ongoing, sophisticated, and accurate monitoring reports to quickly identify issues and correct errors

This white paper will focus on the benefits provided by a unique and advanced analytical approach to study monitoring beyond the minimum necessary RBM approach.