In the context of multicenter clinical research, Centralized Monitoring (CM) is the most efficient way to ensure subject’s safety, trial integrity and data quality. As it permits the study team to proactively detect anomalous data trends, CM improves the quality of the regulatory submissions with a direct impact on the time to marketing approval.
Since publication of the regulatory guidance on Risk-Based Monitoring (RBM) five years ago, the concept of CM has developed amid the emergence of technological enablers that make clinical research more data-driven than ever. Today, regulators encourage the use of CM in conjunction with on-site monitoring to oversee clinical trials. Despite its unique potential for improving the quality of clinical trials, CM can appear so technical that sponsors often elect to renounce its use in favor of costly and less efficient traditional monitoring methods.
In reality, only a few concepts that are relatively easy to master and which most life science people are already familiar with are required to properly implement CM. In fact, to plan a CM strategy, one should be familiar with the concept of risk management which involves identifying risks, estimating their potential impact and devising efficacious mitigation strategies. Then, to perform CM, one needs to understand how simple statistics related to the means and the standard deviations, can be used to detect outliers. Additional CM skills include the ability to detect scientific misconduct using the chi-squared distribution which is closely related to the normal distribution.
CM is relatively easy and accessible to any research professional inspired by the objective of overseeing trials with optimal efficiency while simultaneously saving on site monitoring resources.
For insights on simple ways to perform CM, see my article The Basics of Clinical Trial Centralized Monitoring