Risk-Based Monitoring (RBM) allows targeted monitoring of problematic investigative sites so that issues can be identified, and site performance can be improved, in order to benefit a clinical trial. This is achieved by evaluating the risks at each site and then making a decision on when to send Clinical Research Associates (CRAs) to a site. It is this method of evaluating the site risks with new technologies that has created the concept of Centralized Statistical Monitoring.
Centralized Statistical Monitoring combines Centralized Monitoring with Statistical Monitoring. Centralized monitoring is when site data is evaluated for risks in real time from a single off-site location, rather than reviewing risks directly on site at each investigative site. Statistical Monitoring is the complex statistical algorithms recommended by TransCelerate to discover data outliers and anomalies, the results of which will inform various monitoring, escalation or communication actions in line with the communication plan and the Trial Master Plan (TMP). The combination of these two monitoring practices results in CSM which allows for the potential detection of any erroneous data and site misconduct across multiple sites from a central location.
CSM has an advantage over the use of Key Risk Indicators (KRIs) when reviewing data for effectively targeting on site monitoring visits. KRIs can be subjective and may only reveal specific risks that are identified in the monitoring plan, whereas, CSM is based on all data across the clinical trial, not just proposed risks. For this reason, statistical monitoring algorithms can be used on all variables that influence data quality;
from baseline and clinical data to laboratory data, treatment, and patient-reported outcomes; in fact, every bit of data collected is assessed and all variables are considered equally important. In a clinical study, everything collected should be worth collecting and, therefore, worth checking - Buyse M. Applied Clinical Trials. Mar 24, 2014
In conclusion, CSM improves data integrity of a clinical trial, CSM offers the chance to locate clinical data discrepancies that may occur during study conduct and before any database is locked so that issues may be detected before any major problems occur, which in turn reduces the chance of rejection at regulatory submissions as issues have already been discovered and resolved earlier in the process. The statistical algorithms mentioned are complex, and with the use of latest technologies, allow us to drill down into individual patient data to discover issues early on that could affect the study and risk the success of the regulatory submission. This whole approach of CSM is achieved by a vendor providing Data Quality Oversight (DQO) to a sponsor to improve data integrity. Those responsible for investigator sites become efficient in their process for the detection of site issues and are able to optimize their monitoring activities at their own discretion based on the outcomes of CSM to target sites demonstrating risk.
A further advantage of CSM is that sponsors who decide to outsource to Clinical Research Organizations (CROs) gain improved efficiencies because the CSM method aligns itself as an oversight tool to regularly check the quality of data. As mentioned, the DQO approach brings improved data integrity and sponsors are then able to select the best performing sites when conducting future clinical trials.