As experts in biostatistics and data management, Quanticate have been actively involved in performing Data Quality Oversight (DQO) of subject and site data via industry leading statistical monitoring technologies. DQO is the performance of running statistical analytics using the recommended TransCelerate statistical methodologies and generating reports to improve data integrity as outliers and data anomalies are discovered.
Quanticate have created a suite of technologies that can be used in collaboration with our clinical data managers and biostatisticians, including CluePoints, SAS JMP, Medidata Edge and our own IQ visualization and collaboration Portal.
Statistical consultancy is required at the start of any clinical trial that uses a risk-based approach. Our statisticians can provide input and guidance at the trial design stage in the development of protocols and statistical analysis plans (SAPs), and provide support in the creation of a Risk Assessment Categorization Tool (RACT), which contributes to the creation of a monitoring plan which is inclusive of both generic and study-specific risk factors.
The ICH E6 (R2) addendum states that “reporting on centralized monitoring efforts should be regular and may be independent of site visits”. As a data-focused Clinical Research Organization (CRO) we can be independent of clinical monitoring activities. Our statisticians perform statistical analytic tests to provide signal data points, which are identified in the risk assessment plans, and are reviewed to ensure that the data collected are safe and of a high quality, without the distractions of supplying the monitoring of your trial, thus improving data integrity. By offering Data Quality Oversight across any trial based on the increased review of regulatory submission data to the FDA, Quanticate delivers an independent quality control using data review technologies such as CluePoints, which has become even more relevant when using a risk-based approach to monitoring.
Quanticate statisticians can create regular statistical monitoring reports which are the results of centralized statistical analysis during the DQO process. The findings are presented to sponsors at agreed milestones throughout the trial. Our statisticians can provide these reports with a list of issues that can be ranked based on Key Risk Indicators (KRIs) and potential data queries. Using these reports, sponsors can determine next steps based on the findings, for example, in determining whether sites require additional visits or if it is possible to reduce visits, as the industry accepts the move away from 100% Source Data Verification (SDV).
Examples of data sources that could be provided by a sponsor or partner CRO and analyzed by Quanticate statisticians using DQO include: Laboratory Data, Interactive Web Response Management System (IWRS), Genomic, Electronic Clinical Outcome Assessments (eCOA), Electronic Data Capture (EDC), Sensors, Medical imaging, Operational Data and Clinical Trial Management System (CTMS) Meta Data.
With a focus across the industry of making clinical trials more efficient, the recent addendum to the ICH E6 (R2) has now moved the use of risk‑based approaches from something that innovators were using, to an essential requirement of all clinical trials.
Quanticate is working with CluePoints to offer statistical review of study data to enable decisions to be made around site visits. Our selection of CluePoints is related to the FDA endorsing this technology at submission. By analyzing data using the same technology throughout the trial, our clients can minimize delays in getting their drug approved.
Regulatory authorities recognize the potential of Risk-Based Monitoring (RBM) to improve the conduct of clinical trials of all phases and have published guidance documents on RBM, encouraging sponsors to apply risk-based approaches to study management. RBM is not yet absolutely mandated by any regulator; however, both FDA and EMA accept that less data review is appropriate in lower risk studies and in lower risk periods of an initially higher risk study.
If you decide to use an RBM approach, we can help prevent data issues or surprises at submission.