Effective Risk Management When Using eCOA and ePRO

Contributed Commentary by Steve Young, Chief Scientific Officer, CluePoints 

February 25, 2020 | The use of direct source data capture in clinical research is on the rise, both during clinic visits and remotely by patients. This includes investigator-led and patient self-assessments using laptops or tablet devices, patient diary information using hand-held mobile devices, and wearable sensors that automatically record and transmit various health-related measurements (glucose levels, heart rate, etc.). Patient diaries are increasingly used in clinical trials to capture information such as outcomes or treatment intake directly from the patients. Much of the data collected from these devices represents critical data for the study, supporting key efficacy and safety evaluations, and thus their appropriate use and functioning is of critical importance for the operational success of the study.  

Add to this the complexities of meeting the ICH GCP guidelines and it is easy to see why sponsors, sites, and CROs face so many challenges when it comes to determining the quality, accuracy, and integrity of clinical trial data, both during and after study conduct. Since the initial publication of the ICH GCP guidelines, the way clinical trials operate has changed significantly. The ICH E6 (R2) addendum, which came into effect in June 2017, encourages sponsors to develop a prioritized, risk-based approach to monitoring. However, despite the guidance, some sponsors are still unclear on how to achieve the “gold standard” for monitoring, study execution, and oversight. 

eCOA and ePRO offer many benefits to help accelerate research and improve data quality. They can provide real-time insights into patient safety and study performance, increase patient engagement and compliance, and ultimately facilitate shorter study timelines and reduced study development costs. In the context of risk-based quality management, study teams need to carefully assess, mitigate, and monitor for all operational risks associated with the deployment and use of direct data capture devices. While the use of this mobile technology may introduce unique challenges and risks, it also enables more effective risk detection. This is due to both the near real-time access to generated data as well as the audit trail information which enables evaluation of the actual timing and duration of events associated with the data.   

Let’s look at a real example of ePRO fraud, which illustrates the actual benefit of being able to effectively monitor risk. In one particular clinical trial, a site failed to provision the required ePRO devices to their patients. To cover up their mistake, the study coordinator fabricated daily diary entries for each of their 15 patients. The mis-conduct was first detected by a centralized statistical monitoring test, which discovered that over 70% of this site’s daily diary entries were being logged in the 6 pm hour locally, while the time-of-day distribution of diary entries was much broader at all other sites in the study.  

eCOA devices can also help to improve data quality oversight. Another clinical trial was utilizing eCOA devices for various investigator-led assessments in a Psoriasis study (e.g., PASI, IGA, BSA), where again the statistical evaluation of audit data exposed a site for whom the mean duration of each assessment was extremely small compared to other sites and generally considered un-realistic. This finding was paired with other statistical oddities in the assessment results, which led to confirmation of general misconduct at the site in the application of these assessments.   

Applying a data quality oversight approach efficiently identifies atypical centers, countries, regions, and patients. Clinical and operational data are comprehensively analyzed to ensure that submissions to Regulatory Authorities are of the appropriate quality and integrity. If anomalies are evident then the approach affords sponsors with the ultimate risk mitigation tool to enable Risk-Based Study Execution (RBx). The above examples reinforce an actual benefit in the use of mobile technologies and direct-source data capture in enabling more effective operational risk monitoring and quality oversight. Data quality oversight enables compliance with ICH E6 (R2) and ensures oversight no matter who is managing the trial. Sponsors can identify any discrepancies across primary endpoint and safety data with extreme data scores to focus on areas that matter most in the study and investigate outliers. 

The requirements of ICH E6 R2 sum up perfectly what centralized statistical monitoring is designed to achieve: 

“Review, that may include statistical analyses, of accumulating data from centralized monitoring can be used to: 

a) identify missing data, inconsistent data, data outliers, unexpected lack of variability and protocol deviations.

b) examine data trends such as the range, consistency, and variability of data within and across sites

c) evaluate for systematic or significant errors in data collection and reporting at a site or across sites; or potential data manipulation or data integrity problems

d) analyze site characteristics and performance metrics

e) select sites and/or processes for targeted on-site monitoring” 

It is possible to pinpoint data quality issues by employing a data quality oversight strategy. Combined with an effective risk planning process which considers the risks associated with eCOA and ePRO technologies, we should anticipate and look forward to better quality outcomes in this new paradigm, to accelerate research and protect trials from risk. 

As Chief Operations Officer for CluePoints, Steve Young oversees the research and development of advanced methods for data analytics, data surveillance and risk management, along with providing guidance to customers in RBQM methodology and best practices. Steve worked for three bio-pharmaceutical companies over a span of 15 years and an additional 6 years with eClinical solution providers Medidata and OmniComm. Steve also led a pivotal RBM-related analysis in collaboration with TransCelerate, and is currently leading RBQM best-practice initiatives for several industry RBM consortiums. Steve holds a Master’s degree in Mathematics from Villanova University. He can be reached at steve.young@CluePoints.com.

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