Extended Risk-Based Monitoring Model, On-Demand Query-Driven Source Data Verification, and Their Economic Impact on Clinical Trial Operations

Abstract

Background: Computer-aided data validation enhanced by centralized monitoring algorithms is a more powerful tool for data cleaning compared to manual source document verification (SDV). This fact led to the growing popularity of risk-based monitoring (RBM) coupled with reduced SDV and centralized statistical surveillance. Since RBM models are new and immature, there is a lack of consensus on practical implementation. Existing RBM models’ weaknesses include (1) mixing data monitoring and site process monitoring (ie, micro vs macro level), making it more complex, obscure, and less practical; and (2) artificial separation of RBM from data cleaning leading to resource overutilization. The authors view SDV as an essential part (and extension) of the data-validation process. Methods: This report offers an efficient and scientifically grounded model for SDV. The innovative component of this model is in making SDV ultimately a part of the query management process. Cost savings from reduced SDV are estimated using a proprietary budget simulation tool with percent cost reductions presented for four study sizes in four therapeutic areas. Results: It has been shown that an “on-demand” (query-driven) SDV model implemented in clinical trial monitoring could result in cost savings from 3% to 14% for smaller studies to 25% to 35% or more for large studies. Conclusions: (1) High-risk sites (identified via analytics) do not necessarily require a higher percent SDV. While high-risk sites require additional resources to assess and mitigate risks, in many cases these resources are likely to be allocated to non-SDV activities such as GCP, training, etc. (2) It is not necessary to combine SDV with the GCP compliance monitoring. Data validation and query management must be at the heart of SDV as it makes the RBM system more effective and efficient. Thus, focusing SDV effort on queries is a promising strategy. (3) Study size effect must be considered in designing the monitoring plan since the law of diminishing returns dictates focusing SDV on “high-value” data points. Relatively lower impact of individual errors on the study results leads to realization that larger studies require less data cleaning, and most data (including most critical data points) do not require SDV. Subsequently, the most significant economy is expected in larger studies.

Risk-based Monitoring of Clinical Trials: An Integrative Approach

“…8À12 In essence, RBM separates critical data from critical processes and provides a framework that allows site monitors to prioritize the high-value task of compliance checks over the low-value task of fixing transcription errors, which can be detected much more reliably and efficiently using computer-based techniques, such as edit checks and statistical monitoring. 13 TransCelerate research advocates that an optimal RBM system should excel in the areas of risk assessment and cross-functional risk mitigation planning, data integration, risk indicator data review, and risk and issue tracking, management, and analysis. 10 A number of commercial and home-grown solutions have been developed, which vary greatly in sophistication and completeness.…”

Research methodology and practical issues relating to the conduct of a medical device registry

“…Query-driven monitoring (centralized assessment of systematic trends) can result in cost savings between 3%−14% for smaller studies and 25%−35% for large studies. 76 In a cardiovascular outcomes simulation (assuming 5000 patients, 587 sites), the cost per patient of a pragmatic study was $8000 (compared to $22,000 in a randomized controlled trial). 77…”

Clinical trial monitoring effectiveness: Remote risk-based monitoring versus on-site monitoring with 100% source data verification

“…Reports subsequently showed that a risk-based monitoring approach, which includes targeted SDV and centralized monitoring, was effective in ensuring data integrity and detecting quality issues, and theoretically saving monitoring costs. 13–18 However, most of these reports were retrospective in design and only evaluated partial effectiveness of clinical trial monitoring. Although a few sizable prospective monitoring sub-studies have been conducted to evaluate the effectiveness of on-site monitoring methodologies, including risk-based monitoring, they did not clearly show the overall impact of the monitoring strategies on the reliability of trial results.…”