RESUMO
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.
RESUMO
BACKGROUND: Data quality within the clinical research enterprise can be defined as the absence of errors that matter and whether the data are fit for purpose. This concept, proposed by the Clinical Trials Transformation Initiative, resulted from a culmination of collaboration with industry, academia, patient advocates, and regulators, and it emphasizes the presence of a hierarchy of error types, resulting in a more efficient and modern data-cleaning paradigm. While source document verification (SDV) is commonly used as a quality control method in clinical research, it is disproportionately expensive and often leads to questionable benefits. Although the current literature suggests that there is a need to reduce the burden of SDV, there is no consensus on how to replace this "tried and true" practice. METHODS: This article proposes a practical risk-based monitoring approach based on published statistical evidence addressing the impact of database changes subsequent to SDV. RESULTS: The analysis clearly demonstrates minimal effects of errors and error corrections on study results and study conclusions, with diminishing effect as the study size increases, and it suggests that, on average, <8% SDV is adequate to ensure data quality, with perhaps higher SDV rates for smaller studies and virtually 0% SDV for large studies. CONCLUSIONS: It is recommended that SDV, rather than just focusing on key primary efficacy and safety outcomes, focus on data clarification queries as highly discrepant (and the riskiest) data.
