The high cost of source data verification (SDV), particularly in large trials, has made it a target of scrutiny over the last decade. In addition, the positive impact (ie, cost-benefit ratio of SDV) on overall data quality is often questioned. As a result, regulators and industry groups have started looking at alternative SDV approaches. This article evaluates the FDA-supported risk-based approach to SDV and provides a proposal on how to modify the SDV process without undermining the validity and integrity of the trial data. It summarizes alternative approaches to 100% SDV and evaluates the advantages and disadvantages of risk-based SDV (rSDV). The regulatory, data quality, and cost implications of each approach are considered. The economics of rSDV are discussed and the cost implications of rSDV are presented based on the results of exploratory analyses for four hypothetical trials in cardiology and oncology.
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.
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.
Background In the Gambia, three out of four women of reproductive age have undergone Female Genital Cutting (FGC). Many studies and policy advocates suggest that for such a practice that is deeply rooted in culture, a more holistic approach focusing on educating the population will have sustainable impact. This research examined whether educational level of women has an association with their attitude towards the practice of FGC. Methods Data from the 2013 Gambia Demographic Health Survey (GDHS) were analyzed. The sample included 6217 households: 10,233 females aged between 15 to 49 years and 3831 males between 15–59 years. This study focused only on women participants. The outcome variable was the attitude of women toward the practice of FGC. Results In multivariate regression model, women who were circumcised are found to have 80 times higher odds of supporting FGC [Odds Ratio = 80 (95% CI 50.93–124.4)] compared to uncircumcised women. Women with primary and secondary level education have lower odds of supporting FGC [OR = 0.73 (95% CI 0.915–0.007)) and those with higher education had the lowest odds [OR = 0.28 (95% CI 0.147–0.543)) of supporting FGC relative to women with no education at all. Conclusions Education and awareness programs targeting women who are married and older, those with less education and those who are already circumcised can help change attitudes towards the practice of FGC.
In order to assess the impact of direct data entry (DDE) on the clinical trial process, a single-site, phase 2 clinical trial, under a US investigational new drug application (IND), was performed where the clinical site entered each subject's data into an electronic data capture (EDC) system at the time of the office visit and the clinical research team implemented a risk-based monitoring (RBM) plan. For DDE, the trial used EDC for data collection and the electronic clinical trial record (eCTR) as the subject's electronic source (eSource) record. A clinical data monitoring plan (CDMoP) defined the scope of source document verification, the frequency and scope of online data review, and the criteria for when to perform onsite monitoring. As a result of this novel approach to clinical research operations, (1) there were no protocol violations as screening errors were picked up prior to treatment; (2) because there were minimal transcription errors from paper source records to the EDC system, there was a major reduction in onsite monitoring compared to comparable studies that use paper source records; (3) EDC edit checks were able to be modified early in the course of the clinical trial; (4) compliance issues were identified in real time and corrected; (5) there was rapid transparency and detection of safety issues; and (6) the clinical site indicated that there were major cost savings overall and estimated that just in terms of data entry, it was able to save 70 hours of labor by not using paper as the original source records. It is postulated that once the pharmaceutical industry adopts DDE and RBM, there will be major increases in productivity for sponsors, clinical sites, and CROs, as well as reduced time to database lock and the statistical analyses. In addition to the productivity increases, these processes and tools will improve data integrity and quality and potentially reduce overall monitoring resources and efforts by an estimated 50% to 60%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.