As part of an investigator initiated trial (IIT), the investigator is also the study sponsor and is responsible for monitoring the trial's progress at the site. According to the International Conference on Harmonization (ICH) guideline E6 on Good Clinical Practice (GCP), the sponsor needs to ensure that 1) the rights and well-being of the human participants are protected, 2) the study is being conducted according to the approved protocol and 3) the reported data are accurate, complete, and match that of the original source. The last component is critical to ensure that the study data can be eventually submitted for FDA approval. This is often achieved using source data verification (SDV), but what exactly is SDV?

History of Source Data Verification

SDV acts as a quality control check to ensure that the data recorded in the case report form matches that of the original source. Historically, the FDA required that 100% of the data entered needed to have SDV in order to comply with their requirements for data quality and integrity. However, as you can imagine, this process is extremely resource-intensive and costly. It is estimated that about 25% of the entire clinical budget is allocated to SDV. Although there is the assumption that more SDV leads to better data, there is actually no evidence to indicate the benefits of 100% SDV. In fact, approximately 97% of data that are entered through an electronic capture system is accurate. So, in reality, only about 3% of the data needs to be monitored for accuracy and completeness.

In 2013, the FDA withdrew its original guideline requiring 100% SDV for clinical trials and instead replaced it with risk-based monitoring (RBM). The overall goal was to significantly reduce the costs associated with SDV and allow sponsors more flexibility in alternative monitoring methods. However, since the FDA did not explicitly define what constituted to RBM, the concept and implementation of RBM in a clinical trial are open to interpretation.

Source Data Review

A common strategy for RBM is a source data review (SDR), which unlike SDV, is a more holistic approach that reviews the source data, protocol compliance and ensures that source documentation and critical processes are adequate. It also evaluates the involvement of the investigators and assesses compliance in other study-related areas. The advantage of using SDR is that it reduces costs while still guaranteeing a high quality of data monitoring. Keep in mind that SDR should not replace SDV, rather the two processes should be complementary to each other. SDR helps the clinical monitoring team identify high-risk areas to which SDV should be applied.

RBM Strategies

Since the introduction of RBM, there have been several methods that have been developed to implement RBM in clinical trials. The overall approach to an RBM includes three key steps: 1) detection of critical data processes, 2) assessment of the potential risks and 3) developing a monitoring plan to address these risks. Many of the newly developed methods for RBM utilize technology and software to monitor clinical data collection and indicate potential high-risk areas that require monitoring. Some strategies implemented in RBM include centralized, remote, and reduced monitoring to efficiently allocate resources. It also usually includes statistical monitoring to analyze collected data in real-time and identify potential errors. Trigger monitoring is another strategy in which monitoring occurs when certain triggers occur, such as the occurrence of serious adverse events.

SDV is a crucial component of a clinical study to ensure the quality and integrity of the data. Still, it should be done in a way that maximizes the benefits while reducing the costs. RBM strategies, such as SDR, can help hone in on high-risk areas that require additional monitoring. The best RMB strategy to implement entirely depends on your particular needs and budget. If you need more help on which RMB strategy to use, contact Sengi for further assistance. 

REFERENCES

  1. Andersen JR, Byrjalsen I, Bihlet A, et al. Impact of Source Data Verification on Data Quality in Clinical Trials: An Empirical Post Hoc Analysis of Three Phase 3 Randomized Clinical Trials. Br J Clin Pharmacol 2015;79:660-8.
  2. Miseta E. Source Data Verification: A Quality Control Measure in Clinical Trials. Clinical leader [Available] https://www.clinicalleader.com/doc/source-data-verification-a-quality-control-measure-in-clinical-trials-0001. Accessed: October 22.
  3. Hosely M. Ensuring Quality in Risk-Based Monitoring and Data Protection. Advarra [Available] https://forteresearch.com/news/risk-based-monitoring-and-data-protection/. Accessed: October 22.
  4. Manasco P, Herbel E, Sean Bennett M, et al. Risk-Based Monitoring Versus Source Data Verification. Applied Clinical Trials 2018;27:8-13.

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The Sengi team is led by Dr. Brad Hall, a vision scientist and expert medical writer. A regular peer reviewer for several medical and ophthalmology journals, Dr. Hall has authored a multitude of articles personally, is a successful grant writer, medical writer, and master of the art of simplifying data and statistical analysis. Since launching in 2015, Sengi has provided medical writing and biostatistics analysis expertise to SMBs and researchers around the world that lacked the necessary means to share their scientific breakthroughs outside of the lab. Sengi’s work has enabled these companies to put advanced technology into the hands of those who need them most.