With advancements in technology and increasing emphasis on study efficiency, the process of implementing effective SDV strategies often feels like navigating a complex and constantly evolving maze. The methods and trends surrounding SDV are shifting toward more targeted, technology-driven approaches. To keep pace, sponsors need to find a balance among data accuracy, operational efficiency, and regulatory compliance.
In this article, we review key approaches to SDV, discuss emerging trends, and offer practical tips for selecting and implementing the right SDV strategy to meet the unique needs of each study.
SDV is the process of comparing clinical trial case report forms (CRFs) against the original source data to ensure that the reported information accurately reflects the patient’s clinical experience. This process serves the following core objectives:
There are three primary approaches to SDV:
Complete, or traditional, SDV, where every single data point collected during a clinical trial is manually compared with original source data. This approach provides high levels of data accuracy, but is labor-intensive, time-consuming, and costly, particularly for large-scale trials. Moreover, there is increasing evidence that 100% SDV only minimally impacts overall data quality.1,2
A key theme of the International Council for Harmonisation of Technical Requirements of Pharmaceuticals for Human Use (ICH) Guideline for Good Clinical Practice (GCP) is alignment with quality-by-design (QbD) principles. The guideline makes it clear that the overall quality of a trial is driven by proactively designing quality into the study protocol and processes, with appropriate and fit-for-purpose use of technology.3
Increases in the complexity of clinical trials and the intensity of regulatory scrutiny are contributing to a considerable rise in study costs. Consequently, sponsors and CROs are increasingly adopting risk-based monitoring (RBM), which is a blend of partial and targeted SDV. Recognizing that not all data points carry the same level of importance, RBM focuses on the critical data points that are most likely to impact patient safety or trial outcomes. This strategic approach optimizes resource allocation without compromising data integrity and aligns with the principles of efficiency and risk mitigation. In addition, studies have shown that, compared to traditional SDV, RBM improves data quality with respect to data points that are of major importance to efficacy, safety, and trial outcomes.4
From a technological perspective, advances in electronic health records (EHRs) and electronic data capture (EDC) systems, and eSource data are transforming how SDV is conducted. Integrating automated solutions for data collection and verification with the clinical trial management system (CTMS) helps to minimize human error while enhancing efficiency and real-time access to data. Technologies such as eSource enable automated data capture from external devices and have built-in data validation rules to identify missing data and check for data consistency. With their capability to sift through vast datasets and identify anomalies, patterns, or trends, artificial intelligence (AI) and machine learning (ML) algorithms are also emerging as tools to help monitoring teams identify and prioritize areas of higher risk.
Beyond the benefits of automation, the integration of technology allows for remote source data verification. Instead of relying solely on on-site visits, which can create logistical challenges, a centralized team of clinical monitors can review data from multiple sites or even multiple studies at the same time. The adoption of remote SDV strategies has been driven by the rise of decentralized clinical trials (DCTs), accelerated by the COVID-19 pandemic.
Before choosing an SDV approach, consider disease indication, trial size, protocol complexity, and the criticality of each data element. Typically, in rare diseases, complete SDV is most appropriate as the limited number of patients enrolled augments the importance of every single data point. However, they can still benefit from a centralized data monitoring strategy, including RBQM (risk-based quality management), by implementing dynamic data reviews that consider site performance factors. Of course, if every data point is deemed critical, reduced SDV may compromise study integrity.
As rare disease studies are often limited in patient numbers, resource efficiency is essential to consider, putting heightened importance on each data point.
Highly complex trials that generate large volumes of data from diverse sources may benefit from RBM. To implement RBM, it is essential to perform a risk assessment to rationally and statistically determine which data need full verification and which can be reviewed selectively.
Less complicated trials may blend 100% verification of source data in critical-to-quality areas with remote SDV for less critical data to balance oversight and cost. Recognizing the unique demands of each study, hybrid approaches allow for flexibility by applying more stringent verification to data or phases with a higher risk of error and relying on partial or targeted SDV for less impactful data points.
To ensure comprehensive data quality management and audit readiness, SDV should also be complemented with source data review (SDR), which examines the quality of the source documentation itself.
SDV serves as a gatekeeper, monitoring study data from collection to analysis and ensuring that discrepancies are rectified, protocol deviations are identified, and the conclusions drawn from clinical trials are based on accurate and reliable information. For sponsors and CROs, understanding and implementing effective SDV strategies is crucial not only for complying with regulatory requirements, but also for maintaining the safety of patients and the integrity of trial outcomes. Staying on top of emerging trends and technologies is crucial for identifying and implementing best practices that are tailored to specific study needs and designed to enhance efficiency without sacrificing quality.
At Precision for Medicine, we help sponsors safeguard the credibility and reliability of clinical trial outcomes, through either end-to-end, full-service outsourcing or functional service provider (FSP) clinical monitoring solutions.
Explore how Precision can help identify and implement the right SDV strategy for your study.