Source Data Verification, commonly known as SDV, is a process within clinical research. SDV is a structured activity conducted to ensure the dependability and quality of information collected during a clinical study.
Understanding Source Data Verification
Source Data Verification involves a comparison of data in case report forms (CRFs) or electronic data capture (EDC) systems against original source documents. This process confirms that information transferred into the study database accurately reflects initial recordings. Source data refers to all original records and certified copies of findings, observations, or activities in a clinical trial necessary for the reconstruction and evaluation of the study.
Source documents include patient medical records, laboratory results, imaging studies, patient diaries, and consent forms. Good Clinical Practice (GCP) guidelines mandate this verification process. The aim is to ensure reported trial data are accurate, complete, and verifiable from these original documents.
The Purpose of SDV in Research
SDV upholds the reliability and accuracy of clinical trial data. It confirms that collected data aligns with events and observations made during the study. This verification helps identify and correct any discrepancies or errors that may arise during data collection or transcription.
SDV also safeguards patient safety. By verifying adverse events and adherence to treatment protocols, it ensures all study procedures have been followed. This contributes to research credibility, providing confidence in clinical trial outcomes. It also assists in meeting regulatory requirements, preventing issues that could lead to scrutiny or disapproval of trial results.
How SDV is Conducted
SDV involves trained personnel, such as clinical research associates (CRAs) or monitors. They visit clinical sites or perform remote checks to compare data in CRFs or EDC systems with original source documents. The process includes reviewing patient records, laboratory reports, and other relevant documents to confirm the accuracy of recorded data.
During this comparison, CRAs identify inconsistencies or errors between source and recorded data. Discrepancies are documented, and queries are raised with research site staff. The site investigates and resolves these queries, often by providing clarification or correcting data in the CRF. This systematic approach ensures data integrity is maintained throughout the trial.
Different Strategies for SDV
Historically, clinical trials often used 100% SDV, where every data point in the CRF was manually compared against original source documents. This exhaustive method aims for a high level of data accuracy, but it can be resource-intensive and time-consuming, particularly in large trials.
In contrast, Risk-Based Monitoring (RBM) is an adaptive approach that focuses SDV efforts strategically. RBM involves identifying and assessing risks to data quality or patient safety, then directing monitoring activities to areas of greatest need. This can mean performing targeted SDV on critical data points or at sites identified as higher risk, rather than verifying all data. RBM can optimize resource allocation, potentially reduce costs, and still maintain data quality by concentrating on the most important aspects of the trial.