Source Data Verification (SDV) quality in clinical research: A scoping review

Abstract

Abstract Introduction: The value of Source Data Verification (SDV) has been a common theme in the applied Clinical Translational Science literature. Yet, few published assessments of SDV quality exist even though they are needed to design risk-based and reduced monitoring schemes. This review was conducted to identify reports of SDV quality, with a specific focus on accuracy. Methods: A scoping review was conducted of the SDV and clinical trial monitoring literature to identify articles addressing SDV quality. Articles were systematically screened and summarized in terms of research design, SDV context, and reported measures. Results: The review found significant heterogeneity in underlying SDV methods, domains of SDV quality measured, the outcomes assessed, and the levels at which they were reported. This variability precluded comparison or pooling of results across the articles. No absolute measures of SDV accuracy were identified. Conclusions: A definitive and comprehensive characterization of SDV process accuracy was not found. Reducing the SDV without understanding the risk of critical findings going undetected, i.e., SDV sensitivity, is counter to recommendations in Good Clinical Practice and the principles of Quality by Design. Reference estimates (or methods to obtain estimates) of SDV accuracy are needed to confidently design risk-based, reduced SDV processes for clinical studies.