TY - JOUR
T1 - Source Data Verification (SDV) quality in clinical research
T2 - A scoping review
AU - Hamidi, Muayad
AU - Eisenstein, Eric L.
AU - Garza, Maryam Y.
AU - Morales, Kayla Joan Torres
AU - Edwards, Erika M.
AU - Rocca, Mitra
AU - Cramer, Amy
AU - Singh, Gurparkash
AU - Stephenson-Miles, Kimberly A.
AU - Syed, Mahanaz
AU - Wang, Zhan
AU - Lanham, Holly
AU - Facile, Rhonda
AU - Pierson, Justine M.
AU - Collins, Cal
AU - Wei, Henry
AU - Zozus, Meredith
N1 - Publisher Copyright:
© The Author(s), 2024.
PY - 2024/5/21
Y1 - 2024/5/21
N2 - 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.
AB - 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.
KW - Clinical research
KW - Source Data Verification
KW - clinical trial monitoring
KW - quality
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U2 - 10.1017/cts.2024.551
DO - 10.1017/cts.2024.551
M3 - Article
AN - SCOPUS:85194165434
SN - 2059-8661
VL - 8
JO - Journal of Clinical and Translational Science
JF - Journal of Clinical and Translational Science
IS - 1
M1 - e101
ER -