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

Muayad Hamidi¹, Eric L Eisenstein², Maryam Y Garza³

¹University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.

Journal of Clinical and Translational Science

|December 16, 2024

Summary

Assessing Source Data Verification (SDV) quality in clinical trials is crucial for risk-based monitoring. However, current literature lacks standardized methods and absolute accuracy measures for SDV, hindering effective quality by design implementation.

Keywords:

Clinical research Source Data Verification clinical trial monitoring quality

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Area of Science:

Clinical Translational Science
Clinical Trial Monitoring
Regulatory Science

Background:

Source Data Verification (SDV) is frequently discussed in Clinical Translational Science.
Published assessments of SDV quality, especially accuracy, are scarce.
This scarcity impedes the development of risk-based and reduced monitoring strategies.

Purpose of the Study:

To conduct a scoping review of the literature on SDV quality.
To specifically identify reports assessing the accuracy of SDV.
To understand the current state of knowledge regarding SDV quality metrics.

Main Methods:

A comprehensive scoping review of SDV and clinical trial monitoring literature.
Systematic screening of identified articles.
Summarization of articles based on research design, SDV context, and reported quality measures.

Main Results:

Significant heterogeneity was found in SDV methods, quality domains, and outcome measures.
Variability across studies prevented direct comparison or pooling of results.
No absolute measures of SDV accuracy were identified in the reviewed literature.

Conclusions:

A definitive characterization of SDV process accuracy is currently unavailable.
Reducing SDV without understanding the risk of undetected critical findings (sensitivity) contradicts Good Clinical Practice and Quality by Design.
There is a need for reference estimates of SDV accuracy to design effective risk-based, reduced SDV processes.