A Review of the Source Document Verification Process in Clinical Trials

Schuyl, Malcolm L.; Engel, Thim

doi:10.1177/009286159502900431

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…By definition, SDV is the process of ensuring that the data reported for analyses accurately reflect the source data at the clinical trial site. 2 The information collected during a clinical trial is the source data, and it includes original records documenting clinical findings, observations, and any other activities notable within the clinical trial. 3 SDV focuses on identifying transcription errors, or those errors made in entering the source data into the case report forms (CRFs).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials

Sheetz

Wilson

Benedict

et al. 2014

Ther Innov Regul Sci

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TransCelerate has developed a risk-based monitoring methodology that transforms clinical trial monitoring from a model rooted in source data verification (SDV) to a comprehensive approach leveraging cross-functional risk assessment, technology, and adaptive on-site, off-site, and central monitoring activities to ensure data quality and subject safety. Evidence suggests that monitoring methods that concentrate on what is critical for a study and a site may produce better outcomes than do conventional SDV-driven models. This article assesses the value of SDV in clinical trial monitoring via a literature review, a retrospective analysis of data from clinical trials, and an assessment of major and critical findings from TransCelerate member company internal audits. The results support the hypothesis that generalized SDV has limited value as a quality control measure and reinforce the value of other risk-based monitoring activities.

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Section: Introductionmentioning

confidence: 99%

Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials

Sheetz

Wilson

Benedict

et al. 2014

Ther Innov Regul Sci

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show abstract

“…Too much noncritical data only leads to confusion rather than helping the data management and statistical aspects of the trial and often does not contribute substantially to addressing the main objectives of the trial. The more data that are collected, the more cumbersome and complicated the case record forms become, and the greater the likelihood of confusion and error [5].…”

Section: Impact Of Design On Monitoringmentioning

confidence: 99%

“…The auditor's responsibility is to look at a proportion of the source document verification that has been undertaken to ensure that the system works and is appropriately documented (a quality assurance activity). The auditor will assess whether the site is being monitored in accordance with the protocol specific monitoring plan and applicable SOPs, if the monitoring plan is adequate for the study and site (the frequency of monitoring visits to support the number of subjects enrolled), and if the monitor is effectively monitoring and managing the site to maintain protocol and GCP compliance [5]. Quality assurance audits are performed during enrollment and early in the trial so as to provide feedback to study teams regarding potential problems with the site, the protocol or the management of the study.…”

Section: Auditingmentioning

confidence: 99%

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The other side of clinical trial monitoring; assuring data quality and procedural adherence

Williams

2006

Clinical Trials

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“…The authors also noted, however, that ''there is still much uncertainty surrounding this process, generally referred to as source document verification.'' 2 Since then, SDV has been discussed considerably in the literature, with attention increasingly focused on risk-based monitoring; however, many of these discussions to date have been theoretical rather than data driven. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] Therefore, this study used historical data and simulation methodology to explore the risks (unidentified problems) and benefits (cost reductions) associated with specified reduced SDV scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Risk-Based Source Data Verification

Nielsen

Hyder

Deng

2014

Ther Innov Regul Sci

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Source data verification (SDV) is the process of confirming that reliable, accurate information collected from participants during a clinical trial has been reported successfully to the trial's sponsor by investigators conducting the study. Over the past 15 years or so, there has been considerable discussion in the literature of alternate (reduced and risk-based) approaches to the traditional 100% SDV approach, but these discussions have been theoretical rather than data driven. This research therefore employed data from studies conducted by the authors' company to answer the following research question: Can historical data and simulation methodology be employed to understand the risks (unidentified problems) and benefits (cost reductions) of specific reduced SDV scenarios? The methodological approach was based upon a 2010 paper published in the Drug Information Journal that proposed 4 hypothetical risk-based monitoring approaches. The paper's authors proposed well-thought-out and defined scenarios that were readily replicated in simulation algorithms. These scenarios therefore facilitated the exploration of whether real data could be used to simulate reduced SDV scenarios. These data came from 30 trials that had utilized electronic data capture and were completed between 2005 and 2010. Findings revealed that real study data can successfully be used to simulate reduced SDV scenarios, bringing a data-driven analytical approach to the determination of efficient and effective approaches to reduced SDV, hence translating our theoretical understanding to data-driven methodology.

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A Review of the Source Document Verification Process in Clinical Trials

Cited by 3 publications

References 2 publications

Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials

Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials

The other side of clinical trial monitoring; assuring data quality and procedural adherence

A Data-Driven Approach to Risk-Based Source Data Verification

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