A Review of the Source Document Verification Process in Clinical Trials

Schuyl, Malcolm L.; Engel, Thim

doi:10.1177/009286159903300319

Cited by 12 publications

(7 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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“…12,26,47,[53][54][55] Despite recommendations for measuring and monitoring MRA data quality, 44,48,56 abstraction error usually remains unquanti ed in even the most rigorous clinical studies. 37,46,56,57 Based on the now considerable evidence, we echo recommendations in the MRA Framework for abstractor training, tools, conducive environment and ongoing measurement and control of the MRA error rate 12 and add to the calls for reporting data accuracy measures with research results. 58 Reporting a data accuracy measure with research results should be expected in the same way that con dence intervals are expected; it is di cult, and not recommended, to interpret results in their absence.…”

Section: Addressing Performance Variabilitymentioning

confidence: 95%

Comparing Medical Record Abstraction (MRA) Error Rates in an Observational Study to Pooled Rates Identified in the Data Quality Literature

Garza

Williams

Ounpraseuth

et al. 2023

Preprint

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Background: Medical record abstraction (MRA) is a commonly used method for data collection in clinical research, but is prone to error, and the influence of quality control (QC) measures is seldom and inconsistently assessed during the course of a study. We employed a novel, standardized MRA-QC framework as part of an ongoing observational study in an effort to control MRA error rates. In order to assess the effectiveness of our framework, we compared our error rates against traditional MRA studies that had not reported using formalized MRA-QC methods. Thus, the objective of this study was to compare the MRA error rates derived from the literature with the error rates found in a study using MRA as the sole method of data collection that employed an MRA-QC framework. Methods: Using a moderator meta-analysis employed with Q-test, the MRA error rates from the meta-analysis of the literature were compared with the error rate from a recent study that implemented formalized MRA training and continuous QC processes. Results: The MRA process for data acquisition in clinical research was associated with both high and highly variable error rates (70 – 2,784 errors per 10,000 fields). Error rates for the study using our MRA-QC framework were between 1.04% (optimistic, all-field rate) and 2.57% (conservative, populated-field rate) (or 104 – 257 errors per 10,000 fields), 4.00 – 5.53 percentage points less than the observed rate from the literature (p<0.0001). Conclusions: Review of the literature indicated that the accuracy associated with MRA varied widely across studies. However, our results demonstrate that, with appropriate training and continuous QC, MRA error rates can be significantly controlled during the course of a clinical research study.

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“…Source document verification (SDV), which is not clearly referred to in ICH-GCP, is a systematic process for identifying the completeness, accuracy, and validity of data collected for each study participant and requires comparison between the source data and the case report form (CRF). 8 Source data include all types of information in the original or certified copies of original records obtained during clinical trials. They are contained in the source documents, which are medical charts, laboratory reports, or work sheets, including instruments or tools for patient-reported outcomes.…”

Section: Monitoring Activitymentioning

confidence: 99%

“…Critical data are monitored in all study participants, whereas noncritical data are checked in only a small sample (for example, 25%) of CRFs at a monitoring visit. 8 …”

Section: Monitoring Activitymentioning

confidence: 99%

Monitoring in clinical trials of complementary and alternative medicine

Jung¹,

Kang²,

Kim³

2021

Integrative Medicine Research

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Background Clinical trial monitoring is an essential activity for quality assurance (QA) to ensure the protection of human rights and the reliability and transparency of the data collection process. The purpose of this article is to enhance the understanding of monitoring process and major findings in clinical trials of complementary and alternative medicine (CAM). Methods Based on International Conference on Harmonization of technical requirements for registration of pharmaceuticals for human use (ICH-GCP), we summarized main concept of monitoring process. Personal experiences on monitoring for CAM studies were also narratively described. Results In this brief article, the basic concept of QA and quality control (QC), various monitoring activities during the study process, and major findings regarding clinical trials of CAM are suggested in an effort to improve understanding of monitoring in clinical research on CAM. Conclusion When performing clinical trials for CAM-related interventions, the monitoring recommended in GCP is needed to be recognized as a mandatory element in the course of CAM research.

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A Review of the Source Document Verification Process in Clinical Trials

Cited by 12 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

Comparing Medical Record Abstraction (MRA) Error Rates in an Observational Study to Pooled Rates Identified in the Data Quality Literature

Monitoring in clinical trials of complementary and alternative medicine

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