Quality assurance within the scope of Good Clinical Practice (GCP)—what is the cost of GCP‐related activities? A survey within the Swedish Association of the Pharmaceutical Industry (LIF)'s members

Funning, Sandra; Grahnén, A; Eriksson, Kenneth; Kettis-Linblad, Åsa

doi:10.1002/qaj.433

Cited by 37 publications

(27 citation statements)

References 3 publications

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“…Carraro and Plebani 18 Found error rates of 3.2% and 7.2% in imprecise data entry of specific data points. Funning et al 5 Reported a widespread misunderstanding that the quality of a clinical trial is dependent on the degree of errors discovered. Grahnen et al 19 Illustrated that transcription errors of up to 5% in source data had no significant impact on the outcome of a trial.…”

Section: Us Department Of Health and Humanmentioning

confidence: 99%

“…SDR is a quality management method to evaluate critical processes, including those not associated with a data field in the CRF. On-site monitoring with a primary purpose of SDV is resource intensive, accounting for up to 25% of the total clinical trial cost, 5 and SDV predominantly detects random errors that likely have little impact on the results of clinical trials. Despite this evidence and encouragement from regulators to develop strategic monitoring processes, SDV remains widely engrained in the clinical trial industry as the primary focus of on-site monitoring visits.…”

Section: Introductionmentioning

confidence: 99%

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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: Us Department Of Health and Humanmentioning

confidence: 99%

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

“…SDV is costly and reported to account for an average of 25% of the entire clinical trial budget . The cost‐benefit evaluation of SDV has historically been difficult as very few data are available on the actual benefits of applying a 100% SDV approach.…”

Section: Introductionmentioning

confidence: 99%

Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials

Andersen

Byrjalsen

Bihlet

et al. 2015

Brit J Clinical Pharma

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AIMThe aim of this project was to perform an empirical evaluation of the impact of on site source data verification (SDV) on the data quality in a clinical trial database to guide an informed decision on selection of the monitoring approach. METHODSWe used data from three randomized phase III trials monitored with a combination of complete SDV or partial SDV. After database lock, individual subject data were extracted from the clinical database and subjected to post hoc complete SDV. Error rates were calculated with focus on the degree of on study monitoring and relevance and analyzed for potential impact on end points. RESULTSData from a total of 2566 subjects including more than 3 million data fields were 100% source data verified post hoc. An overall error rate of 0.45% was found. No sites had 0% errors. 100% SDV yielded an error rate of 0.27% as compared with partial SDV having an error rate of 0.53% (P < 0.0001). Comparing partly and fully monitored subjects, minor differences were identified between variables of major importance to efficacy or safety. CONCLUSIONSThe findings challenge the notion that a 0% error rate is obtainable with on site monitoring. Data indicate consistently low error rates across the three trials analyzed. The use of complete vs. partial SDV offers a marginal absolute error rate reduction of 0.26%, i.e. a need to perform complete SDV of about 370 data points to avoid one unspecified error and does not support complete SDV as a means of providing meaningful improvements in data accuracy.

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“…Results from a survey of Swedish pharmaceutical companies in 2005 suggested that fifty percent of the cost of GCP-related activities in phase III trials was due to SDV, with an estimated actual cost of SDV for a phase III program estimated as 90 million US Dollars [7]. In a different study, on-site monitoring was estimated to represent approximately 25 to 30% of costs in phase III cardiovascular clinical trials [8].…”

Section: Introductionmentioning

confidence: 99%

The Value of Source Data Verification in a Cancer Clinical Trial

et al. 2012

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BackgroundSource data verification (SDV) is a resource intensive method of quality assurance frequently used in clinical trials. There is no empirical evidence to suggest that SDV would impact on comparative treatment effect results from a clinical trial.MethodsData discrepancies and comparative treatment effects obtained following 100% SDV were compared to those based on data without SDV. Overall survival (OS) and Progression-free survival (PFS) were compared using Kaplan-Meier curves, log-rank tests and Cox models. Tumour response classifications and comparative treatment Odds Ratios (ORs) for the outcome objective response rate, and number of Serious Adverse Events (SAEs) were compared. OS estimates based on SDV data were compared against estimates obtained from centrally monitored data.FindingsData discrepancies were identified between different monitoring procedures for the majority of variables examined, with some variation in discrepancy rates. There were no systematic patterns to discrepancies and their impact was negligible on OS, the primary outcome of the trial (HR (95% CI): 1.18(0.99 to 1.41), p = 0.064 with 100% SDV; 1.18(0.99 to 1.42), p = 0.068 without SDV; 1.18(0.99 to 1.40), p = 0.073 with central monitoring). Results were similar for PFS. More extreme discrepancies were found for the subjective outcome overall objective response (OR (95% CI): 1.67(1.04 to 2.68), p = 0.03 with 100% SDV; 2.45(1.49 to 4.04), p = 0.0003 without any SDV) which was mostly due to differing CT scans.InterpretationQuality assurance methods used in clinical trials should be informed by empirical evidence. In this empirical comparison, SDV was expensive and identified random errors that made little impact on results and clinical conclusions of the trial. Central monitoring using an external data source was a more efficient approach for the primary outcome of OS. For the subjective outcome objective response, an independent blinded review committee and tracking system to monitor missing scan data could be more efficient than SDV.

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Quality assurance within the scope of Good Clinical Practice (GCP)—what is the cost of GCP‐related activities? A survey within the Swedish Association of the Pharmaceutical Industry (LIF)'s members

Cited by 37 publications

References 3 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

Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials

The Value of Source Data Verification in a Cancer Clinical Trial

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