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Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials
by
Given, Kyle
, Lawton, Andy
, Wilson, Brett
, Nadolny, Patrick
, Young, Stephen
, Sheetz, Nicole
, Benedict, Joanne
, Travers, Mark
, Huffman, Esther
, Florin, Lawrence
in
Accuracy
/ Audits
/ Clinical trials
/ Compliance
/ Data analysis
/ Drug Safety and Pharmacovigilance
/ Onsite
/ Pharmacotherapy
/ Pharmacy
/ Quality control
/ Sponsored Special Section by TransCelerate BioPharma: Challenging the Value of Source Data Verification
2014
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Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials
by
Given, Kyle
, Lawton, Andy
, Wilson, Brett
, Nadolny, Patrick
, Young, Stephen
, Sheetz, Nicole
, Benedict, Joanne
, Travers, Mark
, Huffman, Esther
, Florin, Lawrence
in
Accuracy
/ Audits
/ Clinical trials
/ Compliance
/ Data analysis
/ Drug Safety and Pharmacovigilance
/ Onsite
/ Pharmacotherapy
/ Pharmacy
/ Quality control
/ Sponsored Special Section by TransCelerate BioPharma: Challenging the Value of Source Data Verification
2014
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Do you wish to request the book?
Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials
by
Given, Kyle
, Lawton, Andy
, Wilson, Brett
, Nadolny, Patrick
, Young, Stephen
, Sheetz, Nicole
, Benedict, Joanne
, Travers, Mark
, Huffman, Esther
, Florin, Lawrence
in
Accuracy
/ Audits
/ Clinical trials
/ Compliance
/ Data analysis
/ Drug Safety and Pharmacovigilance
/ Onsite
/ Pharmacotherapy
/ Pharmacy
/ Quality control
/ Sponsored Special Section by TransCelerate BioPharma: Challenging the Value of Source Data Verification
2014
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Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials
Journal Article
Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials
2014
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Overview
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.
Publisher
SAGE Publications,Springer International Publishing,Springer Nature B.V
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