Risk-Based Source Data Verification Approaches: Pros and Cons
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Vadim Tantsyrra, MS, MA,
DrPH (c) Director, Data Management, Infinity Pharmaceuticals Imogene Grimes, PhD Vice President. Statistics and Data Management, Infinity Pharmaceuticals Jrlrs Mitchel, PhD, MBA President. Target Health Inc Kayo Fendt, MSPH Director, Regulatory and Quality. Duke Clinical Research Institute Sorgiy Sirichenko, MS Data Strategist, PAREXEL International Joel Waters, MBA Associate Director, PDG Technical Support. PAREXEL International Jim Crowe, MS Director, Clinical and Nonclinical Statistics, PAREXEL International Barbara Tardiff, MD, MBA Corporate Vice President, Data Sciences, PAREXEL lnterna tional
Key Words Source data verification; Data quality; Site monitoring; Risk-based approach; Key variables Carrespondonce Address Vadim Tantsyura. MS. MA, DrPH (c), 68 Judith Drive, Danbury. CT 068 I I (email: vadim. tantsyura @gmail .corn).
Risk-based Source Data Verification Approaches: Pros and Cons The high cost of source data vmjication (SDV), particulariy in large trials, has made it a target of scrutiny over the last decade. In addition, the positive impact (ie, cost-benefit ratio of SDV) on overall data quality is often questioned. As a result, regulators and industry groups have started looking at alternative SDV approaches. This article evaluates the FDAsupported risk-based approach to SDV and provides a proposal on how to modifv the SDV process without undermining the validity and
integrity of the trial data. It summarizes alternative approaches to 100%SDV and evaluates the advantages and disadvantages of riskbased SDV (rSDV). The regulatory, data quality, and cost implications of each approach are considered. The economics of rSDV are discussed and the cost implications of rSDV are presented based on the results of exploratory analyses for four hypothetical trials in cardiology and oncology.
monitoring, before, during, and after the trial;
INTRODUCTION Source data verification (SDV)is one of many quality steps employed by sponsors and CROs to ensure clinical trial data validity. Other steps include training of clinical investigators and study personnel on the protocol and case report forms (CRFs), data validation procedures using programmed and manual data reviews, and, finally, audits of clinical sites. SDV,in particular, allows for the evaluation of the conformity of clinical trial data presented in the CRF with data collected in the study subject source record at the clinical trial research site. SDV also ensures that "the reported trial data are accurate, complete, and verifiable from source documents" (1). As a component of study quality management, SDV adds to the scientific and ethical integrity of the clinical trial. The extent of the SDV is often debated, as the GCP (ICH E6, 5.18.3 Extent and Nature of Monitoring) language leaves much room for interpretation: The sponsor should ensure that the trials are adequately monitored. The sponsor should determine the appropriate extent and nature of monitoring. The determination of the extent and nature of monitoring should be based
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