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

Abstract

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.

Risk-Based Monitoring: A Closer Statistical Look at Source Document Verification, Queries, Study Size Effects, and Data Quality

“…24 As a result, TransCelerate BioPharma has now developed a methodology that shifts monitoring processes from an excessive concentration on SDV to a more comprehensive risk-driven approach. 24 Lindblad et al 25 concluded that systematic central monitoring of clinical trial data can identify problems at the same trials and sites identified during FDA site inspections, and Nielsen et al 26 concluded that a “mixed approach” (ie, characterized by minimal amount of SDV) appeared to be most efficient.…”

Efficient Source Data Verification Using Statistical Acceptance Sampling: A Simulation Study

“…Other approaches that have been proposed are, for example, “targeted SDV” (ie, focus SDV on high-risk data points), “remote SDV” (allowing SDV to be performed centrally), or hybrid approaches. 1,4 –7,9 –15 The TransCelerate Biopharma Inc guidance on risk-based monitoring estimated that, of all generated queries, the percentage generated through SDV is as low as 7.8% overall and 2.4% in critical data. 20 These numbers may suggest not performing SDV at all.…”

“…[2][3][4][5][6][7][8] Consequently, its use has been a major topic of debate in discussions on the efficiency of clinical trial monitoring, and various alternatives have been proposed. 1,[4][5][6][7][9][10][11][12][13][14][15] One alternative approach (which we term random SDV) is to limit SDV to a random sample of CRF entries: Inspection of a sample may be sufficient to assess the quality of all data provided by an investigational site. 1,[4][5][6][7]11 This view is supported by the Guidelines for Good Clinical Practice, which state that ''statistically controlled sampling may be an acceptable method for selecting the data to be verified.''…”

“…2 –8 Consequently, its use has been a major topic of debate in discussions on the efficiency of clinical trial monitoring, and various alternatives have been proposed. 1,4 –7,9 –15…”

Impacts and Implications of Risk Based Monitoring: A CRO Perspective

“…For e.g. incorporation of centralized monitoring practices, where appropriate, should improve a sponsor's ability to ensure the quality of clinical trial data [1][2][3][4].…”