Extended Risk-Based Monitoring Model, On-Demand Query-Driven Source Data Verification, and Their Economic Impact on...

Area of Science:

• Clinical Trials
• Data Management
• Regulatory Compliance

Background:

• Centralized monitoring and computer-aided data validation offer superior data cleaning compared to manual source document verification (SDV).
• Risk-based monitoring (RBM) is gaining traction, but practical implementation challenges persist due to immature models that often conflate micro and macro monitoring levels and separate RBM from data cleaning.
• The authors advocate for viewing SDV as an integral component of the overall data validation process.

Purpose of the Study:

• To present an efficient, scientifically grounded model for source document verification (SDV).
• To integrate SDV into the query management process for enhanced clinical trial monitoring.
• To estimate cost savings associated with reduced SDV using a simulation tool.

Main Methods:

• Developed an innovative SDV model that incorporates it into the query management workflow.
• Utilized a proprietary budget simulation tool to estimate cost reductions.
• Analyzed cost savings across four therapeutic areas and four study sizes.

Main Results:

• An "on-demand" (query-driven) SDV model can achieve cost savings ranging from 3%–14% in smaller studies.
• Larger studies can realize substantial cost savings of 25%–35% or more with this model.
• High-risk sites do not necessarily require increased SDV; resources may be better allocated to GCP training and risk mitigation.

Conclusions:

• SDV should be integrated with data validation and query management, not GCP compliance monitoring, for improved RBM effectiveness.
• Focusing SDV efforts on data queries is a promising strategy for efficiency.
• Study size impacts monitoring plans; larger studies benefit most from SDV focused on high-value data points due to diminishing returns on data cleaning efforts.