Session 1 - Risk-based Review: Opportunities to Expand the Risk-based Paradigm
Sunday, September 13, 2020: 8:00 AM - 1:00 PM
/ CEU Credits:0.2
SESSION CHAIRS & SPEAKERS
Regulators have been unanimously encouraging trial sponsors to incorporate risk-based approaches into their clinical trial management practices. Recent examples include:
- ICH GCP E6 (R2) Guidance (March 2018):“The methods used to assure and control the quality of the trial should be proportionate to the risks inherent in the trial and the importance of the information collected.”
- MHRA’s ‘GXP’ Data Integrity Guidance and Definitions (March 2018): “Organisations are expected to implement, design and operate a documented system that provides an acceptable state of control based on the data integrity risk with supporting rationale.”
Moreover, regulatory thinking seems to be evolving toward a pragmatic definition of data quality as “the absence of errors that matter”. Following the successful implementation of risk-based approaches to clinical trial monitoring, opportunities seem to now exist to expand the scope of their application to data validation and data review processes. Rather than validating and reviewing data to the same extent regardless of their criticality to the trial conduct and planned analyses—which has largely been the industry’s approach to date - risk-based data review would enable an efficient, fit-for-purpose strategy tailored to the specific requirements of the study.
This session will seek to explore the status of risk-based clinical data review within the industry, including current thinking and actual implementations, ideally with regulatory participation to elicit further dialogue on this topic.
1.Understand high-level regulatory expectations regarding risk-based data review
2.Describe the main principles of the risk-based data review approach
3.Discuss examples of how technology can support risk-based data review
CDM Certification Competencies::
Clinical Trials Processes, Roles, and Responsibilities,Protocol Review,Data Management Plans,Data Base Quality Control Audits
Study Data Manager,DM Management,Statistician