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How local IRBs view central IRBs in the US
1Department of Psychiatry, Columbia University, 1051 Riverside Drive #15, New York, NY, USA. rlk2@columbia.edu
Local Institutional Review Boards (IRBs) are preferred by many for their "local knowledge" and trust, despite the increasing trend towards centralized IRB (cIRB) reviews. Further research is needed to balance the benefits and drawbacks of both models.
Area of Science:
- Medical Ethics
- Clinical Research Administration
- Regulatory Science
Background:
- Centralized Institutional Review Board (cIRB) reviews are increasing in the US and globally.
- The operational models and effectiveness of cIRBs remain unclear.
- Existing literature lacks comprehensive comparisons between local and centralized IRB systems.
Purpose of the Study:
- To explore the perceptions and experiences of IRB professionals regarding local and centralized IRB models.
- To identify the perceived advantages and disadvantages of both local and centralized IRB reviews.
- To inform future policy discussions on IRB review structures.
Main Methods:
- Qualitative study involving interviews with leaders and members of 34 IRBs.
- Surveyed 60 IRBs from top NIH-funded institutions.
- Response rate of 55% from contacted IRB leaders.
Main Results:
- Interviewees favored local IRBs for their "local knowledge," trust-building, and informal PI engagement.
- Local IRBs fostered a sense of responsibility towards "their own" subjects.
- While some advantages of cIRBs were noted (e.g., streamlining), interviewees expressed wariness regarding their variable quality and potential unintended consequences.
- Discrepancies between IRBs seemed linked to institutional culture and personalities, not solely regulatory intent.
Conclusions:
- The debate on IRB centralization should move beyond a binary choice to explore hybrid models and varying degrees of centralization.
- Critical tradeoffs and uncertainties exist with cIRBs, and they may not be a panacea for all IRB issues.
- Future policy must carefully weigh the benefits of local IRBs against their problems and explore systems that retain local advantages while mitigating disadvantages.
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