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Analyzing collaborations in clinical trials in Korea using association rule mining
Ki Young Huh1,2, Ildae Song3
1Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Korea.
Translational and Clinical Pharmacology
|January 13, 2025
Summary
Association rule mining reveals distinct collaboration patterns in clinical trials. Phase 1 trials exhibit more exclusive site collaborations compared to other phases.
Area of Science:
- Clinical Trial Management
- Data Mining
- Health Informatics
Background:
- Understanding collaboration among multicenter trial sites is crucial for efficient trial execution.
- Collaboration dynamics can differ based on study phase and therapeutic area.
- Association rule mining offers a novel approach to analyze these complex relationships.
Purpose of the Study:
- To identify and characterize collaboration patterns among clinical trial sites in Korea.
- To explore how collaboration varies across different study phases and clinical trial domains.
- To leverage association rule mining for uncovering hidden trial site networks.
Main Methods:
- Utilized trial approval data from the Korean Ministry of Food and Drug Safety (2012-2023).
- Employed association rule mining to analyze collaboration networks.
- Categorized 11,107 clinical trials by study phase and domain, involving 209 trial sites.
Main Results:
- Phase 1 trials showed significantly higher lift metrics (mean 5.40) compared to Phase 2 (1.68) and Phase 3 (1.72) trials, indicating more exclusive collaborations.
- Phase 1 trial collaboration networks were highly condensed, particularly in Seoul and Gyeonggi-do.
- Pediatric clinical trial domains exhibited the highest mean and variability in lift metrics, suggesting unique collaboration characteristics.
Conclusions:
- Association rule mining is effective in identifying clinical trial site collaborations.
- Phase 1 trial collaborations are more exclusive than in later phases.
- Collaboration characteristics vary significantly across different clinical trial domains.
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