Anomaly Detection and Cross-Center Consistency Assessment for Multi-Site Clinical Trial Quality Control

Multi-site clinical trials generate heterogeneous data requiring robust quality control mechanisms to ensure data integrity and regulatory compliance. This paper presents a comprehensive framework for automated anomaly detection and cross-center consistency assessment in distributed clinical trials. We propose a multi-layered detection approach combining rule-based thresholds, quantile drift analysis, and graph-structured consistency verification to identify protocol violations and data irregularities across trial sites. The methodology integrates dynamic threshold calibration with historical distributions, hierarchical relationship mapping between centers, investigators, and subjects, and ensemble aggregation techniques to construct audit-friendly evidence matrices. Experimental validation on multi-center trial datasets demonstrates superior detection accuracy, with sensitivity exceeding 87.3%, and early-warning capabilities that identify anomalies after analyzing only 23.5% of accumulated data, compared to 61.2% required by conventional approaches. The framework achieves 42.6% higher sensitivity compared to traditional monitoring approaches while maintaining computational efficiency suitable for real-time deployment. Implementation considerations address regulatory alignment with FDA and EMA guidelines, supporting repeatability, traceability, and auditability principles essential for clinical trial quality assurance.