Federated Meta-Analysis: A Scalable Framework for Collaborative Prognostic Research Across Clinical Trials (Motivated by the Landmark Study on High-Risk Cytogenetic Abnormalities in Multiple Myeloma by Martin F. Kaiser et al.)

Federated Meta-Analysis (FMA) is an innovative, privacy-preserving analytics approach that enables collaborative prognostic research across decentralized clinical trial networks without sharing individual patient data. By using standardized analytic protocols locally and sharing only summary-level results, FMA ensures data privacy, regulatory compliance (e.g., GDPR, HIPAA), and enhanced statistical power. This method addresses traditional meta-analytic challenges, including heterogeneity, publication bias, and limited subgroup analyses, and aligns with modern federated learning frameworks in biomedical research. Results showed consistently poorer outcomes for patients with multiple genetic abnormalities, reinforcing the clinical utility of cytogenetic profiling and highlighting the value of real-world data integration. While offering robust privacy protection and standardization, FMA faces challenges like data harmonization and potential selection bias. Future directions include integrating individual participant data meta-analysis (IPD-MA) and federated machine learning, developing common data models, and enhancing global participation to ensure comprehensive, equitable, and reproducible precision oncology research.