Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment

Area of Science:

• Pharmacovigilance
• Real-world data analytics
• Regulatory science

Background:

• Real-world data (RWD) offers valuable insights for drug safety surveillance.
• Ensuring trust and reliability in RWD studies is crucial for regulatory decision-making.
• The U.S. Food and Drug Administration (FDA) developed the Sentinel System to address these challenges.

Purpose of the Study:

• To describe the best practices implemented by the FDA's Sentinel System for drug safety studies using RWD.
• To outline the system's approach to ensuring transparency, reproducibility, and replicability in real-world evidence generation.

Main Methods:

• Sentinel employs a stepwise schematic for data harmonization, quality checks, query design, implementation, and reporting.
• Data partners convert source data into the Sentinel Common Data Model (SCDM).
• Rigorous quality checks are performed on transformed data before query execution.

Main Results:

• The Sentinel Common Data Model framework, data transformation codes, and quality assurance packages are publicly available.
• Sentinel's querying system allows sophisticated analyses without individual-level data exchange.
• Publicly available documentation, training, and online resources facilitate system use.

Conclusions:

• The Sentinel System demonstrates how RWD can generate regulatory-grade evidence at scale.
• Its transparent, reproducible, and replicable processes enhance trust in drug safety studies.
• Public availability of data models, codes, and study specifications supports scientific rigor.