Active Safety Surveillance Using Real-World Evidence (ASSURE) Implementation: Transparent and Reproducible Real-World...

Area of Science:

• Pharmacovigilance
• Real-world evidence (RWE) generation
• Health data analytics

Background:

• Routine pharmacovigilance relies on timely and accurate safety data.
• Generating applicable and impactful real-world evidence (RWE) is crucial for modern drug safety surveillance.
• Existing methods may face challenges in efficiency and transparency.

Purpose of the Study:

• To summarize experience with Johnson & Johnson's Active Safety Surveillance Using Real-world Evidence (ASSURE) program.
• To evaluate the program's ability to produce efficient, transparent, applicable, and impactful RWE.
• To assess the program's support for routine pharmacovigilance processes.

Main Methods:

• The ASSURE program utilizes a stepwise framework including database diagnostics, phenotype development, and standardized reporting.
• Real-world data (RWD) sources are transformed into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
• Open-source analytic tools are employed for safety signal evaluation, including population characterization and causal effects estimation.

Main Results:

• Over 19 months, ASSURE supported 110 safety signal evaluations across 24 products.
• 67% of evaluations passed study diagnostics and yielded effect estimation results.
• 24% of analyses failed diagnostics, preventing biased results, but still provided population characterizations.

Conclusions:

• The ASSURE framework enables rapid response to medical product safety signals.
• The program produces actionable RWE supporting pharmacovigilance decision-making within regulatory timeframes.
• Collaboration with safety management teams is key to the program's success.