Active Safety Surveillance Using Real-World Evidence (ASSURE) Implementation: Transparent and Reproducible Real-World Evidence Standardized Framework to Support Safety Signal Evaluation
Abstract
Purpose: To summarize experience with Johnson & Johnson's novel Active Safety Surveillance Using Real-world Evidence (ASSURE) program for producing efficient, transparent, applicable, and impactful RWE to support routine pharmacovigilance processes.
Methods: ASSURE follows a stepwise framework that encompasses database diagnostics to assess fit-for-purpose real-world data (RWD) sources in the US, France, Germany, Australia, and Japan, phenotype development, query specification, and analytic implementation, objective study validity diagnostics, and standardized reporting. Licensed RWD sources are transformed into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), from which we generate evidence for safety signal evaluation questions using a suite of open-source analytic tools made publicly available through the OHDSI community. For each signal evaluation request involving a medication exposure and outcome(s), we conducted population characterization and population-level causal effects estimation. Target medication, comparator medication, and indication cohorts utilize predefined phenotypes, when available, for rapid analytics. We provide an evaluation of the initial implementation of this framework, including results from database diagnostics, phenotype development, and objective study validity diagnostics applied prior to unblinding evidence to prevent exposing biased estimates. We defined efficiency as the time from query to results and…
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