Active Safety Surveillance Using Real-World Evidence (ASSURE) Implementation: Transparent and Reproducible Real-World Evidence Standardized Framework to Support Safety Signal Evaluation.

Authors

Kevin Haynes, Jenna Reps, Jill Hardin, Eva-Maria Didden, James Gilbert, Joel Swerdel, Justin Bohn, Mitchell M Conover, Amir Sarayani, Anthony G Sena, Emily Yost, Valerie van Baalen, Kourtney Davis, Patrick Ryan, Martijn Schuemie

Published in

Pharmacoepidemiology and drug safety. Volume 35. Issue 8. Pages e70435.

Abstract

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.
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 integration of results into safety decisions.
The initial 19 months of the ASSURE program supported 110 safety signal evaluations across 24 unique products that arose from 47 requests. 74 evaluations (67%) passed study diagnostics and yielded effect estimation results with 62 (54%) providing propensity-score adjusted comparative cohort method results and 40 (36%) providing self-controlled case series results. Many analyses failed one or more diagnostics, preventing the generation and unblinding of potentially biased results: database (7 evaluations), phenotype specification (3 evaluations), or the requisite objective study validity diagnostics (26 evaluations). These 26 evaluations (24%) all provided population characterizations of exposure, indication, and outcome, including incidence rates.
The ASSURE framework and implementation process, in collaboration with safety management teams, enables rapid response to medical product safety signals under evaluation and produces actionable RWE that supports pharmacovigilance decision making within regulatory timeframes.

PMID:
42494324
Bibliographic data and abstract were imported from PubMed on 24 Jul 2026.

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