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Active Safety Monitoring of New Medical Products Using Electronic Healthcare Data: Selecting Alerting Rules

Gagne, Joshua J.a,b; Rassen, Jeremy A.a; Walker, Alexander M.b,c; Glynn, Robert J.a,b; Schneeweiss, Sebastiana,b

doi: 10.1097/EDE.0b013e3182459d7d

Background: Active medical-product-safety surveillance systems are being developed to monitor many products and outcomes simultaneously in routinely collected longitudinal electronic healthcare data. These systems will rely on algorithms to generate alerts about potential safety concerns.

Methods: We compared the performance of 5 classes of algorithms in simulated data using a sequential matched-cohort framework, and applied the results to 2 electronic healthcare databases to replicate monitoring of cerivastatin-induced rhabdomyolysis. We generated 600,000 simulated scenarios with varying expected event frequency in the unexposed, alerting threshold, and outcome risk in the exposed, and compared the alerting algorithms in each scenario type using an event-based performance metric.

Results: We observed substantial variation in algorithm performance across the groups of scenarios. Relative performance varied by the event frequency and by user-defined preferences for sensitivity versus specificity. Type I error-based statistical testing procedures achieved higher event-based performance than other approaches in scenarios with few events, whereas statistical process control and disproportionality measures performed relatively better with frequent events. In the empirical data, we observed 6 cases of rhabdomyolysis among 4294 person-years of follow-up, with all events occurring among cerivastatin-treated patients. All selected algorithms generated alerts before the drug was withdrawn from the market.

Conclusions: For active medical-product-safety monitoring in a sequential matched cohort framework, no single algorithm performed best in all scenarios. Alerting algorithm selection should be tailored to particular features of a product-outcome pair, including the expected event frequencies and trade-offs between false-positive and false-negative alerting.

Supplemental Digital Content is available in the text.

From the aDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; bHarvard School of Public Health, Boston, MA; and cWorld Health Information Science Consultants, LLC, Newton, MA.

Submitted 14 June 2011; accepted 18 October 2011.

Supported by grants from NIH to Dr. Schneeweiss (RC1-LM010351, RC1-RR028231, R01-LM010213, RC4-HL106376) and the Brigham and Women's Hospital-HealthCore Methods Development Collaboration. Dr. Rassen was funded by a K-award from AHRQ (1 K01 HS018088). Drs. Gagne, Glynn, Rassen, Walker, and Schneeweiss are coinvestigators of the FDA-funded Mini-Sentinel project (PI: Dr. Richard Platt); however, no FDA funding supported this research and the opinions express here are those of the authors and not necessarily of Mini-Sentinel or FDA. Dr. Glynn has received research grants from Novartis and AstraZeneca for clinical trial design, monitoring, and analysis and gave an invited talk at Merck. Dr. Schneeweiss has received investigator-initiated grants from Pfizer and Novartis. The authors reported no financial interests related to this research.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article ( This content is not peer-reviewed or copyedited; it is the sole responsibility of the author.

Correspondence: Joshua J. Gagne, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, 1620 Tremont St, Suite 3030, Boston, MA 02120. E-mail:

© 2012 Lippincott Williams & Wilkins, Inc.