Dr Brown, Dr Kulldorff, Dr Davis, Dr Graham, Dr Raebel, Dr Boudreau, Dr Roblin, Dr Gurwitz, Dr Platt and Dr Gunter and Mr Pettus report no conflict of interest. Dr Chan, Dr Andrade, Dr Herrinton and Dr Smith report receiving industry research funding on issues unrelated to the study.
Article first published online: 22 OCT 2007
Copyright © 2007 John Wiley & Sons, Ltd.
Pharmacoepidemiology and Drug Safety
Volume 16, Issue 12, pages 1275–1284, December 2007
How to Cite
Brown, J. S., Kulldorff, M., Chan, K. A., Davis, R. L., Graham, D., Pettus, P. T., Andrade, S. E., Raebel, M. A., Herrinton, L., Roblin, D., Boudreau, D., Smith, D., Gurwitz, J. H., Gunter, M. J. and Platt, R. (2007), Early detection of adverse drug events within population-based health networks: application of sequential testing methods. Pharmacoepidem. Drug Safe., 16: 1275–1284. doi: 10.1002/pds.1509
Dr Platt has included Figure 3 from this manuscript as an example of active surveillance in the following recent presentations: (1) IOM Forum on Drug Safety (12 March 2007), The Future of Drug Safety—Challenges for the FDA (Drug Safety Symposium, The National Academies, IOM), (2) IOM-FDA meeting (24 April, 2007), Emerging Safety Science: A Forum on Drug Discovery, Development and Translation and (3) Keynote address at AMIA workshop on drug safety (13 June 2007), American Medical Informatics Association, Invitational Conference on Drug Safety and Pharmacovigilance.
- Issue published online: 27 NOV 2007
- Article first published online: 22 OCT 2007
- Manuscript Accepted: 7 SEP 2007
- Manuscript Revised: 29 AUG 2007
- Manuscript Received: 10 NOV 2006
- AHRQ. Grant Number: 2U18HS010391
- adverse drug event;
- sequential analysis;
- drug safety surveillance;
Active surveillance of population-based health networks may improve the timeliness of detection of adverse drug events (ADEs). Active monitoring requires sequential analysis methods. Our objectives were to (1) evaluate the utility of automated healthcare claims data for near real-time drug adverse event surveillance and (2) identify key methodological issues related to the use of healthcare claims data for real-time drug safety surveillance.
We assessed the ability to detect ADEs using historical data from nine health plans involved in the HMO Research Network's Center for Education and Research on Therapeutics (CERT). Analyses were performed using a maximized sequential probability ratio test (maxSPRT). Five drug-event pairs representing known associations with an ADE and two pairs representing ‘negative controls’ were analyzed.
Statistically significant (p < 0.05) signals of excess risk were found in four of the five drug-event pairs representing known associations; no signals were found for the negative controls. Signals were detected between 13 and 39 months after the start of surveillance. There was substantial variation in the number of exposed and expected events at signal detection.
Prospective, periodic evaluation of routinely collected data can provide population-based estimates of medication-related adverse event rates to support routine, timely post-marketing surveillance for selected ADEs. Copyright © 2007 John Wiley & Sons, Ltd.