title: Detecting adverse drug reactions following long-term exposure in longitudinal observational data: The exposure-adjusted self-controlled case series.
abstract: Most approaches used in postmarketing drug safety monitoring, including spontaneous reporting and statistical risk identification using electronic health care records, are primarily suited to pick up only acute adverse drug effects. With the availability of increasingly larger electronic health record and administrative claims databases comes the opportunity to monitor for potential adverse effects that occur only after prolonged exposure to a drug, but analysis methods are lacking. We propose an adaptation of the self-controlled case series design that uses the notion of accumulated exposure to capture long-term effects of drugs and evaluate extensions to correct for age and recurrent events. Several variations of the approach are tested on simulated data and two large insurance claims databases. To evaluate performance a set of positive and negative control drug-event pairs was created by medical experts based on drug product labels and review of the literature. Performance on the real data was measured using the area under the receiver operator characteristics curve. The best performing method achieved an area under the receiver operator characteristics curve of 0.86 in the largest database using a spline model, adjustment for age, and ignoring recurrent events, but it appears this performance can only be achieved with very large data sets.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands m.schuemie@erasmusmc.nl.|Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, USA.
authors.forename: Martijn J
authors.initials: MJ
authors.lastname: Schuemie
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.|Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
authors.forename: Gianluca
authors.initials: G
authors.lastname: Trifirò
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: Preciosa M
authors.initials: PM
authors.lastname: Coloma
authors.affiliation: Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, USA.|Janssen Research and Development LLC, Titusville, USA.
authors.forename: Patrick B
authors.initials: PB
authors.lastname: Ryan
authors.affiliation: Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, USA.|Department of Statistics, Columbia University, New York, USA.
authors.forename: David
authors.initials: D
authors.lastname: Madigan
mesh_terms: D016208:Databases, Factual; D004348:Drug Labeling; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D006801:Humans; D007344:Insurance Claim Reporting; D007348:Insurance, Health / Q000706:statistics & numerical data; D008137:Longitudinal Studies; D064887:Observational Studies as Topic; D012372:ROC Curve; D012107:Research Design
keywords: adverse drug reactions; claims databases; methods analysis; receiver operator characteristics curve; self-controlled case series