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.