A Generalization of the Self-Controlled Case Series Method Allowing for Positive Event Dependence

• Activity Number: 488
• Type: Invited
• Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
• Sponsor: IMS
• Author(s): Shawn E. Simpson*+
• Companies: Columbia University
• Keywords: recurrent events ; self-controlled case series ; drug safety ; event dependence ; conditional Poisson model

Abstract

The self-controlled case series (SCCS) method is used to analyze recurrent events and determine their association with time-varying covariates. It is based on a conditional Poisson regression model, which assumes that events at different time points are conditionally independent given the covariate process. This is particularly problematic when the occurrence of an event can alter the future event risk. In a clinical setting, for example, patients who have a first myocardial infarction may be at higher subsequent risk for a second. In this work we propose a generalization of SCCS that allows the occurrence of an event to increase the future event risk, yet maintains the advantages of the original model by controlling for fixed baseline covariates and relying solely on data from cases. We will focus on the application of postmarketing drug safety surveillance and examine how well our method performs in discerning the relationship between drug exposures and adverse health outcomes.