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A Positive Event Dependence Model for Self‐Controlled Case Series with Applications in Postmarketing Surveillance
Author(s) -
Simpson Shawn E.
Publication year - 2013
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2012.01795.x
Subject(s) - covariate , postmarketing surveillance , context (archaeology) , computer science , inference , event (particle physics) , poisson regression , generalization , econometrics , data mining , statistics , medicine , adverse effect , artificial intelligence , machine learning , mathematics , population , physics , environmental health , quantum mechanics , biology , paleontology , mathematical analysis
Summary A primary objective in the application of postmarketing drug safety surveillance is to ascertain the relationship between time‐varying drug exposures and recurrent adverse events (AEs) related to health outcomes. The self‐controlled case series (SCCS) method is one approach to analysis in this context. 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 requirement is 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 (MI) may be at higher subsequent risk for a second. In this work, we propose the positive dependence self‐controlled case series (PD‐SCCS) method: 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. As in the SCCS model, individual‐level baseline parameters drop out of the PD‐SCCS likelihood. Data sources used for postmarketing surveillance can contain tens of millions of people, so in this situation it is particularly advantageous that PD‐SCCS avoids doing a costly estimation of individual parameters. We develop expressions for large sample inference and optimization for PD‐SCCS and compare the results of our generalized model with the more restrictive SCCS approach.