Evaluation of statistical methods for safety signal detection: a simulation study.

Abstract

Proactive evaluation of drug safety with systematic screening and detection is critical to protect patients' safety and important in regulatory approval of new drug indications and postmarketing communications and label renewals. In recent years, quite a few statistical methodologies have been developed to better evaluate drug safety through the life cycle of the product development. The statistical methods for flagging safety signals have been developed in two major areas - one for data collected from spontaneous reporting system, mostly in the postmarketing area, and the other for data from clinical trials. To our knowledge, the methods developed for one area have not been applied to the other one so far. In this article, we propose to utilize all such methods for flagging safety signals in both areas regardless of which specific area they were originally developed for. Therefore, we selected eight typical methods, that is, proportional reporting ratios, reporting odds ratios, the maximum likelihood ratio test, Bayesian confidence propagation neural network method, chi-square test for rates comparison, Benjamini and Hochberg procedure, new double false discovery rate control procedure, and Bayesian hierarchical mixture model for systematic comparison through simulations. The Benjamini and Hochberg procedure and new double false discovery rate control procedure perform best overall in terms of sensitivity and false discovery rate. The likelihood ratio test also performs well when the sample sizes are large.