A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance

Abstract

After new drugs enter the market, adverse events (AE) induced by their use must be tracked; rare AEs may not be detected during clinical trials. Some organizations have been collecting information on suspected drugs and AEs via a spontaneous reporting system to conduct post-market drug safety surveillance. These organizations use the information to detect a signal representing potential causality between drugs and AEs. The drug and AE data are often hierarchically structured. Accordingly, the tree-based scan statistic can be used as a statistical data mining method for signal detection. Most of the AE databases contain a large number of zero-count cells. Notably, not only an observational zero from the Poisson distribution, but also a true zero exists in zero-count cells. True zeros represent theoretically impossible observations or possible but unreported observations. The existing tree-based scan statistic assumes that all zeros are zero-valued observations from the Poisson distribution. Therefore, true zeros are not considered in the modeling, which can lead to bias in the inferences. In this study, we propose a tree-based scan statistic for zero-inflated count data in a hierarchical structure. According to our simulation study, in the presence of excess zeros, our proposed tree-based scan statistic provides better performance than the existing tree-based scan statistic. The two methods were illustrated using Korea Adverse Event Reporting System data from the Korea Institute of Drug Safety and Risk Management.