Mixed membership effects in adverse event Bayesian hierarchical modelling

Hierarchical Bayesian modelling using groupings of adverse events (AEs) into system organ classes (SOC) are a set of approaches that have been proposed for analysing safety signals in clinical trials. However AEs may be the expression of more than one clinical pathology and the classification of an AE into a single SOC may not always be clear. Further, medical dictionaries may assign AEs which are difficult to classify into a generic disorders SOC. When modelling AE data using SOCs, the misclassification of an AE may lead to either a potential safety signal being missed, or a safety signal being incorrectly flagged. Mixed membership models are one approach to handling this issue. We investigate the effect of introducing mixed SOC membership into an existing Bayesian model. Results indicate that this type of approach does have a real effect on model results, and the implications are discussed.