Bayesian hierarchical model for safety signal detection in multiple clinical trials

Abstract

Clinical safety signal detection is of great importance in establishing the safety profile of new drugs and biologics during drug development. Bayesian hierarchical meta-analysis has proven to be a very effective method of identifying potential safety signals by considering the hierarchical structure of clinical safety data from multiple randomized clinical trials conducted under an Investigational New Drug (IND) application or Biological License Application (BLA). This type of model can integrate information across studies, for instance by grouping related adverse events using the MedDRA system-organ-class (SOC) and preferred terms (PT). It therefore improves the precision of parameter estimates compared to models that do not consider the hierarchical structure of the safety data. We propose to extend an existing four-stage Bayesian hierarchical model and consider the exposure adjusted incidence rate, assuming the number of adverse events (AEs) follows a Poisson distribution. The proposed model is applied to a real-world example, using data from three randomized clinical trials of a neuroscience drug and examine in three simulation studies motivated by real-world examples. Comparison is made between the proposed method and other existing methods. The simulation results indicate that our proposed model outperforms other two candidate models in terms of power and false detection rate.