Bayesian detection of potential risk using inference on blinded safety data

Safety surveillance is a critical issue for ongoing clinical trials to actively identify and evaluate important safety information. With the new regulatory emphasis on aggregate review of safety, sponsors are faced with the challenge to develop systematic and sound quantitative methods to assess risk from blinded safety data during the pre-approval period of a new therapy. To address this challenge, a novel statistical method is proposed to monitor and detect safety signals with data from blinded ongoing clinical trials, specifically for adverse events of special interest (AESI) when historical data are available to provide background rates. This new method is a two-step Bayesian evaluation of safety signals composed of a screening analysis followed by a sensitivity analysis. This Bayesian modeling framework allows making inference on the relative risk in blinded ongoing clinical trials to detect any safety signal for AESI. The blinded safety teams can use this method to assess the signal and decide if any safety signals should be escalated for unblinded review.