Combining safety signals in drug clinical trials and postmarket surveillance using Bayesian modeling

Music City Center 

Clinical trials and postmarket surveillance data both provide valuable information for detecting adverse events (AEs) for drugs. Clinical trials provide estimates for incidence rate but often have insufficient sample size to detect rare adverse events. Post market surveillance data can accumulate more reports for adverse effects, but the data quality is often uneven and cannot provide direct estimate of incidence rate.
Using PD-1 inhibitors as an example, we compared the safety signal in clinical trials and postmarket surveillance. The meta-analysis for clinical trials were based on a sparse Bayesian mixed-effect model. Correspondingly, postmarket reports of adverse events were tabulated from the FDA Adverse Event Reporting System (FAERS) and a sparse Bayesian model was constructed. Finally, a Bayesian model was constructed using prior distributions based on the clinical trial data to guide the inference in the analysis of postmarket surveillance data.
Signals detected in clinical trials data have a high probability to be confirmed in the postmarket surveillance. Integrating clinical trial safety data and postmarket databases appear to be promising in enhancing signals.

adverse event

postmarket surveillance

Bayesian model

clinical trial 

Section on Risk Analysis