Bayesian-Commensurate Approach for Safety Assessment in Clinical Studies with Count Outcomes

• Activity Number: 372
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Wei-Chen Chen* and Judy Li and John Scott and Paul Mintz
• Companies: FDA and FDA and FDA and FDA
• Keywords: Adverse Event ; Log-linear Poisson model ; Commensurate Prior ; Mixture Prior

Abstract

Bayesian commensurate approach has been discussed to be used for efficacy analyses by Hobbs et al. (2011). The approach utilizes commensurate priors to connect current and historical data thoughtfully to improve model inference and decision making for efficacy studies. We extended the approach for the assessment of safety signal in clinical studies where the records of historical adverse event are available to be borrowed. Bayesian log-linear Poisson models with mixture priors (Berry and Berry (2004), Xia et al. (2011), Gould (2013)) as well as different commensurate priors are considered and demonstrated with simulation studies. R, JAGS, and pbdR are used for the programming.