A Bayesian Meta-Analysis Method for Estimating Risk Difference of Rare Events

• Activity Number: 372
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Qi Tang* and Yuanyuan Tang and Yao Yu and Shihua Wen
• Companies: and AbbVie and AbbVie and AbbVie
• Keywords: Meta-analysis ; information synthesis ; rare event ; Bayesian

Abstract

Bayesian meta-analysis has been frequently utilized for synthesizing safety and efficacy information to support landmark decision-making due to its flexibility of incorporating prior information and availability of computing software. However, when outcomes are binary and events are rare, that is zero event counts appear in either one or both study arms, conventional Bayesian meta-analysis methods cannot be directly applied unless artificial continuity corrections are made to zero cells, which may cause biases. To better model zero counts without artificial continuity corrections, we propose a novel Bayesian method, Beta prior BInomial model for Risk Differences (B-BIRD). Simulation studies have shown that compared with the exact method (Tian et al. 2009) the credible intervals obtained from B-BIRD have comparable coverage rates but shorter interval lengths when the event rates are within the (0, 1%) range.