Marginal Meta-Analysis for Combining Multiple Randomized Clinical Trails with Rare Events

• Activity Number: 72 - Methods for Causal and Integrative Analysis in Health Studies
• Type: Contributed
• Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Yun-Ju UMBC Cheng* and Yi Huang and Elande Baro and Guoxing Soon
• Companies: UMBC and UMBC and FDA and FDA
• Keywords: meta-analysis ; randomized clinical trials ; rare event ; safety

Abstract

Since rareness in adverse events studies is commonly seen in medical product associated RCTs, standard Meta-analysis (MA) is facing three challenges in combining those RCTs for safety evaluation: (1)Debate on whether including 0 trials or not continues, add-hoc continuation corrections were proposed to make the best use of available data and improve the MA performance. (2) MA's significant findings may change across different effect measures on the same data. (3) Under same effect measure, MA's significant findings may change after switching MA estimation methods. Moreover, the use of fixed or random effects MA relies on Cochran's heterogeneity test, which has low power with rare events. In this paper, we investigate the validity assumptions behind standard MA and relax assumptions by removing the association to a specific effect measure. We proposed a marginal MA estimator that provides not only a consistent treatment effect estimate for marginal causal effect but also address the challenges. Simulation study shows the proposed estimator performs reasonably well and Avandia as a case study. This is a joint work with Yi Huang, Elande Baro, and Guoxing Soon.