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Activity Number: 164 - Random and Mixed Effect Models
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #323122 View Presentation
Title: Meta-Analysis of Incidence of Rare Events Using Individual Patient-Level Data
Author(s): Chen Chen* and Yan Ma and Yong Ma
Companies: The George Washington University and The George Washington University and FDA/CDER/OTS/OB/DB VII
Keywords: meta analysis ; rare event ; hierarchical model ; simulation study
Abstract:

Individual participant or patient data (IPD) meta-analysis (M-A) is an increasingly popular approach, which provides individual data rather than summary statistics compared to a study-level M-A. By pooling data across multiple studies, meta-analysis increases statistical power. However, existing IPD M-A methods make inferences based on large sample theory and have been criticized for generating biased results when handling rare events/outcomes, such as adverse events in drug safety studies. We propose an exact statistical method based on a Poisson-Gamma hierarchical model in a Bayesian framework to take rare events into account. In addition to the development of the theoretical methodology, we also conduct a simulation study to examine and compare the proposed method with other approaches: the naïve approach of simply combining data from all available studies ignoring the between-study heterogeneity, and a random effects model built on large number theory.


Authors who are presenting talks have a * after their name.

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