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Activity Number: 662 - Methods for Meta-Analysis, and Longitudinal and Clustered Data
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #306794 Presentation
Title: Individual-Level Meta-Analysis for a Pooled Estimate of Incidence Rate for Rare Adverse Events
Author(s): Qing Pan* and Chen Chen and Yan Ma and Yong Ma
Companies: George Washington University and George Washington University and George Washington University and FDA
Keywords: Meta-analysis; Rare adverse events; Individual participant data; Bayesian Hierarchical model

Different clinical trials conducted by different pharmaceutical companies often study the same ingredients and a comprehensive estimate pooling information of individual trials is desirable. At FDA, Individual participant data (IPD) are available and provide a unique opportunity for individual level meta analysis, which adjusts for baseline characteristics and potential confounding variables, leading to increased power. In this study, we propose a novel method for IPD M-A for the incidence rate of rare adverse events. This exact likelihood method, built on a Poisson-Gamma hierarchical model, exhibits superior performance in terms of bias and coverage probability over the conventional approximate approaches including zero inflated models in extensive simulation studies. The methods are illustrated with a real set of trials at FDA.

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

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