Abstract:
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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.
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