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Activity Number: 53
Type: Invited
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #310572 View Presentation
Title: Meta-Analysis of Proportions of Rare Events: A Comparison of Exact Likelihood Methods with Robust Variance Estimation
Author(s): Yan Ma*+ and Haitao Chu and Madhu M. Mazumdar
Companies: Weill Medical College of Cornell University and University of Minnesota and Mount Sinai School of Medicine
Keywords: beta-binomial ; meta-analysis ; proportions ; rare events ; random effects model
Abstract:

The conventional random effects model for meta-analysis of proportions approximates within-study variation using a normal distribution. Due to potential approximation bias, particularly for the estimation of rare events such as some adverse drug reactions, the conventional method is considered inferior to the exact methods based on binomial distributions. We compare two existing exact approaches---beta binomial (B-B) and normal-binomial (N-B)---through an extensive simulation study. In addition, we implement the empirical ("sandwich") estimator of variance into the two models to improve the robustness of the statistical inferences. The simulation study shows that the B-B approach tends to have substantially smaller bias and mean squared error than N-B for rare events with occurrences under five percent, while N-B outperforms B-B for relatively common events. Use of the sandwich estimator of variance improves the precision of estimation for both models. We illustrate the two approaches by applying them to two published meta-analysis from the fields of orthopedic surgery and prevention of adverse drug reactions.


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