Journal
STATISTICS IN MEDICINE
Volume 29, Issue 20, Pages 2078-2089Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/sim.3964
Keywords
cardiac complications; rosiglitazone; Poisson random effects
Categories
Funding
- National Institutes of Health [CA48061, I2B2]
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Meta-analysis provides a useful framework for combining information across related studies and has been widely utilized to combine data from clinical studies in order to evaluate treatment efficacy. More recently, meta-analysis has also been used to assess drug safety. However, because adverse events are typically rare, standard methods may not work well in this setting. Most popular methods use fixed or random effects models to combine effect estimates obtained separately for each individual study. In the context of very rare outcomes, effect estimates from individual studies may be unstable or even undefined. We propose alternative approaches based on Poisson random effects models to make inference about the relative risk between two treatment groups. Simulation studies show that the proposed methods perform well when the underlying event rates are low. The methods are illustrated using data from a recent meta-analysis (N. Engl. J. Med. 2007; 356(24):2457-2471) of 48 comparative trials involving rosiglitazone, a type 2 diabetes drug, with respect to its possible cardiovascular toxicity. Copyright (C) 2010 John Wiley & Sons, Ltd.
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