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Meta‐analysis for rare events
Author(s) -
Cai Tianxi,
Parast Layla,
Ryan Louise
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3964
Subject(s) - meta analysis , rare events , context (archaeology) , inference , computer science , random effects model , poisson regression , poisson distribution , econometrics , statistics , medicine , mathematics , artificial intelligence , paleontology , population , environmental health , biology
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 © 2010 John Wiley & Sons, Ltd.