Meta-analysis for rare events

Stat Med. 2010 Sep 10;29(20):2078-89. doi: 10.1002/sim.3964.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biostatistics
  • Cardiovascular Diseases / chemically induced
  • Humans
  • Hypoglycemic Agents / adverse effects
  • Likelihood Functions
  • Meta-Analysis as Topic*
  • Models, Statistical*
  • Myocardial Infarction / chemically induced
  • Poisson Distribution
  • Risk
  • Rosiglitazone
  • Thiazolidinediones / adverse effects


  • Hypoglycemic Agents
  • Thiazolidinediones
  • Rosiglitazone