Much ado about nothing: meta-analysis with rare events
Mike Bradburn and Jon Deeks, with Jesse Berlin and Russell Localio (Philadelphia)
For rare outcomes, meta-analysis of randomised trials may be the only way to obtain reliable evidence of the effects of healthcare interventions. However, many methods of meta-analysis are based on large sample approximations, and may be unsuitable when events are rare. Through simulation, we evaluate the performance of 12 methods for pooling rare events, considering estimability, bias, coverage and statistical power. Simulations were based on data sets from 3 case studies with between 5 and 19 trials, using baseline event rates between 0.1% and 10% and risk ratios of 1, 0.75, 0.5 and 0.2.
We found that most of the commonly used meta-analytical methods are biased when data are sparse. The bias was greatest in inverse variance and DerSimonian and Laird odds ratio and risk difference methods, and the Mantel-Haenszel odds ratio method using a 0.5 zero-cell correction. Risk difference meta-analytical methods tended to show conservative confidence interval coverage and low statistical power at low event rates. At event rates below 1% the Peto one-step odds ratio method was the least biased and most powerful method, and provided the best confidence interval coverage, provided there was no substantial imbalance in treatment and control group sizes within trials, and treatment effects are not exceptionally large. In other circumstances the Mantel-Haenszel OR without zero-cell corrections, logistic regression and the exact method, perform similarly to each other, and are less biased than the Peto method.
