How does the DerSimonian and Laird procedure for
random effects meta-analysis compare with its more efficient but harder to compute counterparts?

Jackson, D; Bowden, J; Baker, RD

Abstract

The procedure suggested by DerSimonian and Laird is the simplest and most commonly used method for fitting the random effects model for meta-analysis. Here it is shown that, unless all studies are of similar size, this is inefficient when estimating the between-study variance, but is remarkably efficient when estimating the treatment effect. If formal inference is restricted to statements about the treatment effect, and the sample size is large, there is little point in implementing more sophisticated methodology. However, it is further demonstrated, for a simple special case, that use of the profile likelihood results in actual coverage probabilities for 95% confidence intervals that are closer to nominal levels for smaller sample sizes. Alternative methods for making inferences for the treatment effect may therefore be preferable if the sample size is small, but the DerSimonian and Laird procedure retains its usefulness for larger samples.

Citation

random effects meta-analysis compare with its more efficient but harder to compute counterparts?. Journal of Statistical Planning and Inference, 140(4), 961-970. https://doi.org/10.1016/j.jspi.2009.09.017

• Journal Article Type: Article
• Publication Date: Jan 1, 2010
• Deposit Date: Nov 18, 2011
• Journal: Journal of Statistical Planning and Inference
• Print ISSN: 0378-3758
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 140
• Issue: 4
• Pages: 961-970
• DOI: https://doi.org/10.1016/j.jspi.2009.09.017
• Publisher URL: http://dx.doi.org/10.1016/j.jspi.2009.09.017