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

By Dan Jackson, J. Bowden, Rose D. Baker. 2010

223 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cc9cd86f3f9029076ef788151b69792a7bf0de94

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Citations in the corpus

Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis (2014, background): “estimated, and this imprecision is not acknowledged [2].”
Metaprop: a Stata command to perform meta-analysis of binomial data (2014, methodology): “The procedure proposed by Dersimonian and Laird is efficient for the mean but not the heterogeneity parameter [9].”
Estimating heterogeneity variance in meta‐analysis (2013, background): “This fact may explain the large mean-squared error of σ̃2DL which was observed in many simulations (Malzahn et al., 2000; Jackson et al., 2010) and which is discussed further.”
Methods to estimate the between‐study variance and its uncertainty in meta‐analysis† (2015, methodology): “Jackson et al. (2010) investigated the asymptotic efficiency of the DL, ML, and REML methods and showed that ML estimation performs better for small amounts of between-study variance, whereas for large τ2, the DL and REML estimators are more efficient.”