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

Dan Jackson*, Jack Bowden, Rose Baker

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

101 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)961-970
Number of pages10
JournalJournal of statistical planning and inference
Volume140
Issue number4
DOIs
Publication statusPublished - Apr 2010

Keywords

  • Confidence intervals
  • Efficiency
  • Meta-analysis
  • Profile likelihood
  • Random effects

Fingerprint

Dive into the research topics of 'How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts?'. Together they form a unique fingerprint.

Cite this