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

DOI10.1016/j.jspi.2009.09.017zbMath1179.62155OpenAlexW1988172012MaRDI QIDQ2655056

Dan Jackson, Jack Bowden, Rose D. Baker

Publication date: 22 January 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2009.09.017

zbMATH Keywords

confidence intervalsefficiencyrandom effectsprofile likelihoodmeta-analysis

Mathematics Subject Classification ID

Related Items (8)

Outlier detection and accommodation in meta-regression models ⋮ Quantifying uncertainty in method of moments estimates of the heterogeneity variance in random effects meta‐analysis ⋮ Jointly pooling aggregated effect sizes and their standard errors from studies with continuous clinical outcomes ⋮ Inference using an exact distribution of test statistic for random-effects meta-analysis ⋮ Estimating common mean and heterogeneity variance in two study case meta-analysis ⋮ Restricted likelihood representation and decision-theoretic aspects of meta-analysis ⋮ Estimating common parameters in heterogeneous random effects models ⋮ An efficient numerical algorithm for exact inference in meta analysis

Cites Work

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• Using Journal Impact Factors to Correct for the Publication Bias of Medical Studies
• Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood
• Nonparametric estimation of heterogeneity variance for the standardised difference used in meta-analysis
• Publication Bias and Meta-Analysis for 2×2 Tables: An Average Markov Chain Monte Carlo EM Algorithm
• Some general points in estimating heterogeneity variance with the DerSimonian-Laird estimator
• Minimum Variance Estimation Without Regularity Assumptions

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