Random-effects model for meta-analysis of clinical trials: an update.
By R. Dersimonian, Raghu N. Kacker. Contemporary Clinical Trials, 2007
2,248 citations recorded by the source corpus.
Subfields: Statistics and Probability, Surgery
View Paper: https://www.semanticscholar.org/paper/af96cb1d5c7da5418ca7b4310132b5fe7c7364b0
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In Statistics and Probability
- Julian P. T. Higgins
- Georgia Salanti
- Ian R. White
- Dan Jackson
- S. Thompson
- John P. A. Ioannidis
- J. Bowden
- R. Riley
In Surgery
- Ian R. White
- Julian P. T. Higgins
- John P. A. Ioannidis
- Dan Jackson
- S. Thompson
- David J. Spiegelhalter
- Georgia Salanti
- W. Viechtbauer
Citations in the corpus
- A re-evaluation of random-effects meta-analysis (2009, methodology): “Heterogeneity Several estimators of the heterogeneity variance 72 are available that are based on normal likelihoods, including iterative maximum likelihood and restricted maximum likelihood estimates (DerSimonian and Laird, 1986; DerSimonian and Kacker, 2007).”
- Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic (2016, background): “Many estimators of this quantity exist, see for example [29,30,31] .”
- Meta-Analysis in Clinical Trials Revisited (2015, background): “In an update to the original DerSimonian and Laird article, DerSimonian and Kacker [10] presented a unified framework for estimating σ2 and showed that the corresponding estimates for both iterative and non-iterative methods can be derived as special cases of a general method-of-moments estimate eac”
- Estimating heterogeneity variance in meta‐analysis (2013, methodology): “is an analysis-of-variance motivated estimator, which was first employed in meta-analysis by Hedges (1983) and rediscovered in DerSimonian and Kacker (2007). We unify these two important meta-analysis procedures in one class which requires knowledge only of xi and s2 i .”
- Random effects meta‐analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data (2010, background): “Under the normality assumption (1) it is statistically optimal [9].”
- Meta‐analysis using individual participant data: one‐stage and two‐stage approaches, and why they may differ (2016, methodology): “Other non-iterative estimators are also available, which have been shown to improve upon DerSimonian and Laird in some situations [19,20].”
- Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases (2020, methodology): “To comprehensively measure the social and intersectional biases in this distribution, a random-effects model designed to combine effect sizes of similar bias interventions summarizes the overall effect size of bias in the neural language model [17].”
- Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature (2012, methodology): “A random-effects model was selected over the fixedeffects model to accommodate for heterogeneity (DerSimonian & Laird, 1986 ; DerSimonian & Kacker, 2007).”
- Bacterial co-infection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis (2020, methodology): “We pooled proportion data across studies using a random effects meta-analysis with the DerSimonianeLaird method [12].”
- Bias modelling in evidence synthesis (2009, methodology): “estimation (DerSimonian and Kacker, 2007).”