An extended mixed‐effects framework for meta‐analysis
Top Cited Papers
- 24 October 2019
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 38 (29), 5429-5444
- https://doi.org/10.1002/sim.8362
Abstract
Standard methods for meta‐analysis are limited to pooling tasks in which a single effect size is estimated from a set of independent studies. However, this setting can be too restrictive for modern meta‐analytical applications. In this contribution, we illustrate a general framework for meta‐analysis based on linear mixed‐effects models, where potentially complex patterns of effect sizes are modeled through an extended and flexible structure of fixed and random terms. This definition includes, as special cases, a variety of meta‐analytical models that have been separately proposed in the literature, such as multivariate, network, multilevel, dose‐response, and longitudinal meta‐analysis and meta‐regression. The availability of a unified framework for meta‐analysis, complemented with the implementation in a freely available and fully documented software, will provide researchers with a flexible tool for addressing nonstandard pooling problems.Keywords
Funding Information
- Medical Research Council UK (MR/M022625/1 and MR/R013349/1)
This publication has 70 references indexed in Scilit:
- Estimating within‐study covariances in multivariate meta‐analysis with multiple outcomesStatistics in Medicine, 2012
- Multivariate meta‐analysis for non‐linear and other multi‐parameter associationsStatistics in Medicine, 2012
- Meta-Analysis for Linear and Nonlinear Dose-Response Relations: Examples, an Evaluation of Approximations, and SoftwareAmerican Journal of Epidemiology, 2011
- Multivariate meta‐analysis: Potential and promiseStatistics in Medicine, 2011
- A Re-Evaluation of Random-Effects Meta-AnalysisJournal of the Royal Statistical Society Series A: Statistics in Society, 2008
- Facilitating meta‐analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease categoryStatistics in Medicine, 2007
- Recent developments in meta‐analysisStatistics in Medicine, 2007
- Quantifying heterogeneity in a meta-analysisStatistics in Medicine, 2002
- Advanced methods in meta‐analysis: multivariate approach and meta‐regressionStatistics in Medicine, 2002
- Meta-analysis in clinical trialsControlled Clinical Trials, 1986