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An extended mixed‐effects framework for meta‐analysis
Author(s) -
Sera Francesco,
Armstrong Benedict,
Blangiardo Marta,
Gasparrini Antonio
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8362
Subject(s) - pooling , computer science , meta analysis , random effects model , mixed model , multivariate statistics , set (abstract data type) , generalized linear mixed model , variety (cybernetics) , multilevel model , econometrics , data mining , machine learning , artificial intelligence , mathematics , medicine , programming language
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.