An extended mixed-effects framework for meta-analysis

Stat Med. 2019 Dec 20;38(29):5429-5444. doi: 10.1002/sim.8362. Epub 2019 Oct 24.


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: dose-response; longitudinal; meta-analysis; mixed-effects models.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biostatistics
  • Computer Simulation
  • Humans
  • Likelihood Functions
  • Linear Models
  • Longitudinal Studies
  • Meta-Analysis as Topic*
  • Models, Statistical*
  • Multivariate Analysis
  • Network Meta-Analysis
  • Software