The application of meta-analytic (multi-level) models with multiple random effects: A systematic review

In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as amodel with a randomstudy effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models and cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of multilevel meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified randomeffects models are not often used although theymight account better for the meta-analytic data structure of the analyzed datasets. Also, we found that the simulation studies done on multilevel metaanalysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed, and further suggestions are given.

Jaar van publicatie:2020

BOF-keylabel:ja

IOF-keylabel:ja

BOF-publication weight:3

CSS-citation score:3

Auteurs:International

Authors from:Higher Education

Toegankelijkheid:Open