Meta-analysis with robust variance estimation: Expanding the range of working models

Resource type
Journal Article
Authors/contributors
Title
Meta-analysis with robust variance estimation: Expanding the range of working models
Abstract
In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the “metafor” and “clubSandwich” packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Publication
Prevention Science
Pages
No Pagination Specified-No Pagination Specified
Date
2021
ISSN
1573-6695
Short Title
Meta-analysis with robust variance estimation
Library Catalogue
APA PsycNet
Extra
Place: Germany Publisher: Springer
Citation
Pustejovsky, J. E., & Tipton, E. (2021). Meta-analysis with robust variance estimation: Expanding the range of working models. Prevention Science, No Pagination Specified-No Pagination Specified. https://doi.org/10.1007/s11121-021-01246-3
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