Journal
PREVENTION SCIENCE
Volume 23, Issue 3, Pages 425-438Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s11121-021-01246-3
Keywords
Meta-analysis; Meta-regression; Robust standard errors; Dependent effect sizes
Categories
Funding
- NSF [1937633]
- IES [R305B170019]
- Division Of Research On Learning
- Direct For Education and Human Resources [1937633] Funding Source: National Science Foundation
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This paper introduces a method to handle all related dependent effect sizes in a single meta-analysis model, improving efficiency and better capturing the types of data structures that occur in practice. The method also demonstrates practical application in meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults.
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.
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