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Robust variance estimation in meta‐regression with binary dependent effects
Author(s) -
Tipton Elizabeth
Publication year - 2013
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1070
Subject(s) - statistics , estimator , meta analysis , robustness (evolution) , variance (accounting) , confidence interval , estimation , mathematics , econometrics , sample size determination , robust statistics , regression analysis , computer science , medicine , biochemistry , chemistry , accounting , management , economics , business , gene
Dependent effect size estimates are a common problem in meta‐analysis. Recently, a robust variance estimation method was introduced that can be used whenever effect sizes in a meta‐analysis are not independent. This problem arises, for example, when effect sizes are nested or when multiple measures are collected on the same individuals. In this paper, we investigate the robustness of this method in small samples when the effect size of interest is the risk difference, log risk ratio, or log odds ratio. This simulation study examines the accuracy of 95% confidence intervals constructed using the robust variance estimator across a large variety of parameter values. We report results for both estimations of the mean effect (intercept) and of a slope. The results indicate that the robust variance estimator performs well even when the number of studies is as small as 10, although coverage is generally less than nominal in the slope estimation case. Throughout, an example based on a meta‐analysis of cognitive behavior therapy is used for motivation. Copyright © 2013 John Wiley & Sons, Ltd.