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Robust variance estimation in meta‐regression with dependent effect size estimates
Author(s) -
Hedges Larry V.,
Tipton Elizabeth,
Johnson Matthew C.
Publication year - 2010
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.5
Subject(s) - estimator , statistics , mathematics , covariate , econometrics , sample size determination , covariance , conditional independence , variance (accounting) , covariance matrix , sampling (signal processing) , regression , independence (probability theory) , computer science , accounting , filter (signal processing) , business , computer vision
Conventional meta‐analytic techniques rely on the assumption that effect size estimates from different studies are independent and have sampling distributions with known conditional variances. The independence assumption is violated when studies produce several estimates based on the same individuals or there are clusters of studies that are not independent (such as those carried out by the same investigator or laboratory). This paper provides an estimator of the covariance matrix of meta‐regression coefficients that are applicable when there are clusters of internally correlated estimates. It makes no assumptions about the specific form of the sampling distributions of the effect sizes, nor does it require knowledge of the covariance structure of the dependent estimates. Moreover, this paper demonstrates that the meta‐regression coefficients are consistent and asymptotically normally distributed and that the robust variance estimator is valid even when the covariates are random. The theory is asymptotic in the number of studies, but simulations suggest that the theory may yield accurate results with as few as 20–40 studies. Copyright © 2010 John Wiley & Sons, Ltd.