Robust variance estimation in meta-regression with dependent effect size estimates
Robust variance estimation in meta-regression with dependent effect size estimates
复制标题
DOI：
10.1002/jrsm.5
复制
发表时间：
2010-01-01
影响因子：
9.8
通讯作者：
Johnson, Matthew C.
中科院分区：
文献类型：
--
作者：
Hedges, Larry V.;Tipton, Elizabeth;Johnson, Matthew C.
传统的荟萃分析技术依赖于这样的假设，即来自不同研究的效应量估计是独立的，并且具有已知条件方差的抽样分布。当研究基于相同的个体产生多个估计值或存在不独立的研究集群（例如由同一研究者或实验室进行的研究）时，违反了独立性假设。本文提供了一个估计的协方差矩阵的元回归系数，适用于当有集群的内部相关估计。它不假设效应量的抽样分布的具体形式，也不需要了解相关估计值的协方差结构。此外，本文证明了元回归系数是一致的，渐近正态分布，即使协变量是随机的，稳健方差估计是有效的。该理论在研究数量上是渐进的，但模拟表明，该理论只需20-40项研究就可以得出准确的结果。版权所有（C）2010约翰威利父子有限公司
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 (C) 2010 John Wiley & Sons, Ltd.