Robust variance estimation for random effects meta-analysis
Robust variance estimation for random effects meta-analysis
复制标题
DOI：
10.1016/j.csda.2005.07.019
复制
发表时间：
2006-08-01
影响因子：
1.8
通讯作者：
Jonkman, Jeffrey N.
中科院分区：
文献类型：
--
作者：
Sidik, Kurex;Jonkman, Jeffrey N.
在随机效应元分析中，使用加权平均值估计总体效应，其权重基于估计的边际方差。总体效应的方差通常使用估计权重和的倒数来估计，并且关于总体效应的推断通常使用这种“通常”方差估计器进行，它对估计的边际方差中的误差不具有鲁棒性。本文通过考虑一个鲁棒方差估计量与常用方差估计量和另一个不常用的样本方差加权估计量的比较，探讨了加权总体效应估计的渐近方差的鲁棒估计。通过三个实例对三种估计方法进行了论证和比较。此外，还进行了模拟研究，利用估计的权重来评估三种方差估计器的稳健性。仿真结果表明，当由于使用估计的边际方差而导致权重不精确时，鲁棒方差估计器和加权样本方差估计器都比通常的方差估计器更准确地估计整体效应的方差，这在实践中是典型的情况。因此，我们认为关于整体效应的推断应该基于稳健方差估计器或加权样本方差，这为在元分析推断中使用估计权重的做法提供了保护。(C) 2005 Elsevier B.V.版权所有
In random effects meta-ana,lysis, an overall effect is estimated using a weighted mean, with weights based on estimated marginal variances. The variance of the overall effect is often estimated using the inverse of the sum of the estimated weights, and inference about the overall effect is typically conducted using this 'usual' variance estimator, which is not robust to errors in the estimated marginal variances. In this paper, robust estimation for the asymptotic variance of a weighted overall effect estimate is explored by considering a robust variance estimator in comparison with the usual variance estimator and another less frequently used estimator, a weighted version of the sample variance. Three illustrative examples are presented to demonstrate and compare the three estimation methods. Furthermore, a simulation study is conducted to assess the robustness of the three variance estimators using estimated weights. The simulation results show that the robust variance estimator and the weighted sample variance estimator both estimate the variance of an overall effect more accurately than the usual variance estimator when the weights are imprecise due to the use of estimated marginal variances, as is typically the case in practice. Therefore, we argue that inference about an overall effect should be based on the robust variance estimator or the weighted sample variance, which provide protection against the practice of using estimated weights in meta-analytical inference. (C) 2005 Elsevier B.V. All rights reserved.