Inference for correlated effect sizes using multiple univariate meta-analyses.
Inference for correlated effect sizes using multiple univariate meta-analyses.
复制标题
DOI：
10.1002/sim.6789
复制
发表时间：
2016-04-30
影响因子：
2
通讯作者：
Jackson D
中科院分区：
文献类型：
--
作者：
Chen Y;Cai Y;Hong C;Jackson D
多元元分析是一种对不同研究的多个相关结果进行联合分析的方法，受到了广泛的关注。选择多变量方法的一个原因是它能够解释来自同一研究的多个估计之间的依赖性。然而，几乎所有现有的分析多元元分析数据的方法都需要研究内部相关性的知识，这在实践中通常是不可用的。我们提出了一种简单的非迭代方法，可用于分析多变量元分析数据集，它没有收敛问题，也不需要使用研究内相关性。我们的方法使用标准的单变量边际效应方法，但也为多个参数提供了有效的联合推理。该方法可以直接处理随机假设完全缺失情况下的缺失结果。仿真研究表明，该方法提供了无偏估计、良好估计的标准误差和具有良好覆盖概率的置信区间。此外，与已知研究内相关性的传统多元元分析相比，该方法保持了较高的相对效率。我们通过两个真实的元分析来说明所提出的方法，其中估计效应的函数是感兴趣的。©2015作者。医学统计由约翰威利和儿子有限公司出版。
Multivariate meta‐analysis, which involves jointly analyzing multiple and correlated outcomes from separate studies, has received a great deal of attention. One reason to prefer the multivariate approach is its ability to account for the dependence between multiple estimates from the same study. However, nearly all the existing methods for analyzing multivariate meta‐analytic data require the knowledge of the within‐study correlations, which are usually unavailable in practice. We propose a simple non‐iterative method that can be used for the analysis of multivariate meta‐analysis datasets, that has no convergence problems, and does not require the use of within‐study correlations. Our approach uses standard univariate methods for the marginal effects but also provides valid joint inference for multiple parameters. The proposed method can directly handle missing outcomes under missing completely at random assumption. Simulation studies show that the proposed method provides unbiased estimates, well‐estimated standard errors, and confidence intervals with good coverage probability. Furthermore, the proposed method is found to maintain high relative efficiency compared with conventional multivariate meta‐analyses where the within‐study correlations are known. We illustrate the proposed method through two real meta‐analyses where functions of the estimated effects are of interest. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.