一种用于拟合荟萃分析和荟萃回归的多元随机效应模型的基于矩阵的矩量法。
A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression.
作者信息
Jackson Dan, White Ian R, Riley Richard D
机构信息
MRC Biostatistics Unit, Cambridge CB2 0SR, UK.
出版信息
Biom J. 2013 Mar;55(2):231-45. doi: 10.1002/bimj.201200152. Epub 2013 Feb 8.
Abstract
Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example.
摘要
多变量荟萃分析的使用越来越普遍。拟合多变量随机效应模型的方法包括最大似然法、限制最大似然法、贝叶斯估计以及标准单变量矩量法的多变量推广。在此，我们提供一种新的矩量法来估计研究间协方差矩阵，其具有以下特性：（1）它允许完整或不完整的结果；（2）它允许通过元回归纳入协变量。此外，对于完整数据，它在线性变换下是不变的。我们的方法在单维度上简化为DerSimonian和Laird提出的常用单变量矩量法。我们通过模拟研究和一个实际例子来说明我们的方法，并将其与一些其他方法进行比较。
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