Meta-analysis in Stata using gllamm
Meta-analysis in Stata using gllamm
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DOI：
10.1002/jrsm.1157
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发表时间：
2015-12-01
影响因子：
9.8
通讯作者：
Bagos, Pantelis G.
中科院分区：
文献类型：
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作者：
Bagos, Pantelis G.
有多个用户编写的程序用于在 Stata 中执行荟萃分析（Stata 统计软件：College Station，TX：Stata Corp LP）。其中包括 metan、metareg、mvmeta 和 glst。然而，在某些情况下这些程序还不够。例如，没有软件可以进行具有相关估计的单变量荟萃分析、多级或分层荟萃分析或纵向数据荟萃分析。在这项工作中，我们通过实际应用展示了许多不同的模型，包括但不限于前面提到的模型，可以使用 gllamm 进行拟合。该软件用途广泛，可以处理各种模型，并在多个学科中进行应用。这里介绍的方法利用了这些建模功能，并基于协方差矩阵逆矩阵的 Cholesky 分解（称为广义最小二乘法）利用适当的变换来处理相关数据。前面描述的模型可以被认为是一般线性混合模型公式的特殊实例，但据作者所知，迄今为止尚未提出将所有可用的荟萃分析模型合并为特殊情况的一般说明以及将它们拟合到 Stata 中的说明。源代码可在 http: 上获取。版权所有 (c) 2015 约翰·威利父子有限公司
There are several user-written programs for performing meta-analysis in Stata (Stata Statistical Software: College Station, TX: Stata Corp LP). These include metan, metareg, mvmeta, and glst. However, there are several cases for which these programs do not suffice. For instance, there is no software for performing univariate meta-analysis with correlated estimates, for multilevel or hierarchical meta-analysis, or for meta-analysis of longitudinal data. In this work, we show with practical applications that many disparate models, including but not limited to the ones mentioned earlier, can be fitted using gllamm. The software is very versatile and can handle a wide variety of models with applications in a wide range of disciplines. The method presented here takes advantage of these modeling capabilities and makes use of appropriate transformations, based on the Cholesky decomposition of the inverse of the covariance matrix, known as generalized least squares, in order to handle correlated data. The models described earlier can be thought of as special instances of a general linear mixed-model formulation, but to the author's knowledge, a general exposition in order to incorporate all the available models for meta-analysis as special cases and the instructions to fit them in Stata has not been presented so far. Source code is available at http:. Copyright (c) 2015 John Wiley & Sons, Ltd.