The Use of Two-Way Linear Mixed Models in Multitreatment Meta-Analysis
The Use of Two-Way Linear Mixed Models in Multitreatment Meta-Analysis
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DOI：
10.1111/j.1541-0420.2012.01786.x
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发表时间：
2012-12-01
期刊：
影响因子：
1.9
通讯作者：
Madden, L. V.
中科院分区：
文献类型：
--
作者：
Piepho, H. P.;Williams, E. R.;Madden, L. V.
荟萃分析总结了一系列试验的结果。当试验中包括两种以上的治疗方法时，当试验中测试的治疗方法不同时，不同试验的结果组合需要一些注意。为此提出了几种方法，这些方法以不同的标签为特征，例如网络荟萃分析或混合治疗比较。两种类型的线性混合模型可用于元分析。前者表示治疗的预期结果与基线治疗的对比。另一种使用经典的双向线性预测器，主要用于治疗和试验。在本文中，我们比较了两种类型的模型，并探讨在哪些条件下它们给出等价的结果。我们使用两个已发布的数据集来说明双向模型的实际优势。特别是，它表明，试验之间的异质性以及不同类型的试验之间的不一致是直截了当的。
Meta-analysis summarizes the results of a series of trials. When more than two treatments are included in the trials and when the set of treatments tested differs between trials, the combination of results across trials requires some care. Several methods have been proposed for this purpose, which feature under different labels, such as network meta-analysis or mixed treatment comparisons. Two types of linear mixed model can be used for meta-analysis. The one expresses the expected outcome of treatments as a contrast to a baseline treatment. The other uses a classical two-way linear predictor with main effects for treatment and trial. In this article, we compare both types of model and explore under which conditions they give equivalent results. We illustrate practical advantages of the two-way model using two published datasets. In particular, it is shown that between-trial heterogeneity as well as inconsistency between different types of trial is straightforward to account for.