扩展DerSimonian和Laird的方法以进行具有随机不一致效应的网络荟萃分析。
Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects.
作者信息
Jackson Dan, Law Martin, Barrett Jessica K, Turner Rebecca, Higgins Julian P T, Salanti Georgia, White Ian R
机构信息
MRC Biostatistics Unit, Cambridge, UK.
出版信息
Stat Med. 2016 Mar 15;35(6):819-39. doi: 10.1002/sim.6752. Epub 2015 Sep 30.
Abstract
Network meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.
摘要
网络荟萃分析作为一种同时比较多种治疗方法的手段正变得越来越流行。在此，我们开发了一种新的估计方法，用于拟合具有随机不一致效应的网络荟萃分析模型。该方法是对最初由DerSimonian和Laird提出的程序的扩展。我们的方法允许网络内存在不一致性。所提出的程序是半参数的、非迭代的、快速的，并且应用研究人员很容易使用。结果发现，只要样本量足够大且不一致程度不是非常严重，该方法在模拟研究中表现令人满意。我们将我们的方法应用于两个实际例子。
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