Greco Teresa, Edefonti Valeria, Biondi-Zoccai Giuseppe, Decarli Adriano, Gasparini Mauro, Zangrillo Alberto, Landoni Giovanni
Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy; Laboratorio di Statistica Medica, Biometria ed Epidemiologia "G. A. Maccacaro", Dipartimento di Scienze Cliniche e di Comunità, University of Milan, Milan, Italy.
Laboratorio di Statistica Medica, Biometria ed Epidemiologia "G. A. Maccacaro", Dipartimento di Scienze Cliniche e di Comunità, University of Milan, Milan, Italy.
Contemp Clin Trials. 2015 May;42:51-9. doi: 10.1016/j.cct.2015.03.005. Epub 2015 Mar 21.
Meta-analysis is a powerful tool to summarize knowledge. Pairwise or network meta-analysis may be carried out with multivariate models that account for the dependence between treatment estimates and quantify the correlation across studies. From a different perspective, meta-analysis may be viewed as a special case of multilevel analysis having a hierarchical data structure. Hence, we introduce an alternative frequentist approach, called multilevel network meta-analysis, which also allows to account for publication bias and the presence of inconsistency. We propose our approach for a three-level data structure set-up: arms within studies at the first level, studies within study designs at the second level and design configuration at the third level. This strategy differs from the traditional frequentist modeling because it works directly on an arm-based data structure. An advantage of using multilevel analysis is its flexibility, since it naturally allows to add further levels to the model and to accommodate for multiple outcome variables. Moreover, multilevel modeling may be carried out with widely available statistical programs. Finally, we compare the results from our approach with those from a Bayesian network meta-analysis on a binary endpoint which examines the effect on mortality of some anesthetics at the longest follow-up available. In addition, we compare results from the Bayesian and multilevel network meta-analysis approaches on a publicly available "Thrombolytic drugs" database. We also provide the reader with a blueprint of SAS codes for fitting the proposed models, although our approach does not rely on any specific software.
荟萃分析是总结知识的有力工具。可以使用多变量模型进行成对或网络荟萃分析，这些模型考虑了治疗估计之间的依赖性，并量化了各研究之间的相关性。从不同的角度来看，荟萃分析可以被视为具有层次数据结构的多水平分析的一个特例。因此，我们引入了一种替代的频率主义方法，称为多水平网络荟萃分析，它也能够考虑发表偏倚和不一致性的存在。我们针对三级数据结构设置提出了我们的方法：第一级是研究中的各个组，第二级是研究设计中的各项研究，第三级是设计配置。这种策略与传统的频率主义建模不同，因为它直接作用于基于组的数据结构。使用多水平分析的一个优点是其灵活性，因为它自然地允许在模型中添加更多层次，并适应多个结果变量。此外，可以使用广泛可用的统计程序进行多水平建模。最后，我们将我们方法的结果与贝叶斯网络荟萃分析在一个二元终点上的结果进行比较，该终点考察了在最长可用随访期内某些麻醉剂对死亡率的影响。此外，我们还在一个公开可用的“溶栓药物”数据库上比较了贝叶斯和多水平网络荟萃分析方法的结果。我们还为读者提供了用于拟合所提出模型的SAS代码蓝图，尽管我们的方法不依赖于任何特定软件。
