Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK.
Res Synth Methods. 2020 Jan;11(1):105-122. doi: 10.1002/jrsm.1377. Epub 2019 Oct 14.
Standard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, eg, when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we present theoretical arguments as well as results from simulations to illustrate how such practices might lead to exaggerated and overconfident statements regarding relative treatment effects. We discuss how the issue can be addressed via multilevel Bayesian modelling, where treatment effects are modelled exchangeably, and hence estimates are shrunk away from large values. We present a set of alternative models for network meta-analysis, and we show in simulations that in several scenarios, such models perform better than the usual network meta-analysis model.
标准的网络荟萃分析模型同时估计多种相对治疗效果。在实践中，在估计之后，这些多个估计通常会经过正式或非正式的选择程序，例如，当研究人员根据网络中表现最好的治疗效果得出结论时。在本文中，我们提出了理论论据和模拟结果，以说明这些做法如何导致对相对治疗效果的夸大和过度自信的陈述。我们讨论了如何通过多层次贝叶斯建模来解决这个问题，在这种建模中，治疗效果是可交换的建模，因此估计值会从大值中收缩。我们提出了一套用于网络荟萃分析的替代模型，并在模拟中表明，在几种情况下，这些模型的表现优于常用的网络荟萃分析模型。
