跨患者亚组共享信息，从稀疏的治疗网络中得出结论
Sharing information across patient subgroups to draw conclusions from sparse treatment networks
摘要：
网络荟萃分析（NMA）通常提供最高精度的相对效应估计。然而，稀疏的网络，很少有可用的研究和有限的直接证据可能会出现，威胁到NMA估计的鲁棒性和可靠性。在这些情况下，有限的可用信息可能会妨碍对NMA的传递性和一致性假设进行正式评估。此外，来自稀疏网络的NMA估计预计是不精确的，并且可能有偏差，因为它们依赖于在没有足够数据的情况下无效的大样本近似。我们提出了一个贝叶斯框架，允许两个网络之间的信息共享属于不同的人口分组。具体来说，我们使用来自具有大量直接证据的子组（密集网络）的结果来构建目标子组（稀疏网络）中相对效应的信息先验。这是一个两阶段的方法，在第一阶段，我们将密集网络的结果外推到稀疏网络的预期结果。这是通过使用修改后的分层NMA模型来实现的，在该模型中，我们添加了一个位置参数，该参数改变了相对效应的分布，使其适用于目标人群。在第二阶段，这些外推结果被用作稀疏网络的先验信息。我们通过一个激励精神病患者的例子来说明我们的方法。我们的方法可以对相对效应进行更精确和更稳健的估计，并且可以在稀疏网络存在的情况下充分告知临床实践。
英文摘要：
Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large sample approximations which are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.
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