Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Centre for Reviews and Dissemination, University of York, York, North Yorkshire, UK.
Med Decis Making. 2021 Feb;41(2):194-208. doi: 10.1177/0272989X20983315. Epub 2021 Jan 15.
Network meta-analysis (NMA) synthesizes direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modeling a parametric dose-response relationship within an NMA framework. In this article, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks.
We created illustrative data sets by removing studies or treatments from an NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For disconnected networks, we compared MBNMA estimates with NMA estimates from an "augmented" network connected by adding studies or treatments back into the data set.
In connected networks, relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMA v. MBNMA: median = 1.13; range = 1.04-1.68). In disconnected networks, MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15 of 18 data sets. In the remaining 3 of 18 data sets, a more complex dose-response relationship was required than could be fitted with the available evidence.
Where information on multiple doses is available, MBNMA can connect disconnected networks and increase precision while making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship, which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase II trials) on multiple doses of agents where available.
网络荟萃分析（NMA）综合了多项治疗的直接和间接证据，以评估它们的相对疗效。然而，如果不做出强有力的假设，就无法对不相关的治疗方法进行比较。当有包含同一种药物的多种剂量的研究时，基于模型的 NMA（MBNMA）通过在 NMA 框架内对参数剂量-反应关系进行建模，为解决这个问题提供了一种新的解决方案。本文举例说明了几种情况下，剂量反应 MBNMA 可以连接和加强证据网络。
我们通过从偏头痛缓解的曲坦类药物 NMA 中删除研究或治疗方法来创建说明性数据集。我们拟合了具有不同剂量-反应关系的 MBNMA 模型。对于连接的网络，我们比较了 MBNMA 估计值和 NMA 估计值。对于不连接的网络，我们将 MBNMA 估计值与通过将研究或治疗方法添加回数据集而连接的“扩充”网络的 NMA 估计值进行比较。
在连接的网络中，MBNMA 的相对效果估计比 NMA 模型更精确（NMA v. MBNMA 的后验 SD 比：中位数=1.13；范围=1.04-1.68）。在不连接的网络中，MBNMA 为所有不能进行 NMA 的治疗方法提供了估计值，并且对于 18 个数据集中的 15 个数据集中与扩充网络的 NMA 估计值一致。在其余 18 个数据集中的 3 个数据集中，需要的剂量-反应关系比现有证据能够拟合的关系更复杂。
在有多种剂量信息的情况下，MBNMA 可以连接不连接的网络，在做出比替代方法更少的强假设的同时提高精度。MBNMA 依赖于正确的剂量-反应关系规范，这需要在不同剂量下有足够的数据以允许可靠估计。我们建议 NMA 的系统综述搜索并包括可用的多剂量药物的证据（包括 II 期试验）。
