Network Meta-Analysis (NMA) is an increasingly popular evidence synthesis tool that can provide a ranking of competing treatments, also known as a treatment hierarchy. Treatment-Covariate Interactions (TCIs) can be included in NMA models to allow relative treatment effects to vary with covariate values. We show that in an NMA model that includes TCIs, treatment hierarchies should be created with a particular covariate profile in mind. We outline the typical approach for creating a treatment hierarchy in standard Bayesian NMA and show how a treatment hierarchy for a particular covariate profile can be created from an NMA model that estimates TCIs. We demonstrate our methods using a real network of studies for treatments of major depressive disorder.

翻译：网络荟萃分析（NMA）作为一种日益流行的证据综合工具，能够提供竞争性治疗的排序，即治疗层级。治疗-协变量交互作用（TCIs）可纳入NMA模型，使相对治疗效果随协变量取值而变化。本文证明，在包含TCIs的NMA模型中，治疗层级的构建需以特定协变量剖面为基准。我们概述了标准贝叶斯NMA中构建治疗层级的典型方法，并展示了如何从估计TCIs的NMA模型中推导出特定协变量剖面对应的治疗层级。最后，我们通过一个针对重度抑郁症治疗的真实研究网络来演示所提方法。