Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Graduate School for Health Sciences, University of Bern, Bern, Switzerland.
BMC Med Res Methodol. 2024 Aug 5;24(1):169. doi: 10.1186/s12874-023-02130-0.
Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network.
We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments.
The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias.
尽管多数网络荟萃分析（NMA）使用的是来自随机对照试验（RCT）的汇总数据（AD），但其他研究设计（如队列研究和其他非随机研究，NRS）也可以提供有关相对治疗效果的信息。当研究有个体参与者数据（IPD）时，这些数据比 AD 更优，因为其可用于调整重要的参与者特征，并更好地处理网络中的异质性和不一致性。
我们开发了 R 包 crossnma，用于执行跨格式（IPD 和 AD）和跨设计（RCT 和 NRS）的 NMA 和网络荟萃回归（NMR）。这些模型是作为使用 R 环境中的 Just Another Gibbs Sampler（JAGS）软件的贝叶斯三层层次模型来实现的。crossnma R 包包含了用于自动创建 JAGS 模型、重新格式化数据（基于用户输入）、评估收敛和总结结果的功能。我们通过使用比较四种治疗方法的六个试验的网络来演示 crossnma 中的工作流程。
R 包 crossnma 允许用户在贝叶斯框架中使用不同类型的数据进行 NMA 和 NMR，并促进包括所有类型的证据，承认偏倚风险的差异。
