PDF(Pubmed)
Abstract：
BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：尽管大多数网络荟萃分析（NMA）使用来自随机临床试验（RCT）的汇总数据（AD），其他研究设计(例如，队列研究和其他非随机研究，NRS）可以提供有关相对治疗效果的信息。研究的个体参与者数据(IPD)，当可用时,对于调整重要的参与者特征以及更好地处理网络中的异质性和不一致性，都优于AD。
结果：我们开发了R包crossnma，以执行交叉格式（IPD和AD）和交叉设计（RCT和NRS）NMA和网络元回归（NMR）。在R环境中使用另一个吉布斯采样器（JAGS）软件将模型实现为贝叶斯三级分层模型。R包crossnma包含自动创建JAGS模型的函数，重新格式化数据（基于用户输入），评估收敛性并总结结果。我们通过使用六个比较四个治疗方法的试验网络来证明Crosnma内的工作流程。
结论：R包crossnma使用户能够在贝叶斯框架中使用不同数据类型执行NMA和NMR，并有助于纳入所有类型的证据，以识别偏差风险的差异。
结果：我们开发了R包crossnma，以执行交叉格式（IPD和AD）和交叉设计（RCT和NRS）NMA和网络元回归（NMR）。在R环境中使用另一个吉布斯采样器（JAGS）软件将模型实现为贝叶斯三级分层模型。R包crossnma包含自动创建JAGS模型的函数，重新格式化数据（基于用户输入），评估收敛性并总结结果。我们通过使用六个比较四个治疗方法的试验网络来证明Crosnma内的工作流程。
结论：R包crossnma使用户能够在贝叶斯框架中使用不同数据类型执行NMA和NMR，并有助于纳入所有类型的证据，以识别偏差风险的差异。
