Dataset for: Simulation and data-generation for random-effects network meta-analysis of binary outcome

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data- generating models are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multi-arm trials with binary outcome. The only of the common data-generating models which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example.

统计方法的性能通常通过模拟研究予以评估。然而，针对二分类数据的网络Meta分析（network meta-analysis），现有数据生成模型要么仅能纳入双臂试验，要么局限于固定效应模型。基于配对情形下的数据生成范式，我们提出了一种用于模拟随机效应网络Meta分析的框架，该框架可纳入具有二分类结局的多臂试验。当前常见的数据生成模型中，仅有一类可直接适用于随机效应网络场景，但该模型存在极强的限制性假设。为克服上述局限，我们对该方法进行了改进，并推导得到一种以比值比（odds ratios）作为效应量的仿真流程。我们通过合成数据与一则实证案例，对该流程的性能开展了评估。