We show that the covariance matrix of the treatment effect estimates in a network meta-analysis can be obtained without matrix inversion using a geometric series of diffusion matrices. This property extends to the hat matrix and provides a connection between parameter estimation in regression analysis and random walks on the network graph. We also provide a number of visualization tools implemented in R.

翻译：我们证明，通过使用扩散矩阵的几何级数，可以在无需矩阵求逆的情况下获得网络荟萃分析中处理效应估计的协方差矩阵。该性质可推广至帽子矩阵，并揭示了回归分析中的参数估计与网络图上的随机游走之间的联系。我们还提供了若干内置R语言实现的可视化工具。