Hierarchical Bayesian Approaches for Detecting Inconsistency in Network Meta-Analysis

Network meta-analysis (NMA), also known as multiple treatment comparisons, is commonly used to incorporate and compare direct and indirect evidence comparing treatments. With recent advances in methods and software, Bayesian approaches to NMA have become quite popular and allow models of previously unanticipated complexity. However, when direct and indirect evidence conflict in a NMA, the model is said to suffer from inconsistency. Current inconsistency detection in NMA is usually based on contrast-based (CB) models; however, this approach has certain limitations. In this work, we propose an arm-based (AB) random effects model, where we detect discrepancy of direct and indirect evidence for comparing two treatments using the fixed effects in the model, while flagging extreme trials using the random effects. Our approaches permit users to address issues previously tackled via CB models. We compare sources of inconsistency identified by our approach and existing loop-based CB methods using real and simulated datasets, and demonstrate that our methods can offer more powerful inconsistency detection