Network meta-analysis with class effects: a practical guide and model selection algorithm

Network meta-analysis (NMA) synthesizes data from randomized controlled trials to estimate the relative treatment effects among multiple interventions. When treatments can be grouped into classes, class effect NMA models can be used to inform recommendations at the class level and can also address challenges with sparse data and disconnected networks. Despite the potential of NMA class effects models and numerous applications in various disease areas, the literature lacks a comprehensive guide outlining the range of class effect models, their assumptions, practical considerations for estimation, model selection, checking assumptions, and presentation of results. In addition, there is no implementation available in standard software for NMA. This article aims to provide a modeling framework for class effect NMA models, propose a systematic approach to model selection, and provide practical guidance on implementing class effect NMA models using the multinma R package. We describe hierarchical NMA models that include random and fixed treatment-level effects and exchangeable and common class-level effects. We detail methods for testing assumptions of heterogeneity, consistency, and class effects, alongside assessing model fit to identify the most suitable models. A model selection strategy is proposed to guide users through these processes and assess the assumptions made by the different models. We illustrate the framework and structured approach for model selection using an NMA of 41 interventions from 17 classes for social anxiety.