The choice of statistical model used in network meta-analysis (NMA) primarily depends on the type of outcome measured in the trials. In this chapter, we describe the unified generalized linear model (GLM) framework that can handle a wide range of outcomes, including those derived from binary, count, and continuous data types. We present both the fixed and random effects modelling approaches within the GLM framework, explain corresponding assumptions in the context of NMA, and discuss methodological issues and strategies on how to approach the decision between choosing fixed or random effects models. Along with the GLM framework, we present the Bayesian approach for inference, model fit assessment, and ranking treatments. Finally, we apply the Bayesian GLM models to three publicly available datasets, with binary, count, and continuous outcome, and demonstrate the use of deviance statistics to assess model fit for both fixed and random effect models.