The need to combine direct and indirect evidence is increasing in clinical fields, and this is especially true when direct evidence is inconclusive. Thus, in recent years, network meta-analysis has been utilized increasingly in medicine. Network meta-analysis is a statistical method that enables comparison of multiple treatments simultaneously— by combining direct and indirect evidence of the relative treatment effects— to assess the comparative effectiveness of multiple interventions even if there are no head-to-head comparisons. Network meta-analysis has some advantages in addressing all treatments for a specific condition, comparing interventions and ranking the efficacy and safety of multiple treatments, and increasing the certainty of evidence by pooling direct and indirect evidence to generate overall estimates. The major assumption in network meta-analysis is exchange-ability of the studies, and other key assumptions include similarity, consistency, and transitivity. The Bayesian approach is used most commonly in network meta-analysis because it provides greater flexibility that allows for the use of more complex models and can produce estimates of rank probabilities. Bayesian network meta-analysis produces treatment rankings according to the probability of being the best treatment, the second best, third best, and so forth. Network meta-analysis is an interesting method that provides useful information for use in by rheumatologists in decision-making.