Network meta-analysis using Stata: Principles and applications

There is an urgent need to translate experimental interventions from research environments into clinical practice, which requires the comparison of effectiveness and safety among several interventions used to treat the same condition and select the most appropriate (comparative effectiveness research - CER). Classically, it has been used for conventional pairwise meta-analysis to compare the effects between treatments based on head-to-head comparisons; however, data from direct comparisons are relatively limited, hampering the knowledge translation and clinical decision making. An alternative analytical approach called network meta-analysis (NMA) was developed to include in the meta-analysis not only direct comparisons but also indirect comparisons based on logical inference (and assumptions) from the network model. This approach is being used rapidly because it maximizes the way we use evidence to clinical decision making. In this lecture, we will introduce NMA definitions, relevant statistical concepts, and the frequentist NMA analytics process to be implemented using Stata with a practical example from the fibromyalgia literature.