Meta-analysis is a statistical technique that compares the effectiveness or safety of two treatments by incorporating the findings from several independent studies (DerSimonian and Laird, 1986). Network meta-analysis (NMA), also sometimes referred to as multiple (or mixed) treatment comparisons (MTCs), is the extension of the traditional meta-analysis of two treatments to simultaneous incorporation of multiple treatments, where in most cases none of the studies compared all the treatments at one time. The goal of NMA is to address the comparative effectiveness or safety of interventions while accounting for all sources of data (Hoaglin et al., 2011; Jansen et al., 2011). In such analysis, there are two types of evidence: direct and indirect (Gartlehner and Moore, 2008; Lumley, 2002). Suppose that two studies investigate Placebo (P) versus Drug A and three studies investigate Placebo versus Drug B. In this example, every observed comparison (P versus A and P versus B) is a direct evidence; the phrase “direct head-to-head comparison” refers to the case where the two treatments are active, such as A versus B within the same trial. To make an inference comparing A and B, conducting a new head-to-head trial is an ideal solution but may be infeasible in many situations, for example, when the active control groups vary for different countries or regions. Thus, we have to rely on the comparative effectiveness of safety between Drugs A and B by using only indirect information (say, the P versus A and P versus B studies). While a traditional meta-analysis depends solely on direct evidence, NMA combines direct and indirect evidence to compare multiple treatments.