Network meta-analysis is a methodology used to compare the efficacy and safety of multiple medical interventions by synthesizing data across clinical studies. The term "network" is coined because each medical intervention can be represented as a node in a network and any two nodes are linked when there is at least one study that compares the two medical interventions. Most of the current literature focuses on connected networks, which arise when there is at least one path that connects all the nodes. When this is not the case, the network is said to be disconnected. Although disconnected networks arise rather frequently, their analysis is not common practice because the standard (contrast-based) method (with fixed baseline effects) used in connected networks is thought not to work in disconnected networks. In this talk, we will provide theoretical confirmation that the standard contrast-based method with fixed baseline treatment effects does not work in disconnected networks. As an alternative to synthesizing evidence in disconnected networks, we explore empirically the suitability of using random baseline treatment effects in a contrast-based approach.