OBJECTIVES: Develop a method to quickly test whether a network meta-analysis evidence network is connected. BACKGROUND Network meta-analysis, or mixed treatment comparisons, is a method to combine evidence on multiple treatments that have been compared in randomised controlled trials that form a connected network of treatment comparisons.  Evidence networks consist of nodes, representing treatments, and edges, representing clinical trials comparing two treatments.  If nodes corresponding to treatments are not connected, they cannot be compared. Connectedness is typically tested by visual inspection, however this is time consuming when there are many separate networks representing different outcomes, subgroups, and scenarios, and also prone to error, especially in large networks .  Path finding algorithms can be used to automate testing for connectedness, but these are slow and inefficient. We present a fast and simple approach to test connectedness. METHODS: Our method constructs a symmetric square matrix, called the direct connection matrix, with the number of rows and columns equal to the number of treatments in the network. We fill this matrix with ones where treatments of the corresponding row and column have been compared in a trial, and zeros otherwise. The diagonal is filled with ones. Exponentiation of the matrix to the number of treatments, minus one, gives the indirect connection matrix. Non-zero entries of this final matrix represent treatment combinations that can be compared using available evidence, and vice versa. This test is easy to implement in software and can be conducted rapidly. We prove the validity of the method mathematically and illustrate with application to a network of anticoagulants for the prevention of stroke in atrial fibrillation. CONCLUSIONS: We have developed a simple and rapid test of connectedness of networks that is easy to automate and can be applied to any network meta-analysis.