PreTA: A network meta-analysis ranking metric measuring the probability of being preferable than the average treatment

Background: Network meta-analysis (NMA) produces complex outputs as many comparisons between interventions are of interest and a treatment ranking is often included in the aims of the evidence synthesis. The estimated relative treatment effects are usually displayed in a forest plot or in a league table and several ranking metrics are calculated and presented, such as the median and mean treatment ranks. Methods: We estimate relative treatment effects of each competing treatment against a fictional average treatment using the deviation from the means coding that has been used to parametrize categorical covariates in regression models. Based on this alternative parametrization of the NMA model, we present a new ranking metric (PreTA: Preferable Than Average) interpreted as the probability that a treatment is better than a fictional treatment of average performance. Results: We compare PreTA with existing probabilistic ranking metrics in 232 networks of interventions. We use two networks of interventions, a network of 18 antidepressants for acute depression and a network of four interventions for heavy menstrual bleeding, to illustrate the methodology. The agreement between PreTA and existing ranking metrics depends on the precision with which relative effects are estimated. Conclusions: PreTA is a viable alternative to existing ranking metrics which can be interpreted as the probability of being better than the average treatment. It enriches the decision-making arsenal with a ranking metric which is interpreted as a probability and considers the entire ranking distributions of the involved treatments.