Network meta-analysis results against a fictional treatment of average performance: treatment effects and ranking metric.

Nikolakopoulou, Adriani; Mavridis, Dimitris; Chiocchia, Virginia; Papakonstantinou, Theodoros; Furukawa, Toshi A; Salanti, Georgia (2021). Network meta-analysis results against a fictional treatment of average performance: treatment effects and ranking metric. Research Synthesis Methods, 12(2), pp. 161-175. Wiley 10.1002/jrsm.1463

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BACKGROUND

Network meta-analysis (NMA) produces complex outputs as many comparisons between interventions are of interest. 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.

METHODS

In this paper, we estimate relative treatment effects of each competing treatment against a fictional treatment of average performance using the 'deviation from the means' coding that has been used to parametrize categorical covariates in regression models. We then use this alternative parametrization of the NMA model to present a ranking metric (PreTA: Preferable Than Average) interpreted as the probability that a treatment is better than a fictional treatment of average performance.

RESULTS

We illustrate the alternative parametrization of the NMA model using two networks of interventions, a network of 18 antidepressants for acute depression and a network of four interventions for heavy menstrual bleeding. We also use these two networks to highlight differences among PreTA and existing ranking metrics. We further examine the agreement between PreTA and existing ranking metrics in 232 networks of interventions and conclude that their agreement depends on the precision with which relative effects are estimated.

CONCLUSIONS

A forest plot with NMA relative treatment effects using 'deviation from means' coding could complement presentation of NMA results in large networks and in absence of an obvious reference treatment. PreTA is a viable alternative to existing probabilistic ranking metrics that naturally incorporates uncertainty. This article is protected by copyright. All rights reserved.

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