Articlegold2022

Answering complex hierarchy questions in network meta-analysis

Theodoros Papakonstantinou · Georgia Salanti · Dimitris Mavridis · Gerta Rücker · Guido Schwarzer · Adriani Nikolakopoulou

10 citations0/0 files storedBMC Medical Research Methodology

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Meta-analysis and systematic reviews Statistical Methods in Clinical Trials Delphi Technique in Research

Abstract

BACKGROUND: Network meta-analysis estimates all relative effects between competing treatments and can produce a treatment hierarchy from the most to the least desirable option according to a health outcome. While about half of the published network meta-analyses present such a hierarchy, it is rarely the case that it is related to a clinically relevant decision question. METHODS: We first define treatment hierarchy and treatment ranking in a network meta-analysis and suggest a simulation method to estimate the probability of each possible hierarchy to occur. We then propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. The steps of the approach are summarized as follows: a) a question of clinical relevance is defined, b) the hierarchies that satisfy the defined question are collected and c) the frequencies of the respective hierarchies are added; the resulted sum expresses the certainty of the defined set of criteria to hold. We then show how the frequencies of all possible hierarchies relate to common ranking metrics. RESULTS: We exemplify the method and its implementation using two networks. The first is a network of four treatments for chronic obstructive pulmonary disease where the most probable hierarchy has a frequency of 28%. The second is a network of 18 antidepressants, among which Vortioxetine, Bupropion and Escitalopram occupy the first three ranks with frequency 19%. CONCLUSIONS: The developed method offers a generalised approach of producing treatment hierarchies in network meta-analysis, which moves towards attaching treatment ranking to a clear decision question, relevant to all or a subset of competing treatments.

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References

2010Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial 2004Combination of direct and indirect evidence in mixed treatment comparisons 2015Ranking treatments in frequentist network meta-analysis works without resampling methods 2012Indirect and mixed‐treatment comparison, network, or multiple‐treatments meta‐analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool 2014Evaluating the Quality of Evidence from a Network Meta-Analysis 2013Conceptual and Technical Challenges in Network Meta-analysis 2011Interpreting Indirect Treatment Comparisons and Network Meta-Analysis for Health-Care Decision Making: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 1 2012Quantifying the impact of between‐study heterogeneity in multivariate meta‐analyses

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