gms | German Medical Science

62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

17.09. - 21.09.2017, Oldenburg

Connecting the disconnected: New statistical methodology or new clinical research?

Meeting Abstract

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  • Gerta Rücker - Institut für Medizinische Biometrie und Medizinische Informatik, Freiburg, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg, 17.-21.09.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocAbstr. 180

doi: 10.3205/17gmds041, urn:nbn:de:0183-17gmds0412

Veröffentlicht: 29. August 2017

© 2017 Rücker.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Network meta-analysis, like standard pairwise meta-analysis, is classically based on relative effects, i.e., contrasts between treatments that have been directly compared in a trial. This approach, called contrast-based or treatment effect model, automatically preserves randomisation. Recently, some authors proposed arm-based approaches (also called treatment response models) to network meta-analysis and also models combining both approaches (baseline response plus treatment effect models). This provoked a controversial discussion [1], [2], [3].

Disconnected networks may arise in a variety of situations, e.g., when there is no accepted standard of care, when there has been a major paradigm shift in treatment, or for particular outcomes, where the network is connected for other outcomes. Based on the approach by Hong 2015 [2], Goring 2016 [4] presented an approach for analysing disconnected networks using an arm-based approach, arguing that the data in a disconnected network do not provide enough information for modelling the relative effects for the disconnected treatments.

In my talk I want to critically discuss the arguments given by the proponents of connecting disconnected networks. Particularly, a so-called “major paradigm shift in treatment” is no reason for abandoning randomized trials. As an essential presumption in clinical research, such a shift should be justified by clinical trials comparing the new treatment to the best standard of treatment before. The right thing to do would be to set up a trial that closes the gap. This can be done based on information from network meta-analysis [5], [6], [7]. On the other hand, if such a trial seems unethical because the new treatment is thought better anyway, then there is no reason to conduct a network meta-analysis including the “old” treatments, unless they can serve as bridge comparators just in order to obtain a connected network.In conclusion, disconnected networks should be analysed as separate networks. Rather than being fixed by intricate methodology, gaps identified in a network should motivate new primary research.

Der Vortrag gehört zum Workshop "Methods for Generalized Evidence Synthesis".

Organisatoren: R. Bender, K.H. Herrmann, K. Jensen, D. Hauschke, F. Leverkus & T. Friede

Die Autoren geben an, dass kein Interessenkonflikt besteht.

Die Autoren geben an, dass kein Ethikvotum erforderlich ist.


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