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

Cross-design synthesis of evidence in small populations

Meeting Abstract

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  • Steffen Unkel - Universitätsmedizin Göttingen - Georg-August-Universität Göttingen, Göttingen, 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. 181

doi: 10.3205/17gmds042, urn:nbn:de:0183-17gmds0428

Veröffentlicht: 29. August 2017

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



Double-blind randomised controlled trials (RCTs) are the gold standard design of clinical research to assess therapeutic interventions. Although there are scenarios in which one phase III trial with exceptionally compelling and clinically relevant results is sufficient to demonstrate efficacy and safety for marketing authorization, usually two independent confirmatory trials are conducted to provide the variety of data needed to confirm the usefulness of an intervention in the intended population. However, in small populations the conduct of even a single RCT with a sufficient sample size might be extremely difficult or not feasible. This is particularly the case in paediatric studies, if the intervention is to treat a rare disease or if randomization is challenging. In these instances, there may be some randomised evidence, but a decision might be taken to consider further observational evidence in addition to the randomised evidence. In this talk, we consider the scenario of a single RCT comparing an experimental treatment to a control in a small patient population. Inspired by a paediatric trial in Alport syndrome, we assume that information external to the randomised comparison, such as data arising from disease registries, is available. When observational data are considered in combination with randomised evidence, the question arises as to how to sensibly synthesise the two forms of evidence. Hierarchical models provide a natural framework for cross-design synthesis of evidence from different study designs. There is an extension to the standard random-effects model for meta-analysis in the sense that an extra level of variation is modelled to allow for variability in effect sizes between the two sources of evidence. The hierarchical models are fitted under the Bayesian paradigm. The performance of the proposed methods are evaluated under different scenarios by means of experiments. This research has received funding from the EU's 7th Framework Programme for research, technological development and demonstration under grant agreement number FP HEALTH 2013 - 602144 with project title (acronym) "Innovative methodology for small populations research'' (InSPiRe).

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.