Model averaging for robust extrapolation in evidence synthesis
2018-09-17 | journal article. A publication with affiliation to the University of Göttingen.
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- Authors
- Röver, Christian ; Wandel, Simon; Friede, Tim
- Abstract
- Extrapolation from a source to a target, e.g., from adults to children, is a promising approach to utilizing external information when data are sparse. In the context of meta-analysis, one is commonly faced with a small number of studies, while potentially relevant additional information may also be available. Here we describe a simple extrapolation strategy using heavy-tailed mixture priors for effect estimation in meta-analysis, which effectively results in a model-averaging technique. The described method is robust in the sense that a potential prior-data conflict, i.e., a discrepancy between source and target data, is explicitly anticipated. The aim of this paper to develop a solution for this particular application, to showcase the ease of implementation by providing R code, and to demonstrate the robustness of the general approach in simulations.
- Issue Date
- 17-September-2018
- Journal
- Statistics in Medicine
- Project
- info:eu-repo/grantAgreement/EC/FP7/602144/EU//INSPIRE
- Organization
- Institut für Medizinische Statistik
- ISSN
- 0277-6715
- Language
- English