  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.
