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arXiv · 1805.10890

Model averaging for robust extrapolation in evidence synthesis

Christian Röver·Simon Wandel·Tim Friede
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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.

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Christian Röver, Simon Wandel, Tim Friede. 2018-05-28. Model averaging for robust extrapolation in evidence synthesis. https://doi.org/10.1002/sim.7991

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