Model averaging for robust extrapolation in evidence synthesis

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.

arXiv comment18 pages, 7 figures, 5 tables

References in corpus (5)

• Bayesian random-effects meta-analysis using the bayesmeta R package
• Meta-analysis of few small studies in orphan diseases
• Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases
• Interleukin-2 receptor antagonists for pediatric liver transplant recipients: A systematic review and meta-analysis of controlled studies
• Discrete approximation of a mixture distribution via restricted divergence

Cited by in corpus (8)

• On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis
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• Bayesian Model-Averaged Meta-Analysis in Medicine
• Dynamically borrowing strength from another study through shrinkage estimation
• Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis
• Applying Meta-Analytic-Predictive Priors with the R Bayesian evidence synthesis tools
• Methods of multi-indication meta-analysis for health technology assessment: a simulation study
• Eliciting prior information from clinical trials via calibrated Bayes factor