Abstract
The synthesis of evidence from trials and medical studies using meta-analysis is essential for Evidence Based Medicine. However, problematical outlying results often occur even under the random-effects model. We propose a model that allows a long-tailed distribution for the random effect, which removes the necessity for an arbitrary decision to include or exclude outliers. In this approach, they are included, but with a reduced weight. We also introduce a modification of the forest plot to show the downweighting of outliers. We illustrate the methodology and its usefulness by carrying out both frequentist and Bayesian meta-analyses using data sets from the Cochrane Collaboration.
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Baker, R., Jackson, D. A new approach to outliers in meta-analysis. Health Care Manage Sci 11, 121–131 (2008). https://doi.org/10.1007/s10729-007-9041-8
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DOI: https://doi.org/10.1007/s10729-007-9041-8