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This framework might be used to analyze a new study in the light of previous data, which might differ in their design (e.g., a randomized controlled trial and a clinical registry). We show how the common study weights arise in effect and shrinkage estimation, and how these may be generalized to the case of Bayesian meta\u2010analysis. Next we develop simple ways to compute bounds on the weights, so that the contribution of the external evidence may be assessed a\u00a0priori. These considerations are illustrated and discussed using numerical examples, including applications in the treatment of Creutzfeldt\u2013Jakob disease and in fetal monitoring to prevent the occurrence of metabolic acidosis. The target study's contribution to the resulting estimate is shown to be bounded below. 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