Shrinkage estimation in a meta-analysis framework may be used to facilitate dynamical borrowing of information. 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-analysis. Next we develop simple ways to compute bounds on the weights, so that the contribution of the external evidence may be assessed a priori. These considerations are illustrated and discussed using numerical examples, including applications in the treatment of Creutzfeldt–Jakob 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. Therefore, concerns of evidence being easily overwhelmed by external data are largely unwarranted.

中文翻译：

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收缩估计中外部数据权重的界限

元分析框架中的收缩估计可用于促进信息的动态借用。该框架可用于根据以前的数据分析一项新研究，这些数据可能在设计上有所不同（例如，随机对照试验和临床注册）。我们展示了常见的研究权重如何在效应和收缩估计中出现，以及这些权重如何推广到贝叶斯荟萃分析的情况。接下来，我们开发了计算权重界限的简单方法，以便可以先验地评估外部证据的贡献。使用数值例子说明和讨论了这些考虑因素，包括在克雅氏病的治疗和胎儿监测中的应用，以防止代谢性酸中毒的发生。目标研究' s 对结果估计的贡献如下所示。因此，对证据容易被外部数据淹没的担忧在很大程度上是没有根据的。